ELE Times

Federated Learning (FL) has been termed a revolutionary manner of machine learning because it provides the capability of collaborative model training across devices in a decentralized manner while preserving data privacy. Instead of transferring data to a centralized server for training, devices train locally, and only their model updates are shared. This way, it finds applicability in sensitive areas like healthcare, finance, and mobile applications. As Federated Learning continues to evolve, an increasingly diverse array of algorithms has emerged each designed to enhance communication efficiency, boost model accuracy, and strengthen resilience against data heterogeneity and adversarial challenges. This article will delve into the types, examples, and top 10 Federated Learning Algorithms.

Types of federated learning algorithms:

Federated Learning algorithms get classified by how data is laid out, by the system structure, and by the privacy requirements. Horizontal FL covers clients with the same features but distinct data points. Vertical FL captures the case where features are different but clients overlap. When users and features are both different, we use Federated Transfer Learning. Decentralized FL, as opposed to Centralized FL, doesn’t use a central server and instead allows for peer-to-peer communication. In terms of FL deployment methods, Cross-Silo FL consists of powerful participants like hospitals and banks, while Cross-Device FL focuses on lightweight devices, such as smartphones. In addition, Privacy-Preserving FL protects user data with encryption, differential privacy, and other techniques, and Robust FL attempts to protect the system from malicious, adversarial, or broken clients.

Examples of federated learning algorithms:

Examples of Federated Learning Algorithms: A number of algorithms have been created to overcome challenges specific to Federated Learning problems. The basic approach of Federated Learning is FedAvg, which, in contrast, models client averaging. FedProx, which is designed to work well with data heterogeneity, is a more advanced approach. For personalization, FedPer customizes top layers for each client, and pFedMe applies meta-learning techniques. Communication-efficient algorithms like SCAFFOLD and FedPAQ reduce bandwidth usage and client drift. Robust algorithms such as Krum, Bulyan, and RFA filter out malicious or noisy updates to maintain model integrity. Privacy-focused methods like DP-FedAvg and Secure Aggregation ensure data confidentiality during training. These algorithms are often tailored or combined to suit specific domains like healthcare, finance, and IoT.

Top 10 Federated Learning Algorithms:

1. Federated Averaging (FedAvg):

FedAvg stands as the founding algorithm for Federated Learning. The weight averaging is performed after models are trained locally on each client for updating the global model. Due to its simple design and the ease with which one can scale, it has been widely implemented in practice.

2. FedProx

FedProx improves upon FedAvg by adding a proximal term to the loss function. FedProx builds upon FedAvg by introducing a proximal term in the loss function. By penalizing local updates that diverge too much from the global model, this term helps stabilize training in settings with widely differing client data distributions. It is especially helpful in fields like healthcare and finance, where heterogeneous data is prevalent.

3. FedNova (Federated Normalized Averaging)

To address the drift of the client, FedNova normalizes updates with respect to the number of local steps and learning rates. This ensures each client has an equal contribution to the global model regardless of its computational capabilities or data volume. This further favors convergence and fairness in heterogeneous setups.

4. SCAFFOLD

SCAFFOLD, an abbreviation for Stochastic Controlled Averaging for Federated Learning, employs control variates to make corrections to the client’s updates. This limits the variance that exists owing to non-IID data and speeds the convergence. It is particularly effective in an edge computing environment, where data come from various sources.

5. MOON (Model-Contrastive Federated Learning)

MOON brings contrastive learning into FL by aligning local and global model representations. It enforces consistency of models that are particularly necessary when client data are highly divergent. MOON should often be used for image and text classification tasks for very heterogeneous user bases.

6. FedDyn (Federated Dynamic Regularization)

FedDyn incorporates a dynamic regularization term in the loss function to enable the global model to accommodate client-specific updates better. Because of this, it can withstand situations involving extremely diverse data, such user-specific recommendation systems or personalized healthcare.

7. FedOpt

FedOpt substitutes in place of the vanilla averaging mechanisms with advanced server-side optimizers like Adam, Yogi, and Adagrad. Using these optimizers leads to faster and more stable convergence, which is paramount in deep learning tasks with large neural networks.

8. Per-FedAvg (Personalized Federated Averaging)

Personalized Federated Averaging hopes to balance global generalization with local adaption by allowing clients to fine-tune the global model locally. Because of this, Per-FedAvg is suitable for personalized recommendations, mobile apps, and wearable health monitors.

9. FedMA (Federated Matched Averaging)

The distinguishing feature of this method is the matching of neurons across client models before averaging. This retains the architecture of a deep neural network and hence allows for much more meaningful aggregation, especially for convolutional and recurrent architectures.

10. FedSGD (Federated Stochastic Gradient Descent)

A simpler alternative to FedAvg, FedSGD sends gradients instead of model weights. It’s more communication-intensive but can be useful when frequent updates are needed or when model sizes are small.

Conclusion:

These algorithms represent the cutting edge of federated learning, each tailored to address specific challenges like data heterogeneity, personalization, and communication efficiency. As FL continues to grow in importance especially in privacy-sensitive domains these innovations will be crucial in building robust, scalable, and ethical AI systems.

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• Bharat set to welcome delegates from 33 Countries, 50+ CXOs,  350 Exhibitors
• At country’s biggest Semiconductors & Electronics Show in New Delhi from 2-4 September 2025
• Over 50+  Eminent Global Visionary Speakers
• Event To Highlight Robust Local Semiconductor Ecosystem Expansion and Industry Trends

The fourth edition of SEMICON India 2025 will be officially inaugurated by Hon’ble Prime Minister Shri. Narendra Modi on 2nd September 2025 at Yashobhoomi (India International Convention and Expo Centre), New Delhi. Staying true to its legacy of positioning India as a global Semiconductor powerhouse, the fourth edition of SEMICON India 2025 will convene key stakeholders including global leaders, semiconductor industry experts, academia, government officials and students.

Under the Semicon India program, 10 strategic projects have been approved across high-volume fabs, 3D heterogeneous packaging, compound semiconductors (including SiC), and OSATs, marking a significant milestone for the country. Recognizing semiconductors as a foundational technology, over 280 academic institutes and 72 startups have been equipped with state-of-the-art design tools, while 23 startups have already been approved under the DLI scheme. These initiatives are driving innovations in critical applications such as CCTV systems, navigation chips, motor controllers, communication devices, and microprocessors—strengthening India’s journey towards Atmanirbhar Bharat.

Accelerating India’s semiconductor revolution, SEMI, the global industry association prompting the semiconductor industry and India Semiconductor Mission (ISM), Ministry of Electronics and Information Technology (MeitY), announced the programming for SEMICON India 2025 at a press conference held in the national capital.

Under the theme Building the Next Semiconductor Powerhouse, the event will offer valuable insights into innovations and trends in key areas such as Fabs, Advanced packaging, smart manufacturing, AI, supply chain management, sustainability, workforce development, Designs and Start Up’s along with 6 country round tables.

The SEMICON India exhibition will feature nearly 350 exhibitors from across the global semiconductor value chain including 6 county Round Tables, 4 country pavilions,  9 states participations and over 15000 expected visitors providing South Asia’s single largest platform for showcasing the latest advancements in the semiconductor and electronics industries, said Shri S Krishnan, Secretary, MeitY.

“SEMI is bringing the combined expertise and capabilities of our member companies across the global electronics design and manufacturing supply chain to SEMICON India, helping to advance both India’s semiconductor ecosystem expansion and industry supply chain resiliency,” said Ajit Manocha, President and CEO, SEMI. “The event will feature signature SEMICON opportunities for professional networking, business development, and insights into technology and market trends from a star-studded lineup of leading industry experts.”

SEMICON India 2025 is designed to maximize technological advancements in the semiconductor and electronics domain and highlight India’s policies aimed at strengthening its semiconductor ecosystem.

The event is a remarkable convergence of ideas, collaboration and innovation, and provides a unique opportunity to address complex challenges of tomorrow while fostering collaboration across the semiconductor ecosystem. We are looking forward to an astounding number of participations this year, Said Shri Amitesh Kumar Sinha, Additional Secretary , MeitY and CEO ISM.

“India’s semiconductor industry is poised for a breakthrough, with domestic policies and private sector capacity finally aligning to propel the nation to global prominence. As we navigate this transformative landscape, collaboration and ecosystem building will be key to unlocking the next wave of growth and breakthroughs and SEMICON India 2025 plays the catalyst for this.” said Ashok Chandak, President, SEMI India and IESA.

In addition to distinguished government officials, this year’s event will also feature an impressive lineup of industry leaders from top companies including Applied Materials, ASML, IBM, Infineon, KLA, Lam Research, MERCK, Micron, PSMC, Rapidus, Sandisk, Siemens, SK Hynix, TATA Electronics, Tokyo Electron, and many more.

Over the span of three days, the flagship event will feature a diverse range of activities including high profile keynotes, panel discussions, fireside chats, paper presentations, 6 international roundtables and more that will converge to drive the next wave of semiconductor innovation and growth. The event will also include a ‘Workforce Development Pavilion’ to showcase microelectronics career prospects and attract new talent.

SEMICON India is one of eight annual SEMICON expositions worldwide hosted by SEMI that bring together executives and leading experts in the global semiconductor design and manufacturing ecosystem. The upcoming event marks the beginning of an exciting journey into the future of technological innovation, fostering collaboration and sustainability in the global semiconductor ecosystem.

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The new BBA series features higher field strengths for critical test environments up to 18 GHz

Rohde & Schwarz, a leading global supplier of test and measurement equipment and a reliable partner for turnkey EMC solutions, has expanded its broadband amplifier portfolio of the R&SBBA300 family with the two innovative amplifier series R&SBBA300-F for 6 to 13 GHz and R&SBBA300-FG for 6 to 18 GHz with additional power classes such as 90W, 180W and 300W.

Together with the already successfully introduced broadband amplifier series R&SBBA300-CDE for 380 MHz to 6 GHz and R&SBBA300-DE for 1 to 6 GHz, Rohde & Schwarz now offers compact dual-band amplifiers covering the entire frequency range from 380 MHz to 18 GHz in 4HU desktop models only.

The R&SBBA300 family is the new generation of compact, solid-state broadband amplifiers, designed for high availability and a linear output across an ultra-wide frequency range. It supports amplitude, frequency, phase, pulse and complex OFDM modulation modes and is extremely robust under all mismatch conditions, providing reliable test results in all circumstances.

Typical applications include EMC, co-existence and RF component tests during development, compliance test and production. The very wide frequency range makes them ideal for wireless and ultra-wideband testing.

The R&SBBA300-F series is a cost-effective solution for applications between 6 GHz and 13 GHz; the R&SBBA300-FG series covers a continuous frequency band from 6 GHz to 18 GHz. The two amplifier series can be used for ultrawideband applications as well as to address various EMC standards within mobile communications (FCC, ETSI), automotive (ISO), aerospace (DO-160), and military (MIL-STD-461). Both the R&SBBA300-F and the R&SBBA300-FG are now available in the power classes 30 W, 50 W, 90 W, 180 W, 300 W.

The R&SBBA300 broadband amplifier family offers two powerful tools for tailoring the RF output signal to the application: adjusting the amplifier either for excellent linearity or faithful reproduction of pulse signals by shifting the operating point between class A and class AB, and setting the amplifier for maximum tolerance to output mismatch or for maximum RF output power to utilize the power reserves for the application.

This allows users like developers, test engineers, integrators, or operators to optimize the output signal and react flexibly to a wide variety of requirements. Both parameters can be changed during amplifier operation.

“In addition to high linearity and excellent harmonic properties, our users also need extremely wide, continuous frequency bands at high RF output power,” said Michael Hempel, product manager for amplifier systems at Rohde & Schwarz. “The BBA300 series is our direct response to these requirements, offering outstanding bandwidth with high output power.”

Rohde & Schwarz also provides fully compliant EMI test receivers, signal generators, antennas, software and other essential system components and service for EMC testing.

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Empowering a New Era of Physical AI and Robotics Development in the Asia-Pacific Region

EDOM Technology announced the official distribution of NVIDIA’s latest NVIDIA Jetson Thor module and developer kit, built for physical AI and general robotics. This move is set to accelerate technological upgrades and local deployment of applications such as intelligent robotics, AMR (Autonomous Mobile Robot), AIoT, and smart manufacturing across the region.

Jetson Thor is the most powerful edge AI module in the NVIDIA Jetson series. Built on NVIDIA Blackwell GPU architecture, it delivers over 2,070 TFLOPS of AI inference capability, specifically designed for humanoid robots, AMRs, and industrial smart devices. Its highly integrated computing architecture supports multi-sensor fusion, Transformer model inference, and real-time motion control, enabling a deep integration of generative AI and the physical world. Jetson Thor seamlessly integrates with NVIDIA Isaac ROS, NVIDIA Omniverse, and NVIDIA Isaac GR00T, forming a complete AI toolchain from data generation and simulation training to edge deployment. This significantly accelerates the adoption and commercialization of Physical AI applications, making it a key enabler of next-generation edge AI and robotics intelligence.

As NVIDIA’s long-standing partner and authorized distributor of Jetson series modules in the Asia-Pacific, EDOM brings around 30 years of experience in distribution and technical integration, covering AI modules, embedded systems, sensor integration, industrial automation, and component applications.

EDOM provides comprehensive product offerings of the Jetson Thor platform, including the Jetson AGX Thor Developer kit and Jetson T5000 module. Equipped with NVIDIA Holoscan Sensor Bridge for real-time data processing, along with high-speed interfaces such as GMSL, MIPI, 25GbE, 5G, and Wi-Fi modules, as well as high-performance storage interfaces, these solutions effectively meet the stringent low-latency and high-bandwidth demands of edge computing. Additionally, EDOM supports custom hardware design and system integration reference solutions, fully assisting customers in accelerating product development and deployment processes.

Jeffrey Yu, CEO at EDOM Technology stated:
“Jetson Thor represents a major breakthrough in NVIDIA’s physical AI and robotics applications. We are honored to be the authorized distributor for Jetson Thor in the Asia-Pacific. By combining technical supports, educational resources, and platform ecosystems, we aim to help customers accelerate innovation and advance the deployment of generative and physical AI technologies.”

With the launch of Jetson Thor, the module is expected to see wide adoption in fast-growing physical AI and robotics sectors across Asia-Pacific, including smart manufacturing, AMRs, smart transportation, and service robots. For example:

• In high-precision AOI (Automated Optical Inspection), Jetson Thor can process large-scale image data in real time and perform inference, improving yield rates and automation in factories.
• In AMR factory logistics, through multi-sensor fusion and real-time motion control, it enables autonomous navigation and smart scheduling in complex environments.
• In humanoid and companion robots, Jetson Thor’s integration with GR00T multimodal models and visual recognition enables highly interactive scenarios, ideal for applications in aging societies and public services.
• With support for multiple GMSL cameras and high-speed Ethernet, Jetson Thor is also well-suited for smart city traffic nodes, performing real-time image analysis and behavior recognition.

These applications demonstrate Jetson Thor’s powerful computing capabilities and provide developers and enterprises in Asia-Pacific a complete path from AI training to edge deployment.

EDOM will continue to act as a critical bridge between technology and the market, working with system developers, integrators, and academic institutions. By driving the local deployment of the NVIDIA Jetson platform across key sectors—such as smart transportation, AIoT, and smart manufacturing—EDOM is accelerating the development and implementation of generative AI and Physical AI throughout the Asia-Pacific region.

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MeitY approved 23 chip design projects under its Design Linked Incentive (DLI) scheme, thus strengthening the semiconductor design ecosystem in India. This favors domestic start-ups and MSMEs that work on chips applicable in surveillance cameras, smart energy meters, among others.

Alongside these approvals, 72 companies now have access to industry-grade EDA tools to shorten development time and improve design capabilities in India.

One of the beneficiaries is Bengaluru-based Vervesemi Microelectronics, which is designing integrated circuits for smart energy, motor control, and aerospace. Its forthcoming designs include chips for weighing scales, energy meters, small appliances, electric vehicles, and avionics.

Vervesemi is developing ICs for strategic and consumer applications, including ASICs for weighing scales, smart energy meters, BLDC motor controllers, EVs, drones, and aerospace systems. Sampling for most designs is expected between late 2025 and 2026.

The company mentioned that these efforts display India’s growing ability to design high-performance, Made-in-India ICs for the strategic and consumer market in import substitution and raising the country’s stature at a global level in the semiconductor field.

The DLI program is at the core of India’s $10 billion semiconductor mission, which aims to reduce reliance on imports while simultaneously fostering domestic talent and creating a robust ecosystem for chip design and development.

The broader impact of the DLI scheme is already visible, as India moves toward building a robust fabless semiconductor ecosystem. By empowering startups and MSMEs with access to Electronic Design Automation (EDA) tools and financial incentives, MeitY is laying the groundwork for a self-reliant design-to-deployment pipeline. Experts believe that this momentum, combined with strategic investments and global partnerships, could help India emerge as a competitive force in the global semiconductor supply chain, reducing reliance on imports and boosting domestic innovation.

The post Govt Sanctions 23 Chip Design Ventures Under DLI Scheme appeared first on ELE Times.

India’s electronics sector, one of the major achievements under the Make in India initiative, could witness long-term benefits following China’s decision to ease export restrictions on rare earth metals and critical minerals.

Rare earth elements (REEs) comprise the very stuff in a myriad of devices-hardly just companies assembling smartphones, laptops, gaming consoles, and electric vehicles, but also advanced display technologies. Rare earth elements (REEs) were an eternal source of limited supply, coupled with China dominating the supply chains worldwide, thereby posing an impediment to global electronics manufacturers.

The relaxation in export restrictions by China could go a long way in reducing supply constraints in India thus helping the domestic industry grow faster, and in removing supply-chain bottlenecks-according to experts of the industry. This is extremely important in sectors like semiconductors, consumer electronics, and electric mobility, where India aspires to build some competitive advantage.

Industry leaders believe that stable prices for critical minerals should lower production costs and foster further investment in the electronics ecosystem in India, thus quickening the country’s path to being recognized as a-global electronics manufacturing hub.

The change in policy comes at a time when India is enhancing PLI schemes to pull in global multinationals engaged in electronics and semiconductor fabrication. With a better supply assurance for rare earths, India should now find itself better placed to enter into global value chains and curtail its dependence on expensive imports from various markets.

Despite China being India’s strategic competitor, the loosening of export controls unexpectedly highlights global supply chains’ interconnectivity. Analysts say that the way is now clear for the electronics and EV sectors of India to be among the biggest beneficiaries if India continues to develop domestic processing and value addition firms.

The post Rare Earth Export Curbs Lifted by China: India’s Semiconductor and Electronics Sectors Poised to Benefit appeared first on ELE Times.

The Ministry of Electronics and Information Technology (MeitY) has indicated that two to three small semiconductor projects may soon be announced, using leftover funds from the ₹76,000 crore India Semiconductor Mission (Semicon 1.0). Most of the outlay has already been committed to chip fabrication facilities, the Semiconductor Laboratory in Mohali, and the Design-Linked Incentive scheme.

The government is also working out the framework for Semicon 2.0 with the aim of further mainstreaming the semiconductor ecosystem in India.

The announcement comes as a precursor to SEMICON India 2025, taking place from September 2 to 4 at Yashobhoomi, Delhi, the inauguration of which shall be graced by Prime Minister Narendra Modi. This edition has almost doubled in scale and has attracted more global and state participation, with all exhibition spaces being booked to capacity.

MeitY has further said that the first commercially produced Made-in-India chips may just be out by the end of 2025, with several companies racing to reach this milestone.

Industry observers say the combination of policy support, global partnerships, and rising investor interest has positioned India as an increasingly credible player in the global semiconductor supply chain.

Global players including Applied Materials, IBM, Infineon, LAM Research, Merck, Siemens, TSMC, and Tata Electronics will join the event, underscoring rising international confidence in India’s semiconductor push.

The post MeitY May Announce 2–3 Small Semiconductor Projects Soon appeared first on ELE Times.

The New High-Efficiency Audio Solution Ideal for Dashboard, eCall, and T-Box Applications

Nuvoton announced NAU83U25YG, a new automotive-grade Class-D audio amplifier. The NAU83U25YG Class-D amplifier features high-efficiency stereo, digital input, and delivers up to 3W (4 Ω load) or 1.7W (8 Ω load) output power. Featuring a two-wire gain adjustment interface, it is the ideal choice for automotive electronics applications such as dashboards, eCall, and T-Box systems.

As automotive electronics enter the era of the “smart cockpit,” vehicle intelligence has become a key industry focus. This trend is driving increasing functional requirements for audio solution providers in automotive applications. Nuvoton Technology strictly adheres to automotive industry standards, offering AEC-Q100 qualified products for automotive applications. To simplify system design, our solutions support digital I2S audio signal input from the vehicle’s main controller, reducing the need for external components and minimizing PCB size. Additionally, our digital amplifiers help prevent circuit interference and effectively solve EMI issues.

The NAU83U25YG stereo Class-D audio amplifier has advanced features like 80 dB PSRR, 90% efficiency, ultra-low quiescent current (i.e. 2.1 mA at 3.7V for 2 channels) and superior EMI performance. It offers lower distortion, reduced background noise, and a wider dynamic range. Additionally, this new amplifier supports comprehensive device protection.

NAU83U25YG Key Features

1. Gain Setting via I²C interface, 22 dB to -62 dB
2. Powerful Stereo Class-D Amplifier, 2ch x 3.0W (4Ω @ 5V, 10% THD+N)
3. Low Output Noise: 18 μVrms @ 0 dB gain
4. Comprehensive Device Protection:

• Overcurrent Protection (OCP)
• Undervoltage Lockout (UVLO)
• Overtemperature Protection (OTP)
• Clock Termination Protection (CTP)

5. Click-and-Pop Suppression
6. Package: QFN-20
7. Operating Temperature Range: -40℃ ~ +105℃
8. Automotive Grade: AEC-Q100 qualification & TS16949 compliant

Superior EMI Performance, Filter-Free
The NAU83U25YG amplifier stands out by eliminating the need for an external output filter, thanks to its spread-spectrum-oscillator technology and slew-rate control, effectively reducing electromagnetic interference (EMI). Moreover, it offers enhanced immunity and power supply rejection ratio (PSRR) of > 80 dB at 217 Hz. Making the NAU83U25YG an excellent fit for Class-D audio amplifiers in wireless and AM (Amplitude Modulation) frequency band applications.

Leap Forward in Efficiency, Power
The Class-D topology represents a significant leap forward in both power efficiency and noise minimization in audio devices. By generating a binary square wave, Class-D amplifiers efficiently amplify the signal through power device switching. Compared to Class-AB devices, Class-D amplifiers offer power efficiencies that are two-thirds better.

The NAU83U25YG Class-D audio amplifier excels in driving a 4 Ω load with an impressive output power of up to 3W and fast start-up time of just 14 msec.

NAU83U25YG Target Applications

The new Class-D audio amplifier is designed for automotive electronics applications including dashboards, eCall, ADAS (Advanced Driver Assist Systems) and T-Box.

The post Nuvoton Introduces Automotive-grade, Filter-Free 3W Class-D Audio Amplifier NAU83U25YG appeared first on ELE Times.

India is now the biggest supplier of smartphones to the US, overtaking China, marking a momentous change in global manufacturing. According to research firm Canalys, cited by the PIB, the share of Indian smartphone imports into the US had abruptly shot up to 44% during April-June 2025, whereas it was meager 13% in the same quarter of the previous year. On the other hand, China saw its share plummeting to just around 25% from 61% in 2024.

Such an extraordinary development has been largely attributed to government interventions like Make in India and the PLI scheme, transforming India’s electronics sector into what it is today.

The Ministry of Electronics & IT recently spoke about the growth journey of India, pointing out its tremendous rise both in production and exports between 2014-15 and 2024-25. From ₹18,000 crore, mobile phone production soared to ₹5.45 lakh crore, while exports surged from just ₹1,500 crore to ₹2 lakh crore a 127-fold increase in exports.

Overall electronics production grew from ₹1.9 lakh crore to ₹11.3 lakh crore, a sixfold increase.

There were only two production units in 2014–15, and by 2024–25, the number had grown to 300 units a massive expansion.

The increase in smartphone exports from India to the United States, which now accounts for 44% of US smartphone imports, is a key factor driving this shift in global manufacturing.This rapid scaling of production, reduction in imports, and emergence as an alternate tech supply chain to China have been largely driven by Make in India and PLI initiatives.

The rise of India as the top exporter of smartphones to the US represents a significant change in the dynamics of global manufacturing. This change has altered the electronics landscape of the nation and is being fueled by strategic initiatives such as the Production Linked Incentive scheme and Make in India.

The post India Surges Ahead of China in Smartphone Exports to US appeared first on ELE Times.

The New High-Efficiency Audio Solution Ideal for Dashboard, eCall, and T-Box Applications

Nuvoton announced NAU83U25YG, a new automotive-grade Class-D audio amplifier. The NAU83U25YG Class-D amplifier features high-efficiency stereo, digital input, and delivers up to 3W (4 Ω load) or 1.7W (8 Ω load) output power. Featuring a two-wire gain adjustment interface, it is the ideal choice for automotive electronics applications such as dashboards, eCall, and T-Box systems.

As automotive electronics enter the era of the “smart cockpit,” vehicle intelligence has become a key industry focus. This trend is driving increasing functional requirements for audio solution providers in automotive applications. Nuvoton Technology strictly adheres to automotive industry standards, offering AEC-Q100 qualified products for automotive applications. To simplify system design, our solutions support digital I2S audio signal input from the vehicle’s main controller, reducing the need for external components and minimizing PCB size. Additionally, our digital amplifiers help prevent circuit interference and effectively solve EMI issues.

The NAU83U25YG stereo Class-D audio amplifier has advanced features like 80 dB PSRR, 90% efficiency, ultra-low quiescent current (i.e. 2.1 mA at 3.7V for 2 channels) and superior EMI performance. It offers lower distortion, reduced background noise, and a wider dynamic range. Additionally, this new amplifier supports comprehensive device protection.

NAU83U25YG Key Features

1. Gain Setting via I²C interface, 22 dB to -62 dB
2. Powerful Stereo Class-D Amplifier, 2ch x 3.0W (4Ω @ 5V, 10% THD+N)
3. Low Output Noise: 18 μVrms @ 0 dB gain
4. Comprehensive Device Protection:

• Overcurrent Protection (OCP)
• Undervoltage Lockout (UVLO)
• Overtemperature Protection (OTP)
• Clock Termination Protection (CTP)

5. Click-and-Pop Suppression
6. Package: QFN-20
7. Operating Temperature Range: -40℃ ~ +105℃
8. Automotive Grade: AEC-Q100 qualification & TS16949 compliant

Superior EMI Performance, Filter-Free
The NAU83U25YG amplifier stands out by eliminating the need for an external output filter, thanks to its spread-spectrum-oscillator technology and slew-rate control, effectively reducing electromagnetic interference (EMI). Moreover, it offers enhanced immunity and power supply rejection ratio (PSRR) of > 80 dB at 217 Hz. Making the NAU83U25YG an excellent fit for Class-D audio amplifiers in wireless and AM (Amplitude Modulation) frequency band applications.

Leap Forward in Efficiency, Power
The Class-D topology represents a significant leap forward in both power efficiency and noise minimization in audio devices. By generating a binary square wave, Class-D amplifiers efficiently amplify the signal through power device switching. Compared to Class-AB devices, Class-D amplifiers offer power efficiencies that are two-thirds better.

The NAU83U25YG Class-D audio amplifier excels in driving a 4 Ω load with an impressive output power of up to 3W and fast start-up time of just 14 msec.

NAU83U25YG Target Applications

The new Class-D audio amplifier is designed for automotive electronics applications including dashboards, eCall, ADAS (Advanced Driver Assist Systems) and T-Box.

The post Nuvoton Introduces Automotive-grade, Filter-Free 3W Class-D Audio Amplifier NAU83U25YG appeared first on ELE Times.

New Cadence Palladium Dynamic Power Analysis App enables designers of AI/ML chips and systems to create more energy-efficient designs and accelerate time to market

Cadence announced a significant leap forward in the power analysis of pre-silicon designs through its close collaboration with NVIDIA. Leveraging the advanced capabilities of the Cadence Palladium Z3 Enterprise Emulation Platform, utilizing the new Cadence Dynamic Power Analysis (DPA) App, Cadence and NVIDIA have achieved what was previously considered impossible: hardware accelerated dynamic power analysis of billion-gate AI designs, spanning billions of cycles within a few hours with up to 97 percent accuracy. This milestone enables semiconductor and systems developers targeting AI, machine learning (ML) and GPU-accelerated applications to design more energy-efficient systems and accelerate their time to market.

The massive complexity and computational requirements of today’s most advanced semiconductors and systems present a challenge for designers, who have until now been unable to accurately predict their power consumption under realistic conditions. Conventional power analysis tools cannot scale beyond a few hundred thousand cycles without requiring impractical timelines. In close collaboration with NVIDIA, Cadence has overcome these challenges through hardware-assisted power acceleration and parallel processing innovations, enabling previously unattainable precision across billions of cycles in early-stage designs.

“Cadence and NVIDIA are building on our long history of introducing transformative technologies developed through deep collaboration,” said Dhiraj Goswami, corporate vice president and general manager at Cadence. “This project redefined boundaries, processing billions of cycles in as few as two to three hours. This empowers customers to confidently meet aggressive performance and power targets and accelerate their time to silicon.”

“As the era of agentic AI and next-generation AI infrastructure rapidly evolves, engineers need sophisticated tools to design more energy-efficient solutions,” said Narendra Konda, vice president, Hardware Engineering at NVIDIA. “By combining NVIDIA’s accelerated computing expertise with Cadence’s EDA leadership, we’re advancing hardware-accelerated power profiling to enable more precise efficiency in accelerated computing platforms.”

The Palladium Z3 Platform uses the DPA App to accurately estimate power consumption under real-world workloads, allowing functionality, power usage and performance to be verified before tapeout, when the design can still be optimized. Especially useful in AI, ML and GPU-accelerated applications, early power modeling increases energy efficiency while avoiding delays from over- or under-designed semiconductors. Palladium DPA is integrated into the Cadence analysis and implementation solution to allow designers to address power estimation, reduction and signoff throughout the entire design process, resulting in the most efficient silicon and system designs possible.

The post Cadence Accelerates Development of Billion-Gate AI Designs with Innovative Power Analysis Technology Built on NVIDIA appeared first on ELE Times.

A form of distributed machine learning known as “federated learning” uses data from edge devices, such as laptops, smartphones, and wearable technology, to train machine learning and deep learning algorithms without transferring the data to a central server.

Among the several advantages it confers are meeting latency constraints, promoting data privacy and security, and making parameter updates in a distributed manner.

It is thus a decentralized approach to machine learning, where, across multiple organizations or devices, data can be used to collaboratively build machine learning models without anyone sharing the actual private data. Instead of raw data being moved to some central server, only some updates or parameter values are exchanged, thus ensuring the privacy of the data and also its security.

Federated learning is an approach that thereby supports data privacy on the one hand, in that training data remains local and only aggregated insights are exchanged, while on the other hand, the federated data are used for improving model accuracy.

Types of Federated Learning:

• Horizontal Federated Learning:

Horizontal federated learning protects privacy by allowing several parties with distinct users but comparable data attributes to work together to build a model without exchanging raw data.

• Vertical Federated Learning

Vertical Federated Learning occurs when multiple clients share the same users but possess different features. It enables collaborative model training across organizations that hold complementary data about the same individuals, without exchanging raw data.

• Federated Transfer Learning:

Federated transfer learning is basically making federated learning meet transfer learning so that clients with different data can collaborate. This allows models to transfer knowledge even if the clients have different features and user distributions, thus aiding a common project in optimizing its performance without the exchange of raw data.

Federated learning can also be divided into two categories based on the size of the participating clients: Cross-Device Federated Learning and Cross-Silo Federated Learning.

How federated learning works:

Federated learning is a privacy-preserving machine learning technology by which multiple devices or organizations set about building a shared model collaboratively without disclosing any raw data. A central server starts the process by selecting a global model and disseminating it among their client devices. Each client trains the model on their own private dataset, hence sensitive information remains on the client device. When training is completed, clients submit only the updated model parameters (weights or gradients) to the server. The aggregator then combines the clients’ updates, usually performing an averaging operation known as Federated Averaging or FedAvg, with to update the global model. This improved global model is redistributed for more rounds of training, and so on. Consequently, the model learns from several data sources while ensuring the privacy and security of the data. This is especially useful in hospitals, finances, and mobile apps.

Applications of Federated Learning:

• Autonomous Vehicle:

Federated learning enables self-driving cars to be safer and smarter through real-time awareness of road terrain, faster decisions on the spot, and continuous model updating. Vehicles share insights locally like hazards or weather changes without sending raw data, allowing onboard AI to react instantly while improving overall system accuracy over time.

• Mobile and Edge Devices

FL enables more intelligent and private user experiences in mobile technologies. For instance, Google Gboard learns from user typing behaviors right on the device to enhance text predictions. Through local training, voice assistants such as Google Assistant and Siri improve speech recognition and customisation. Without jeopardizing user privacy, FL also offers individualized content recommendations.

• Industrial IoT

Federated learning in IoT allows machines and sensors to train models locally with their own data while not actually sharing it. Only these model updates are communicated to the central server for a combined update in performance. This serves predictive maintenance and anomaly detection while rendering operational data private and secure.

• Finance

In the financial industry, FL is a method for banks and other financial institutions to collaborate against fraud, measuring creditworthiness, evaluating market risks, and so forth. Training of the model occurs on distributed data sources, thus providing institutions with a wider perspective while preserving customer building laws with regards to data sovereignty.

• Cybersecurity

With the FL approach, one can detect anomalies and forecast malicious threats on the basis of observed local attack patterns. This constitutes a decentralized approach toward developing defense, thereby ensuring that sensitive logs are not merged together. Biometric authentication systems take one step further by endowing local training that keeps personal identifiers locked on the device.

Federated learning advantages:

Federating learning keeps data in a local device, which enhances privacy and security. It reduces bandwidth usage, supports personalized models, and allows learning from a broader and diversified data source without centralizing information that is disclosive.

Federated learning disadvantages:

It requires high resources on the device, lacks consistency in data distribution over users, and faces barriers of coordination and debugging. It may also train slow the models and be less accurate than centralized ones.

Federated Learning Examples:

Some of the use cases for Federated learning are:

• Google Gboard: Improve predictive text and suggestions without the need to upload data of user typing.
• Healthcare: Hospitals train the model on patient data-essentially-training without sharing sensitive records.
• Finance: Banks employ federated learning to detect fraud across institutions without exposing customer data.
• Google: Google uses FL to enhance on-device machine learning systems, such as the “Hey Google” detection in Google Assistant, enabling users to issue voice commands.

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India has fast emerged as a global AI and deep learning innovation hub.India is become a hub for some of the most discerning deep learning applications in retail, healthcare, banking, and autonomous systems due to the rising demand.Many small, medium, and large enterprises are integrating artificial intelligence technologies to gain competitive advantage both in the domestic and overseas markets.This article will explore the top 10 deep learning companies in India.

1. Tata Consultancy Services (TCS)

With its Ignio platform, TCS is leading the way in enterprise-grade deep learning solutions. Neural networks are used for predictive analytics, intelligent automation, and anomaly detection. To improve operations and decision-making, it is extensively used in banking, retail, and healthcare.

2. Infosys

Infosys Nia is an AI platform, powered by deep learning, developed by Infosys, that enables usage scenarios such as automation, business intelligence, and predictive modeling. It is used in industries to help streamline processes, predict trends, and improve customer service.

3. Wipro AI

Wipro concentrates on deep learning techniques in NLP and computer vision. Their solutions target cybersecurity, cloud AI, and digital transformation; they allow the clients to detect threats and automatically analyze visual data.

4. Arya.ai

Arya.ai builds deep learning platforms such as BUDDHA to assist enterprises in deploying AI models with little human intervention. It specializes in automated architecture search, model explainability, and compliance-ready systems, particularly for regulated sectors like finance and insurance.

5. HCL Tech

HCL Tech has its own applications for deep learning in predictive maintenance, healthcare diagnosis, and IT infrastructure management. Models are built not only to detect failures of systems before they actually do, but also assist in medical image analysis for speedy diagnoses.

6. Tech Mahindra

Tech Mahindra applies deep learning into telecom, 5G and IoT ecosystems. Through these AI-powered platforms, the customer experience is enhanced by real-time personalization, and network performance is optimized via smart data modeling.

7. Mad Street Den

Mad Street Den, through its platform Vue.ai, focuses on computer vision applications in retail automation. Their deep learning-based models enable visual search, automated tagging, and personalized styling, consequently revolutionizing e-commerce experience.

8. Fractal Analytics

Fractal Analytics works in applying deep learning to provide AI solutions in customer analytics, forecasting, computer vision, and NLP (natural language processing) in the sectors of healthcare and finance. Furthermore, it imparts AI training through its own institute, the Fractal Analytics Academy, and pursues the implementation of fractal machine learning for enhancing model efficiency and scalability.

9. Haptik’s

Haptik’s deep learning abilities cover real-time analytics, customer self-service, and pre-sales guidance, giving enterprises a complete conversational experience.

10. Zensar Technologies

Zensar Technologies furthers deep learning in AI and ML activities. The company uses deep learning techniques as part of the Vinci AIOps platform, an operational platform that improves IT operations through event correlation, anomaly detection, root cause analysis, and intelligent automation. This system thereby uses deep learning and NLP to learn and respond intelligently to IT systems.

Conclusion:

India’s deep learning ecosystem is rising at lightning pace. Indian companies, from the likes of established IT giants TCS and Infosys to swanky startups like Mad Street Den, contributing to shaping the global AI landscape with revolutionary applications.

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Decade-long partnership overcame complex payload design challenges to empower next-generation environmental research from space

• A deeply-coupled partnership between TI and SAC-ISRO helped enable the mission payloads for the NISAR satellite, which is currently orbiting Earth.
• TI’s space-grade power management, mixed signal and analog technologies optimize system performance and allow the satellite to operate in the harsh environment of space over the mission’s lifetime.
• NISAR is the first satellite to use dual-band synthetic aperture radar technology to monitor the Earth’s ecosystems, natural hazards and climate patterns.

Texas Instruments (TI) semiconductors are enabling the radar imaging and scientific exploration payloads for the NASA-Indian Space Research Organization (ISRO) synthetic aperture radar (NISAR) satellite, which was recently launched into orbit. The launch of the satellite culminates a decade-long partnership between TI and the ISRO to optimize the performance of the electronic systems responsible for this Earth-observation mission. NISAR is equipped with TI’s radiation-hardened and radiation-tolerant products that enable designers to maximize power density, precision and performance in their satellite systems.

Engineering a first-of-its-kind satellite for Earth observation

The ISRO describes NISAR as the first Earth-observation mission to use dual-band synthetic aperture radar (SAR) technology, enabling the system to capture precise, high-resolution images during the day, night and all weather conditions. TI’s technology is enabling the satellite’s next-generation capabilities through efficient power management, high-speed data transfer, and precise signal sampling and timing.

The NISAR satellite will image the entire planet every 12 days, offering scientists greater understanding of changes to Earth’s ecosystems, ice mass, vegetation biomass, sea-level rise and groundwater levels. The agencies also expect the data to improve real-time monitoring of natural hazards such as earthquakes, tsunamis, volcanoes and landslides.

“From selecting the right products to ensuring consistent support across development cycles, TI’s technical expertise helped us navigate complex payload requirements,” said Shri Nilesh Desai, Director, Space Applications Centre (SAC), ISRO. “A deeply coupled partnership, specifically focused on high-impact mixed signal and analog semiconductors, enabled ISRO to meet the system-level requirements for a satellite in low Earth orbit. Together, we achieved the space-grade performance standards needed for this important mission.”

Addressing complex design challenges with TI’s space-grade portfolio

Throughout the project life cycle, TI’s system expertise and space-grade semiconductors, which are designed to withstand the harshest space environments, helped enable the advanced S-band SAR capabilities of the NISAR mission. The company provided:

• Radiation-hardened power management die for SAC-ISRO developed point-of-load hybrid power module, helping optimize size, weight and power for the mission payloads.
• Analog-to-digital converters with ultra-high sampling rates and high resolution, allowing the satellite payload to generate fine-grained, high resolution radar imagery.
• High-performance interface technology, which enables high-speed data transfer between different satellite subsystems to ensure reliable communication.
• A clocking solution that enables the precise time alignment and synchronous, coherent sampling required for high-precision SAR systems.

“As the NISAR satellite is now in orbit, I reflect on the decade-long partnership that brought us here and how our teams are already looking to what’s next, developing new technologies that will enable future missions,” said Elizabeth Jansen, TI India’s sales and applications director. “Building on more than 60 years of expertise, TI’s radiation-hardened and radiation-tolerant semiconductors are ready to meet the evolving demands of the space market. Our broad and reliable space-grade portfolio is ever-expanding and pushing the limits of what’s possible in the next frontier.”

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• Infineon India has signed a Memorandum of Understanding (MoU) with the Department for Promotion of Industry and Internal Trade (DPIIT)
• This further strengthens Infineon’s long-standing commitment to foster the country’s startup ecosystem
• Recent startup success stories contribute to energy-efficient e-mobility and smart e-health solutions

India is rapidly emerging as a hub for semiconductor innovation. As a global leader in power semiconductors and the Internet of Things (IoT), Infineon has been collaborating with Indian start-ups for years, recognizing the importance of this in accelerating innovation. With a focus on supporting advancement and entrepreneurship in the country the company has formed partnerships with various organizations, including NITI Aayog, Startup India, and the Ministry of Electronics and Information Technology (MEITY), to promote the “Make in India” initiative and foster startup growth.

Memorandum sparks startup innovation in IoT, electromobility, and security

As part of its ongoing efforts, Infineon India has signed a Memorandum of Understanding (MoU) with the Department for Promotion of Industry and Internal Trade (DPIIT) this year. The MoU aims to develop, foster, and promote the country’s innovation ecosystem by encouraging and supporting engineering students, product startups, innovators, and entrepreneurs through design challenges using Infineon’s innovative products to address applications of relevance for India.

“We are committed to empowering India’s startup ecosystem in microelectronics”, said Vinay Shenoy, Managing Director of Infineon India. “Partnerships such as the MoU with DPIIT allow us to work with innovative startups, giving them access to state-of-the-art technologies and our local and global networks. In return, we tap into their agility and entrepreneurial spirit, driving mutual growth and strengthening India’s innovation ecosystem.”

Propelling the Indian startup ecosystem

Infineon India has collaborated with various incubators and innovation ecosystems for years, including the Foundation for Science Innovation & Development at IISC Bangalore, IIT Madras Incubation Cell, and Artpark, AI & Robotics Technology Park @IISC. These partnerships have enabled the company to support startups and innovators in the country, and provide them with access to resources, expertise, and funding. Some of the key initiatives undertaken by Infineon India include the AI Challenge with Startup India and AGNIi, the solar pump motor drive challenge, and the MoU with MEITY to support the MEITY startup hub. These initiatives have helped to promote innovation and entrepreneurship in the country and have provided a platform for startups and innovators to showcase their ideas and products.

Startup collaborations for sustainable e-mobility and smart e-health

Recent Infineon partnerships with startups like e-Drift Electric, EYDelta or Mimyik are successful examples of collaboration with significant impact on e-mobility and e-charging as well as smart health solutions.

As part of Infineon’s co-innovation program, e-Drift Electric is contributing to the development of electric vehicle (EV) charging infrastructure. The start-up is focusing on creating energy-efficient modules using Infineon’s Si-SiC-MOSFET portfolio. As the adoption of EVs accelerates, it is increasingly important to develop an energy-efficient and robust charging infrastructure to ensure a cleaner and greener future for transportation in India.

For EYDelta the partnership with Infineon enables a faster product development and manufacturing of electric motors and motor controllers for multiple sectors like e-mobility, drones, and aerospace. By integrating AI-driven diagnostics and cloud-connectivity the solutions enable smarter IoT-ecosystems, help optimizing energy consumption, reduce emissions, and drive sustainable transportation systems in India and abroad.

The cooperation with Mimyk, a startup, spun out of the Indian Institute of Science Bangalore, is focusing on metabolic health monitoring. Infineon provided latest microcontrollers as well as access to the global Infineon semiconductor network. This partnership will accelerate development cycles and transform health monitoring to make health tracking smarter and easily accessible for everyone.

This demonstrates how Infineon’s co-innovation program fosters a strong ecosystem in India, empowering startups to grow as well as accelerating innovation-to-customer value, together.

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By: STMicroelectronics

Smartphones have become some of the most ubiquitous devices in modern history. For most of us, the smartphone is an indispensable tool to not only communicate, but to manage our lives – work, personal relationships, travel, shopping, entertainment, photography, video creation. In short, smartphones have become a hub for life. 

 The touchscreen was transformational in the smartphone’s adoption and use. But in the future, the smartphone is set to become a platform for immersive experiences. And when aligned to innovations that will extend battery life and even see smartphones harvesting their own energy, along with new ways to stay constantly connected, their usefulness will only increase.  

 A powerful processor in your pocket

Smartphones have become incredibly powerful processing devices. Indeed, in comparison to the most powerful supercomputers of the 1980s, today’s smartphones can process information more than 5,000 times faster. 

In some ways, however, the way that we interact with our smartphones has progressed least since their arrival. For many people, the touchscreen remains the primary – if not only – way that they access and view the interactive services and rich experiences provided by their smartphone. The coming years will see that transformed and, with it, the idea of what a smartphone is. 

A reduced reliance on the smartphone display as the principal way to interact with the device and receive information fundamentally changes the role of the smartphone. As a powerful computing device in its own right, but also connected to cloud-based computing resources, the smartphone potentially becomes a platform for delivering immersive experiences and valuable services to the user in numerous new ways.  

New models for smartphone interaction

Voice assistants have become one of the first steps into a new world of accessing services via our smartphones. Whether issuing voice commands and queries directly into the device or having these relayed via connected headphones and earbuds, consumers are realising the convenience of voice and audio interaction. An additional benefit, of course, is that the smartphone itself can remain in a pocket or bag, out of harm’s way. 

Eyeglasses featuring augmented reality (AR) display technology are an ideal solution. These can visually display directions in the user’s eyeline, while also overlaying other useful or interesting information. With more information and experiences layered over the real world, discovering a new city will be more rewarding than ever before, with less potential for a misstep along the way. 

Artificial intelligence (AI) will also enable proactive and predictive services that help us manage our daily lives. For example, by understanding the current traffic conditions, AI might bring an alert for your next meeting across town 30 minutes earlier. With the alert appearing on your smartwatch, more efficient travel could be proposed, with directions to the closest public transport appearing in your eyeglasses’ AR display. 

Gesture recognition and haptic feedback

Gesture recognition is emerging as another way to interact with services provided by smartphones. Less obvious that either using a touchscreen or voice, subtle gestures to make or answer calls or respond to messages will be quick and convenient methods of interaction. Who knows, you might well respond to the latest message received with an actual thumbs up, rather than having to find and type the emoji itself.  

We might be on the cusp of a whole new vocabulary of gestures as commands. Google is one company looking at how devices can be controlled by natural human gestures, many of which we use subconsciously. Other advances in hardware, such as the latest generation of Time-of-Flight (ToF) sensors, will support more accurate detection of gestures in and around smartphones. 

Haptic feedback is the use of vibrations or sensations to enrich the experience of using a device. At a basic level, most of us already experience haptic feedback in our smartphone use. Vibrations rather than a ringtone to signify an incoming call is a simple example, but the nature and application of haptic feedback is rapidly evolving. 

Imagine shopping online and being able to ‘feel’ different types of fabric through haptic feedback via your smartphone’s screen. Subtle vibrations from different parts of smart eyeglasses could be used to enrich visual experiences or help with directions. Research is even looking at ultrasound and “mid-air” haptics, where the sensation of physical touch is created in the air. Such haptic feedback could augment gesture control or enhance touchless interfaces. 

The potential for neural interfaces

Though still in its early stages, the idea of interacting with devices merely by thinking is becoming more real. Various non-invasive neural interfaces are in development.  

Electroencephalography (EEG) sensors placed on the head via headsets, or potentially even embedded in hats and headbands, are a direct way to tap into the brain’s activity. Neural wristbands detect signals from nerves connecting the brain to an individual’s hands, whereby just thinking about a gesture or action could act as a command.  

So-called “silent speech” interfaces detect subtle changes in expression or movements in vocal chords, where simply mouthing words would be detected as accurately as voice. Data from wearables such as smartwatches, rings, and earbuds, could identify cognitive load and emotional state, triggering proactive alerts, suggestions, or experiences to help alleviate issues.  

Projecting further into the future, neural interfaces and advanced haptic feedback could be combined to create a new world of deeply immersive experiences, all powered by the not-so-humble smartphone. 

Always connected

Of course, this vision of the smartphone as a platform for new services and experiences relies on an almost constant connection to cloud-based computing resources. Fortunately, alongside the innovations in smartphone interface technologies, we’re seeing continued development of technologies that ensure we remain connected, wherever we are. 

As we recently highlighted, the need to connect the world of increasingly intelligent “things” – not only smartphones, but billions of sensors, machines, and consumer products – is being supported by innovation in communications technology. This includes further evolution of established infrastructure, with 6G telecommunications networks arriving in the coming years, but also the significant expansion of satellite-based communications networks.  

When the smartphone arrived it was exactly that: a phone with additional capabilities. We can all appreciate how far it has moved beyond that simple description, and over a relatively short period of time. While we might need a new name for the device, we certainly need to change our understanding of what this powerful pocket processing device represents.

New ways to interact with our smartphones, innovation in the delivery of seamless immersive experiences, universal connection, and improved battery life and self-charging, will see them become the primary digital platform for every aspect of our lives.

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A subfield of machine learning called “deep learning” uses artificial neural networks to learn from data in an attempt to mimic human learning. Artificial neural networks, inspired by the human brain, are versatile enough to tackle a wide array of issues from speech recognition to image recognition and natural language processing. The top 10 deep learning applications & use cases that are spurring innovation worldwide will be examined in this article.

1. Autonomous Vehicle

Deep learning is crucial for self-driving cars, allowing them to interpret more-or-less simultaneous data streaming from sensors, cameras, and radar systems as they move through the world. Allowing real-time models to engage in split-second decisions, these models help vehicles identify pedestrians, traffic signs, and other vehicles so that safety is ensured. The companies working with these models are at the forefront of autonomous mobility, aiming for fewer accidents and more efficient transport through these means.

2. Healthcare

In medicine, deep learning helps in disease diagnosis and treatment. Cancer, heart illness, and tumors are detected on the basis of medical images such as X-rays, MRIs, and CT scans evaluated with higher accuracy by algorithms. It is useful for drug discovery, remote health monitoring, and personalized medicine as well.

3. Natural Language Processing (NLP)

Natural language processing is a significant feature of deep learning systems that work on text and speech interpretation. Natural language Processing serves certain applications like sentiment analysis, language translation, and customer support chatbots.

4. Facial Recognition

Deep learning-based facial recognition systems essentially identify and verify individuals based on facial characteristics. Its uses for smartphones include a secure method for unlocking, for airports in passenger verification, and for public safety surveillance.

5. Fraud Detection & Finance

Financial entities use deep learning in order to detect fraudulent transactions and cyber threats. These models conduct an agglomeration of data points numbering in the millions as patterns to flag an anomaly that might constitute identity theft, credit card fraud, or insider trading. This proactive approach helps protect the consumer and builds confidence in digital banking systems.

6. Satellite Imaging and Earth Observation

Such deep learning technologies assist in analyzing satellite imagery for climate monitoring, urban planning, and disaster-management applications. It can track deforestation, glacial movement, or the magnitude of damage caused by a natural disaster.

7. In-Vehicle Personalization

Deep learning enhances the driving experience by adapting vehicle settings and features to individual preferences. These systems learn from driver behavior and environmental conditions to optimize comfort, convenience, and entertainment.

8. Robotics and Industrial Automation

Robots with deep learning enable them to perform complex tasks such as object recognition, defect detection, and predictive maintenance in the manufacturing and logistics arenas. These intelligent systems decrease operating expenses, increase efficiency, and lessen human mistake. Robots powered by artificial intelligence are changing industrial processes, from precise assembly lines to warehouse automation.

9. Predictive Maintenance

Predictive maintenance powered by deep learning helps industries anticipate equipment failures before they happen, minimizing downtime and reducing repair costs.

10. Cybersecurity

To avoid hacking and illicit access, deep learning models detect anomalies in network traffic of an automobile. This role grows more and more critical as cars get more and more connected.

Conclusion:

From health care to cyber-security, applications of deep learning are serving as building blocks for the future of technology. This, in turn, shows the process by which AI is mixing itself in everyday life. With more applications being discovered, deep learning will be building smarter, safer, and more efficient systems for all sectors of economy.

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Infineon Technologies AG announced that its AIROC CYW20829 Bluetooth LE microcontroller (MCU) and Software Development kit (SDK) are now verified as part of the Engineered for Intel Evo laptop accessory program, the first of its kind in the Bluetooth human interface device (HID) industry. Through this collaboration with Intel, vendors developing next generation HID devices can confidently “ditch the dongle” using CYW20829. With this new solution, designers can achieve a best-in-class direct-to-host connection which has been rigorously tested against Intel’s strict KPIs and experience requirements.

“At Infineon, we strive to provide a leading wireless experience and convenience to consumers and designers,” said Shantanu Bhalerao, Vice President of Wireless Products at Infineon Technologies. “With over two decades of wireless HID experience, we are delighted to support the Engineered for Intel Evo program with our CYW20829, optimizing the user experience for both vendors and customers by helping them to truly eliminate the dongle.”

The Intel Evo platform is Intel’s superior consumer brand for premium laptops. Designed to provide a premium computing experience, laptop designs that meet the Intel Evo requirements undergo rigorous testing measurement and verification to ensure they deliver exemplary performance in terms of responsiveness, battery life, charging capability, form factor, and more. Engineered for Intel Evo Laptop Accessories Program defines strict Intel requirements for Bluetooth PC Peripherals, maximizing the end-to-end user experience when paired with Intel Evo laptops.

“Wireless peripherals are essential ingredients for the execution of many of our day-to-day computing activities,” said Eric McLaughlin, VP & GM, Connectivity Solutions Group at Intel Corporation. “For this reason, Intel works closely with key partners to ensure their devices meet technical requirements in the areas of performance, reliability, and ease of use. We’re excited that the Engineered for Intel Evo accessory program is expanding to include Infineon’s AIROC CYW20829 Bluetooth LE MCU and SDK, further extending Intel’s industry collaboration towards the goal of enabling amazing end-to-end connected user experiences.”

Infineon’s CYW20829 Bluetooth LE microcontroller (MCU) was designed with HID applications in mind to include:

• RF Performance: Featuring a dual demodulator architecture, long-standing Rx Blocker IP, and link budget of 108 dB, CYW20829 delivers unprecedented wireless robustness.
• Power Optimization: CYW20829 delivers up to 20 percent better battery life over leading devices in the market through its ARM Cortex-M33 clocking at 48 or 96 MHz, hardware keyscan matrix to buffer user inputs without core activation, and superb Tx & Rx current consumption.
• Cost: Highly integrated and unburdened with unnecessary peripherals, the CYW20829 is cost competitive with low-cost package offerings and the ability to route on 2-layer non-HDI PCB.
• Security: CYW20829 features support for the emerging Cyber Resiliency Act (CRA) and Radio Equipment Directive (RED) security regulations which will be mandatory for wireless products being sold on the European market.
• Zephyr: Enabled and supported on CYW20829, Zephyr addresses the growing number of OEMs leveraging the operating system.

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The best substation training program gives energy professionals the tools and knowledge to navigate the complexity of modern power systems. This sector involves advanced technology and evolving regulations that demand precision and adaptability. These programs focus on the real-world skills to keep operations safe, reliable and compliant with industry standards. 

Substations serve as critical hubs in the power grid, and even small errors can lead to costly downtime or serious safety risks. This makes specialized training a must for technicians and engineers. Combining technical expertise with a deep understanding of safety protocols prepares professionals to work confidently with high-voltage equipment, adapt to new digital systems and meet the regulatory demands of the energy sector.

• TRC Power Academy

TRC Power Academy is a top-tier training provider for substation professionals, thanks to its industry-aligned, hands-on approach that mirrors real utility environments. It has a cutting-edge training center in Lancaster, Pennsylvania, with a full-scale mock substation, complete with control house, yard, circuit-breaker and transformer simulators. Its facilities let engineers and technicians learn in a safe yet realistic setting.

Backed by experienced utility engineers, its courses cover relay protection, substation drawings, power transformers and outage planning. These programs are delivered through instructor-led sessions and self-paced online modules that meet the International Electrical Testing Association (NETA) standards. 

Beyond fundamentals, TRC Power Academy includes a 10-week training sequence followed by six months of field application and advanced refresher sessions. This rare opportunity fosters real professional connections while reinforcing safety and technical consistency. For energy and inspection professionals who want deep learning in a practical, engaging environment, TRC Power Academy delivers with clarity, credibility and career-building momentum.

• Siemens Power Academy

Siemens Power Academy offers users a highly interactive and expert training experience tailored to energy professionals. It offers more than 300 curated courses — including core substation automation, protection, digital substations and cybersecurity — through hands-on workshops, virtual sessions and simulation-based options.

Its structured course catalog spans crucial topics, such as substation automation, distributed energy automation, self-consistent-charge parameterization and Process Bus integration. It covers station- and process-level digital substation design and cutting-edge modules on microgrids, grid planning and smart communication systems.

Participants benefit from training personalization through Siemens’ consultative approach, which ensures each learner’s unique goals align with their curriculum. They can enjoy flexible delivery formats — classroom, on-site, e‑learning or simulator‑based — to fit different learning styles and schedules. For professionals in energy and inspection services seeking advanced, credible and future-ready training, Siemens offers the depth in substation technology and delivery flexibility to stay ahead in the field.

• GE Digital Energy Training Programs

The GE Digital Energy Training Program is an exceptional partner for substation training because it offers a comprehensive and flexible learning ecosystem that blends technical depth with real-world relevance. Its global network of technical institutes delivers hands‑on courses in electrical grid safety, equipment operations, protection, control and network management. These programs are taught by seasoned experts using full‑size gas-insulated bays, air-insulated substation components and real equipment to bridge theory with practice.

Participants can undergo modern training formats, including virtual reality modules that simulate real-world procedures, modular classroom instruction and customizable certification tracks. Courses cover various topics such as gas-insulated substation fundamentals, digital substation systems and high‑voltage substation environments. These modules deliver practical skills and theoretical understanding in formats tailored to engineers and new professionals.

The GE Digital Energy Training Programs come with assessments and certification, which ensure learning matches individual needs and modern delivery preferences. It equips energy and inspection professionals with cutting-edge, adaptable training that builds competence and career momentum.

• ABB Power Grids Learning Center

ABB Power Grids Learning Center provides a robust training ecosystem through its ABB University. It offers targeted programs on digital substation products and modern protection systems. These programs offer flexible delivery — interactive classroom sessions, e-learning, webinars and fully tailored on-site courses — so learners can engage in the format that best fits their goals.

Trainees gain hands-on access to protection and control relays with the guidance of expert trainers. They can master everything, such as basic relay operations, advanced engineering, application specifics and critical topics like cybersecurity and fault management. ABB’s commitment to customization ensures its training comes in multiple languages and is delivered securely via VPN when necessary.

ABB offers up-to-date course content, global accessibility, a practical focus and a strong emphasis on safe, reliable system operation. With its legacy of innovation behind it, its training stands out as a compelling choice for professionals aiming to boost their substation expertise and operational confidence.

• Eaton Electrical Engineering Services & Training

Eaton Electrical Engineering Services & Training delivers exceptional substation and electrical training. Its experience centers give energy professionals a versatile, high-impact learning path in real-world environments. With over a century of expertise in power systems and decades of hands-on experience, Eaton combines deep industry knowledge with flexible course formats. 

It offers in‑person instruction at world-class facilities in Pittsburgh and Houston, remote instruction, eLearning and even virtual simulations. Learners can train anywhere on topics like power distribution equipment, testing, safety, relay and transformer maintenance, compliance, and arc flash protection. Its instructors are seasoned field engineers who actively shape industry safety standards, which ensures training stays current and authoritative. 

Eaton’s hands-on offerings include courses like Basic Protective Relay Testing, Transformer Startup and Maintenance, Power Quality Monitoring and Analysis, and Electrical and Arc Flash Safety. It blends practical insight, recognized credentials and schedule-friendly delivery, making it a smart, credible choice for substation-focused learning in the energy and inspection sector.

• Megger Training Services

Megger Training Services equips technicians and apprentices with hands‑on skills and solid technical know‑how. Its Substation Maintenance I course guides participants in safely maintaining and testing industrial and utility substation equipment. It includes immersive lab sessions focused on medium-voltage circuit breakers and switchgear, which help learners spot weak components and ensure operational readiness.

For those ready to level up, Substation Maintenance II provides advanced training centered on transformer-related operations, expanding on the foundational skills from the first course. These courses feed into a robust Substation Technician Certification program that validates mastery in maintaining breakers, transformers, safety protocols and relevant OSHA standards.

Beyond classroom training, Megger enhances ongoing skill development via expert-led webinars and a rich online knowledge hub with technical articles and support. Megger combines practical, high-impact classroom experiences with accessible learning tools and recognized credentials. It gives energy professionals the tools, confidence and recognition they need to excel in substation operations.

• Schweitzer Engineering Laboratories (SEL) University

Schweitzer Engineering Laboratories (SEL) University delivers a standout substation training experience. It equips engineers and managers with a full spectrum of learning options, including in-person, virtual and self-paced e-learning formats to suit every schedule and learning preference.

Its programs span a range of specialized courses. The Transmission Substation Relay Testing class teaches learners to input settings, test, commission and troubleshoot relays via immersive exercises. Meanwhile, Substation Equipment Protection dives deep into protection schemes for high-voltage transformers, buses, capacitor banks and reactors. It includes real-world fault analysis and relay setting calculations.

Beyond course delivery, SEL diligently instills real-world safety. Its Charlotte facility features a simulator of a substation control house and yard where staff train on hazard avoidance in realistic scenarios. SEL University offers unmatched expertise, credibility and adaptability in substation education. It’s ideal for energy and inspection professionals seeking deep, practical and grounded in real control environments.

• American Public Power Association (APPA) Academy

The American Public Power Association (APPA) Academy supports public power utilities with tailored workforce development. It offers in‑person seminars, certificate programs, webinars and on-demand training that often include critical substation safety and maintenance content in its utility-focused courses.

For example, Snohomish County Public Utilities District hosted a five‑day course blending electrical theory and hands‑on training. It taught technicians how to test equipment and identify trends that signal imminent failure, which reinforces reliability and safety in substation operations. Working with APPA, the Missouri Public Utility Alliance also launched a two‑week apprenticeship that includes substation safety training. It helped apprentices gain real‑world substation skills alongside foundational distribution practices.

Alongside supportive resources like the APPA Safety Manual, these offerings make APPA a valuable ally for utilities aiming to elevate substation training, maintain compliance and reinforce safety. Engaging with APPA and its network means tapping into community-curated tools, collaborative training opportunities and up-to-date safety frameworks that help energy and inspection professionals.

• Electric Power Research Institute (EPRI) Training Programs

The Electric Power Research Institute (EPRI) Training Programs offer impressive, on-demand webinars and workshops tailored to substation topics. These include dissolved gas analysis, partial discharge detection, transformer monitoring and circuit-breaker restrike explanations. Each program delivers deep technical insight in a flexible, self-paced format.

These training modules blend research-backed content with real-world application. They are ideal for energy and inspection professionals seeking to sharpen their expertise on transformer condition monitoring, substation equipment behavior or fault detection methods. Further, EPRI offers instructor-led workshops like the Substation Ground Grid Inspection Workshop, delivering hands-on inspection techniques and peer-to-peer learning that bring theory to life.

As a nonprofit research authority, EPRI delivers up-to-date best practices rooted in industry science and innovation. It’s a smart, credible choice for professionals who value evidence-based learning and want to stay ahead in substation operations and reliability.

Skills and Competencies Gained From Substation Training

The best substation training program gives energy professionals the well-rounded expertise to excel in the power industry. It builds technical competencies by teaching precise equipment operation, thorough switchgear maintenance and the intricate workings of relay protection systems.

Safety and compliance are woven into every lesson, with practical instruction in electrical hazard awareness, lockout/tagout procedures and arc flash protection that keeps people and infrastructure secure. Learners also develop sharp inspection and testing skills, using diagnostic tools, thermography and proper gas handling to spot issues before they escalate.

To future-proof their careers, participants gain insight into emerging technologies like digital substations, smart monitoring systems and remote diagnostics. These skills align with the industry’s shift toward smarter, more connected grids. By blending hands-on practice with forward-looking knowledge, the best programs ensure professionals can work confidently, meet compliance demands and adapt to whatever the grid’s future holds.

Maximizing Career Impact From the Best Substation Training Program

Certifications from reputable training programs are powerful career accelerators in the energy sector. They signal to employers and clients that a professional meets recognized industry standards and has the skills to handle complex, high-stakes work. They often open doors to higher-level positions, specialized project assignments and increased earning potential. 

Many training providers also foster valuable networking opportunities, which connect participants with peers, industry veterans and potential employers through workshops and alumni networks. These connections can lead to collaborations, mentorships and job referrals that would be hard to find otherwise. 

Ongoing certification also helps professionals stay ahead of compliance and safety requirements. It ensures their work practices align with evolving regulations, technical standards and industry best practices, keeping careers and operations future-ready.

Industry Trends Shaping Substation Training

The best substation training program prepares professionals to thrive in a sector reshaped by digitalization and automation, and they use smart systems to streamline operations and boost efficiency. Participants learn to work with AI-driven monitoring tools that enhance predictive maintenance, detect faults in real time and improve grid reliability.

With renewables integration and energy storage transforming how substations balance supply and demand, the program equips learners with the skills to manage these new complexities while maintaining stability and compliance. It also embraces the post-pandemic shift toward remote training technologies, offering interactive virtual simulations and online modules that make advanced learning accessible without sacrificing hands-on experience.

How to Choose the Best Substation Training Program

Choosing the right substation training program can significantly impact your career growth, technical skills and safety expertise. With so many options available, evaluating each program based on your professional goals, industry requirements and preferred learning style is important. The best choice will deepen your technical knowledge and provide recognized credentials and practical experience that translate directly to your work in the field.

• Check industry recognition and accreditation: Ensure the program is backed by reputable organizations and aligns with standards from bodies like NETA or OSHA.
• Look for experienced instructors: Choose providers whose trainers have real-world substation and utility experience.
• Evaluate hands-on training opportunities: Prioritize programs that offer lab work, equipment simulations or on-site practice to apply concepts in realistic scenarios.
• Confirm coverage of compliance and safety: Make sure the curriculum includes current safety protocols and hazard mitigation strategies.
• Review available learning formats: Select a delivery method that fits your schedule and learning style.
• Assess technology and facilities: For in-person training, look for modern equipment and simulation setups that mirror real substation environments.
• Consider post-training support: See if the provider offers refresher courses, alum networks or ongoing technical resources.
• Match the program to your career stage: Beginners may need foundational courses, while seasoned professionals might benefit more from advanced or specialized modules.

Staying Ahead Through Continuous Substation Training

Continuous learning in substation operations ensures professionals stay ahead of technologies, safety standards and regulatory requirements. You gain skills that strengthen performance and career growth by choosing the best substation training program. Evaluate programs based on current industry demands and the competencies you will need for the future.

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Deep learning architecture pertains to the design and arrangement of neural networks, enabling machines to learn from data and make intelligent decisions. Inspired by the structure of the human brain, these architectures comprise many layers of nodes connected to one another to gain increasing abstraction. As data goes through these layers, the network learns to recognize patterns, extract features, and perform tasks such as classification, prediction, or generation. Deep learning architectures have brought about a paradigm shift in the fields of image recognition, natural language processing, and autonomous systems, empowering computers with a degree of precision and adaptability to interpret inputs brought forth by human intelligence.

Deep Learning Architecture Diagram:

Diagram Explanation:

This illustration describes a feedforward network, a simple deep learning model wherein data travels from input to output in one direction only. It begins with an input layer, where, for example, every node would be a feature, fully connecting with nodes in the next hidden layer. The hidden layers (two layers of five nodes each) now transform the data with weights and activation functions, while every node in one layer connects with every node in the other layer: this complexity aids the network in learning complicated patterns. The output layer produces the final prediction-fully connected with the last hidden layer, it uses sigmoid in case of binary classification or softmax in case of multi-class. The arrows represent weights, which get adjusted during training to minimize the cost function.

Types of Deep Learning Architecture:

1. Feedforward Neural Networks (FNNs)

The simplest cases of neural networks used for classification and regression with a unidirectional flow of data from input to output form the basis for more complicated architectures

2. Convolutional Neural Networks (CNNs)

CNNs process image data by applying convolutional layers to detect spatial features. They are widely used in image classification, object detection, and medical image analysis because they can capture local patterns.

3. Recurrent Neural Networks (RNNs)

RNNs are ideal for working with sequential data such as time series or text data. The loops hold in memory information or state of previous computations, which prove useful in speech recognition and language modeling.

4. Long Short-Term Memory Networks (LSTMs)

LSTMs, which in turn are a type of RNN, can learn long-term dependencies as they utilize gates to control the flow of information through the cell. Some of their main uses include machine translation, music generation, and text prediction.

5. Variational Autoencoders (VAEs)

With the addition of probabilistic elements, a VAE extends the traditional autoencoder and can, therefore, generate new data samples. They find their use in generative modeling of images and text.

6. Generative Adversarial Networks (GANs)

GANs work by pitting two networks, a generator and a discriminator, against each other to create realistic data. They are known for producing high-quality images, deepfakes, and art.

7. Transformers

Transformers use self-attention to study sequences in parallel, making them excellent models in natural language processing. Models like BERT, GPT, and T5 use the Transformer as their backbone.

8. Graph Neural Networks (GNNs)

GNNs operate on graph-structured data; for example: social networks, or molecular structures. They learn representations by aggregating information from neighboring nodes-and are powerful for relational reasoning.

9. Autoencoders

These are unsupervised models that learn to compress and then reconstruct data. Autoencoders are also used for dimensionality reduction, anomaly detection, and image denoising.

10. Deep Belief Networks (DBNs)

DBNs are networks with multiple layers of restricted Boltzmann machines. They are used for unsupervised feature learning and pretraining of deep networks, which are then fine-tuned with supervised learning.

Conclusion:

Deep learning architectures are the backbone of modern AI systems. Each type, be it a simple feedforward network or an advanced transformer, possesses unique strengths suited to particular applications. With the continuing evolution of deep learning, hybrid architectures and efficient models are poised to spark breakthroughs in healthcare, autonomous systems, and generative AI.

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