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Distribution Partnership to Expand Presence Across APAC

Lotus Microsystems ApS, a power management solutions company, and EDOM Technology, one of the Top 10 Global Distributors, jointly announced the signing of a strategic distribution agreement for the Asia-Pacific region.

This collaboration combines Lotus Microsystems’ innovative power management solutions with EDOM Technology’s extensive distribution network, strong field application engineering (FAE) force, and deep market expertise. The partnership is designed to accelerate customer adoption, deliver superior technical support, and strengthen the presence of both companies across the fast-growing APAC markets.

Power and thermal management are crucial aspects of electronic design, especially in the rapidly developing computing, networking, and IoT markets. Effective thermal management ensures that devices operate within a safe temperature range, optimizing performance and extending their operational life. Lotus Microsystems’ work on high-efficiency power modules supports more sustainable computing by reducing energy losses and improving overall power usage effectiveness.

“This partnership with Lotus Microsystems allows us to bring differentiated and forward-looking solutions to our customers in the APAC region. We see great potential in Lotus Microsystems’ technology and are confident it will contribute to the success of our ecosystem.”
— Jeffrey Yu [CEO of EDOM Technology]

“We are delighted to partner with EDOM Technology, a recognized leader in distribution across Asia. This agreement marks an important step in our global expansion, enabling Lotus Microsystems to better serve customers in key APAC markets with the strong support and capabilities that EDOM provides.”
— Hans Hasselby-Andersen [CEO of Lotus Microsystems]

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By: STMicroelectronics

Vehicles across the continent are increasingly shifting from ‘passive’ to ‘active’ safety features with the aim of preventing crashes from happening at all – particularly since an EU mandate came into effect. One part of the shift towards active safety is the evolution of in-cabin driver and occupant monitoring systems. These systems combine critical data within one central intelligence network to create a better picture of human behavior in the car and contributing to a safer driving experience.

But the other – perhaps more crucial part – will be in the connected infrastructure that supports drivers on the road. Technologies like RADAR (Radio Detection and Ranging) and LiDAR (light detection and ranging) are set to detect traffic, perceive potential obstacles and understand the environment outside the vehicle. With these technologies combined, cars will be able to ‘talk’ to the environment around them – with the potential to usher-in a new era of connectivity and road safety.

RADAR vs LiDAR

Let’s dive into these technologies and how they work together. Both RADAR and LiDAR enable accurate depth sensing. RADAR uses radio waves to provide long-range object detection in even the most adverse weather conditions. LiDAR emits laser light pulses to offer high precision and detail for 3D mapping. By emitting radio waves that bounce off objects and return to the sensor, RADAR systems can detect how far away an object is, the relative speed of that object, the direction of its movement and – depending on resolution and signal processing – the size and shape of that object.

With this information, vehicles are better equipped to identify potentially dangerous situations and prevent crashes. Radar technology, for example, can detect if the car in front suddenly slows down and as the gap between vehicles decreases, automatic emergency braking systems can be deployed. On a technical level, automotive radar solutions typically use 24 GHz or 77 GHz bands, balancing range and resolution requirements. While 24 GHz radars are used in Advanced Driver Assistance Systems (ADAS) to provide safety features such as blind-spot detection, rear cross traffic alerts and collision avoidance, 77 GHz radars can detect obstacles like other vehicles, cyclists or pedestrians in the 30 to 250 meter range, even in low visibility conditions like fog, rain and snow.

By comparison, LiDAR systems emit a series of short bursts of light that reflect off objects and surfaces before returning to the sensor. The “time of flight” data is used to calculate the distance to an object and create a dense collection of 3D points, mapping out a detailed 3D model. This precise object detection and classification is ideal for more complex or even semi-autonomous driving scenarios, where distinguishing between pedestrians, vehicles, and road edges is crucial. LiDAR is typically more precise than RADAR, however LiDAR is more susceptible to distortion or lower performance in fog or rainy conditions.

The V2X layer

Many modern vehicles combine RADAR and LiDAR to formulate a more detailed and complete picture of the vehicle’s surroundings – and enables a future of Vehicle-to-Everything (V2X) communication. V2X refers to an intelligent ecosystem where cars are just one piece of the puzzle. With V2X communication, vehicles, road infrastructure, pedestrians and cellular networks or cloud-based services can exchange information in real-time.

The symbiotic relationship between these technologies is certainly exciting – with the potential to improve journeys and reduce road accidents. Environmental sensors would monitor road surface conditions. When moisture levels build up, this data can be communicated to adaptive road signs to automatically reduce speed limits. Thinking of congestion, traffic sensors can measure vehicle volumes and speeds, feeding into traffic signals so green lights can be extended when necessary to reduce traffic jams on the roads. This technology is yet to be deployed on a massive scale, but is already being tested in cities across the United States, China and Portugal – and the momentum will only increase as the benefits are felt by road users.

A connected ecosystem of cars

V2X technology transforms connected cars into mobile sensors. Each car will collect anonymized data about road conditions, hazards, and traffic patterns – and also hard braking events and airbag deployments to areas of poor visibility – to benefit all road users without compromising driver privacy. In this world, authorities could apply automatic speed limitations based on real-time data from vehicle clusters – by sending a warning to the driver about a pothole, or the car auto-adjusting for those conditions.

Pedestrians would also be safer. If a vehicle’s sensors may not “see” a child on a bike about to emerge from behind a parked car, a smart roadside unit equipped with V2X technology may catch it from another vantage point to warn nearby drivers so they can slow down or even trigger automatic braking.

Data governance in the V2X age

Questions of data privacy and accountability are also emerging as V2X capabilities continue to scale. Who is accountable if a software update introduces a safety flaw? And should anonymized safety data – from near-miss incidents to driver behavior patterns – be shared between automotive manufacturers to improve system-wide learning?

Safety improvements need to be balanced with the diminishing role of human agency in the vehicle. Though we are some years from fully autonomous vehicles, the shift is underway to reduce the margin for human error on the roads. Yet if drivers are less engaged or less able to intervene quickly if an incident arises, it could paradoxically increase risk in situations where manual override becomes necessary. The evolution of increasingly connected and autonomous vehicles must go hand-in-hand with transparency, good data stewardship, and appropriate human oversight if the industry is to build trust in a V2X-enabled future.

Driving 2.0

With RADAR, LIDAR and V2X technology, vehicles are on track to become one node in a much larger and more intelligent ecosystem. They will be able to make sense of the world around them, detecting and interacting with the road, other vehicles and their wider external environment so evasive action can be taken early to avoid collisions.

A comprehensive 360-degree view of a vehicle’s surroundings combined with the potential to safely share anonymized data will enable a new era of road safety.

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Tata Electronics Private Limited has signed a strategic Memorandum of Understanding (MoU) with Merck, a global leader in science and technology, to accelerate the development of India’s semiconductor ecosystem. The agreement, finalized, underscores a joint commitment to building robust capabilities in materials, fabrication, and supply chain infrastructure.

Under the partnership, Merck will prepare a full suite of advanced solutions for Tata Electronics, including high-purity electronic materials, advanced gas and chemical delivery systems, and turnkey fabrication infrastructure services. Merck’s AI-enhanced Material Intelligence solutions will also aid operations at Tata’s Semiconductor Fabrication Plant in Dholera, Gujarat.

The partnership encompasses more than just the transfer of technology. Merck will provide guidance on safety and production excellence practices and grant access to Athinia, a secure data analytics platform that enables collaboration at scale. The contract also foresees the establishment of local warehouses, the development of raw material supply chain, and talent development programs, all aimed at bolstering India’s position in the semiconductor sector in the world.

Tata Electronics has promised to invest ₹91,000 crore ($11 billion) in creating the Dholera semiconductor fabrication plant, the first of its kind in India. Once operational, the fab will manufacture chips for applications ranging from automotive and mobile devices to artificial intelligence and advanced computing, catering to both domestic and international markets.

This partnership is viewed as a major step in furthering the goals of the India Semiconductor Mission, establishing Merck and Tata Electronics as important figures in determining the future of high-tech production in the nation.

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The Uttar Pradesh government has introduced a draft policy aimed at strengthening electronics and smartphone manufacturing in the state, with a particular focus on the Noida region. The initiative is part of the state’s broader goal of becoming a $1 trillion economy by 2030.

The draft policy titled “UP Electronics Component Manufacturing Policy 2025” has a goal of providing an ecosystem to nurture domestic and international investors. A variety of incentives, such as capital investment subsidies, stamp and electricity duty waivers, and participation interest grants are proposed to gain more participants.

The state’s IT and electronics department confirmed that the policy was approved by the cabinet in September 2025 and has been made effective retrospectively from April 1, 2025.

The policy aims to achieve $50 billion worth of electronics production within the next five years. Electronics production from U.P. is expected to grow, attracting serious investment, creating massive employment, and cementing the state’s position as a major player in India’s U.P. electronics manufacturing is expected to grow multi-fold within that period.

With Noida as a confirmed centre for electronics and smartphone production, the policy is expected to enhance the state’s role in global supply chains supporting the greater vision of India as a hub for electronics manufacturing.

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Prime Minister Narendra Modi inaugurated Semicon India 2025, positioning India as a rising powerhouse in the global semiconductor industry. Addressing the summit, he said the world now looks to India not only as a trusted partner but as a future leader in chip innovation.

“Oil has been referred to as black gold in the semiconductor industry, but chips are the digital diamonds,” Modi said, highlighting India’s determination to become a full-stack semiconductor nation. Even though we started our trip later than others, we are now unstoppable. The world’s largest revolution will soon be made possible by India’s smallest chip.

Under the Atmanirbhar Bharat vision, the Prime Minister underlined that India’s efforts go beyond chip production and instead concentrate on creating a comprehensive semiconductor ecosystem that boosts competitiveness and self-reliance.

He further elaborated on the Indian semiconductor plan by connecting the dots with India’s stronger economic output. “GDP figures released for the first quarter indicate that India’s GDP is growing at a remarkable 7.8 percent. The growth is seen in every sector of the economy,” he said, putting semiconductor development into the picture of the national economy.

The summit came after Modi’s trip to Japan, where he visited Tokyo Electron Miyagi Ltd., a notable company in semiconductor technology. He explained the complementary relationship between Japan’s advanced technology and India’s nascent semiconductor manufacturing ecosystem and implied that there is more collaboration from other countries to come.

India’s semiconductor market, estimated to be worth between $45 and $50 billion in FY2024–2025, is expected to more than double to $100–110 billion by 2030, according to industry projections presented at the event. Together with international collaborations and regulatory backing, this quick growth is anticipated to solidify India’s position as one of the world’s most important chip-making destinations.

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In the heart of India’s rapidly transforming digital economy, a silent revolution is underway women are stepping into the circuits and chips of the electronics industry, carving out spaces in labs, production floors, design centers, and boardrooms. Once considered a male-dominated field, the Indian electronics sector is now increasingly recognizing the power of diversity, and women are playing a key role in this evolution.

From the factory lines in Noida to innovation hubs in Bengaluru, women are taking up the soldering iron, the oscilloscope, and the executive chair. And at the heart of this transformation is the Electronics Sector Skills Council of India (ESSCI)—a catalyst for empowering women through targeted skilling and industry-aligned training.

The Expanding Electronics Landscape

India’s electronics industry is expected to surpass USD 300 billion by 2026, fuelled by global shifts in supply chains, robust government incentives like the Production Linked Incentive (PLI) scheme, and rising domestic consumption of electronic goods. As India moves towards becoming a global hub for electronics manufacturing and design, the demand for a skilled, innovative, and diverse workforce is growing exponentially.

This surge brings with it immense career opportunities for women, especially in:

• Electronics Manufacturing Services (EMS)
• Semiconductor design and embedded systems
• Mobile and consumer electronics repair
• PCB assembly and quality control
• IoT, Robotics, and Automation
• Solar electronics and green energy solutions

The industry’s demand for precision, discipline, and focus makes women particularly well-suited for many of these roles. However, to fully harness this potential, skilling and upskilling are non-negotiable—and that’s where ESSCI plays a pivotal role.

ESSCI: The Enabler Behind the Change

Established under the Ministry of Skill Development and Entrepreneurship (MSDE), ESSCI is the nodal body dedicated to creating a skilled ecosystem for the electronics sector. With over 75 job roles developed and aligned to National Skill Qualification Framework (NSQF), ESSCI has been instrumental in mainstreaming women into electronics-related job roles.

Key Initiatives Include:

• Women-Centric Skilling Programs for roles like LED assembly, mobile repair, solar installations, and PCB soldering.
• Industry-Academia Partnerships to ensure real-world exposure and better placement outcomes.
• National Apprenticeship Promotion Scheme (NAPS) facilitation to integrate women into mainstream apprenticeships.
• Train-the-Trainer Models to build a strong base of female instructors, creating ripple effects in communities.

How to Get Started

1. Education: Pursue a B.Tech/B.E. in Electronics and Communication Engineering, Electrical Engineering, or related fields from private institutions. Specialized courses in VLSI, IoT, or embedded systems enhance employability.
2. Certifications: Enroll in ESSCI courses for industry-recognized certifications in semiconductor design, IoT, and AI.

Career Paths Open to Women

Whether a woman is a school dropout, an ITI student, or an engineering graduate, the electronics sector has space for everyone:

1. Skilled Technicians and Operators

Women are increasingly hired in electronics factories for their dexterity, precision, and focus, particularly in roles like soldering, assembling, testing, and quality control for products like smartphones, consumer durables, and electric vehicles (EVs). For example, Tata Motors employs 1,500 women in its SUV production line, and MG Motor India has 37% women on its shop floor.

Women with short-term skill training can begin careers in:

• Electronic assembly
• PCB soldering
• Component testing
• Quality inspection

These roles are in high demand in electronics manufacturing clusters like Sriperumbudur, Noida, and Pune.

2. Mid-Level Technical Jobs

Diploma holders and trained candidates can explore:

• Service and repair of smartphones, TVs, and consumer electronics
• Solar system installation and maintenance
• EV charging station technicians
• Automation and IoT device installation

3. Engineering and R&D Careers

Women are excelling in chip design, verification, and testing. The semiconductor industry is projected to grow significantly, with women’s participation expected to rise from 24–28% in 2020 to over 30% by 2027. Roles include VLSI design engineer and semiconductor manufacturing engineer. For B.Tech or M.Tech graduates in ECE or related fields, opportunities lie in:

• VLSI and embedded systems
• Hardware design and validation
• Product testing and compliance
• Robotics and sensor integration

With remote work and flexible hours becoming more acceptable, women engineers can balance family responsibilities and professional growth effectively.

4. Entrepreneurship

Skilled women are also turning into job creators by starting:

• LED bulb manufacturing units
• Repair centers for electronics and white goods
• Retail of components and accessories
• Local e-waste collection and recycling businesses

ESSCI supports such ventures by linking women to funding agencies, mentoring, and digital platforms.

Industry Trends Supporting Women

• Growth of the Electronics Sector: India’s electronics industry is projected to grow significantly, with the semiconductor market alone expected to reach $100–110 billion by 2030, driven by technologies like AI, IoT, 5G, and EVs. This creates a high demand for skilled professionals, including women.
• Gender Diversity Initiatives: Companies like Micron (28% women workforce) and NXP (24% women workforce) are fostering inclusive environments with flexible work policies, maternity benefits, and return-ship programs for women re-entering the workforce.
• Government Support:
  • Science and Technology for Women Program promotes women’s participation in STEM through research and skill development.
  • Skill India Initiatives provide training in VLSI, AI, and IoT, targeting women to bridge the skill gap.
  • The 2017 Maternity Bill and policies addressing workplace safety support women’s retention in the workforce.

High-Demand Roles and Salaries

• VLSI Design Engineer: ₹5–10 LPA (entry-level), ₹15–20 LPA (senior).
• Embedded Systems Engineer: ₹5–8 LPA (entry-level), ₹10–15 LPA (mid-level).
• PCB Design Engineer: ₹4–7 LPA (fresher), up to ₹12 LPA (experienced).
• Semiconductor Manufacturing Engineer: ₹6–10 LPA (entry-level), ₹15 LPA+ (senior).

Conclusion: 

The journey for women in electronics has just begun, and the signal is strong: India’s electronics industry needs women—not just as workers, but as leaders, innovators, and entrepreneurs. With the right mix of policy support, industry collaboration, and targeted skilling initiatives like those from ESSCI, the future circuit boards of India will not only carry current—they’ll carry the hopes of empowered women everywhere.

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Kaynes Semicon Private Limited, a pioneering Indian semiconductor manufacturer and Infineon, a global leader in semiconductor solutions, has signed a Memorandum of Understanding (MoU) to explore strategic collaboration opportunities in India’s fast- growing semiconductor market.

Strengthening India’s Semiconductor Supply Chain

This collaboration will add a significant milestone with the launch of the first Kaynes Semicon MEMs Microphone, featuring Infineon’s reliable and market proven bare die, a breakthrough in domestic semiconductor module manufacturing. This “Made in India” MEMs Microphone will target for especially TWS earbuds, positioning Kaynes Semicon at the forefront of next generation wearable tech.

Additionally, Infineon will supply high-performance power solution bare die wafers to Kaynes Semicon, which will package them into discrete and module semiconductor products tailored for Indian customers.

By combining Infineon’s leadership with Kaynes Semicon’s advanced semiconductor packaging expertise, the two companies aim to strengthen India’s domestic reach and its global supply chain position. This collaboration will ensure a cost-optimized, locally integrated supply chain that delivers high-performance, reliable, and energy-efficient solutions with significantly reduced lead times for customers.

Driving Innovation Across Key Industries

By working together, Infineon and Kaynes Semicon will address critical semiconductor needs across various sectors, including:

• Energy Semiconductors & Renewable Solutions – Delivering high-efficiency technologies for solar, wind, and energy management applications.
• Industrial & Consumer Applications – Enhancing energy efficiency and performance in smart appliances and manufacturing processes.

With the Indian government prioritizing semiconductor self- reliance, this collaboration supports India’s goal of strengthening local production and reducing import dependency. It also lays the foundation for future innovation and deeper engagement in advanced semiconductor technologies, catering to India’s evolving needs in next-generation electronics.

“Infineon’s industry-leading solutions are known for its high performance, efficiency, and reliability across various applications, including automotive, consumer, industrial, renewable energy and data centers. By bringing together our know-how in semiconductors, with the semiconductor packaging and supply chain expertise of Kaynes Semicon, we are confident this partnership will drive India’s high-tech manufacturing push to greater heights. Congratulations to Kaynes Semicon on the opening of their new Gujarat plant, and we look forward to closer collaboration in the future,” said CS Chua, President and Managing Director, Infineon Technologies Asia Pacific.

“The launch of our first ‘Made in India’ MEMs Microphone, powered by Infineon’s technology, is a milestone moment for the Indian semiconductor industry. We are proud to be enabling next-gen innovations across wearable tech, renewables, and industrial sectors with a trusted global leader.” said Mr. Raghu Panicker, CEO, Kaynes Semicon.

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Delta, a global leader in power management and smart green solutions, unveiled a comprehensive portfolio of next-generation innovations at SEMICON India 2025, inaugurated by the Hon’ble Prime Minister of India, Shri Narendra Modi. The showcase features Delta’s Collaborative Robot, the DIATwin Virtual Machine Development Platform, advanced Smart Screwdriving System and Semiconductor Assembly Solutions, and an integrated Smart Manufacturing Architecture. Delta also demonstrated its Smart Green Facility Monitoring & Control Systems and Energy Management Solutions, along with SEMI E187 cybersecurity certification, reinforcing its commitment to advancing India’s semiconductor ecosystem with precision, resilience, and sustainability.

Speaking on India’s semiconductor journey, the Hon’ble Prime Minister said,“Semiconductor factories are coming up in India, and the country will see its first domestically made chip in the market by the end of 2025. Work is also progressing rapidly on developing a ‘Made in India’ 6G network.”

Aligning with this vision, Benjamin Lin, President, Delta Electronics India, said, “India is at the cusp of a semiconductor and electronics revolution, and Delta is proud to contribute to this transformation with future-ready technologies. By combining our deep global expertise with localized innovation, we aim to empower manufacturers with reliable, secure, and intelligent solutions that strengthen competitiveness and create long-term value for India’s high-tech ecosystem. Our efforts are deeply aligned with the Government’s Semiconductor Mission and Make in India initiative.”

Delta’s Collaborative Robot boasts payloads from 6 to30 kg, reach ranges from 800–1,800 mm, and IP66-rated protection. Equipped with Reflex Safety for instant stoppage on contact and the AI Cognitive Module kit for intuitive interaction via speech, gesture, and 3D object recognition.  In addition, the DIATwin Virtual Machine Development Platform shortens new product development by 20%, linking virtual production lines with real data to enable high-fidelity simulation and improved first-pass yield.

Niranjan Nayak, Managing Director, Delta Electronics India, added, “At Delta, we believe India’s journey to becoming a global semiconductor powerhouse will be driven by a strong digital and sustainable backbone. Through investments in collaborative robotics, digital twins, and green technologies, we are ensuring that India’s manufacturing ecosystem is not only competitive but also resilient and sustainable. Delta’s vision is to stand alongside India as it accelerates toward this milestone.”

Highlights of Delta’s booth at SEMICON India 2025 include:

• The showcase includes a Silicon Die Handling Solution for heterogeneous integration, a high-speed wafer feeder, and the High-Speed Die Pick-and-Place Solution powered by CODESYS controllers, enabling high-precision semiconductor assembly.
• Smart Screwdriving System – Torque up to 7.5 N·m, dual-tool capability (one controller managing two screwdrivers), and storage of 200,000 tightening results, ensuring unmatched assembly accuracy across automotive, aerospace, electronics, and medical sectors.
• Smart Manufacturing Architecture – Integrates OT and IT through DIASECS Semiconductor Equipment Standard Communication and Control Application Software), DIAEAP+ Equipment Automation Program, DIASPC Statistical Process Control, and DIAWMS Warehouse Management System, enabling predictive maintenance, process optimization, and seamless factory digitalization.
• Smart Green Facility Monitoring & Control Systems and Energy Management Solutions – Enable enterprises to optimize operations, reduce energy costs, and embed sustainability into production systems.

Dr Sanjeev Srivastava, Business Head- Industrial Automation SBP, Delta Electronics India, said, “India’s Semiconductor Mission and Make in India program are bold and visionary initiatives, and achieving them requires robust digital and automation backbones. With Digital Twin, Smart Manufacturing, and precision robotics, Delta is helping manufacturers move from concept to execution faster, safer, and more efficiently—positioning India as a global hub for high-tech manufacturing.”

Alongside its hardware innovations, Delta also showcased its software portfolio, including DIASECS Semiconductor Equipment Standard Communication and Control Application Software for standardized equipment communication, DIAWMS Warehouse Management System, DIAEAP+ Equipment Automation Program for efficient data collection, and DIASPC Statistical Process Control. Together, these platforms enable seamless integration, higher operational efficiency, and quality assurance while adhering to global semiconductor industry standards.

Anil Chaudhry, Head of Robotics & IA Solutions, Delta Electronics India, added, “Our technologies are not just about automation they are about resilience, agility, and long-term growth. By bridging IT and OT, we help companies break down silos, predict challenges, and adapt seamlessly to market volatility. This is the future of intelligent manufacturing, and we are proud to bring it to India under the vision of the Semiconductor Mission and Make in India.”

Delta is also setting a new benchmark in semiconductor equipment cybersecurity by adopting the SEMI E187 certification, ensuring greater reliability, resilience, and trust for customers operating critical manufacturing platforms.

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Decision tree algorithms continue to be one of the most reliable methods for converting unprocessed data into useful insights as artificial intelligence transforms various industries. With its quickly expanding tech sector, India is home to a number of businesses that are highly skilled at developing and implementing decision tree-based solutions in a variety of sectors, including banking, healthcare, retail, telecommunications, and agriculture. In order to provide highly accurate, scalable AI solutions, these companies use decision trees not only for classification and regression tasks but also incorporate them into sophisticated ensemble techniques like Random Forests and Gradient Boosted Trees. This article will examine the top 10 companies that are at the forefront of machine learning innovation powered by decision trees.

1. TCS

TCS uses decision tree models in Ignio in IT automation, including anomaly detection and predictive analytics. Its solutions span banking, manufacturing, and retail, assisting organizations in making reliable scalable advanced data-backed decisions.

2. Infosys

With its proprietary Nia platform, Infosys is able to use decision tree algorithms for customer analytics, supply chain optimization, and fraud detection. This company is also known for combining decision trees with deep learning to improve both the interpretability and accuracy of the system.

3. Entropik Tech

Entropik uses decision tree algorithms in emotion AI to classify user responses and predict behaviour. Their platforms combine decision trees with computer vision and EEG data to help brands decode consumer sentiment and improve engagement strategies.

4. Wipro

With the help of Wipro’s HOLMES AI and Automation platform, decision tree models can be used for cognitive automation, IT service management, and predictive maintenance. Wipro also combines decision trees with reinforcement learning and NLP to provide smart solutions in the healthcare, energy, and telecommunications industries.

5. Artivatic.ai

Artivatic.ai uses decision trees for its underwriting, fraud detection, and claims automation in insurance technology. Using them along with neural networks, Artivatic.ai’s platform provides explainable AI in health and life insurance, where decision trees are commonly used.

6. Fractal Analytics

Fractal uses algorithms based on decision trees in its Qure.ai and Cuddle.ai platforms, which specialize in healthcare diagnostics and business intelligence. By integrating decision trees with deep learning, they strive to elevate the interpretability and accuracy of their solutions in critical settings.

7. HCLTech

HCLTech’s DRYiCE suite uses decision tree algorithms to improve business functions, pinpoint anomalies, and improve workflows. Their models are applied and further developed with other methods in financial services, the automotive industry, and life sciences to improve functionality and scalability.

8. Zensar Technologies

Zensar uses decision tree algorithms in the reshaping of customer experiences, predictive analytics, and in the digital supply chain. Their AI-powered platforms deliver retail and logistics business intelligence and leverage decision trees to provide real-time analytics for better business decision-making.

9. Mu Sigma

Mu Sigma exploits decision tree techniques in their decision sciences, facilitating risk, churn, and operational optimization analytics for Fortune 500 firms. The company’s unique frameworks integrate decision trees with Bayesian methods, yielding more reliable analyses.

10. Tredence

Tredence creates AI-driven models for retailers by integrating decision trees with demand prediction, inventory management, and customer segmentation. The models function on analytics platforms and can scale on the cloud.

Conclusion:

The use of machine learning models based on decision trees has become pivotal in India’s evolving AI landscape. Numerous organizations, ranging from major IT corporations like TCS, Infosys, and Wipro to niche analytical businesses like Fractal Analytics, Mu Sigma are showcasing the capabilities of decision trees particularly in conjunction with ensemble methods like Random Forests and Gradient Boosted Trees in offering actionable, explainable, and scalable industry solutions.

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Union Electronics and IT Minister Ashwini Vaishnaw has said that India is making rapid strides in the semiconductor sector and is on track to be among the world’s top five chip‑making nations by 2032.

In a recent interaction, Vaishnaw mentioned that SEMICON India 2025 would be an important event to gain global partnerships, attract investments, and showcase India’s growing semiconductor ecosystem. He added, “With policy support and industry collaboration, our aim is to turn India into the semiconductor hub of the world.”

Recalling the chip making vision of the government, Vaishnaw talked about achievements in chip design, advanced packaging, and talent development. Regarding this, he mentioned that the first commercially available semiconductor chip made in India would be released soon.

The India Semiconductor Mission has allocated $10 billion for its first phase. This funding incorporates a plethora of initiatives such as manufacturing incentives, display fabrication units, compound semiconductors, design linked schemes, and research driven collaborations.

An end to end semiconductor ecosystem approach is being adopted encompassing chip design, equipment, materials and manufacturing so that India is fully plugged into the global semiconductor value chain. This approach is expected to lay a strong foundation for sustained industry growth.

Regarding the talent, 270 universities and 70 startups have been provided with advanced semiconductor design tools. Students have already designed 20 chipsets, several of which have been sent for fabrication, showcasing the country’s growing design capabilities.

With six semiconductor production facilities currently authorized or in development nationwide, manufacturing momentum is increasing in the meantime. In order to increase domestic output and lessen dependency on imports, these facilities are expected to be essential.

He emphasised India’s competitive advantage as policy support, engineering talent, and industry collaboration. The country’s electronics exports have already crossed $40 billion, which is an eightfold increase over the last 11 years.

Vaishnaw stressed the Indian Electronics System Design and Manufacturing (ESDM) industry’s strengths because of the semiconductor companies’ policy support and the robust engineering talent base. The ecosystem is getting more robust with several global semiconductor companies starting large R&D and design centres in India.

As India gears up to host SEMICON India 2025, it is expected that the semiconductor industry officials and the policy makers will come up with a roadmap that will expedite the journey of India to become semiconductors self-reliant and also strengthen the role in global supply chain.

By 2032, if present trends continue, India may rank among the world’s top five semiconductor powers, revolutionizing the country’s electronics manufacturing sector and making a substantial contribution to economic growth.

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• The upcoming facility in Gwalior, Madhya Pradesh, will have an annual capacity of producing 105MW of electrolysers, with a roadmap to scale up to 500 MW by 2030, contributing direction to the National Green Hydrogen Mission.
• Supported by ₹157.5 crore Production Linked Incentive (PLI) subsidy, the project stems from a landmark MoU between GH2 Solar and AHES Ltd. It will be backed by GH2 Solar’s UK based partner Rhizome Energy.
• ₹400 crore of total investment, with ₹100 crore allocated in the first phase to set up up a 3 GWh BESS assembly line, and the remaining ₹300 crore to be invested in phases by 2030 to expand the facility.
• The facility is expected to create 300+ direct jobs in the clean energy sector.
• Supported by Invest India and Skill council for Green Jobs to build renewable energy capabilities and train future workforce in line with the Government of India’s Atmanirbhar Bharat Mission.

GH2 Solar Limited, a next generation renewable energy company and one of only five companies in India Government’s PLI scheme for both green hydrogen production and electrolyser manufacturing, announced a major milestone under India’s Green Hydrogen Mission its upcoming state-of-the-art Green Hydrogen Electrolyzer Manufacturing Facility in Gwalior, Madhya Pradesh, in joint venture with South Korea-based Advanced Hydrogen Energy Solutions (AHES) Ltd.

The facility located in Pipersewa, Morena district (Madhya Pradesh), will begin with an annual manufacturing capacity of 105 MW awarded under SECI’s SIGHT program, supported by ₹157.5 crore Production Linked Incentive (PLI) subsidy. The total investment in the project is approximately ₹400 crore, with ₹100 crore allocated in the first phase to set up a 3 GWh BESS assembly line, and the remaining ₹300 crore to be invested in phases by 2030 to expand the facility. GH2 Solar has also outlined plans to expand the electrolyser capacity to 500 MW by 2030, directly contributing to the National Green Hydrogen Mission’s target of producing 5 million tonnes of green hydrogen annually by 2030. The announcement was marked by a Bhoomi Pujan ceremony in Gwalior, graced by Shri Dr. Mohan Yadav Ji, Hon’ble Chief Minister of Madhya Pradesh. The project was formally announced by Mr. Anuraj Jain, CEO and Founder of GH2 solar, alongside Prof. Joong-Hee Lee, CEO of AHES Ltd and Mr. Raj Sharma, Director of Rhizome Energy, UK, both key international partners of GH2 Solar’s green hydrogen journey.

Through the JV with AHES Ltd, GH2 Solar will bring advanced alkaline electrolyzer technology to India, with future expansion into PEM and other generation systems. In addition, GH2 Solar’s partnership with Rhizome Energy (UK) will embed sustainable design principles and advanced engineering practices,  to ensure the facility is competitive, efficient and manufactures tailored solutions as per Indian conditions, strengthening the vision of making India a global hub for green hydrogen.

Speaking on the occasion, Mr. Anurag Jain, Founder and CEO, GH2 Solar, “As India advances towards energy independence and transitions from fossil fuels to green hydrogen, our Electrolyser Manufacturing Facility will play a critical role in this journey. Through  global partnerships, we are bringing cutting-edge decarbonization technologies, while government support enables us to effectively leverage local resources. We are also committed to collaborating with academic institutions and skill development centers to train engineers and technicians, ensuring India has a robust workforce to drive green hydrogen technologies forward. Ultimately, our goal is to build a complete clean energy ecosystem that positions India as a leading producer and exporter of green hydrogen, with the workforce and technology to truly realize the vision of Atmanirbhar Bharat”

Adding to his perspective, Prof. Joong-Hee Lee, CEO of AHES, said, “The future is green, and no nation can achieve it alone. The world must unite in its commitment to sustainable energy. Our joint venture with GH2 Solar, brings this vision closer by producing electrolysers in India for the world. India already has skilled manpower, strong public institutions, and crucial government policy and funding support. We are happy to contribute to this ecosystem and believe our Gwalior facility will be an important step in shaping the world’s green future.”

On the public institution side, the project is supported by Invest India and Skill Council for Green Jobs. The facility’s operations are expected to create over 300 direct jobs in manufacturing, operations and research, along with hundreds of secondary jobs across supply chain, logistics, and renewable energy services. By building renewable energy capabilities and training future workforce, the facility also makes a significant contribution to the Government of India’s Atmanirbhar Bharat Mission.

The project supports the Government of India’s National Green Hydrogen Mission, which targets 5 million metric tonnes of annual green hydrogen production by 2030 and underpins India’s ambition to achieve net zero by 2070. The Gwalior facility is expected to play a crucial role in decarbonizing high-emission sectors such as steel, fertilizers, and refineries, while also creating opportunities for export to Europe and East Asia.

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Decision Tree learning is a widely used method in machine learning and data analysis for making decisions and predictions. It employs a tree-like model of decisions, where each internal node represents a test on a feature, each branch corresponds to an outcome of the test, and each leaf node signifies a final decision or classification. The process begins at the root node, which encompasses the entire dataset, and progressively splits into branches based on feature values, ultimately leading to distinct outcomes. This hierarchical structure allows for intuitive visualization and interpretation of decision-making processes. Decision Trees are incredibly versatile and find applications across a wide range of fields. Highlighted below are the top 10 decision tree learning real-world applications and use cases.

1. Fraud Detection

Identifying and preventing fraudulent transactions is one of the primary use cases of Decision Trees, and they are especially beneficial in banking as well as e-commerce centers. For instance, Decision Trees can flag suspicious transactions such as sudden exorbitant spending or transactions from new locations, which helps enterprises to minimize financial risks and combat security threats.

2. Customer Segmentation

Decision Trees are particularly useful in marketing, where customers can be classified into groups based on age, income, and even purchase and browsing history. This form of segmentation is especially useful for marketing as it helps personalize communication and enhances engagement by ensuring the right message is delivered to the appropriate audience.

3. Medical Diagnosis

Decision trees in the healthcare sector are essential for assisting clinicians in making predictions about the likelihood of a disease for a patient. This is derived from the patient’s symptoms, tests, and previous medical records. The trees’ logic is clear, which gives the doctors a chance to follow each step of reasoning, and this makes the tools invaluable in clinical decision support systems.

4. Recommendation systems

Decision trees are used in recommendation systems, such as on Netflix and Amazon, to suggest items, movies, or services by analyzing user preferences, browsing history, and ratings. These models help personalize the user experience and increase engagement by suggesting items that align with individual tastes.

5. Predictive Maintenance

In the sectors of manufacturing and transportation, decision trees based on sensor data, usage patterns, and equipment operating conditions are used to forecast equipment failure. This provides timely maintenance and improves the chance to provide uninterrupted service.

6. Autonomous Driving Decision Systems

Decision trees are important to the development of autonomous vehicles because they incorporate decision making models in driving systems. With their complex environments, these vehicles have to make safe and efficient decisions while learning the rules of the road, functionality of other vehicles, and traffic control. The vehicles accelerate, brake, and even change lanes based on the output of decision trees.

7. Cybersecurity Threat Detection

The use of decision trees in threat detection provides a more in-depth look into network traffic, different login schemes and their failures, as well as different system behaviors. Their use aids in the prevention of attacks and protection of crucial information.

8. Filtering of Email Spam

In order to classify messages, email providers analyze the words used, the sender’s reputation, and the structure of the message. They classify the messages using decision trees as either spam or legitimate email. Making email spam free and increasing security for the users.

9. Space Agencies and Aerospace Companies

Space and aerospace companies use decision trees in monitoring spacecraft systems and in predicting component failure and assist in mission planning. They help ensure safety and reliability in high-stakes environments.

10. Navigation and GPS Functionality

Decision trees are used by mapping and navigation software to provide the best possible route possibilities while accounting for user preferences, roadwork, and traffic conditions. Decision trees also consider the user’s objectives, whether to minimize travel time, fuel consumption, or increase safety. 

Conclusion:

Decision trees learning have a wide array of uses in data driven decision making, and thus can be considered a very strong and useful methodology. Their unique and flexible structure, ease of understanding and use, and transparency make decision trees very useful from the healthcare sector and the finance sector all the way to public administration and environmental care sectors. Decision trees can be used and are very crucial in the healthcare sector to help make very important and life saving decisions, and businesses also stand to benefit through the use of decision trees in optimizing their strategies. The impact of decision trees is very important and will grow even further as technology advances.

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Prime Minister Narendra Modi and his Japanese counterpart Shigeru Ishiba visited the Tokyo Electron Factory (TEL Miyagi) in Sendai. This visit was significant because it marked a focus of India and Japan’s cooperation in advanced technologies, especially semiconductors. The two leaders also emphasised the importance of this industry by taking the bullet train from Tokyo to Sendai, which is more than 300 km.

During the visit, Modi engaged with TEL executives regarding their position in the global semiconductor ecosystem and future partnerships with India. He emphasized how India’s growing manufacturing ecosystem and Japan’s cutting-edge semiconductor machinery and technology work in tandem.

In his remarks at the India–Japan Economic Forum, Modi highlighted semiconductors, batteries, and robotics as focus areas for Make in India collaborations. Prime Minister Ishiba laid out three goals: building stronger people-to-people ties, fusing technology with green initiatives, and boosting cooperation in high-tech fields, especially semiconductors.

The visit to Sendai came as a follow-up of the bilateral agreements made under the India-Japan Industrial Competitiveness Partnership and the Economic Security Dialogue. Both these agreements cover fields like critical minerals, ICT, pharmaceuticals, and more. An understanding was made to speed up the projects in these fields alongside semiconductors.

Involvement from the private sector is increasing steadily. Japanese firms have entered into around 150 MOUs over the last two years in sectors such as aerospace, automotive, semiconductors, energy, and human resources, as per the Ministry of External Affairs of India. Modi also remarked that the Digital Partnership 2.0, AI collaboration, and work on rare earth minerals will continue to be the focus of partnership.

Modi and Ishiba reiterated their vision of developing strong and trusted supply chains and India and Japan’s roles as critical partners in the framework of global technology security by keeping semiconductors as the focus of this visit.

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Union Minister Ashwini Vaishnaw inaugurated India’s first tempered glass manufacturing facility in Noida, marking a major milestone in the country’s electronics manufacturing ecosystem.

Noida now owns the distinction of having inaugurated India’s first tempered glass manufacturing unit, a step ahead in the electronics manufacturing journey.

The plant built in collaboration US technology giant Corning is owned and operated by Optiemus infracom. The factory will manufacture tempered glass for smartphones and other electronic devices, which is used as a protective layer and is used extensively.

Optiemus has emerged as a key player in India’s electronics manufacturing ecosystem, known for its strategic partnerships and innovation, Minister Vaishnaw described Optiemus, “a new gem in India’s fast-growing electronics manufacturing ecosystem,” and further stated that production of covered glass with Corning’s collaboration is slated to begin before the end of this year.

Investment and Production Capacity:

The Noida facility has been built with an initial investment of ₹70 crore and is, and it is furnished with an annual capacity of 2.5 crore units. In addition to supporting domestic manufacturing, the plant is projected to generate more than 600 direct jobs in the area.

Optiemus has set forth expansion plans of a larger scale. In the second phase of growth, the company aims to significantly increase its capacity to 20 crore units per year for the domestic market as well as for exports.

Phase 2 Expansion: 

For the next phase, the company wishes to open another plant in Noida with an annual capacity of 10 crore units. In addition, a new plant in southern India with a capacity of 15 crore tempered glass units is planned. An additional ₹800 crore is earmarked for this expansion, with the southern plant receiving more than ₹450 crore.

In addition, the company plans to launch its own brand of tempered glass, RhinoTech, in September 2025. Emphasizing domestic manufacturing, a ‘Made in India’ tag will be attached to the product. RhinoTech will have consumer-friendly features. For instance, it will be covered by a one-year warranty with unlimited replacement, which is bound to add value to the product in the market.

While speaking at the event, Minister Vaishnaw focused on the achievements of India’s electronics sector. In the past 11 years, this sector’s production value has increased six times, reaching ₹11.5 lakh crore. Exports have also grown to more than ₹3 lakh crore, and the industry supports 25 million jobs both directly and indirectly across the country.

The inauguration of this factory marks India’s entry into the tempered glass manufacturing industry, which was previously reliant on imports. The impact of this development is the expected improvement of the supply chain for smartphones and other electronic devices, which is in line with the government initiative to make India a global hub for electronics production.

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Reinforcement learning (RL), a subfield of machine learning in which agents learn by interacting with their surroundings, is gaining significant popularity in India’s quickly developing AI ecosystem. RL is being used in a variety of areas, including financial modeling, smart energy grids, and autonomous systems. Indian businesses are using RL to innovate and create scalable solutions that are on par with international standards, rather than merely adopting it. The top 10 reinforcement learning companies in India will be explored in this article:

1. Tata Consultancy Services (TCS)

As the global IT leader, TCS focuses on integrating RL into supply chain optimization, autonomous systems, and intelligent automation. It is AI laboratories work on adaptive algorithms that learn from changing environments in logistics, manufacturing, and operations for better decision making. The company also uses its platform TCS iON to apply RL to the fields of education and skill development, employing gamified and tailored learning to increase motivation and achieve better educational results.

2. Infosys

As led by the Infosys Topaz platform, the AI-first initiative of the company shows faster advances in Reinforcement Learning (RL). The platform’s robotics, enterprise automation, and conversational AI are improved by RL and RLHF (Reinforcement Learning with Human Feedback). The completion and integration of these technologies enable the creation of adaptive, scalable, and self-learning enterprise solutions, such as automated fraud detection systems, predictive analytics, and enhanced customer care.

3. Wipro

Wipro is currently engaging with Reinforcement Learning (RL) to upgrade automation, simulation, and intelligent systems across multiple sectors. The company utilizes RL in industrial automation and flight simulation, employing adaptive learning models to improve control mechanisms and decision-making procedures. Wipro’s investigations also extend to scalable RL methodologies for manufacturing and financial services, which facilitate more intelligent resource allocation and operational forecasting.

4. HCL Technologies

HCL Technologies is continuously refining the applications of Reinforcement Learning (RL) across various focus areas, including cybersecurity, workforce analytics, and education. In workforce analytics, HCLTech uses RL for the customization of learning pathways and the prediction of talent development, enabling companies to match employee evolution with their strategic objectives. Their partnership with Pearson brings even greater value in the education sector, where RL-driven adaptive learning systems customize services to the learners and enhance the mastery of skills.

5. ValueCoders

ValueCoders is an Indian software company specializing in adaptive smart system software development for healthcare, finance, and education sectors. They use computer vision, reinforcement learning, and MLOps to ease decision automation, enhance personalization, and boost system performance over time for their clients.

6. Locus

Locus is a top-class supply chain and logistics company that focuses on streamlining and automating supply chain operations with the use of reinforcement learning (RL). With Locus, businesses can now enhance the planning of delivery routes, scheduling of deliveries, and even the allocation of resources. This allows companies to better control and reduce costs, increase the efficiency of their operations, and better respond to fluctuating demand and traffic conditions.

7. Mad Street Den

Mad Street Den is the only company to blend reinforcement learning and computer vision through its Vue.ai platform to enhance personalized retail experiences. Their adaptive systems are designed to optimize merchandising, styling, and customer engagement on behalf of global fashion and e-commerce brands.

8. Arya.ai

With a deep focus on reinforcement learning and deep neural networks, Arya.ai addresses autonomous decision systems. Their SaaS products with real-time adaptation enabled for finance, insurance, and robotics industries address fraud detection, claims automation, and smart underwriting.

9. Infilect

Infilect uses visual intelligence platforms to implement RL in retail. Their technologies optimize pricing, merchandising, and shelf availability using RL-driven analytics, which helps brands lower stockouts and increase in-store compliance.

10. Flutura Decision Sciences

The major industries of oil and gas, chemicals, and heavy machinery benefit from Flutura Decision Sciences’ artificial intelligence and reinforcement learning approaches to machine learning, which are used to develop their industrial internet of things platform, Cerebra. With Flutura, these industries can improve asset performance, anticipate failures, and minimize downtime. To offer complex system digital twins, Cerebra delivers diagnostics and prognostics, which are supported by physics models, heuristics, and machine learning.

Conclusion:

With smart healthcare, smart agriculture, and smart city systems, autonomous systems powered by reinforcement learning are ready to take off, marking the beginning of the AI revolution. With the development of edge AI and quantum computing, real-time decision-making will be dominated by RL. Due to the culture of innovation, availability of skilled resources, and the country’s bold vision, India has the potential to lead the world in adaptive intelligent systems in the upcoming years.

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High Security Integration, Low Power, and Small Package, Providing Cost-Effective RoT

Nuvoton Technology released the upgraded NuMicro M2354, tailored for applications such as server RoT, smart city, IoT, and smart metering.

NuMicro M2354 is an Arm TrustZone microcontroller based on the Armv8-M architecture and powered by the Arm Cortex-M23 CPU, designed to enhance IoT security. It is suitable for long-term confidentiality requirements and highly sensitive data protection scenarios.

The M2354 operates at frequencies up to 96 MHz, offers a wide operating voltage range of 1.7V to 3.6V, and a broad operating temperature range of -40°C to +105°C. The power consumption is 89.3 μA/MHz in LDO mode and 39.6 μA/MHz in DC-DC mode. The Standby Power-down mode consumes less than 2 µA, and the Deep Power-down mode without VBAT consumes less than 0.1 µA, effectively extending the device’s battery life and meeting the needs of long-term IoT operation.

For Secure FOTA, the M2354 has built-in dual-bank Flash Memory of up to 1024 KB and 256 KB of SRAM. In addition to supporting eXecute-Only-Memory (XOM) to prevent code theft, it also integrates a cryptographic hardware accelerator that supports FIPS PUB 197/180/180-2/180-4 and NIST SP 800-38A, as well as a hardware key store to protect against side-channel and fault injection attacks. In terms of secure boot mechanism, the upgraded M2354 supports the Root of Trust architecture based on DICE, implemented in Mask ROM, and supports ECDSA P-521. This feature automatically generates a unique device identity and establishes a chain of trust during boot, effectively verifying firmware version and preventing firmware rollback and tampering attacks. Furthermore, M2354 is compliant with PSA Level 3 and SESIP Level 3 security certifications, which meet the demands of the EU’s Cyber Resilience Act (CRA).

M2354 supports a wide range of peripherals, including CAN, USB 2.0 full-speed OTG, PWM, UART, SPI/I2S, Quad-SPI, I²C, and RTC.

M2354 also integrates several analog components, including analog comparators, ADC, and DAC.

The package options include LQFP-48, LQFP-64, and LQFP-128. The upgraded M2354 also offers a compact WLCSP49 package. With support of the SPDM (Security Protocol and Data Model) secure communication protocol, the upgraded M2354 is well-suited for Root of Trust applications in server motherboards and daughterboards.

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Reinforcement Learning (RL), unlike other machine learning (ML) paradigms, notably supervised learning, has an agent learning to act optimally within a given environment, one step at a time. At each step, it is given feedback in the form of a reward or a penalty. The goal is to learn a policy a strategy for selecting actions that maximize the total reward over a certain time horizon. There are no inputs or outputs to fit to (as in traditional supervised learning), so RL agents must balance exploring unknown actions to discover their worth and exploiting known good actions to maximize rewards.

Reinforcement Learning History:

Reinforcement learning began with behavioural psychology’s theory of behaviourism in the early 1900s. Behaviourism postulated learning as a trial and error process propelled by rewards and punishments. This concept was later adapted and formalised into computer science mathematical models that paved the way for the development of optimisation and machine learning algorithms. Reinforcement learning is akin to optimising methods where the desired function is not explicitly given but is instead hinted at through trial and error.

How does reinforcement learning work:

To enhance decision-making, reinforcement learning works by training an agent to interact with an environment. The agent gets to perform actions. After each action, the agent gets feedback in terms of rewards or penalties associated with the specific action.

Types of Reinforcement Learning:

1. Value-Based Reinforcement Learning

This method requires an agent to learn a value function that predicts the reward for performing an action in a particular state and Q-learning is the most well-known. An agent updates its Q-values in Q-learning according to the received reward and acts to maximize these Q-values.

2. Policy-Based Reinforcement Learning

Policy-based methods focus on learning the policy itself, which is the set of rules mapping states to actions, instead of estimating value functions. This is crucial in cases with complex or continuous action spaces. Methods like REINFORCE and Proximal Policy Optimization (PPO) are good examples of algorithms that follow this paradigm.

3. Model-Based Reinforcement Learning

This refers to methods which try to construct a model of the environment that can predict the following state and reward given the current state and action. Using this model, the agent can plan and make decisions ahead of time. While this method is efficient in terms of samples, its implementation can be complicated to do correctly.

4. Actor-Critic Methods 

These hybrid methods combine the strengths of value-based and policy-based approaches. The actor updates the policy based on feedback from the critic, which evaluates the action taken. This results in more stable and efficient learning, especially in complex environments.

Applications of Reinforcement Learning:

1. Self-Driving Cars

Self-driving cars use reinforcement learning to understand their surroundings. They identify the best routes, change lanes, avoid obstacles, and optimize their overall driving.

2. Automated Machines

Automated machines use reinforcement learning to master new skills like walking, picking up objects, and putting them together. As they deal with new items and different tasks, they improve how they do things in due course.

3. Medicine

Personalized treatment is now possible because of reinforcement, which allows crafting adaptive treatment plans for patients. It is also useful in optimizing clinical trials and in the management of chronic illness.

4. Investment

In portfolio management and trading, reinforcement learning technologies attempt to make investment choices by evaluating prevailing market patterns and modifying tactics geared towards greater returns.

5. Recommendation Systems

Reinforcement learning is used to improve the recommendation systems. As users interact with the content, the system learns users preferences and dynamically suggests content making the platform personalized and more engaging.

Reinforcement Learning Examples:

Reinforcement learning is integrated into numerous fields enabling the technology to thrive. In game playing, RL has enabled breakthroughs like AlphaGo which mastered complex games such as Go and chess through self-play. In autonomous driving, self-driving cars use RL to make decisions like lane changes and obstacle avoidance by learning from real and simulated environments. In robotics, RL helps machines learn tasks like walking, grasping, and assembling by adapting to physical feedback. In finance, RL algorithms optimize trading strategies and portfolio management by analyzing market data. Lastly, in recommendation systems, platforms like Netflix and Amazon use RL to suggest content or products based on user behavior, enhancing engagement and satisfaction.

Reinforcement Learning Advantages:

Reinforcement learning is adaptive and its methods are goal driven. As an example, it can be very effective in environments that are constantly changing and that require very little supervision. It is a type of learning that is guided by rewards or feedback, in which an agent learns to improve its behavior over time based on interaction with the environment.

Conclusion:

As the rest of intelligent systems, reinforcement learning is, for now, an incredible advancement and is bound to become even more so. The level of innovation that RL will bring about will be unimaginable given the availability of more processing power and much more sophisticated algorithms. Preemptive systems, self-learning autonomous agents, and machines that collaborate with humans are only the beginning. Personalized medicine, self-developing robots, and adaptive learning systems will all lean on RL technologies. These technologies will not just adapt to the world, but will actively ‘mold’ it, in essence, making the word ‘transformative’ obsolete in describing the level of change these technologies will bring.

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Infineon Technologies AG announced that its microcontrollers (MCUs) in the new PSOC Control C3 Performance Line family are compliant with Post-Quantum Cryptography (PQC) requirements for firmware protection outlined in the Commercial National Security Algorithm (CNSA) Suite 2.0. The MCUs also support PSA (Platform Security Architecture) Level 3 compliance. By complying with both standards, Infineon’s PSOC Control C3 Performance Line meets the security needs of a wide range of industrial applications and eases their transition to increased security in the PQC era.

“With the PSOC Control C3 family, we are setting a new standard for security in industrial microcontrollers, building on decades of proven experience in MCUs and secured electronic systems,” said Steve Tateosian, SVP and General Manager, IoT, Consumer and Industrial MCUs, Infineon Technologies. “Infineon is committed to meeting and evolving industry requirements for MCU embedded security that provides stringent protection against quantum-based attacks on critical systems.”

Changes in security architecture for the PQC era include the replacement of Elliptic Curve Cryptography (ECC) based asymmetric cryptography as well as increasing the size of Advanced Encryption Standard (AES) keys and Secure Hash Algorithm (SHA) hash sizes. The algorithms and implementation guidelines provided by CSNA 2.0 help to facilitate a smoother transition to Post-Quantum Cryptography.

About PSOC Control C3 family

The PSOC Control C3 family of MCUs provide real-time control for motor control and power conversion applications. New MCUs of the PSOC Control C3 Performance Line enable system performance at high switching frequencies and increase control loop bandwidth. That is achieved with proprietary autonomous hardware accelerators as well as high resolution and high performing analog peripheral support. The family supports systems designed with wide-bandgap switches while achieving best-in-class control loop frequencies, accuracy and efficiency for applications such as data centers, telecom, solar and electric vehicle (EV) charging systems.

Specific security features include support for Leighton-Micali Hash-Based Signatures (LMS), which is an efficient post-quantum cryptography FW verification algorithm integrated with SHA-2 hardware acceleration for peak performance. To maximize ease of use, Infineon’s Edge Protect Tools and ModusToolbox will support everything a customer needs to provision LMS keys as well as options for hybrid post-quantum cryptography where customers may use both LMS and ECC to sign firmware updates which can be verified by Infineon chips.

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Decision Tree Learning is a type of supervised machine learning used in classification as well as regression problems. It tries to mimic real-world decision making by representing decisions and their possible outcomes in the form of a tree. Each internal node in the tree denotes a test on a feature, each branch denotes an outcome of the test, and the leaf node gives the final decision. It is easy to understand, requires no complex data preprocessing, and is visually very informative.

Decision tree learning history:

The concept of decision trees has roots in decision analysis and logic, but their formal application in machine learning began in the 1980s. The ID3 algorithm, developed by Ross Quinlan in 1986, was one of the first major breakthroughs in decision tree learning. It introduced the use of information gain as a criterion for splitting nodes. This was followed by C4.5, an improved version of ID3, and CART (Classification and Regression Trees), developed by Breiman et al, which used the Gini index and supported both classification and regression tasks. These algorithms laid the foundation for modern decision tree models used today.

How does decision tree learning work:

Decision tree learning is a type of algorithm in machine learning where data gets split into smaller subsets and gets organized in the form of a tree. The splitting is based on the value of the data features. At the beginning, with the root node, a feature of the data gets selected. This selection feature tends to be the one that gets deemed most informative by the Gini impurity or entropy criteria. As mentioned earlier, internal nodes get to represent a certain decision rule. This process continues until the data is sufficiently partitioned or a stopping condition is met, resulting in leaf nodes that represent final predictions or classifications. The tree structure makes it easy to interpret and visualize how decisions are made step by step.

Types of Decision Trees:

1. Classification Trees

These are utilized when the dependent variable is categorical. Such trees assist in categorizing the dataset into specific categories (e.g., spam and non-spam). Each split aims to enhance class separation based on certain features.

2. Regression Trees

These trees are used when the dependent variable is continuous. Unlike categorization, these trees aim to provide numerical predictions (e.g., house prices). The split in these trees is done for minimizing prediction error.

Examples of Decision Tree Learning:

• Email Filtering: Marking emails as spam or not using keywords and sender details.
• Loan Approval: Deciding loan approval using income, credit score, and employment status.
• Medical Diagnosis: Identifying a disease with the help of symptoms and test results.
• Weather Prediction: Predicting rain using humidity, temperature, and wind speed.

Applications of Decision Tree Learning:

1. Finance

Decision trees analyze customer data and transaction behavior for credit scoring, fraud detection, and risk management.

2. Healthcare

With the use of medical records and test outcomes, they aid in disease diagnosis, treatment suggestions, and patient outcome predictions.

3. Marketing

Segmenting customers, predicting buying behavior, and optimizing campaign strategies based on demographic and behavioral data.

4. Retail

Forecasting sales, managing inventory, and personalizing product recommendations.

5. Education

Predicting student performance, dropout risk, and tailoring learning paths based on academic data.

Decision Tree Learning Advantages:

Decision Tree learning has numerous benefits, all of which contribute to its widespread use in machine learning. It is simple to grasp and analyze because the structure of the tree is akin to human decision-making and can be easily visualised. It can process both numerical and categorical data without the need for advanced data preprocessing or feature scaling. Decision trees are not affected by outliers or missing data, and they can model non-linear patterns in data. It requires very little in the way of data preparation and is immensely powerful and user-friendly because it inherently takes into account feature combinations through its hierarchical splits.

Conclusion:

Decision Tree Learning is going to mature into a dynamic, real-time intelligence system processing complex data, providing direction to autonomous systems, and enabling accountable decision-making in all sectors. These trees will, in time, become self-optimizing systems that reason, tell stories, and co-exist with human cognition, and they will serve as the ethical and intellectual foundation of future AI.

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New solution empowers engineers to meet HDMI Forum compliance standards while optimizing signal integrity and performance across high-bandwidth video applications

Keysight Technologies, Inc. announced the release of its enhanced physical layer compliance test solution for high-definition multimedia interface (HDMI), delivering robust compliance and performance validation capabilities for transmitter [source] and cable devices. The Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI addresses the growing complexity, and bandwidth demands of modern HDMI applications, including ultra-high definition (UHD) video, high dynamic range (HDR) content, and immersive audio experiences.

With the rising demand for 8K/12K video, HDR content, and high-speed connectivity, engineers face growing challenges in maintaining signal integrity across HDMI interfaces. The recent release of the HDMI 2.2 test specification by the HDMI Forum introduces more stringent compliance requirements for transmitters and cables, highlighting gaps in traditional test coverage. Without robust validation tools, manufacturers risk costly redesigns and certification delays. As HDMI technology advances, the need for comprehensive, automated test solutions is critical to ensure performance, reliability, and faster time-to-market.

As the HDMI ecosystem evolves to support higher resolutions, faster refresh rates, and increased bandwidth demands, the Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI offers a fully automated and scalable platform for professionals in design, engineering, and compliance testing to validate device performance with confidence and precision. The new test solution provides a unified platform for automated electrical testing as specified in the HDMI 2.2 test specification, ensuring that device manufacturers can confidently validate product performance at the transmission and cable, while reception testing is introduced at a later stage.

Keysight’s physical layer compliance test solution for HDMI meets the latest technical and procedural demands of the HDMI Forum. Designed for precision and efficiency, the solution integrates high-bandwidth measurement hardware with automated compliance workflows to manage complex test scenarios across transmitters and cables. The modular architecture of the solution supports flexible test configurations, while built-in diagnostics provide deep insight into the root causes of signal degradation. This enables design and validation teams to not only verify compliance but also optimize performance early in the development cycle.

Han Sing Lim, Vice President and General Manager of Keysight’s General Electronic Measurement Division, said: “With the introduction of the Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI, our customers can accelerate time-to-market for next-gen consumer electronics while ensuring robust integrity and regulatory compliance. By incorporating the latest version of HDMI technology in our solution, we are enabling leading consumer electronics designers and manufacturers to continue to push the boundaries of digital display and multimedia performance.”

Backed by Keysight’s global expertise in compliance testing and proven in high-volume production environments, the new solution delivers a trusted path to certification readiness and superior end-product performance.

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