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A complex web of communication systems inextricably links the modern world. From the simple act of placing a phone call to the intricate orchestration of global data networks, these systems have become the lifeblood of our societies, driving innovation and shaping every aspect of our lives.

Advanced communication systems are not only driving technological progress but are also shaping industries, economies, and lifestyles. However, the relentless march of technological progress demands a constant evolution of these systems to meet the ever-growing demands of a hyper-connected world.

From the rapid expansion of 5G to the groundbreaking development of quantum communication, this article explores the latest advancements, challenges, and transformative potential of these systems.

Evolution of Communication Systems

The journey of communication systems has been marked by exponential growth, from basic telegraphs to sophisticated wireless networks. Each generation of communication technology has introduced faster speeds, higher reliability, and greater accessibility:

• 1G to 4G: Early systems focused on enabling voice communication, followed by text and basic data services. The advent of 4G revolutionized mobile broadband with faster internet and video streaming capabilities.
• 5G Revolution: The fifth generation (5G) marked a paradigm shift, enabling ultra-low latency, high-speed connectivity, and massive device interconnectivity.

As the world moves beyond 5G, research on 6G and other cutting-edge technologies is gaining momentum, promising to redefine communication systems yet again.

Key Advancements in Communication Systems

1. 5G and Beyond

5G networks are unlocking new possibilities across industries, with key features such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). These capabilities are driving applications like autonomous vehicles, smart cities, and industrial automation.

Looking ahead, 6G networks are projected to offer:

• Terahertz (THz) frequency bands for ultra-high data rates.
• AI-driven network optimization.
• Integration of communication and sensing for applications like precision healthcare and environmental monitoring.

2. Quantum Communication

Quantum communication is emerging as a revolutionary technology for secure data transmission. Leveraging the principles of quantum mechanics, such as superposition and entanglement, it ensures unparalleled security through Quantum Key Distribution (QKD). Recent advancements include:

• Satellite-based quantum communication networks, exemplified by China’s Micius satellite.
• Integration of quantum repeaters to extend the range of quantum networks.

3. Satellite Communication

The rise of Low Earth Orbit (LEO) satellite constellations, such as SpaceX’s Starlink and OneWeb, is transforming global connectivity. These systems offer high-speed internet to remote and underserved regions, addressing the digital divide. Advancements in phased-array antennas and laser-based inter-satellite links are further enhancing satellite communication capabilities.

4. Software-Defined Networking (SDN) and Network Function Virtualization (NFV)

SDN and NFV are revolutionizing network architecture by decoupling hardware from software. These technologies enable:

• Dynamic network management and traffic optimization.
• Cost-efficient deployment of virtualized network functions (VNFs).
• Faster rollout of updates and new services.

5. Artificial Intelligence and Machine Learning

AI and ML are becoming integral to modern communication systems, enhancing efficiency, reliability, and adaptability. Key applications include:

• Predictive network maintenance to minimize downtime.
• Intelligent resource allocation for optimized bandwidth usage.
• Real-time anomaly detection and cybersecurity.

6. Optical Communication

Optical communication, leveraging fibre-optic technology, continues to advance with innovations such as:

• Multi-core and hollow-core fibres for higher data transmission rates.
• Free-space optical communication (FSO) for wireless, high-speed data transfer.
• Integration with 5G networks for enhanced backhaul and fronthaul.

Applications of Advanced Communication Systems

1. Healthcare

Telemedicine, remote patient monitoring, and robotic surgeries are becoming more feasible with advanced communication technologies. For instance:

• 5G networks support real-time video consultations and transmission of high-resolution medical imaging.
• IoT-enabled medical devices ensure continuous health monitoring and timely alerts.

2. Autonomous Systems

From self-driving cars to unmanned aerial vehicles (UAVs), advanced communication systems are critical for enabling autonomy. Ultra-reliable and low-latency networks ensure seamless data exchange between devices, improving safety and efficiency.

3. Smart Cities

Smart cities rely on interconnected systems to optimize urban infrastructure. Advanced communication systems enable:

• Smart grid management for energy efficiency.
• Intelligent transportation systems (ITS) to reduce traffic congestion.
• Real-time environmental monitoring and disaster response.

4. Industrial Automation

Industry 4.0 is driven by advanced communication technologies that facilitate:

• Real-time data exchange between machines, robots, and sensors.
• Predictive maintenance to reduce downtime and operational costs.
• Digital twins for process simulation and optimization.

Challenges in Advanced Communication Systems

1. Spectrum Scarcity

The increasing demand for wireless communication is putting pressure on the available radio frequency spectrum. Efficient spectrum management and the exploration of higher frequency bands, such as millimeter waves and THz waves, are crucial to address this issue.

2. Security and Privacy

As networks become more interconnected, they become more vulnerable to cyberattacks. Ensuring robust encryption, secure authentication protocols, and real-time threat detection is imperative.

3. Infrastructure Costs

Deploying advanced communication networks, especially in remote or underserved areas, involves significant investment. Innovations in cost-effective technologies and public-private partnerships are needed to bridge this gap.

4. Environmental Impact

The energy consumption of communication networks is rising with the proliferation of data-intensive applications. Developing energy-efficient network components and leveraging renewable energy sources are essential for sustainable growth.

Future Directions

1. Integration of AI and 6G

The synergy between AI and 6G will enable intelligent and adaptive networks, capable of self-optimization and self-healing. AI-driven communication systems will also support advanced applications such as immersive extended reality (XR) and holographic telepresence.

2. Global Quantum Internet

The vision of a global quantum internet is becoming a reality, with efforts focused on building scalable quantum networks. This technology will redefine secure communication, scientific research, and even financial systems.

3. Interplanetary Communication

As space exploration intensifies, advanced communication systems are being developed to support interplanetary missions. NASA’s Deep Space Optical Communications (DSOC) project aims to enable high-speed data transmission between Earth and spacecraft.

4. Convergence of Technologies

The convergence of communication, computing, and sensing technologies will lead to new paradigms, such as the Internet of Everything (IoE), where devices, data, and humans interact seamlessly.

Conclusion

Advanced communication systems are transforming the way we connect, collaborate, and innovate. By pushing the boundaries of speed, security, and scalability, these technologies are laying the foundation for a smarter and more sustainable future. As we navigate the complexities of deployment and adoption, a focus on innovation, inclusivity, and sustainability will be critical in harnessing the full potential of advanced communication systems.

The post The Future is Connected Embracing the Advanced Communication Revolution appeared first on ELE Times.

Quintessent Inc of Santa Barbara, CA, USA — which specializes in heterogeneous integration of quantum dot lasers and silicon photonic integrated circuits (PICs) — and epiwafer and substrate maker IQE plc of Cardiff, Wales, UK have partnered to establish what is said to be the world’s first large-scale quantum dot laser and semiconductor optical amplifier (SOA) epitaxial wafer supply chain. Supported by purchase order commitments from Quintessent, the collaboration will see IQE deliver production quantities of epitaxial wafers to Quintessent throughout 2025...

📰 Газета "Київський політехнік" № 3-4 за 2025 (.pdf) kpi пт, 01/24/2025 - 10:20

The escalating global energy crisis, coupled with the urgent need to mitigate climate change, demands a radical shift in our energy consumption patterns. This necessitates a two-pronged approach: enhancing energy efficiency and transitioning to low-carbon emission sources. These two facets are not mutually exclusive but rather symbiotic, driving a virtuous cycle of sustainability.

In the era of climate change and environmental challenges, the twin goals of enhancing energy efficiency and reducing carbon emissions have become pivotal for global sustainability. As industries, governments, and researchers seek solutions to minimize ecological footprints, advancements in technology and policy innovations have opened new pathways toward achieving these goals. This article delves into the latest trends, strategies, and technologies in energy efficiency and low carbon emissions, shedding light on their implications for a sustainable future.

The Energy-Carbon Nexus

Energy consumption is a primary contributor to greenhouse gas (GHG) emissions, particularly from sectors such as power generation, transportation, and manufacturing. According to the International Energy Agency (IEA), energy-related CO2 emissions account for nearly 75% of global GHG emissions. Tackling this issue requires a dual approach: improving energy efficiency to reduce consumption and transitioning to low-carbon energy sources.

Innovations Driving Energy Efficiency

1. Smart Grids and IoT Integration

Smart grids leverage Internet of Things (IoT) devices, sensors, and real-time analytics to optimize energy distribution and consumption. These grids enable demand response strategies, where electricity usage is adjusted based on supply conditions, reducing waste and enhancing grid stability. For instance, smart thermostats and lighting systems can significantly cut residential and commercial energy usage.

2. Advanced Building Technologies

Buildings account for 40% of global energy consumption. Modern energy-efficient building materials, such as aerogels and phase-change materials, provide superior insulation and thermal regulation. Additionally, building automation systems (BAS) equipped with AI algorithms can optimize HVAC (heating, ventilation, and air conditioning) systems, further reducing energy needs.

3. High-Efficiency Industrial Processes

Industrial processes are energy-intensive, but advancements in technologies like waste heat recovery, precision manufacturing, and energy-efficient motors have made significant progress. For example, deploying variable frequency drives (VFDs) in motor systems can reduce energy consumption by 30-50%.

4. Electrification of End-Uses

The electrification of transportation, heating, and cooking—coupled with clean electricity—is a cornerstone of energy efficiency. Electric vehicles (EVs), heat pumps, and induction stoves consume less energy compared to their fossil fuel-based counterparts while eliminating direct emissions.

Decarbonizing the Energy Sector

The transition to low-carbon energy sources is critical for achieving global climate goals. Recent innovations are accelerating this shift:

1. Renewable Energy Expansion

The deployment of solar, wind, and hydropower technologies has reached unprecedented levels. Innovations in photovoltaic (PV) materials, such as perovskite solar cells, promise higher efficiency and lower production costs. Offshore wind turbines with capacities exceeding 15 MW are now operational, significantly enhancing energy output.

2. Green Hydrogen

Green hydrogen, produced via electrolysis powered by renewable energy, is emerging as a versatile solution for decarbonizing hard-to-abate sectors like steelmaking, aviation, and maritime transport. Recent advancements in electrolyzer efficiency and cost reduction have accelerated its adoption.

3. Energy Storage Technologies

The intermittent nature of renewable energy necessitates robust storage solutions. Lithium-ion batteries dominate the market, but next-generation technologies such as solid-state batteries, redox flow batteries, and gravity-based storage systems are gaining traction. These innovations promise longer lifespans, higher energy densities, and reduced environmental impacts.

4. Carbon Capture, Utilization, and Storage (CCUS)

CCUS technologies capture CO2 emissions from industrial and power generation processes, preventing them from entering the atmosphere. The captured CO2 can be utilized to produce synthetic fuels, chemicals, or building materials, creating a circular carbon economy. Companies like Climeworks and CarbonCure are pioneering such solutions.

Policy and Market Drivers

Governments worldwide are implementing policies to incentivize energy efficiency and low-carbon technologies. Examples include:

• Carbon Pricing Mechanisms: Carbon taxes and cap-and-trade systems encourage industries to reduce emissions by assigning a cost to carbon pollution.
• Energy Efficiency Standards: Mandates for appliances, vehicles, and industrial equipment ensure a baseline level of efficiency.
• Renewable Energy Targets: Countries like India, Germany, and the United States have set ambitious goals for renewable energy capacity.
• Green Financing: Initiatives like green bonds and sustainability-linked loans provide capital for clean energy projects.

Challenges and Opportunities

Despite progress, significant barriers remain. The high upfront costs of energy-efficient technologies and renewables can deter adoption, particularly in developing regions. Additionally, integrating high shares of renewables into the grid poses technical challenges related to stability and storage.

These challenges also offer opportunities for innovation and investment. Digital twins, for instance, enable virtual simulations of energy systems, optimizing design and operations. Artificial intelligence (AI) and machine learning (ML) are being harnessed to predict energy demand, optimize renewable integration, and enhance grid resilience.

Case Studies: Real-World Impacts

1. Singapore’s Green Building Initiative

Singapore has implemented stringent green building standards, leading to a 28% reduction in energy consumption per building. The city-state’s Green Mark certification incentivizes energy-efficient designs and retrofits, demonstrating the impact of policy-driven action.

2. Tesla’s Energy Ecosystem

Tesla’s integrated approach—combining solar panels, battery storage, and EVs—offers a glimpse into a sustainable energy future. The company’s Gigafactories focus on scaling production while reducing costs, making clean energy solutions more accessible.

3. Europe’s Offshore Wind Success

Europe’s offshore wind sector exemplifies the potential of renewable energy. Projects like Dogger Bank in the UK, set to be the world’s largest offshore wind farm, highlight advancements in turbine technology and supply chain efficiencies.

The Road Ahead

Achieving a sustainable, low-carbon future requires collective effort across sectors. Key priorities include:

1. Scaling Innovation: Continued research and development are crucial to drive down costs and improve performance.
2. Equitable Access: Ensuring that developing nations benefit from clean technologies and financing mechanisms is essential for global impact.
3. Collaboration: Partnerships between governments, private sectors, and academia can accelerate deployment and knowledge sharing.
4. Behavioral Change: Public awareness campaigns and incentives can encourage energy-saving behaviors and adoption of clean technologies.

Conclusion

Energy efficiency and low carbon emissions are not just environmental imperatives but also economic opportunities. By embracing cutting-edge technologies, fostering policy innovation, and promoting global collaboration, we can pave the way for a resilient and sustainable future. As we stand at the crossroads of energy transformation, the choices we make today will shape the world for generations to come.

The post The Race to Net-Zero: Accelerating Efficiency & Renewables appeared first on ELE Times.

If there's interest I'll post more. submitted by /u/Switchlord518 [link] [comments]

https://preview.redd.it/37pzjodu1uee1.png?width=667&format=png&auto=webp&s=b857e9367e4bed1ab1bde3e166b238d67f0ee14e Source: Single supply opamp design techniques submitted by /u/StopShoutingCrofty [link] [comments]

submitted by /u/le_intrude [link] [comments]

NUBURU Inc of Centennial, CO, USA — which was founded in 2015 and develops and manufactures high-power industrial blue lasers — has been notified by NYSE American Market that it has resolved the deficiencies identified by it on 18 November 2024 (relating to NUBURU having an insufficient number of independent directors), and NUBURU is now compliant with NYSE American Market’s continued listing standards...

Infineon’s first PSOC Control MCUs, based on Arm Cortex-M33 processor, enable secured motor control and power conversion. Supported by Modus Toolbox design tools and software, the entry and mainline devices offer varied performance, features, and memory options.

PSOC Control MCUs—C3M for motor control and C3P for power conversion—can be used in appliances, industrial drives, robots, light EVs, solar systems, and HVAC equipment. Their Cortex-M33 processor runs at up to 180 MHz with a DSP and FPU, while a CORDIC math coprocessor accelerates control loop calculations.

Entry-line MCUs (C3M2, C3P2) feature high-resolution, high-precision ADCs and timers, while mainline MCUs (C3M5, C3P5) add high-resolution PWMs for faster real-time response. The devices are PSA Certified Level 2/EPC2 and include Class B and SIL 2 safety libraries. A crypto accelerator, Arm TrustZone, and secure key storage enable IP protection and firmware updates.

The PSOC Control C3 entry and main lineup comprises 34 devices, all available now.

PSOC Control C3 product page

Infineon Technologies 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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Chiplet PHY Designer 2025 from Keysight offers simulation capabilities for UCIe 2.0 and support for the Open Compute Project Bunch of Wires (BoW) standard. Tailored to AI and data center applications, this digital chiplet design and die-to-die (D2D) platform enables pre-silicon level validation, streamlining the path to tapeout.

The Chiplet PHY Designer aids chiplet development by ensuring interoperability with UCIe 2.0 and BoW standards, enabling seamless integration within advanced packaging ecosystems. It accelerates time-to-market by automating simulation and compliance testing setup, including Voltage Transfer Function (VTF) analysis, simplifying design workflows.

Enhancing design accuracy, the toolset provides insight into signal integrity, bit error rate (BER), and crosstalk analysis, minimizing the risk of costly silicon re-spins. It also optimizes clocking designs by supporting advanced schemes like quarter-rate data rate (QDR), ensuring precise synchronization for high-speed interconnects.

To read about what’s new in Chiplet PHY Designer 2025, click here.

Chiplet PHY Designer product page 

Keysight Technologies 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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Guerrilla RF’s GRF0020D and GRF0030D GaN-on-SiC HEMT power amplifiers deliver up to 50 W of saturated power. Available as bare die, these discrete transistors are intended for wireless infrastructure, military, aerospace, and industrial heating applications, supporting integration into custom MMICs.

Each device operates from either 50-V or 28-V supply rails, covering multiple octaves of operational bandwidth for continuous wave, linear, and pulsed modulation. When using a 50-V rail, the GRF0030D delivers 50 W (PSAT) from DC to 6 GHz, with gain ranging from 13.5 dB to 23.7 dB. At 28 V, it provides up to 27.5 W of saturated output power.

The GRF0020D offers up to 30 W at 50 V and 19 W at 28 V. This lower-power HEMT supports frequencies up to 7 GHz and provides gain between 13.8 dB and 24.3 dB.

The GRF0020D and GRF0030D are 100% DC production tested to ensure known good die (KGD) compliance. They are available for order, with samples ready for distribution. Prices start at $30 each for quantities of 100 units.

GRF0020D product page 

GRF0030D product page 

Guerrilla RF 

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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R&S offers a load pull test setup with wideband modulated signals using the RTP oscilloscope for non-linear device characterization. Compared to conventional vector network analyzers, this setup enables wideband modulation characterization of RF frontends across varying impedances. It allows precise validation of key performance indicators, such as error vector magnitude and adjacent channel leakage ratio, to support the development of RF components for next-generation wireless technologies.

Designed to verify power amplifier performance when connected to an antenna with dispersive impedance, the setup is based on the RTP084 oscilloscope with the wideband modulated load-pull option RTP-K98, paired with the SMW200A vector signal generator. The oscilloscope’s internal architecture ensures precise phase and time synchronization for forward and reverse wave measurements. Meanwhile, the dual-path vector signal generator provides accurate timing and phase stability between the input and tuning signal for load pull operation.

The RTP-K98 software processes the oscilloscope’s measured data, performs the necessary calculations to achieve the target impedance, and controls the signal generator. It is well-suited for verifying the performance of RF frontends, typically used across wider frequency ranges and multiple transmission bands, such as 5G or Wi-Fi.

Option RTP-K98 is available now. For more information on load pull testing, click here.

RTP product page

Rohde & Schwarz  

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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The SiT5977 Super-TCXO from SiTime is a single-chip timing device that achieves 3X better synchronization than its predecessor and enables 800G network connectivity. Part of the Elite RF family, this differential-ended TCXO optimizes AI compute efficiency in large data centers.

With a dedicated low-phase-noise MEMS resonator driving its integrated PLL, the SiT5977 simplifies AI system architectures by replacing multiple timing components. This ultra-stable, low-jitter TCXO provides a 156.25-MHz output with 80-fs phase jitter and LVDS outputs, supporting 800G and higher links. Integrated digital control adds system-level programmability.

The SiT5977 offers ±0.1 to ±0.25 ppm frequency stability, ensuring precise timing for high-speed networks and AI systems. Designed for demanding environments, it features a ±1-ppb/°C frequency slope (dF/dT) for resilience against airflow and thermal shock. Its digitally controlled tuning allows fine frequency adjustments with a ±400-ppm pull range and 0.05-ppt (5e-14) resolution via an I2C/SPI interface, facilitating embedded control loops for real-time compensation.

Housed in a compact 5.0×3.5-mm package, the SiT5977 Super-TCXO is now in production, with samples available.

SiT5977 product page 

SiTime

Find more datasheets on products like this one at Datasheets.com, searchable by category, part #, description, manufacturer, and more.

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https://preview.redd.it/mnyawhbh9ree1.png?width=871&format=png&auto=webp&s=d688ac5303303a2a568bda89e81a15d85403ae9a https://preview.redd.it/h7i0ilih9ree1.png?width=820&format=png&auto=webp&s=d45444dc244e22fa98adb821c132c176ad199434 https://preview.redd.it/rvvhpcph9ree1.png?width=1119&format=png&auto=webp&s=17f646f4978ebde94ad8919a014078c41ae44b4e submitted by /u/tiadore [link] [comments]

I was having a bottle of iced tea one day when I noticed something. Please see this excerpt from the bottle’s label referring to a 2,000 calories per day of personal intake (Figure 1).

I confirmed that caloric number independently to learn that it’s considered an average, but still an essentially correct daily caloric intake number.

Figure 1 Bottle label excerpt referring to a 2000 calories per day standard diet.

If we look up the word “calorie”, we find that one calorie equals 4.184 Joules (Figure 2).

Figure 2 A quick word search reveals that a single calory is the equivalent of 4.184 Joules.

If then we consume 2000 calories per day, we find the following in Figure 3.

Figure 3 The human body power usage based upon the 2,000 calories per day reference for a standard diet.

Two thousand calories per day equals 8368 joules per day which then comes to 0.096852 joules per second which is just under 97 mW. We’ll call that 100 mW just for convenience of thought. We are dealing with approximations, of course.

If all of one’s caloric intake is ultimately dispersed as body heat, that 100 mW of body heat could plausibly be imparted to whatever delicate unit under test (UUT) one happens to be working on while working diligently inside an environmental test chamber.

I once saw such an environmental test chamber in which there was a bank of 100-W lamps mounted “over there”. Those lamps were kept constantly lit and running except that if one more person were to enter the room, one lamp would then be extinguished. This was supposed to help hold the thermal environment of the UUT as invariant as possible.

If we say the following:

• That each light bulb was providing 100 W over a spherical area of 4*π*R² where R is the radial distance from the light bulb
• That the UUT has 1 square foot (1 ft2)of presented area receiving that bulb’s thermal radiation
• That the bulk of the 100 mW of a human body was also impinging on that UUT

We can then seek that value of R for which the 100 W  of one bulb was down by a factor of 1000 to yield the equivalent human presentation of 100 mW to that UUT.

Noting that 100 W divided by 100 mW equals 1,000, we seek that value of R at which the area of the posited sphere around the bulb comes to 1,000 ft2.

Where 4*π*R² = 1,000, we find that R is nominally 8.92 ft.

For all of these crude approximations, y’know what?? That is indeed just about how far away those light bulbs were positioned with respect to the UUT at hand in that chamber.

John Dunn is an electronics consultant, and a graduate of The Polytechnic Institute of Brooklyn (BSEE) and of New York University (MSEE).

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• Heart rate monitor using a programmable SoC

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Robot System Products (RSP), a global leader in high-performance industrial robot accessories, has officially opened its first production facility outside of Sweden, located in Chennai, India, through its subsidiary company, Scandinavian Robot Systems India Private Limited.

RSP’s Indian subsidiary began operations in November 2023 in Tamil Nadu, one of India’s primary automotive hubs. Since then, the company has been supplying its industry-leading range of robot accessories to customers across India. In 2024, RSP experienced significant growth, especially within the automotive and automotive ancillary (Tier-1) sectors in India. To further enhance customer service, the company also established a branch office in Pune, strengthening its presence in western India.

The new facility in Chennai will focus on manufacturing key products or modules such as automatic tool changers, swivels, tool parking stands, and cable & hose management solutions. RSP is leveraging the established supplier network in Tamil Nadu and Karnataka to ensure high-quality production.

According to the International Federation of Robotics (IFR), India was the fastest-growing industrial robot market in 2023, with annual installations increasing by 50%, reaching 8,500 units. This surge in demand has positioned India as the 10th largest robotics market globally. “We are confident in our decision to establish operations in India, and the rapid market growth reinforces that India will deliver on its potential,” said Eddie Eriksson, President and CEO of Robot System Products AB.

RSP’s products are designed to enhance manufacturing flexibility and reliability across all major robot brands. As a leading innovator in the industrial automation space, RSP delivers cutting-edge solutions that improve robot performance and versatility. Among its key products, the automatic tool changers stand out for their efficiency, enabling robots to seamlessly switch between various tools—such as grippers, welders, and drills—without downtime, optimizing productivity and throughput.

Arvind Vasu, Managing Director of RSP’s India subsidiary, commented, “Indian industries are poised to boost productivity through robot-based automation, particularly in automotive electric vehicles, electronics, and other manufacturing sectors. With India’s ambitious ‘Make in India’ initiative, RSP is well-positioned to offer local industries reliable, high-quality, and flexible solutions to help them automate and achieve their goals.”

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Servotech Renewable Power System Ltd. (Formerly known as Servotech Power Systems Ltd.) (NSE: SERVOTECH), India’s largest manufacturer of Electric Vehicle (EV) chargers, and a leading provider of Solar solutions, EV Charger components and Power-Backup solutions, presented its quarterly financial results for the quarter and nine months ended 31st December, 2024 at its Board of Directors meeting on 21st January, 2025.

FINANCIAL HIGHLIGHTS Consolidated

• Total Revenue witnessed stellar growth of 3% in Q3 FY25 to Rs. 21,683.2 lacs from Rs. 5,220.6 lacs in Q3 FY24
• EBITDA increased by 1% from Rs. 321.6 lacs in Q3 FY24 to Rs. 1,672.8 lacs in Q3 FY25
• PBT stood at 1,282.5 lacs in Q3 FY25, compared to Rs. 185.8 lacs in Q3 FY24, witnessing a growth of 590.2%
• PAT stood at 798.7 lacs in Q3 FY25, compared to Rs. 111.4 lacs in Q3 FY24, witnessing a growth of 616.8%
• In terms of 9M performance, total revenue increased by 1% and stood at Rs. 52,934.0 lacs in 9M FY25 compared to Rs. 21861.0 lacs in 9M FY24
• EBITDA increased by 1% from Rs. 1,631.5 lacs in 9M FY24 to Rs. 4,455.9 lacs in 9M FY25
• PAT increased by 1% and stood at Rs. 2,371.9 lacs in 9M FY25 compared to Rs. 834.8 lacs in 9M FY24, margins improved to 4.5% from 3.8% during the same period

Standalone

• Total Revenue witnessed stellar growth of 8% in Q3 FY25 to Rs. 18,185.7 lacs from Rs. 4,984.5 lacs in Q3 FY24.
• EBITDA increased by0% from Rs. 318.6 lacs in Q3 FY24 to Rs. 1,698.1 lacs in Q3 FY25
• PBT stood at 1,309.4 lacs in Q3 FY25, compared to Rs. 182.9 lacs in Q3 FY24, witnessing a growth of 616.0%
• PAT stood at 829.7 lacs in Q3 FY25, compared to Rs. 109.2 lacs in Q3 FY24, witnessing a growth of 659.8%
• In terms of 9M performance, total revenue increased by 6% and stood at Rs. 46,236.9 lacs in 9M FY25 compared to Rs. 18,601.6 lacs in 9M FY24
• EBITDA increased by 8% from Rs. 1,596.7 lacs in 9M FY24 to Rs. 4,499.9 lacs in 9M FY25
• PAT increased by 3% and stood at Rs. 2,441.8 lacs in 9M FY25 compared to Rs. 813.0 lacs in 9M FY24, margins improved to 5.3% from 4.4% during the same period

Commenting upon the results, Raman Bhatia, Managing Director, Servotech Renewable Power System Ltd. (Formerly known as Servotech Power Systems Ltd.) said, “The quarter experienced stellar growth, fueled by our unwavering commitment to delivering cutting-edge, technology-driven solutions in electric vehicles and solar energy. As a market leader in India’s EV charging sector, currently holding a 35-40% market share, we are strategically positioned to capture 50-55% by manufacturing 12,000 DC fast chargers in FY25, meeting the rapidly surging demand for EV infrastructure. EV chargers are projected to constitute 70-75% of our total revenue, with solar products contributing the remaining portion. This dual-pronged focus on innovation reflects our dedication to creating long-term value for our stakeholders while simultaneously contributing to a sustainable future. The trust and enthusiasm of our customers and shareholders inspire us to push boundaries and set higher benchmarks. We are optimistic about the future and committed to driving growth and establishing ourselves as a global leader in renewable energy and smart energy solutions.”

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India’s unmanned aerial vehicle (UAV) industry has emerged as a global hotspot for innovation and technological advancement, driven by rapid adoption across defense, agriculture, infrastructure, and surveillance sectors. With a strong focus on indigenization and cutting-edge research, Indian UAV companies are not only meeting domestic demands but also establishing a significant presence in international markets. As of January 2025, several Indian firms have taken the lead in developing advanced drone solutions, offering a range of applications tailored to meet diverse operational requirements.

This article highlights the top 10 UAV companies in India that are shaping the future of drone technology and revolutionizing industries with their innovative offerings.

1. ideaForge Technology Limited: Established in 2007, ideaForge is a prominent UAV manufacturer based in Mumbai. The company designs and develops drones for mapping, security, and surveillance applications, catering to defense forces and various government departments. In July 2023, ideaForge successfully launched its initial public offering (IPO), marking a significant milestone in its growth trajectory.

2. Asteria Aerospace: Asteria Aerospace is a key player in the Indian drone industry, focusing on the development of UAVs for various applications, including defense and industrial sectors. The company offers a range of drone solutions tailored to meet specific operational requirements.

3. Zen Technologies: Specializing in defense training solutions, Zen Technologies has expanded into the UAV market, providing drones and related systems for military applications. The company’s expertise in simulation and training complements its UAV offerings.

4. Paras Defence and Space Technologies: Paras Defence is involved in the design, development, and manufacturing of a wide range of defense and space engineering products, including UAVs. The company serves various segments of the Indian defense industry, contributing to the nation’s strategic capabilities.

5. Garuda Aerospace: Garuda Aerospace is a Chennai-based company that provides drone-based solutions across multiple sectors, including agriculture, infrastructure, and surveillance. The company focuses on delivering cost-effective and efficient UAV services.

6. Aarav Unmanned Systems: Aarav Unmanned Systems focuses on providing drone solutions for industrial applications, including mining, urban planning, and agriculture. The company’s UAVs are designed to deliver high-quality data for decision-making processes.

7. Dhaksha Unmanned Systems: Dhaksha Unmanned Systems offers a variety of UAVs for applications such as agriculture, surveillance, and logistics. The company emphasizes indigenous design and manufacturing to cater to local and international markets.

8. Marut Drones: Marut Drones provides drone-based solutions focusing on environmental and agricultural applications. The company develops UAVs aimed at improving efficiency in sectors like crop management and afforestation.

9. SkyLark Drones is a Bengaluru-based drone technology company specializing in providing end-to-end drone solutions for industrial applications. Founded in 2014, the company leverages drone-based data to drive operational efficiencies and decision-making across sectors such as infrastructure, mining, agriculture, and energy. SkyLark Drones offers services including drone mapping, surveying, and aerial analytics, supported by a proprietary platform that transforms drone-captured data into actionable insights.

10. Adani Defence & Aerospace: As a division of Adani Enterprises, Adani Defence & Aerospace has ventured into UAV manufacturing through collaborations and acquisitions. Notably, the company has set up a manufacturing facility for unmanned aerial vehicles in Hyderabad, Telangana, in partnership with Elbit Systems.

 

The post Top 10 UAV Companies in India appeared first on ELE Times.

The rapid evolution of artificial intelligence (AI) has been marked by the rise of large language models (LLMs) with ever-growing numbers of parameters. From early iterations with millions of parameters to today’s tech giants boasting hundreds of billions or even trillions, the sheer scale of these models is staggering.

Table 1 outlines the number of parameters in the most popular LLMs today.

Table 1 The number of parameters in today’s most popular LLMs reaches into the billions if not trillions. Source: VSORA

To understand why leading-edge LLMs are scaling so rapidly, we must explore the relationship between parameters, performance, and the technological advancements driving this trend.

Role of parameters in language models

In neural networks, parameters represent the weights and biases that the model captures and modifies. They are analogous to synaptic connections in the human brain.

From a computational architecture perspective, parameters act as the model’s memory, storing the complex relationships and subtle nuances within the input data. Intuitively, an increase in the number of parameters in a language model translates to enhanced ability to understand context, generate coherent text, and even perform tasks for which they were not explicitly trained.

Today, the largest models exhibit behaviors such as advanced reasoning, creativity, and the ability to generalize across diverse domains, reinforcing the notion that scaling up is essential for pushing the boundaries of what AI can achieve.

Scaling laws and diminishing returns

Early LLMs demonstrated that increasing the size of models led to predictable improvements in performance, especially when paired with larger datasets and superior computational power. However, these improvements follow a diminishing returns curve. As models grow larger, the incremental benefits become smaller, requiring exponentially more resources to achieve significant gains.

Despite this, the race to build bigger models persists because the returns, while diminishing, remain valuable for high-stakes applications. For instance, in areas like medical diagnostics, scientific research and autonomous systems, even marginal improvements in AI performance can have profound implications.

Drivers of parameter explosion

Modern LLMs are trained on vast and diverse datasets encompassing entire libraries of books, research papers, studies, analyses of a wide range of human endeavors, extensive software code repositories, and many more data sources. The breadth of these datasets necessitates larger models with billions of parameters to fully exploit the richness of the data.

Multimodal capabilities

Leading-edge LLMs are not limited to processing text alone; many are designed to handle multimodal inputs, integrating text, images, and other types of data. Expanding the parameter count allows these models to process and draw connections between various data types, thus enabling them to perform tasks that involve more than one type of input—such as image captioning, generating audio responses, and cross-referencing visual data with textual information.

The trend toward multimodal capabilities requires a significant increase in parameters to manage the added complexity. The added computational storage enables richer representations of different data modalities and deeper cross-modal understanding, making these models more versatile and valuable for practical applications.

Zero-shot/few-shot learning

One standout advancement in LLMs has been their proficiency in zero-shot and few-shot learning. These models can perform new tasks with minimal examples or even without explicit task-specific training. GPT-3 popularized this capability, showing that an appropriately large model could infer task instructions from just a few examples.

Achieving this level of generalization requires a massive number of parameters so that the model can encode a wide variety of linguistic and factual knowledge into its architecture. This capability is particularly useful in real-world applications where training data may not be available for every conceivable task. Expanding parameter counts helps LLMs build the necessary knowledge and contextual flexibility to adapt to various tasks with minimal guidance.

The competitive AI landscape

The competitive nature of AI research and development also fuels parameter explosion. Companies and research institutions strive to outdo each other in developing state-of-the-art models with more impressive capabilities.

The metric of “parameter count” has become a benchmark for gauging the power of an LLM. While sheer size is not the sole determinant of a model’s effectiveness, it has become an important factor in competitive positioning, marketing, and funding within the AI field.

Challenges in computational power and training infrastructure

The dramatic rise in parameter counts for AI models would not have been possible without parallel advancements in computational power and the supporting infrastructure. For decades, AI progress was hindered by the limitations of the central processing unit (CPU), the dominant computing architecture since its inception in the late 1940s. CPUs, while versatile, are inefficient at parallel processing, a critical capability for training modern AI systems.

A turning point occurred about a decade ago with the adoption of graphics processing units (GPUs) for executing deep neural networks. Unlike CPUs, GPUs are designed for efficient parallel computation, enabling rapid acceleration in AI capabilities.

Today, LLMs leverage distributed training across thousands of GPUs or specialized hardware such as tensor processing units (TPUs), combined with optimized software frameworks. Innovations in cloud computing, data parallelism, and sophisticated training algorithms have made it feasible to train models containing hundreds of billions of parameters.

Techniques like model parallelism and efficient gradient-based optimization have further advanced the field by distributing training tasks across multiple processors and clusters.

However, while larger parameter counts unlock unprecedented potential, they also bring significant challenges, chief among them being the soaring hardware computing resource demands. These demands inflate the total cost of ownership, encompassing not only sky-high upfront hardware acquisition costs but also steep operational and maintenance expenses.

Training vs. inference

Training: A computational beast

Training involves processing massive amounts of unstructured data to achieve accurate results, regardless of how long the task takes. It’s an extremely computationally intensive process, often reaching performance levels in the ExaFLOPS range.

Achieving these results typically demands months of continuous 24/7 operation on cutting-edge hardware. Today, this is conducted on thousands of GPUs, installed on large boards in vast numbers only available in the largest data centers. These setups come at enormous costs, but they are essential investments as no viable alternative exists at present.

Inference: A different approach

Inference operates under a distinct paradigm. While high performance remains critical, whether conducted in the cloud or at the edge, inference typically handles smaller, more targeted datasets. The primary objectives are achieving fast response times (low latency), minimizing power consumption, and reducing acquisition costs. These attributes make inference a more cost-effective and efficient process compared to training.

In data centers, inference is still executed using the same hardware designed for training—an approach that is far from ideal. At the edge, a variety of solutions exist, some outperforming others, but no single offering has emerged as a definitive answer.

Rethinking inference for the future

Optimizing inference requires a paradigm shift in how we approach three interconnected challenges:

1. Reducing hardware requirements
2. Accelerating latency
3. Enhancing power efficiency

Each factor is critical on its own but achieving them together is the ultimate goal for driving down costs, boosting performance, and ensuring sustainable scalability.

Reducing hardware requirements

Lowering the amount of hardware needed for inference directly translates to reduced acquisition costs and a smaller physical footprint, making AI solutions more accessible and scalable. Achieving this, however, demands innovation in computing architecture.

Traditional GPUs, today’s cornerstone of high-performance computing, are reaching their limits in handling the scaling of AI models. A purpose-built architecture can significantly reduce the hardware overhead by tailoring design to the unique demands of inference workloads, delivering higher efficiency at lower costs.

Accelerating latency

Inference adoption often stalls when query response times (latencies) fail to meet user expectations. High latencies can disrupt user experiences and erode trust in AI-driven systems, especially in real-time applications like autonomous driving, medical diagnostics, or financial trading.

The traditional approach to reducing latency—scaling up hardware and employing parallel processing—inevitably drives up costs, both upfront and operational. The solution lies in a new generation of architectures designed to deliver ultra-low latencies intrinsically, eliminating the need for brute-force scaling.

Enhancing power efficiency

Power efficiency is not just an operational imperative; it is an environmental one. Energy-intensive AI systems contribute to rising costs and a growing carbon footprint, particularly as models scale in size and complexity. To address this, inference architectures must prioritize energy efficiency at every level, from the processor core to the overall system design.

Breaking through the memory wall

At the core of these challenges lies a shared bottleneck: the memory wall. Even with the rapid evolution of processing power, memory bandwidth and latency remain significant constraints, preventing full utilization of available computational resources. This inefficiency is a critical obstacle to achieving the simultaneous reduction in hardware, acceleration of latency, and improvement in power efficiency.

Transformation of AI systems

The rapid expansion of parameters in cutting-edge LLMs reflects the industry’s unyielding drive for superior performance and enhanced capabilities. While this progress has unleashed groundbreaking possibilities, it has also exposed critical limitations in current processing hardware.

Addressing these challenges holistically will open the path forward to wide adoption of inference as a seamless, scalable process that performs equally well in both cloud and edge environments.

In 2025, innovative solutions are expected to redefine the hardware landscape, paving the way for more efficient, scalable, and transformative AI systems.

Lauro Rizzatti is a business advisor to VSORA, a startup offering silicon IP solutions and chips. He is a verification consultant and industry expert on hardware emulation.

 

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• Solving AI’s Power Struggle
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• Key technologies push AI to the edge
• AI Inference: Unveiling the Future of Neural Processing
• Deep learning model optimization reduces embedded AI inference time

The post A closer look at LLM’s hyper growth and AI parameter explosion appeared first on EDN.

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