Feed aggregator

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

Deep Learning Architecture Diagram:

Diagram Explanation:

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

Types of Deep Learning Architecture:

1. Feedforward Neural Networks (FNNs)

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

2. Convolutional Neural Networks (CNNs)

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

3. Recurrent Neural Networks (RNNs)

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

4. Long Short-Term Memory Networks (LSTMs)

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

5. Variational Autoencoders (VAEs)

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

6. Generative Adversarial Networks (GANs)

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

7. Transformers

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

8. Graph Neural Networks (GNNs)

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

9. Autoencoders

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

10. Deep Belief Networks (DBNs)

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

Conclusion:

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

The post Deep Learning Architecture Definition, Types and Diagram appeared first on ELE Times.

The scree got water damage, but this still works, the tubes will light up. Also, these transformers look quite similar to the ones found in CRTs. I wonder... submitted by /u/Greedy-Kangaroo-4674 [link] [comments]

In an exclusive interview with ELE Times, Mr. Deep Hans Aroraa, Co-Founder & Director at JSD Electronics, discussed how the company is redefining IoT-enabled manufacturing through multi-layered cybersecurity, AI-driven quality control, and advanced testing methodologies. From embedding secure boot and end-to-end encryption into connected devices, to deploying machine vision and predictive analytics for defect prevention, JSD is committed to delivering reliable, compliant, and future-ready electronics. The conversation also explored the company’s use of digital twins, ERP-MES integration, and embedded software innovations that power smarter, more resilient products across industries. Excerpts:

ELE Times: How are you integrating cybersecurity measures into your IoT-enabled products to ensure data safety and compliance? 

Deep Hans Aroraa: At JSD, we integrate multiple layers of cybersecurity into our IoT-enabled products to protect data integrity, confidentiality, and availability while ensuring compliance with industry standards.

Key Measures Implemented:

• Secure Boot – Only authenticated firmware is allowed to run.
• Firmware Signing & Verification – Prevents malicious or unauthorized firmware updates.
• End-to-End Encryption – TLS 1.3 / DTLS for secure data transmission.
• Mutual Authentication – Both device and server authenticate each other before communication.
• VPN or Private APN – Used for sensitive industrial and enterprise deployments.
• Data-at-Rest Encryption – AES-256 encryption for onboard storage and databases.
• API Authentication & Authorization – Implemented via OAuth 2.0 and JWT tokens.
• Cloud IAM (Identity & Access Management) – Restricts access to IoT data.
• Intrusion Detection Systems (IDS) – Specialized for IoT protocols such as MQTT and CoAP.
• Secure OTA Updates – Safely push patches and firmware updates throughout the device lifecycle.

ELE Times: What advanced testing or simulation technologies does JSD use to ensure the reliability of mission-critical devices like medical equipment and GPS systems?

Deep Hans Aroraa: For stable IoT device manufacturing aligned with global quality standards, JSD employs the following advanced testing and simulation technologies:

• Hardware-in-the-Loop (HIL) Testing
• Environmental & Stress Testing (Thermal Cycling, Shock Testing, Vibration, Humidity, and Corrosion Testing)
• RF & Wireless Performance Testing
• Functional Qualification Testing
• Protocol & Interoperability Testing (MQTT, TCP/IP, HTTP, etc.)
• Cybersecurity Penetration Testing
• Power Consumption & Battery Life Simulation
• EMC/EMI Compliance Testing
• Field Trials & Operational Evaluation

ELE Times: In what ways is JSD using data analytics or AI to optimize production lines and improve product quality?

Deep Hans Aroraa: Data plays a vital role in optimizing manufacturing operations. JSD has implemented ERP systems for production and inventory management, along with a Manufacturing Execution System (MES), enabling data capture from multiple sources such as machines, operators, environmental conditions, materials, and quality inspections.

Data Analytics Applications:

• Bottleneck Analysis (Time-Series) – Identify and resolve process slowdowns.
• Predictive Maintenance – Anticipate equipment failures before they occur.
• Process Parameter Optimization – Fine-tune machine settings for maximum efficiency.
• Dynamic Scheduling – Adjust production plans in real-time to changing conditions.
• Energy Optimization – Reduce energy consumption without impacting output.

Quality Improvement with AI:

• AI-Powered Visual Inspection – Detect the smallest defects in real time.
• In-Process Quality Prediction – Forecast potential quality issues before final assembly.
• Defect Root Cause Analysis – Pinpoint exact defect causes.
• Supplier Quality Analytics – Correlate incoming material quality with production outcomes.

These initiatives deliver better insights, higher yields, greater reliability, improved efficiency, and significant cost savings.

ELE Times: How is JSD integrating machine vision systems or AI-driven defect detection into its quality control processes?

Deep Hans Aroraa: JSD Optical Inspection & AI-Driven Quality Control Workflow

• Inward Material Inspection – Optical systems check incoming components for dimensional accuracy, labeling, and surface defects.
• PCBA Solder Paste Inspection (SPI) – Measures solder paste volume, height, and alignment before placement to ensure optimal solder joints.
• Pre-AOI – Verifies part type, polarity, and position after component placement.
• Post-AOI – Detects solder bridging, tombstoning, missing components, and misalignments after reflow soldering.
• Final Product Digital Inspection (PDI) – High-resolution imaging and visual inspection for cosmetic finish, assembly quality, and labeling.

AI-Enhanced Workflow:

• Image Capture – AOI systems record detailed PCB images.
• AI Analysis – Detects solder defects, missing components, and misalignments with high precision.
• Defect Logging – Records in MES with batch and machine data.
• Real-Time Alerts – Flags issues immediately for rework.
• Continuous Learning – Uses stored defect images for AI retraining and root cause prevention.

ELE Times: What role does embedded software development play in JSD’s product strategy, especially for smart connected devices?

Deep Hans Aroraa: Embedded software development is central to JSD’s product strategy, serving as the intelligence that transforms hardware into connected, adaptive, and differentiated products.

It defines core functionality, manages connectivity (Wi-Fi, Bluetooth, Zigbee, LoRa, 5G), and ensures security through secure boot, encryption, authentication, and OTA updates. Embedded software also enables scalability and future-proofing, allowing features and compliance updates without hardware redesign.

Additionally, it powers edge intelligence, processing data locally for faster response and reduced bandwidth, and governs data collection and transmission—critical for analytics, predictive maintenance, and new revenue streams.

ELE Times: How does JSD leverage digital twins or virtual prototyping before moving to full-scale manufacturing?

Deep Hans Aroraa: JSD uses digital twins and virtual prototyping to reduce risk, improve design quality, and accelerate time-to-market by creating virtual replicas for simulation and validation before physical production.

• Design Validation – Identify and fix flaws before building prototypes.
• Process Optimization – Simulate assembly lines and workflows to remove bottlenecks.
• Performance Testing – Model real-world stress, thermal, and EMI conditions.
• IoT-Driven Insights – Use sensor data for predictive improvements.
• Cost & Sustainability – Test materials and processes for efficiency and eco-impact.
• Training – Prepare teams in a simulated environment before production ramp-up.

The post How JSD Electronics Uses AI and Machine Vision to Deliver Zero-Defect Electronics appeared first on ELE Times.

Commerce and Industry Minisher Piyush Goyal has said that India’s electronics production has seen an increase of more than four times in the last decade from $31 billion in 2014-15 to $133 billion in 2024-25. He mentioned that such magnificent growth was accompanied by a tremendous increase in exports, which have increased by more than 47% in Q1 FY26 over the same quarter last year.

According to data by India Cellular and Electronics Association (ICEA), electronics exports were $12.4 billion in Q1 FY26 compared to $8.43 billion in Q1 FY25. Riding on this momentum, the industry body hopes to reach exports to the tune of $46–50 billion by the fiscal year.

Goyal further elaborated upon the long-term transformation seen in the sector. He said, “The mobile phone industry has essentially been at the heart of this journey. A decade ago, India was largely an importer of mobile phones. Today, we have become a global hub for mobile manufacturing and exports. Mobile exports themselves grew 55 per cent in Q1 FY26, rising from $4.9 billion in the same quarter last year to an estimated $7.6 billion.” Non-mobile exports grew at 36% from $3.53 billion to an estimated $4.8 billion.

These includes solar modules, networking equipment, charger adapters, and electronic components: thus, widening India’s export portfolio.

He added that electronics not just strengthened the exports but also created employment opportunities on a large scale with the support of policy initiatives like Phased Manufacturing Programme (PMP), Production Linked Incentive (PLI) schemes, and close industry-state coordination.

Electronics exports from India have experienced sustained double-digit growth in multiple product segments and are set to record a landmark in FY26, thus placing the country firmly in the global supply chains.

The post India’s Electronics Production Climbs to $133 Billion, Export Growth Accelerates appeared first on ELE Times.

A new multilayer diffractive optical processor blocks images in one direction while passing them in the other.

submitted by /u/Practical-Friend-960 [link] [comments]

This is just a blink sketch, but my projects will become miniaturized. submitted by /u/Big_Lack_ [link] [comments]

Keysight says the updated EMI receiver significantly improves test throughput, reduces debugging time, and optimizes EMC chamber efficiency.

📚 Набір на Базовий курс підготовки до НМТ 2026 для учнів 9, 10 та 11-х класів відкрито! kpi пн, 08/18/2025 - 17:33

Back in 2016, I did a teardown of Actiontec’s ECB2200 MoCA adapter, which had fried in response to an EMP generated by a close-proximity lightning bolt cloud-to-cloud spark (Or was it an arc? Or are they the same thing?). As regular readers may recall, this was the second time in as many years that electronics equipment had either required repair or ended up in the landfill for such a reason (although the first time, the lightning bolt had actually hit the ground). And as those same regular readers may already be remembering, last August it happened again.

I’ve already shared images and commentary with you of the hot tub circuitry that subsequently required replacement, as well as the three-drive NAS, the two eight-port GbE switches and the five-port one (but not two, as originally feared) GbE switch. And next month, I plan to show the insides of the three-for-three CableCard receiver that also met its demise this latest lightning-related instance. But this time, I’ll dissect Actiontec’s MoCA adapter successor, the ECB2500C:

I’d already mentioned the ECB2500C a decade back, actually:

The ECB2500C is the successor to the ECB2200; both generations are based on MoCA 1.1-supportive silicon, but the ECB2500C moves all external connections to one side of the device and potentially makes other (undocumented) changes.

And as was the case back in 2016, the adapter in the master guest bedroom was the MoCA network chain link that failed again this time. Part of the reason why MoCA devices keep dying, I think, is due to their inherent nature. Since they convert between Ethernet and coax, there are two different potential “Achilles Heels” for incoming electromagnetic spikes. Plus, the fact that coax routes from room to room via cable runs attached to the exterior of the residence doesn’t help. And then there’s the fact that the guest bedroom’s location is in closest proximity (on that level, at least) to the Continental Divide, from whence many (but not all) storms source.

This time, however, the failure was more systemic than before. The first thing I did was to test the wall wart’s DC output using my multimeter:

Dead! Hey…maybe the adapter itself is still functional? I grabbed the spare ECB2500C’s wall wart, confirmed it was functional, plugged it into this adapter and…nope, nothing lit up on the front panel, so the adapter’s dead, too. Oh well, you’ll get a two-for-one teardown today, then!

Let’s start with the wall wart, then, as usual accompanied by a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes:

Specs n’ such:

Time to break out the implements of destruction again (vise not shown this time):

Progress…

Success!

No “potting” in this case; the PCB pulls right out:

The more interesting side of the PCB, both in penny-inclusive and closer-up perspectives:

The same goes for the more boring (unless you’re into thick traces, that is) side:

And now for some side views:

I didn’t see anything obviously scorched, bulged, or otherwise mangled; did you? Let me know in the comments if I missed something! Now on to the adapter, measuring 1.3 x 3.8 x 5.5 in. (33 x 97 x 140 mm). I double-checked those dimensions with my tape measure and initially did a double-take until I realized that the published width included the two coax connectors poking out the back. Subtract 5.8” for the actual case width:

You may have already noticed the four screw heads, one in each corner, in the earlier underside shot. You know what comes next, right?

That was easy!

The PCB then (easily, again) lifts right out of the remaining top half of the case:

Light pipes for the LEDs, which we’ll presumably see once we flip over the PCB:

Let’s stick with this bottom side for now, though:

The lone component of note is a Realtek RTL8201EL Fast Ethernet PHY. The mess of passives below it is presumably for the system processor at that location on the other side of the PCB:

Let’s see if I’m right:

Yep, it’s Entropic’s EN2510 single-chip MoCA controller, at lower left in the following photo. To its left are the aforementioned LEDs. At upper left is an Atmel (now Microchip Technology) ATMEGA188PA 8-bit AVR microcontroller. And at upper right, conveniently located right next to its companion Ethernet connector, is a Magnetic Communications (MAGCOM) HS9001 LAN transformer:

Switching attention to the other half of the PCB upper half, I bet you’re dying to see what’s underneath those “can” and “cage” lids, aren’t you? Me, too:

Your wish is my command!

As with the wall wart, and unlike last time when a scorched soldered PCB pad pointed us to the likely failure point, I didn’t notice anything obviously amiss with the adapter, either. It makes me wonder, in fact, whether either the coax or Ethernet connector was the failure-mechanism entry point this time, and whether the failure happened in conjunction with last August’s lightning “event” or before. The only times I would ever check the MoCA adapter in the master guest bedroom were when…umm…we were prepping for overnight guests to use that bedroom.

Granted, an extinguished “link active” light at the mated MoCA adapter on the other end, in the furnace room, would also be an indirect tipoff, but I can’t say with certainty that I regularly glanced at that, either. Given that the wall wart is also dead, I wonder if its unknown-cause demise also “zapped” the power regulation portion of the adapter’s circuitry, located at the center of its PCB’s upper side, for example. Or maybe the failure sequence started at the adapter and then traveled back to the wall wart over the conjoined power tether? Let me know your theories, as well as your broader thoughts on what I’ve covered today, in the comments!

—Brian Dipert is the Editor-in-Chief of the Edge AI and Vision Alliance, and a Senior Analyst at BDTI and Editor-in-Chief of InsideDSP, the company’s online newsletter.

Related Content

• Teardown: MoCA adapter succumbs to lightning strike
• Devices fall victim to lightning strike, again
• Lightning strike becomes EMP weapon
• Lightning strikes…thrice???!!!
• A teardown tale of two not-so-different switches
• Dissecting (and sibling-comparing) a scorched five-port Gigabit Ethernet switch
• LAN security for MoCA and powerline

The post Broke MoCA II: This time, the wall wart got zapped, too appeared first on EDN.

While the Silicon 100 report was being compiled and curated to profile the most promising startups in the semiconductor industry in 2025, two prominent chiplet upstarts were already taken. First, Qualcomm announced its acquisition of chiplet interconnect developer Alphawave Semi in the second week of June 2025.

Nearly a month later, Tenstorrent snapped Blue Cheetah Analog Design, another supplier of die-to-die interconnect IPs. These two deals highlight the red-hot nature of the chiplets world and how this new multi-die technology landscape is emerging despite geopolitical headwinds.

In this year’s Silicon 100 report, there are eight startup companies associated with chiplet design and manufacturing work. In the chiplet design realm, DreamBig Semiconductor develops chiplet platforms and high-performance accelerator solutions for 5G, artificial intelligence (AI), automotive, and data center markets. Its core technology includes a chiplet hub with high-bandwidth memory (HBM).

Founded by Sohail Syed in 2019, the San Jose, California-based chiplet designer is using Samsung Foundry’s SF4X 4-nm process technology and is backed by the Samsung Catalyst Fund and the Sutardja family investment.

Eliyan, another well-known chiplet firm, offers PHY interconnect that enables high-bandwidth, low-latency, and power-efficient communication between chiplets on both silicon and organic substrates. The company, co-founded in 2021 by serial entrepreneur Ramin Farjadrad, completed the tapeout of its NuLink PHY in a ×64 UCIe package module on Samsung Foundry’s SF4X 4-nm manufacturing process in November 2024.

Figure 1 The die-to-die PHY solution for chiplet interconnect achieves 64 Gbps/bump. Source: Eliyan

While design startups are mostly engaged in die-to-die interconnect and related aspects, chiplet manufacturing realm seems far more expansive and exciting. Take AlixLabs, for instance, a 2019 startup spun off from Sweden’s Lund University. It specializes in atomic layer etch (ALE) equipment to develop a technique called ALE pitch splitting (APS), which enables atomic-scale precision in semiconductor manufacturing at dimensions below 20 nm.

Figure 2 The ALE-based solutions perform atomic-level processing to reduce the number of process steps required to manufacture a chip while increasing throughput. Source: AlixLabs

Then there is Black Semiconductor, developing manufacturing methods for back-end-of-line use of graphene to create optical chip-to-chip connections. The company is currently building a manufacturing facility at its new headquarters in Aachen, Germany. FabONE is expected to be operational in 2026, with pilot production scheduled to start in 2027 and full-volume production by 2029.

Figure 3 FabONE will be the world’s first graphene photonics fab. Source: Black Semiconductor

Next, Chipletz, a fabless substrate startup, is working on chiplet-based packaging. Established in 2016 as an activity within AMD and then spun off in 2021, its smart substrate enables the heterogeneous integration of multiple ICs within a single package. That, in turn, eliminates the need for a silicon interposer by providing die-to-die interconnects and high-speed I/O. It also supports different voltage domains from a single supply, outperforming traditional multichip modules and system-in-package (SIP) solutions.

Silicon Box is another semiconductor packaging upstart featured in the Silicon 100 report; it specializes in the production of multi-die components based on chiplet architecture. It currently operates a factory and R&D facility in Singapore and has raised $3.5 billion to build a semiconductor assembly and testing facility in Piedmont, Italy.

Silicon 100 offers a glimpse into the startup ecosystem of 2025 and beyond, highlighting firms that work on various aspects of chiplet design and manufacturing. And their potential is inherently intertwined with another 2025 star: AI and data center semiconductors. One common factor that both chiplets and AI semiconductors share is their association with advanced packaging technology.

Find out more about upstarts focusing on chiplet design and manufacturing in “Silicon 100: Startups to Watch in 2025” by uploading a copy of the report here.

Related Content

• The Age of Chiplets is Upon Us
• Startup Aims to Improve Chiplet Packaging
• Chiplets diary: Three anecdotes recount design progress
• Silicon Box to Invest €3.2B in a Semiconductor Fab in Italy
• Eliyan Breaks Chiplet Memory Wall With Standard Packaging

The post Silicon 100: Chiplet work spanning interconnect PHYs to smart substrates appeared first on EDN.

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

Deep learning algorithms are a category of machine learning methods that draw inspiration from the workings of the human brain. Such methods use artificial neural networks made up of interconnected nodes or neurons in handling data. Deep learning algorithms are the driving force behind modern artificial intelligence. They enable machines to learn from vast amounts of data, recognize patterns, and make decisions with minimal human intervention. These algorithms are modeled after the structure and function of the human brain, using artificial neural networks composed of layers of interconnected nodes.

Usually, deep learning algorithms are divided into groups according on the neural network architecture they employ:

• Feedforward neural networks (FNNs): The fundamental architecture of feedforward neural networks (FNNs) allows data to flow in a single direction.
• Convolutional neural networks, or CNNs, are specialized for analyzing images and videos.
• Recurrent neural networks (RNNs): These networks are made to process sequential data, such as language or time series.
• Autoencoders: For dimensionality reduction and unsupervised learning.
• Generative models, such as GANs and VAEs, generate new data instances.
• GNNs (Graph Neural Networks): Utilize data that is graph-structured.
• Transformers: Using attention mechanisms, they transformed NLP tasks.

Examples of Deep Learning Algorithms:

• Image Classification: CNNs used for facial identification or medical imaging.
• Speech recognition: RNNs and LSTMs are utilized in virtual assistants.
• Text Generation: Chatbots and translation use transformers like GPT.
• Anomaly Detection: Fraud detection using autoencoders.
• Data Synthesis: GANs that produce lifelike pictures or movies.

Top 10 deep learning algorithms:

1. Convolutional Neural Networks (CNNs)

Convolutional Neural Networks are applied to process grid-like data such as images by convolution layers that can identify spatial hierarchies and patterns such as edges and textures. It is widely used in image recognition applications ranging from facial recognition to medical imaging for tumor detection and object detection in autonomous vehicles.

2. Recurrent Neural Networks (RNNs)

Recurrent Neural Networks were designed to work with sequences of data through loops in the network to keep a memory of past inputs. They are thus best suited for tasks such as speech recognition, time-series forecasting (e.g., stock prices), and natural language processing where context from previous data points is essential.

3. Long Short-Term Memory Networks (LSTMs)

LSTMs are specialized RNNs that can learn longer-term dependencies and avoid the vanishing gradient problem. They are best suited for applications like machine translation, predictive text input, and chatbots, in which realizing the bigger picture of a conversation or an incoming sentence is advantageous.

4. Generative Adversarial Networks (GANs)

GANs consist of two networks-the discriminator and the generator-that compete against one another in order to create realistic synthetic data. These models are used in generating lifelike images, creating deepfake videos, producing art, and augmenting datasets-classifying certain datasets-so that their training with respect to other models can be improved.

5. Autoencoders

Autoencoders are types of unsupervised learning models that map input data into a lower-dimensional representation, then reconstruct this representation. They are used for anomaly detection in cybersecurity, image denoising, and dimensionality reduction for visualization or further high-end analysis.

6. Deep Belief Networks (DBNs)

DBNs are layered networks built using Restricted Boltzmann Machines that learn to represent data hierarchically. They’re useful for tasks like image and speech recognition, where uncovering hidden patterns and features in large datasets is essential.

7. Variational Autoencoders

VAEs are a probabilistic extension of autoencoders that learn latent representations of data with some added regularization. They are commonly found being used in drug discovery for generating new molecules, handwriting synthesis, speech synthesis, or just compression of data in a way that retains important features.

8. Graph Neural Networks (GNNs)

GNNs were built to work with data structured as graphs and capture relationships between nodes. They are especially useful in social network analysis, recommendation systems, and fraud detection, wherein understanding the relationships between entities is key.

9. Transformers

Transformers rely on attention mechanisms to attribute relative importance to different chunks of input data. This has ushered in advancements in NLP tasks—translation, summarization, and question answering to name a few—while also leading to their use, to some extent, in vision tasks like image captioning and object detection.

10. Multilayer Perceptron (MLP)

MLPs stand for multilayer perceptrons, or feedforward neural networks with more than one layer of neurons separating input and output. They are suited for handwritten digit recognition, fraud detection, and customer churn prediction, where structured data and non-linear relationships have to be modeled.

Conclusion:

The latest changes in AI are powered by deep learning algorithms. These algorithms are used with varying strengths and applications, for instance, CNNs that study images, and Transformers that understand human language.

Following the implementation of AI for applications in health sciences, financial management, autonomous systems, and content creation, possessing knowledge about these top 10 deep learning algorithms becomes essential for both practitioners and researchers.

The post Top 10 Deep Learning Algorithms appeared first on ELE Times.

Independent power electronics company Quantum Power Transformation (QPT) Ltd of Cambridge, UK — which was founded in 2019 and develops gallium nitride (GaN)-based electric motor controls — has unveiled MicroDyno, a low-voltage motor drive test platform which demonstrates the key benefits of ultrahigh-frequency GaN-based motor drives. The solution enables greater control and efficiency with lower system complexity and cost, providing significant benefits in the fast-growing cobot market...

I know it is of no use,just wanted to learn to host local servers submitted by /u/LegalAd8550 [link] [comments]

Tokyo-based Oki Electric Industry Co Ltd has developed Tiling crystal film bonding (CFB) technology using its proprietary CFB technology. The technology enables the heterogeneous integration of small-diameter optical semiconductor wafers onto 300mm silicon wafers, previously not possible due to wafer size restrictions, and will contribute to the advancement of rapidly growing photonics-electronics convergence technology. OKI aims to achieve early commercialization through collaboration with partner companies and universities...

КПІ ім. Ігоря Сікорського поглиблює освітню й наукову співпрацю з Королівством Марокко kpi пн, 08/18/2025 - 10:18

India’s first quarter of 2025–2026 had a strong increase in electronics exports of over 47% over the same time in 2024–2025, according to Commerce and Industry Minister Piyush Goyal.

In a social media post, the Minister described growth as a “sweet success story” of the Make in India initiative describing the changes in electronics manufacturing witnessed by the country in the last decade. According to him, electronics production in India grew from USD 31 billion in 2014-15 to USD 133 billion in 2024-25.

He said that policy support and government initiatives were indispensable in making India Aatmanirbhar in manufacturing. “India has moved from having just two mobile manufacturing units in 2014 to more than 300 today,” he said. He further said India has completely revolutionized from a mobile phone importer into the second-largest manufacturer in the world.

The Minister further noted that the electronics sector has emerged as a key driver for employment generation. Along with mobile phones, solar modules, networking devices, charger adapters, and other electronic components have played a crucial role in strengthening the export basket of India.

The steep increase in electronics exports is symbolic of the government’s efforts to make India a global center for hi-tech manufacturing and to further the cause of economic self-reliance.

The post India’s Electronics Exports Strengthen with 47% Jump, Says Piyush Goyal appeared first on ELE Times.

As industrial automation enters the collaborative era of Industry 5.0, ensuring the safety of human-robot interaction is more critical than ever. Toradex, a leader in embedded computing solutions, today announced its collaboration with QNX, a division of BlackBerry Limited, to help manufacturers meet the stringent requirements of the updated ISO 10218 standard for industrial robotics safety.

In dynamic, high-risk environments, ranging from smart factories to unmanned systems and autonomous mobile robots (AMRs) operating in public spaces, safety is a foundational requirement. While automation drives productivity, recent real-world incidents highlight the urgent need for embedded functional safety (FuSa) at every stage of design and development.

The newly revised ISO 10218-1 and 10218-2 standards introduce more rigorous frameworks for safety, including comprehensive risk assessments, stricter verification of safety functions, improved protocols for human-robot collaboration, and integrated cybersecurity measures. Compliance requires achieving IEC 61508 SIL 3 rating, validating the system’s safety integrity from architecture through deployment.

Key Takeaways from the Toradex and QNX Collaboration:

• Investment into QNX SDP 8.0 support to deliver embedded innovation with unmatched reliability, seamless integration, and real-time performance.
• ISO 10218 Compliance, Simplified: Certifiable software and hardware minimize risk and reduce time to market.
• Microkernel Architecture Advantage: QNX OS for Safety provides fault isolation, high determinism, and robust security.
• Hardware Platform Flexibility: QNX is working with Toradex across scalable hardware, from lower to higher-end chips like the Verdin iMX95, with pin-to-pin compatibility. Support can extend to additional form-factors, like the Toradex SMARC.
• Hardware-Software Synergy: Toradex System on Modules (SoMs) offer high reliability, configurability, and industrial-readiness. A perfect fit for QNX’s real-time OS.
• Built for Industry 5.0: Designed for true human-machine collaboration, not just coexistence.
• Engineered for Safety-Critical Systems: The offering from Toradex using QNX software meets the demanding needs of industrial robotics and enables the next-generation of safety-critical environments.
• Accelerated Certification: Ready-to-use QNX Board Support Packages (BSPs) for Toradex hardware streamline development and functional safety certification.
• Long-Term Reliability: Industrial-grade components and long-term support ensure a stable platform for mission-critical deployments.

Toradex hardware supported by QNX software as of today, are:

• Verdin iMX8M Plus (QNX SDP 8.0 and 7.1)
• Apalis iMX8 (QNX SDP 8.0, 7.1 and 7.0)
• Colibri iMX8X (QNX SDP 7.1 and 7.0)
• Apalis iMX6 (QNX SDP 7.0)

“Robotics safety isn’t just a compliance checkbox, it’s a core enabler of innovation,” said Grant Courville, SVP Products and Strategy, at QNX. “With our QNX OS for Safety and this collaboration with Toradex, we’re offering a certifiable platform that allows manufacturers to accelerate development while maintaining the highest safety standards. Together, we’re building a foundation of trust for the next generation of collaborative robotics.”

QNX OS for Safety and QNX Hypervisor for Safety, both certified to IEC 61508 SIL 3, feature a microkernel-based architecture purpose-built for real-time, fault-tolerant applications. This approach isolates safety-critical components, enhances system predictability, and directly supports compliance with updated ISO 10218 standards.

“Robot safety can no longer be bolted on after deployment,” added Daniel Lang, CMO at Toradex.” By combining QNX’s safety-certified RTOS with our scalable and reliable hardware, we deliver a robust platform that enables manufacturers to develop certifiable robotic and unmanned systems more rapidly and efficiently.”

Webinar: Achieving ISO 10218 Compliance with Toradex and QNX
Toradex and QNX recently hosted a joint webinar, Achieving ISO 10218 Compliance in Industrial Robotics with Toradex and QNX: Enhancing Safety and Performance, focusing on the practical implications of the updated ISO 10218 standard for industrial robotics. The session included expert perspectives on how to navigate new safety requirements, with a specific look at the role of software architecture, functional safety certification, and hardware-software integration.

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