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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...

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.

The post Toradex and QNX Address Industrial Robot Safety Amidst ISO 10218 Standard Updates appeared first on ELE Times.

In an efficient collaboration, Rohde & Schwarz has developed the first integrated testing solution for the Taiwan Space Agency (TASA), enabling seamless EMC and antenna measurements within a single chamber. The project underscores the growing demand for advanced satellite testing systems as Non-Terrestrial Network technologies evolve.

Rohde & Schwarz has taken a significant step forward in satellite testing technology by delivering a fully customized solution to the TASA. For the first time, Rohde & Schwarz integrated EMC and antenna measurement capabilities into a single test chamber, addressing a complex challenge in satellite payload testing. The innovative system, now operational at TASA’s facility, reflects the growing need for advanced satellite testing methodologies. With the increasing adoption of Non-Terrestrial Network (NTN) technologies, precise validation of satellite performance in complex environments is becoming essential. This includes ensuring seamless integration between satellites and cellular base stations — an area where conventional testing methods often fall short.

The project’s complexity stemmed from TASA’s dual testing requirement: a single chamber capable of performing both EMC and antenna measurements. To meet this demand, Rohde & Schwarz’s solution includes a R&S ZNA43 vector network analyzer, R&S AMS32 customized measurement software, and engineering services. The system also incorporated a Compact Antenna Test Range (CATR) reflector, developed in partnership with a Taiwan third-party supplier.

A key innovation in the project was the introduction of a patented diamond-shaped reflector design. This reflector minimizes interference within the chamber and reduces interactions between the reflector, feed, and Device Under Test (DUT) positioner. The result is improved accuracy and reliability in measuring complex satellite payloads.

For Rohde & Schwarz, this project reinforced its expertise in integrating third-party components. The collaboration between a third-party supplier and TASA highlights the company’s adaptability in addressing specialized customer requirements while maintaining high standards of performance and reliability.

As satellite-based connectivity becomes more integral to global communications, the industry faces rising expectations for robust and reliable testing processes. Satellites play a critical role in ensuring seamless communication across a wide range of applications, from remote sensing tools to mobile networks.

The post Rohde & Schwarz collaborates successfully with the Taiwan Space Agency to develop a dual-function satellite test solution appeared first on ELE Times.

This is my first project for the electronics technical specialty, it's a telegraph transcriber with Arduino. The final presentation is in three days. It's almost ready, aesthetic details would need to be adjusted submitted by /u/Economy-Internet-272 [link] [comments]

In this article, we'll introduce the fundamental concepts of direct FM generation and examine a key FM modulation circuit: the reactance modulator.

The problem: I share a long driveway with my neighbor who runs an Airbnb and I’m tired of telling the guests to slow down. This device monitors the car speed, takes a photo of the car if it exceeds a set point, uploads the photo and data to a server and emails several people automatically. It’s powered by a solar panel with battery. submitted by /u/nutstobutts [link] [comments]

Just finished my first PCB! It's a TTL clock using mostly 74LS90 ICs. Didn't expect it to work on the first try :3 submitted by /u/SuperSuperCat [link] [comments]

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I have just started looking and playing with electronic abt 2 months ago .I have also made a power bank and few other plug n play devices with the help of Arduino . Now I am looking for a new project suggestions to explore more electrical components, thanks Btw here's the link to this project working https://drive.google.com/file/d/19qMVNdBbIMH3ztrHKshNMicb7OIMiIwN/view?usp=drivesdk submitted by /u/in_for_de_mony [link] [comments]

He told me there was a wiring mistake and then he sent me the second picture. It tracks time from a gps and it's awesome! It works perfectly and I love it! submitted by /u/llapizz [link] [comments]

Today I successfully milled my first PCB and soldered ESP32-WROOM-32 on it. Next step: Upload a sketch. submitted by /u/Big_Lack_ [link] [comments]

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Hi again =D Big thanks to everyone here for the feedback and encouragement on my very first PCB! I took all that advice, went back to the drawing board, and I’m excited to share the second revision—this one includes a bunch of improvements based on suggestions from my previous post. The design files are up on my GitHub if you’d like to dig into the details, and I’ve also included some gallery shots above (hopefully a bit more presentable this time). Honest feedback is always welcome—it really helps me level up as a beginner. Technical Overview Project: Mainboard for a Bluetooth-controlled RC car Tooling: Designed in KiCAD 9 Form factor: 4 mounting holes, 4 copper layers F.Cu → signal layer (traces, routing) In1.Cu → +5V power plane In2.Cu → GND power plane B.Cu → secondary signal layer Assembly: Through-hole (all components except battery holders) Core MCU: Arduino Nano Features Dual DC motor control (L293D IC) Servo motor control Rear status LEDs (brake, reverse, turn signals—modeled after real cars) LED blinking logic (555 timer in monostable mode + 74HC00 NAND gate) Bluetooth communication via HC-05 module Audio signals: 2 piezo buzzers (horn + alarm) Power source: two 18650 Li-ion cells (7.4V) regulated through a 7805 submitted by /u/Practical-Friend-960 [link] [comments]

I've been wanting to make a card that has the usually hidden SMBus and JTAG signals in a PCIe slot available to the user. I've also made 3.3V, 5V and 12V rails available. If you want to checkout the project go checkout the github. submitted by /u/SuperCookieGaming [link] [comments]

I wanted to put together a minimal LED flasher circuit using only a few components. No microcontrollers, no 555 timer, just good old analog behavior. Parts used: 1 × BC547 transistor 1 × 1000µF capacitor 1 × 1k resistor 1 × LED How it works: The capacitor charges slowly through the resistor. Once it hits a threshold voltage, the transistor conducts and lights the LED briefly. Then the cap discharges and the process repeats, it creates a satisfying blink loop. It starts flashing after a short delay and just keeps going. I’d love to hear ideas on how to iterate on this, different values, transistor types, or ways to expand it? I posted a short 30s demo in the top comment if you'd like to see it in action. submitted by /u/ftuncer59 [link] [comments]

Behind the foundational and advanced radar textbooks of our day stood the brilliant engineer and researcher Merrill Skolnik.