Збирач потоків

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Man the difference between linear and logarithmic pots and faders for volume is pretty interesting. This is my third TX-6 style mixer that I had time to finally finish. The first used linear faders and pots, and the second had faders that were too high value resistance so it was more on the quiet side. submitted by /u/Edboy796 [link] [comments]

«Золоті почесні знаки» від Національної технічної організації Федерації науково-технічних товариств Польщі KPI4U-1 пт, 05/08/2026 - 23:15

maybe it's time to start using PCBs submitted by /u/Effective_Vacation21 [link] [comments]

For more detail: https://blog.mehmetasaf.me/how-to-build-a-uart-to-half-duplex-converter-for-your-servo-projects/ Tomorrow, I will build this schematic on a breadboard. I might add some pictures later. Thanks for reading. submitted by /u/PineappleOk7203 [link] [comments]

Нова лабораторія керування промисловими системами на ФЕЛ kpi пт, 05/08/2026 - 17:48

I have designed and built a test jig that will automatically test a small USB output for bench power supplies adapter called USBpwrME. The USBpwrME allows users to connect USB powered electronics to a power supply during test, evaluation troubleshooting etc. Test jig in action The test jig is built around the PIC18F27K22. This is my goto chip at the moment. It has a lot of configurable peripherals, ADC with really high resolution and a huge amount of memory for being a small MCU. And wide supply voltage range! Test sequence will cover all the functions of the USB adapter with as few operator interactions as possible. One "funny" mistake i made during the design was not noticing that the relays i use has actually polarized coil so the pos/neg has to be connected in correct way to make the relay click. I missed this so i needed to hand modify all three relays. Second mistake i made was actually a bit harder to foresee. One test that is performed is to invert the the input polarity to the USBpwrME to see that the polarity protection works. Well the design mistake was that the GND between the jig and the adapter is connected together thru the GND shield of the USB cables. So when the polarity switches the test jig short-circuits itself and restarts. I solved this by adding in the test sequence when to actually connect the USB cables and performing the polarity test just before. Even my eight year old son can operate it :) :) Quite happy although with the result submitted by /u/KS-Elektronikdesign [link] [comments]

Noise, India’s leading connected lifestyle brand, announces the launch of NoiseFit Halo 3, a bold, design, first round dial smartwatch crafted to seamlessly blend style, productivity and AI-powered utility. Design for those who refuse to compromise, Halo 3 combines the refined aesthetics of a classic dress watch with the intelligence and functionality of a modern smartwatch. It delivers what consumers have long sought: a timeless round-dial design paired with meaningful smart capabilities. Building on the Halo legacy, Halo 3 features a sculpted integrated-strap silhouette, a vibrant 1.43″ AMOLED display with 1000 nits brightness, and Noise AI Pro, a productivity-first AI ecosystem offering voice commands, voice recording and transcription, health insights, and personalised wallpapers. 

With Noise Vault for QR pass access, a customizable Smart Dashboard, one-tap health checks and up to 7 days of battery life, Halo 3 is built for the modern man who wants to make an impression, moving effortlessly from a boardroom meeting to a boarding gate, with a watch that transitions as fluidly as he does.

Noise AI Pro with Smart Productive Dashboard

At the core of Halo 3 lies Noise AI Pro, a productivity-first AI layer built for modern routines. Voice commands enable hands-free actions, morning briefs summarise sleep and activity insights, and AI Transcription transcribes voice notes into clean notes. Super Notifications refine alerts by surfacing contextual updates like OTPs, ride statuses and delivery notifications (Android supported). Complementing this intelligence is a customizable Smart Dashboard that supports up to five widgets,  from music control and AQI to sleep insights and hydration tracking, ensuring the most relevant information is always within reach.

Round-Dial Design with AMOLED Brilliance, built to command attention

NoiseFit Halo 3 features refined curves that flow into an integrated strap design, creating a cohesive, sculpted silhouette. Precision cuts along the dial edge add depth and character, while the 1.43” AMOLED display with 1000 nits brightness delivers striking clarity and effortless visibility across lighting conditions. Available in metal, leather and silicon strap options, Halo 3 adapts seamlessly from boardrooms to social settings, offering long-wear comfort without compromising on presence.

Noise Vault & Seamless Utility, scan and move

Halo 3 introduces Noise Vault, allowing users to store QR codes for flights, concerts, movies and more directly on the watch. Acting as a digital passbook, it enables seamless, hands-free scanning at entry points and boarding gates, reducing dependence on the phone during high-movement moments.

Health Insights & Week-Long Battery, built for uninterrupted days

The smartwatch supports one-tap heart rate, stress and SpO₂ monitoring alongside continuous tracking throughout the day. Backed by up to 7 days of battery life, Halo 3 ensures users stay informed and connected without frequent charging interruptions.

Price and Availability

Available in four elegant colours with strap options – Metal (Black) , Leather (Brown, Blue) & Silicon (Black),  the NoiseFit Halo 3 is live on sale, at an introductory price of 5,499 on gonoise.com, Amazon and Flipkart. 

Product Specifications
NoiseFit Halo 3

Specification	Details
Display	1.43″ AMOLED, 1000 nits
Strap options	Metal (Black), Leather (Brown, Blue), Silicon (Black)
Core AI	Noise AI Pro: Voice commands, Morning briefs, AI Transcription, Super Notifications (Android-only advanced notifications)
Health	One-tap Heart Rate, Stress, SpO₂; continuous tracking
Compatibility	Android & iOS
Battery	Up to 7 days

 

About Noise

Noise is India’s leading smartwatch and connected lifestyle brand. The brand prioritises consumer centricity, design innovation, and product excellence to constantly reinvent and introduce future-forward innovations in audio, wearables, and the connected lifestyle ecosystem. As a homegrown brand, it is committed to creating an experience-led ecosystem through futuristic yet meaningful technology. With patents and a strong R&D focus, their innovation arm, Noise Labs, boasts many industry-first breakthroughs and houses some stellar technologies across categories. 

Noise is leading the charge to foster the growth of the industry and the nation’s vision by boosting the manufacturing efforts under the Make in India initiative, fostering a strong community of people who want to connect on health, lifestyle, and fitness on the NoiseFit App, while helping businesses ensure their employee wellbeing through the Corporate Wellness Program.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The post Next-Gen Upgrade to the Halo Series, NoiseFit Halo 3 brings Presence-Led Design and AI to the Wrist appeared first on ELE Times.

Renesas Electronics Corporation is a premier supplier of semiconductor solutions. Today, it announces that a subsidiary of Renesas has completed the acquisition of Irida Labs, a Greece-based company specialising in embedded software for AI-powered visual perception systems. The acquisition strengthens Renesas edge AI embedded processing offerings, a key secular growth area for Renesas. It also enables system-level solutions that integrate physical AI vision systems across industrial, robotics, smart city, IoT, agriculture and healthcare markets. As a part of Renesas’ digitalisation strategy, Irida Labs software and tools will be integrated into Renesas 365, a newly released platform that unifies electronics system development from discovery to development and lifecycle management.

While the demand for intelligent systems at the edge continues to soar across industries, developers must often overcome the growing complexity of AI system development. This includes the integration of power-constraint embedded processors and software, training, deploying AI models and addressing latency and security risks associated with data transmission. Vision AI software plays a critical role in interpreting and processing visual data from cameras and sensors widely used in industrial inspection, robotics guidance, in-cabin automotive sensing, traffic and infrastructure monitoring, smart retail analytics and safety and security systems.

The addition of Irida Labs to Renesas’ product portfolio addresses these emerging challenges. By combining Renesas’ AI-enabled RA microcontrollers (MCU) AND RZ microprocessors (MPU) with Irida Labs comprehensive tool suite and lightweight Vision AI software, Renesas can now delebier high performance, power-efficient edge AI solutions that are ready for deployment. Together, these capabilities reinforce Renesas’ progress towards fully integrated Vision AI system solutions.

Vassilis Tsagaris, CEO & Co-Founder of Irida Labs, added, “The joining of Irida Labs into Renesas marks an important milestone in our edge vision AI journey. By combining Irida Labs’ edge Vision AI expertise and our PerCV.ai software with Renesas hardware and global ecosystem, we open up exciting new opportunities to deliver meaningful impact on edge AI worldwide. I am proud of what the team has built, and genuinely excited to take it forward together with Renesas, turning our shared vision into reality.”

Before the acquisition, Renesas and Irida Labs collaborated as partners to develop solutions combining Irida Labs’ PerCV.ai software with Renesas’ RA and RZ devices. Bringing these capabilities in-house enables Renesas to deliver more tightly integrated solutions quickly. Renesas also plans to integrate Irida Labs software and tools into its newly introduced intelligent, open cloud-based development platform, Renesas 365.

The post Renesas Completes Acquisition of Irida Labs to Expand Vision AI Software Capabilities and Accelerates System-Level Vision Solutions appeared first on ELE Times.

День пам’яті та перемоги над нацизмом у Другій світовій війні 1939–1945 років KPI4U-2 пт, 05/08/2026 - 11:56

Infineon Technologies AG of Munich, Germany says that the Full Commission of the US International Trade Commission (ITC) has affirmed the ITC’s initial determination from December 2025 that China-based Innoscience (Suzhou) Technology Holding Co Ltd, which manufactures GaN-on-silicon power chips on 8” silicon wafers, infringed an Infineon patent concerning gallium nitride (GaN) technology and ordered import and sales bans against Innoscience. The Commission’s final decision and the bans are subject to a 60-day review period of the US President...

Rhode & Schwartz and Greenerwave conduct a joint measurement trial that demonstrates a near-field system. It can record the full radiation pattern of a 50 cm Ku band electronically steerable array for a SATCOM antenna in just half an hour. The result matches simulation models within a decibel, that make this approach rapid and precise. For manufacturers of SATCOM systems facing large chamber constraints, it offers a clear path towards quick and cost-effective testing.

Electronically Steerable Array (ESA) antennas are a key component in modern SATCOM systems. Accurate knowledge of their radiation pattern is required for reliable operation in LEO, MEO, and GEO orbits. However, conventional far-field testing demands chambers that are often larger for Ku or Ka band antennas, especially when the aperture of the Antenna Under Test (AUT) reaches half a meter or more. Compact Antenna Test Range (CATR) is relatively large for AUTs and are time consumiong dual-axis positioning of AUT to map the radiation pattern.

Greenerwave’s innovative SATCOM user terminals are based on Reconfigurable Intelligent Surface (RIS), allowing the company to design electronically steerable antennas that deliver high-performance connectivity while reducing energy consumption and reliance on semiconductors compared with conventional solutions.

For the joint measurement campaign, T&M expert Rhode & Schwarz provided its R&STS8991 over-the-air and antenna measurement system, equipped with a conical cut positioner, and its R&SZNA vector network analyser. Together, they evaluated Greenerwave’s passive single-aperture ESA that uses RIS technology for beamforming. The Antenna Under Test (AUT) features a 50x 50cm aperture and is designed for low power consumption and easy integration.

The measurement covered an extended upper hemisphere down to a polar angle of 120 degrees, using a one-degree step size. Ten Ku band frequencies were recorded in a total of 32 minutes due to the system’s hardware trigger function. Data was processed using the R&SAMS32 antenna measurement software, which applied the FIAFTA near-field to far-field transformation.

Comparison with the original simulation based on a numerical twin model and with results from Greenerwave’s CATR setup showed peak gain or directivity variations, validating the accuracy of the near-field solution. The trial shows that even large SATCOM antennas can be characterised quickly and accurately, providing a practical alternative to large-sized far-field CATRs. This system can be used by other SATCOM makers testing broadband, research lab environment, IoT for applications requiring flexible beam control and high data rates.

The post Rohde & Schwarz and Greenerwave Achieve an Efficient ESA Antenna Charecterisation Using Near-Field Technology appeared first on ELE Times.

Deposition equipment maker Aixtron SE of Herzogenrath, near Aachen, Germany has supplied Renesas Electronics Corp of Tokyo, Japan with multiple Planetary G5+C systems to expand its gallium nitride (GaN) production in high-volume manufacturing (HVM) environments. The collaboration helps to strengthen Renesas’ GaN production capabilities in response to surging demand across critical power electronics applications...

The current robotics narrative is heavily weighted toward artificial intelligence (AI). The prevailing assumption is that more parameters, larger models, and better reinforcement learning pipelines will eventually grant machines human like dexterity. This belief has shaped research agendas, funding priorities, and public expectations.

However, for engineers designing hardware that must survive millions of high-velocity cycles at companies like Amazon Robotics, a different truth is apparent. In the lab, the focus is on the brain, but on the production floor, robots fail for mechanical reasons far more often than algorithmic ones.

In high duty cycle environments, the primary drivers of unplanned downtime are wear, compliance, thermal drift, misalignment, and mechanical fatigue. These are not failures of perception or planning. No amount of neural network tuning can compensate for a linkage that deflects under load or an end effector that cannot maintain repeatability. As the industry continues to chase AI-centric solutions, it risks overlooking the fundamental engineering disciplines that determine whether a robot succeeds in the physical world.

The robotics community is at a crossroads. The last decade has delivered extraordinary advances in machine learning, but the physical reliability of robotic systems has not kept pace. The result is a widening gap between what robots can demonstrate in controlled environments and what they can sustain in real production settings.

Closing this gap requires a shift in mindset. The next leap in robotics will not come from larger models or more training data. It will come from better mechanisms, better actuation, and better physical architectures.

The reliability gap

The industry has spent a decade optimizing the brain while neglecting the body. This imbalance has created what can be described as the reliability gap. As a technical judge for MassChallenge and for university capstone programs at Worcester Polytechnic Institute and Boston University, I have observed a recurring pattern.

Startups and student teams often present systems that segment objects perfectly in simulation, classify scenes with remarkable accuracy, and demonstrate impressive reinforcement learning policies. Yet when these systems are deployed in the physical world, they fail after only a few hours of operation.

The reason is straightforward. AI amplifies a robot’s capability, but the mechanism defines the physical boundary. If a kinematic chain introduces unpredictable hysteresis, software cannot compensate its way to a reliable solution. If a transmission loses stiffness under load, no amount of perception accuracy will restore positional integrity. If an end effector cannot generate stable contact forces, even the most advanced grasping model will fail.

The robotics industry must acknowledge a practical reality. Software and AI are essential, but they cannot overcome fundamental mechanical limitations. The most successful robotic systems in history have not been those with the most advanced algorithms, but those with the most deterministic mechanical behavior. Reliability is not an emergent property of software. It’s engineered into the physical system from the beginning.

Determinism and the voyager philosophy

True industrial progress requires a return to mechanical rigor, specifically a focus on what can be called deterministic mechatronics. This philosophy suggests that the most successful robotic systems are those engineered for passive stability, predictable behavior, and graceful failure. A useful analogy comes from deep space engineering.

Voyager 1, launched nearly half a century ago, remains operational in one of the harshest environments imaginable. NASA has occasionally uploaded new command sequences, performed resets, and adjusted subsystems to extend its life. These interventions succeed because the underlying mechanical and electrical systems were engineered for extreme reliability. The spacecraft’s longevity is not the result of software alone or hardware alone, but the synergy between robust physical design and intelligent control.

Industrial robotics should adopt this same mindset. The next leap in automation will come from kinematic architectures that reduce inertia, precision transmissions that maintain sub-millimeter accuracy under load, and actuation strategies that prioritize physical determinism. The goal is not to diminish the role of AI, but to ensure that AI is built on a stable mechanical foundation.

A deterministic mechanism reduces the burden on perception and control. It narrows the solution space. It transforms a difficult control problem into a manageable one. When the physical system behaves predictably, the software becomes simpler, more robust, and more efficient.

Case study: The apparel challenge

The manipulation of non-rigid materials, such as apparel, provides a clear example of this principle. Handling folded fabric is traditionally viewed as an AI problem. The common assumption is that complex pose estimation, dense depth reconstruction, and advanced vision models are required to manage the noise introduced by folds and wrinkles.

However, breakthroughs in this field, including those protected under U.S. Patents 11268223 and 11939714, demonstrate that the solution is primarily mechanical. By designing a compliant yet deterministic gripping architecture, the physics of the material can be used to the machine’s advantage.

When the kinematic chain is engineered to minimize shear forces, the physical interaction becomes predictable. When the mechanism constrains the degrees of freedom in a way that aligns with the material’s natural behavior, the need for complex perception is reduced.

In these systems, AI still plays a meaningful role. It identifies features, guides sequencing, and handles variability. But it succeeds because the underlying mechanism provides a stable substrate. The machine does the heavy lifting so the software can remain efficient. This balanced approach is what the industry needs. Instead of using software to compensate for mechanical unpredictability, the mechanism is engineered to reduce the burden on software.

This approach scales. It is robust. It is repeatable. And it is the foundation on which industrial grade automation must be built.

A new hierarchy of design

To unlock the next stage of automation, the engineering community must rebalance its priorities. The hierarchy of design must shift.

First, the industry must invest in mechanism research and development with the same intensity it brings to AI. For every dollar spent on perception, equal resources should be allocated to transmissions, linkages, and end effectors. Mechanisms are not a solved problem. They are the frontier that will determine the next decade of progress.

Second, the industry must build reliability-first architectures. Robots should be engineered with the longevity of aerospace systems, not the lifecycle of consumer electronics. This requires a shift in mindset. Reliability is not a feature. It’s a design philosophy.

Third, the industry must foster a new breed of roboticists. The next generation of engineers must be equally proficient in kinematics and PyTorch, equally comfortable with finite element analysis and neural network training and equally invested in mechanical determinism and algorithmic efficiency. The future belongs to engineers who can bridge the physical and digital domains.

Finally, the industry must resist the temptation to chase demos. The goal is not to produce systems that perform well in controlled environments, but systems that operate reliably in the real world. The measure of success is not a viral video, but a robot that performs millions of cycles without failure.

The next decade of robotics

Artificial intelligence is an extraordinary amplifier, but it’s not the foundation of robotics. Intelligence can only be as effective as the physical vessel through which it acts. The next decade of robotics will be defined by the engineers who recognize that mechanisms, transmissions, and physical architectures are not secondary considerations. They are the core of the system.

The future of robotics does not belong to the AI-first approach or the mechanism-first approach. It belongs to the integration of both into a single, reliable, and deterministic system. When the body and the brain evolve together, automation will finally achieve the scale, reliability, and capability that the industry has been pursuing for years.

This is the mechanism-centric future of robotics. And it’s long overdue.

Santosh Yadav is senior mechanical engineer and robotics researcher at ASME MBE Standards Committee.

Special Section: Smart Factory

• Rethinking machine vision in industrial automation
• Smart factory: The rise of PoE in industrial environments
• Precision lasers boost safety and efficiency in smart factories
• Tale of 3 sensors operating in smart factory environments
• From edge AI to physical AI in smart factories: A shift in how machines perceive and act

The post Robots: Why AI alone will not deliver the next leap in automation appeared first on EDN.

📰 Газета "Київський політехнік" № 17-18 за 2026 (.pdf) Інформація КП пт, 05/08/2026 - 10:00

Of course, the process was not completely smooth. I wanted to add reverse polarity protection to V1, which the prototype did not have. In the first design, I built and tested a reverse polarity protection circuit with a single P-Channel MOSFET. However, I had missed one scenario: although a single P-Channel MOSFET can be enough in some cases, it could not block reverse current coming from inside the device. Even when the IRFP260N MOSFETs were off, reverse current could pass through the body diodes and put the connected power supply into a short-circuit condition. To solve this problem, I reworked the PCB to convert the power input block to a back-to-back P-Channel MOSFET structure. I used the banana sockets on the front panel as the protected input, designed to support an 8-30V range. The XT60 connector on the right works as the unprotected input and supports a 0-30V input range. After the rework, the protected power input caused significant heating at 8V and below because it left the protection MOSFETs partially on. For the next PCB revision, I plan to redesign the power input block using an ideal diode controller and two low-RDS(on) N-Channel MOSFETs in a back-to-back structure. Also, because of the two P-Channel back-to-back MOSFETs, the protection MOSFETs heated to unsafe levels at my target 200W test power. For safe operation, I limited the device to 150W. The device can support voltage and current values up to 30V and 10A within this limit. On the software side, with AI assistance, I developed control, protection and monitoring functions such as toggling load draw with the RST button, overcurrent warning, reverse polarity notification, temperature tracking and fan control. For the V2 revision, I aim to improve the device with more functional features and design a structure with higher power capacity. Overall, this project was a very educational and experience-building work for me in power electronics, measurement, PCB design, mechanical design, rework and fault analysis. https://omerikinci.github.io/projects/electronic-dummy-load.html submitted by /u/Aggravating-Safe5352 [link] [comments]

Kept redoing the same impedance calculations during SI work, so I built this into a proper tool. It covers: Microstrip, Stripline, Coaxial, CPW, GCPW, Differential Pair — gives you Z₀, εeff, propagation delay, loss, VSWR, Smith Chart, dispersion analysis, and parametric heatmaps. Runs in browser, no login, no account. Link: tools.vyomex.in/Impedance_calculator Happy to hear if anything is off or if there are topologies you'd want added. submitted by /u/Existing-Milk3177 [link] [comments]

submitted by /u/1Davide [link] [comments]