# Computer Vision Published articles for Computer Vision. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## New AI technique could make minimally invasive surgeries safer and more precise DevFeed: [New AI technique could make minimally invasive surgeries safer and more precise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-16T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [3D](), [navigation]() Tags: [3d](), [ai](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computer-vision](), [electrical-engineering-and-computer-science-eecs](), [health-care](), [images](), [imaging](), [jameel-clinic](), [machine-learning](), [medical-devices](), [medical-imaging](), [minimally-invasive-surgery](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [model](), [national-institutes-of-health-nih](), [navigation](), [paper](), [polina-golland](), [precision](), [real-time](), [research](), [school-of-engineering](), [vision](), [vivek-gopalakrishnan]() ### AI overview MIT researchers and collaborators developed xvr, an AI method that adapts to individual patients and rapidly aligns intraoperative X-rays with preoperative 3D medical scans. The technique is intended to improve surgical navigation for minimally invasive procedures. ### Source excerpt This patient-specific method, called xvr, helps doctors use X-rays for surgical navigation in fields such as orthopedics and neurosurgery. ## Arduino announces a live build of a privacy-focused smart doorbell on the Arduino UNO Q DevFeed: [Arduino announces a live build of a privacy-focused smart doorbell on the Arduino UNO Q]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-16T14:06:15Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Computer vision](), [Security]() Tags: [arduino](), [computer-vision](), [model](), [notify](), [privacy](), [security](), [smart-doorbell](), [uno-q]() ### AI overview Arduino announces a live build showing how to create a smart doorbell using a computer vision model that runs locally on an Arduino UNO Q board. The event is scheduled for September 22 at 3 PM CET / 9 AM ET and will include questions for the Arduino team. ### Source excerpt Go on your favorite online shopping platform, and you'll find any number of smart doorbell options. Click to purchase, have it delivered, install it, download some app. But where's the fun in that? And also, don't you wonder how that thing works? That thing that watches you and your loved ones go in and out, [...] The post Build your own smart doorbell and protect your privacy - in one hour, with Massimo Banzi appeared first on Arduino Blog. ## CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding) DevFeed: [CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding)]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-09-16T00:00:55Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Artificial Intelligence](), [Embedded Systems](), [AI Inference](), [V](), [Arm](), [cpu](), [Open Source](), [Ethernet](), [Linux](), [Toit]() Tags: [ai](), [arm](), [artificial-intelligence-ai](), [camera](), [computer-vision](), [cpu](), [debug](), [edge-ai](), [embedded](), [ethernet](), [hardware](), [inference](), [kickstarter](), [linux](), [module](), [open-source](), [real-time](), [risc-v](), [robotics](), [rt-thread](), [soc](), [sophgo](), [tinyml](), [usb](), [video](), [vision]() ### AI overview The AIMORELOGY Ovis is an open-source modular AI vision camera built around the CVITEK CV1842H-P SoC. It provides full-color 1080p night vision, 1.5 TOPS edge AI inference, USB and Ethernet connectivity, and a dual-OS environment using Linux and RT-Thread. ### Source excerpt The AIMORELOGY Ovis is an open-source AI vision camera module built around the CVITEK CV1842H-P SoC, with full-color 1080p night vision and 1.5 TOPS edge AI inference in a compact modular design. It is designed for drones, robotics, security systems, smart cameras, and custom embedded vision products. The camera features a compact stacked design, with a 20 x 20 mm Core board that includes the CVITEK CV1842H-P SoC, 2 Gbit NAND flash, USB, and UART debug pads. A Sensor board with the SC235HAI image sensor connects on top and also adds Ethernet and UART interfaces. An optional CVBS board goes between the Sensor board and the Ovis Core board. AIMORELOGY Ovis specifications: Ovis Core Board SoC - CVITEK CV1842H-P CPU - 1x Arm Cortex-A53 core @ 1.1 GHz, 1x RISC-V C906 core @ 800 MHz NPU - 1.5 TOPS @ INT8 with BF16 support ISP - AI-ISP with real-time 1080p [...] The post CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding) appeared first on CNX Software - Embedded Systems News. ## MYWAI ports its VILMA visual imitation learning toolkit to Arduino UNO Q and VENTUNO Q DevFeed: [MYWAI ports its VILMA visual imitation learning toolkit to Arduino UNO Q and VENTUNO Q]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-15T14:26:17Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Robotics](), [Artificial Intelligence](), [Computer vision](), [Arduino](), [Qualcomm](), [UNO Q](), [VENTUNO Q]() Tags: [ai](), [arduino](), [computer-vision](), [edge-ai](), [industrial](), [machine-learning](), [qualcomm](), [robotics](), [robots](), [uno-q](), [ventuno-q](), [warehouse]() ### AI overview The article describes VILMA, an AI-powered toolkit from MYWAI that enables robots and humanoids to learn manipulation tasks from one-shot human demonstrations. It reports that the toolkit is being ported to Arduino UNO Q and VENTUNO Q boards powered by Qualcomm Dragonwing processors. ### Source excerpt Every day, hundreds of thousands of kits are prepared in warehouses before components ever reach an automotive production line. While robots have become commonplace in modern manufacturing, many upstream logistics activities still rely heavily on human operators performing repetitive pick-and-place and kitting tasks. What if robots could learn these operations the same way humans do: [...] The post MYWAI™ VILMA™ is designed to bring human-like learning to robots via one-shot demonstration appeared first on Arduino Blog. ## Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers DevFeed: [Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T13:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [Inference](), [Computer vision](), [servers](), [dell](), [RISC-V]() Tags: [ai](), [computer-vision](), [dell](), [enterprise](), [inference](), [risc-v](), [servers]() ### AI overview Axelera AI is shipping Europa, a second-generation AI Processing Unit, in bare-chip and PCIe card configurations. The company says the 45W device delivers 629 TOPS and supports on-premises inference workloads including generative AI, vision-language models, and computer vision. The Edge 232p card is shipping in validated Dell XE5 and Supermicro 111AD systems. ### Source excerpt Axelera AI is shipping Europa, the second-generation AI Processing Unit (AIPU) it has been previewing since last year, and it's launching with validated servers from Dell and Supermicro attached. The Eindhoven company's pitch is inference on infrastructure the customer controls: agentic systems, vision-language models, generative AI, and computer vision running in a standard rackmount server The post Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers appeared first on StorageReview.com. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU DevFeed: [NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-12T02:19:36Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [Microcontroller](), [wifi 6](), [Hardware](), [object-detection]() Tags: [c-c-plus-plus](), [camera](), [computer-vision](), [edge-ai](), [embedded-systems](), [hardware](), [mcu](), [object-detection](), [open-source](), [stm32](), [stmicro-stm32](), [vision](), [wifi-6]() ### AI overview The article reports on CamThink's NeoEyes NE302, a compact USB-C-powered Wi-Fi 6 edge AI vision camera built around the STM32N6 MCU. It describes the device's reduced memory and feature set compared with the NE301, along with its camera, wireless, debugging, video-encoding, object-detection, networking, OTA, and security capabilities. ### Source excerpt CamThink NeoEyes NE302 is a tiny WiFi 6 Edge AI camera based on an STM32N6 Arm Cortex-M55 MCU with Neural-ART NPU which the company says is "especially suitable for developers and makers". I initially thought about it as a smaller version of the CamThink NeoEyes NE301 based on the same STM32N6 MCU and 4MP OS04C10 camera sensor. But it's quite a different device. First, it doesn't have a battery, and the USB-C is only used for power. Memory and storage capacities have been reduced to 32 MB PSRAM and 64 MP SPI flash, and a range of built-in and optional features have been removed, including 4G LTE module (global or US), audio wafers, PIR motion connector, and a 16-pin GPIO header, as well as support for PoE power. CamThink NeoEyes NE302 specifications: Vision MCU STMicro STM32N6 MCU Core - Arm 32-bit Cortex-M55 CPU @ up to 800MHz with Arm Helium [...] The post NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU appeared first on CNX Software - Embedded Systems News. ## Building smarter AMRs with the Arduino® VENTUNO™ Q board DevFeed: [Building smarter AMRs with the Arduino® VENTUNO™ Q board]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-11T11:10:16Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Physical AI](), [Microcontroller](), [Linux](), [Computer vision](), [real-time](), [systems]() Tags: [amr](), [arduino](), [autonomous-mobile-robots](), [computer-vision](), [featured](), [linux](), [mcu](), [physical-ai](), [real-time](), [robotics](), [robots](), [ros](), [sensor](), [sensors](), [ventuno-q]() ### AI overview The article explains how Arduino's VENTUNO Q board could support autonomous mobile robots by combining a Linux-capable MPU with a real-time MCU. The MPU can run Linux, ROS 2, navigation, computer vision, and AI workloads, while the MCU handles motor control, encoder feedback, inertial measurements, local sensing, and motor-driver communication. ### Source excerpt Physical AI is based on the idea that intelligence shouldn't stop at perception: instead, it should bring to life systems able to sense their environment, reason about it, and act on it - all in one continuous loop. It's what makes the difference between a device that observes and one that acts. Autonomous mobile robots [...] The post Building smarter AMRs with the Arduino® VENTUNO™ Q board appeared first on Arduino Blog. ## Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications DevFeed: [Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-11T03:13:41Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Arm](), [cpu](), [Embedded Systems](), [Artificial Intelligence](), [Internet of things](), [Hardware](), [object-detection](), [Neural Network](), [Transformer]() Tags: [aiot](), [amlogic](), [arm](), [armv9](), [camera](), [computer-vision](), [cortex-a320](), [edge-ai](), [embedded](), [ethernet](), [h-264](), [hardware](), [iot](), [linux](), [llm](), [low-power](), [neural](), [npu](), [object-detection](), [processor](), [security](), [soc]() ### AI overview Amlogic has announced the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered edge AI and IoT devices. The preliminary specifications include video encoding and decoding, NPUs, image signal processing, camera interfaces, networking, USB, and low-power features. The article notes that full specifications, block diagrams, and software details are not yet available. ### Source excerpt Amlogic has unveiled the A123X quad-core and C305X2 dual-core Arm Cortex-A320 SoCs for industrial and battery-powered Edge AI and IoT applications such as robots, dashcams, IP cameras, video conferencing equipment, and so on. The Arm Cortex-A320 low-power Armv9 CPU core was introduced in February 2025, and Amlogic is the first silicon vendor to announce Cortex-A320 SoCs. Details are sparse, with no full specifications or block diagrams and limited software information, but let's see what we know so far. Amlogic A123X Amlogic A123X specifications: CPU - Quad-core Arm Cortex-A320 processor (Armv9.2-A, SVE2) GPU - None or not disclosed VPU H.264/H.265 encoding at 4K @ 60fps H.264/H.265 decoding at 4K @ 30fps AI 4 TOPS ADLA2 NPU for object detection and tracking CNN models 8 TOPS ADLA3 NPU supporting hardware-accelerated Transformer operations for ViT, LLM, etc. Neural network-based hardware SED engine for low-power audio event identification ISP - Low-light HDR ISP Supports [...] The post Amlogic A123X and C305X2 Arm Cortex-A320 SoCs target industrial and low-power AIoT applications appeared first on CNX Software - Embedded Systems News. ## Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering DevFeed: [Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering]() Original publisher: [Read original article]() Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [LLM evaluation / benchmarking](), [Ground truth / benchmark quality](), [Hallucination detection]() Tags: [benchmark](), [computer-vision](), [evaluation](), [metrics](), [multimodal](), [research](), [video]() ### AI overview The article introduces CapQuiz, a reference-free benchmark for evaluating video-caption quality through human-verified multiple-choice questions. It also proposes CapF1, combining factuality and visual-information coverage, and reports stronger correlation with human judgments than existing metrics. ### Source excerpt Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth references. This paradigm suffers from the "one-to-many" nature of video description, where high-quality captions are often penalized for lexical mismatches or valid shifts in visual focus. Furthermore, such assessments are typically one-dimensional, failing to provide a fine-grained analysis of caption quality. To address this, we redefine caption quality via information fidelity: A caption must maximize the coverage... ## Automatic image cropping in Appwrite with AutoGravity DevFeed: [Automatic image cropping in Appwrite with AutoGravity]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Computer vision](), [Code](), [Go]() Tags: [announcements](), [automatic](), [computer-vision](), [go](), [image](), [object-detection](), [open-source](), [storage](), [web]() ### AI overview Appwrite introduces AutoGravity, an open-source Go service that adds automatic image cropping to Appwrite Storage. It uses a model pipeline to identify a focal point and returns normalized coordinates that Appwrite's existing image transformation pipeline uses for cropping. The article explains that transformed images are cached and identifies U²-Net as the main model behind the feature. ### Source excerpt AutoGravity brings automatic image cropping to Appwrite Storage. Learn how saliency detection and face detection pick the focal point behind gravity=auto. ## The Reflexes Machine turns a reaction game into an interactive experience DevFeed: [The Reflexes Machine turns a reaction game into an interactive experience]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-09T12:37:27Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [UNO Q](), [Computer vision](), [webcam](), [real-time](), [Hardware]() Tags: [architecture](), [arduino](), [building](), [camera](), [computer-vision](), [hardware](), [i2c](), [led](), [modulino-nodes](), [project](), [reaction-game](), [real-time](), [reflex-game](), [reflexes-machine](), [sensors](), [uno-q]() ### AI overview An Arduino community member built Reflexes Machine, an arcade-style reaction game using an Arduino UNO Q board, Modulino nodes, a USB camera, illuminated buttons, LED Matrix displays, and sound effects. The camera detects a player's face and automatically starts the countdown. ### Source excerpt What happens when you combine a little imagination with a powerful dual-brain board and a handful of building-block sensors? In the case of Arduino community member LucaDilo, you get a reaction game that doesn't simply wait for someone to press a button... it actually notices when you walk up and invites you to play. Reflexes [...] The post The Reflexes Machine turns a reaction game into an interactive experience appeared first on Arduino Blog. ## Circuit Valley CHC5 - A modular USB, HDMI, and Ethernet camera system (Crowdfunding) DevFeed: [Circuit Valley CHC5 - A modular USB, HDMI, and Ethernet camera system (Crowdfunding)]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-02T07:25:10Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [webcam](), [Hardware](), [USB](), [hdmi](), [Ethernet](), [SOC](), [Embedded Systems](), [Linux](), [SDK](), [Open Source]() Tags: [arm](), [buildroot](), [c-c-plus-plus](), [camera](), [computer-vision](), [edge-ai](), [embedded-systems](), [ethernet](), [fpga](), [hardware](), [hdmi](), [kickstarter](), [linux](), [open-source](), [poe](), [sdk](), [soc](), [usb](), [video](), [xilinx-zynq]() ### AI overview CNX Software reports on Circuit Valley's CHC5, a modular camera system built around an AMD/Xilinx Zynq-7020 SoC FPGA. It supports interchangeable sensor and lens configurations, USB, HDMI, and Gigabit Ethernet interfaces, and image-processing and machine-vision workloads. ### Source excerpt Circuit Valley's CHC5 is a modular camera system based on a Core board powered by an AMD/Xilinx Zynq-7020 SoC FPGA, a range of interface boards with USB, HDMI, or Ethernet (PoE), various sensor boards, and a metal enclosure. The solution can be customized with different lens objectives and different sensors with MIPI D-PHY, LVDS, Sub-LVDS, Low Voltage LVDS, or SLVS interfaces. The Zynq-7020's Cortex-A9 cores can handle complex tasks, while the FPGA fabric takes care of high-speed calculation and correction on the image, and can also be leveraged for AI/machine-vision-specific algorithms or complex low-latency hardware acceleration. CHC5 specifications: Core board SoC - Xilinx AMD Zynq-7020 SoC FPGA CPU - Dual-core Arm Cortex-A9 processor FPGA - 85K logic cells, 4.9Mb Block RAM, 220 DSP slices System Memory - 1GB DDR3 Storage 1 Gbit flash memory MicroSD card slot ISP - Full-fledged ISP implemented on FPGA fabric Multiple interface boards with 10Gbps [...] The post Circuit Valley CHC5 - A modular USB, HDMI, and Ethernet camera system (Crowdfunding) appeared first on CNX Software - Embedded Systems News. ## Why Output Metrics Can Miss Problems in Complex Production Systems DevFeed: [Why Output Metrics Can Miss Problems in Complex Production Systems]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-09-02T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [Monitoring](), [dashboards](), [robot sense of touch](), [Embedded Software Dev](), [Databases](), [Computer vision]() Tags: [algorithm](), [computer-vision](), [databases](), [embedded](), [feature-articles](), [manufacturing](), [metrics](), [monitoring](), [robotics](), [widget]() ### AI overview This commentary examines a metrics-driven manufacturing team whose automated production line combines robotics, embedded firmware, web-based monitoring tools, and PLC code. It argues that tracking output and limited performance metrics does not adequately explain how such a complex system behaves or why bottlenecks occur. ### Source excerpt Rachel joined a new team which was proudly "metrics driven". When she first met with her boss, Zane, he explained his thinking. "We need to be data-driven to make good decisions, right? We're a manufacturing company. We make widgets. At the end of the day, we need to make the most widgets for the lowest cost of goods sold. So we track that, and that feeds into every decision." The team oversaw an automated production line, which meant the software was a mix of robotics, embedded firmware, high-level web based monitoring tools, and thickets of dreaded PLC code. And because you can't build an entire factory for test purposes, they only way they could test real-world scales with real-world data was to roll changes out to production. They could simulate, they could run tests on subsets of the system, but a change in the production line software couldn't truly be validated until it rolled out into the real world. Rachel's first task on the new team involved making some changes to their metrics dashboard. It was viewed as a good way to get her feet wet with the new team. As it turned out, the metrics dashboard was a Google Sheet, with a complex series of formulas that involved multi-level INDEX functions- essentially querying the spreadsheets like they were a database. Why not use an actual database? Oh, they did -- six actually -- but the company obeyed Remy's Law of Requirements Gathering: "no matter what the requirements the users ask for, what they really wanted was Excel". The database data was pulled into the spreadsheet for reporting. Now, a complicated sheet pulling in data from not one, but six different databases, they must have a pretty complex model to explain how changes to their software would impact productivity. And since they needed to model the software to make predictions about how it'd behave in production, that model must be extremely useful. Of course it wasn't. The only metrics they tracked were output metrics, variations on "widgets produced per unit ## Build Your Own Face Recognition Tool With Python DevFeed: [Build Your Own Face Recognition Tool With Python]() Original publisher: [Read original article]() Author: Kyle Stratis Published: 2026-09-01T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Machine learning](), [object-detection](), [Training AI Models](), [Command-line interface]() Tags: [command-line](), [computer-vision](), [machine-learning](), [python](), [testing](), [train](), [tutorial]() ### AI overview A Python tutorial for building a command-line face-recognition tool that detects faces in images, trains and validates a model, and labels detected faces with bounding boxes. ### Source excerpt In this tutorial, you'll build your own face recognition command-line tool with Python. You'll learn how to use face detection to identify faces in an image and label them using face recognition. With this knowledge, you can create your own face recognition tool from start to finish! ## D-Robotics RDK S100P - A 128 TOPS alternative to NVIDIA Jetson Orin NX 16GB with Cortex-A78AE/R52+ cores DevFeed: [D-Robotics RDK S100P - A 128 TOPS alternative to NVIDIA Jetson Orin NX 16GB with Cortex-A78AE/R52+ cores]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-08-31T08:06:13Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Jetson](), [Robotics](), [Embedded Systems](), [Microcontroller](), [Jetson Orin](), [SOC](), [cpu](), [Arm](), [real-time](), [GPU](), [Linux](), [Nvidia]() Tags: [arm](), [artificial-intelligence-ai](), [camera](), [computer-vision](), [cortex-a78](), [cpu](), [deep-learning](), [development-kit](), [dfrobot](), [edge-ai](), [embedded](), [embedded-systems](), [gpu](), [hardware](), [jetson](), [jetson-orin](), [linux](), [mcu](), [networking](), [nvidia](), [os](), [processors](), [real-time](), [robotics](), [ros](), [safety](), [sensor](), [sensors](), [single-board-computer](), [soc](), [software](), [som](), [specifications](), [systems](), [ubuntu]() ### AI overview The D-Robotics RDK S100P is a robotics single-board computer featuring a 6-core Cortex-A78AE CPU, 4-core Cortex-R52+ MCU domain, 128 TOPS INT8 Nash BPU, Mali-G78AE GPU, and 24 GB of LPDDR5. Its MCU supports real-time motor and sensor control, potentially reducing the need for a separate real-time controller depending on the robot's requirements. ### Source excerpt D-Robotics RDK S100P development kit is a robotics SBC built around an S100P-based module providing an alternative to NVIDIA Jetson Orin NX 16GB with a 6-core Cortex-A78AE application cluster, a 4-core Cortex-R52+ MCU domain, a Nash BPU rated at 128 TOPS INT8, a Mali-G78AE GPU, and 24 GB of LPDDR5. The four R52+ cores can be configured for reliable real-time control: two cores run in lockstep for safety, and the other two support split-lock operation. In practice, the A78AE CPU and BPU can handle Linux and vision processing, while the MCU handles time-critical tasks such as motor and sensor I/O. This can reduce the need for a separate real-time controller, although whether it eliminates one depends on the robot and its requirements. RDK S100P specifications: SoC - D-Robotics S100P CPU - 6x Arm Cortex-A78AE @ 2.0 GHz (safety-capable AE cores) MCU - 4x Arm Cortex-R52+ @ 1.2 GHz (1x DCLS [...] The post D-Robotics RDK S100P - A 128 TOPS alternative to NVIDIA Jetson Orin NX 16GB with Cortex-A78AE/R52+ cores appeared first on CNX Software - Embedded Systems News. ## Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino DevFeed: [Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-08-27T13:18:53Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Artificial Intelligence](), [Computer vision](), [Neural Network](), [object-detection](), [C](), [datasets]() Tags: [ai](), [ai-powered-livestock-monitoring](), [arduino](), [computer-vision](), [data](), [livestock-monitoring](), [model-training](), [monitoring](), [neural-networks](), [uno-q](), [yolo]() ### AI overview An Arduino Blog article profiles four 17-year-olds in Italy developing an AI-powered livestock monitoring system with the UNO Q. The project uses computer vision, custom-trained neural networks, and diverse image data to identify animals, count them, and detect possible health problems in real time. ### Source excerpt Pietro Maria Piazza, Alessandro Nesci, Davide Santucci, and Matteo Angiolillo are not waiting to finish school before starting to build something real. Based in Forlì, Italy, the four friends behind Blue Proton Initiative strive to develop an AI-powered livestock monitoring system designed to help farmers identify individual animals and detect early signs of health problems [...] The post Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino appeared first on Arduino Blog. ## Luce: Relightable Gaussians for 3D Asset Generation DevFeed: [Luce: Relightable Gaussians for 3D Asset Generation]() Original publisher: [Read original article]() Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Artificial Intelligence](), [Benchmark]() Tags: [3d](), [ai](), [benchmark](), [computer-vision](), [generation](), [images](), [mesh](), [models](), [multimodal](), [techniques](), [vision]() ### AI overview Luce is a multimodal 3D representation for generating relightable assets from a single image. It combines geometry with physically based materials in a voxelized Gaussian cloud, compresses them into a material-aware latent space, and generates relightable PBR Gaussians and optional textured meshes. On Toys4K, it reports a 28% FID improvement over the strongest baseline and improves alignment on an AI-generated image benchmark. ### Source excerpt High-fidelity image-to-3D generation requires a 3D representation that captures both geometry and appearance. To support relighting and integration into standard rendering pipelines, the representation should include physically based rendering (PBR) modalities such as albedo, metallic-roughness, and surface normals. We propose Luce, a 3D representation that unifies geometry and PBR materials within a voxelized multimodal Gaussian cloud, using dedicated Gaussian primitives for each modality. A variational autoencoder compresses this representation into a unified material-aware latent space. A... ## \[July 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[July 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-08-24T02:23:20Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [AI Agent](), [Agentic development](), [Artificial Intelligence](), [SDK](), [Orchestration](), [Computer vision](), [Microsoft Agent Framework]() Tags: [2026](), [agentic-development](), [ai](), [ai-agents](), [computer-vision](), [google](), [google-ai](), [google-antigravity](), [orchestration](), [sdk]() ### AI overview A July 2026 roundup highlights Google AI community projects built with the Antigravity SDK and related tools. The featured work covers asynchronous triggers, autonomous and self-correcting agents, approval-gated workflows, computer vision operations, and parallel multi-agent orchestration. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravity Antigravity has no task queue. Meet @trigger, its real async primitive by AI GDE Omotayo Aina (UK) explores the design philosophy behind Antigravity SDK, detailing how it leverages asyncio and triggers instead of a traditional task queue. It demonstrates how to construct asynchronous patterns like bounded task queues and cron-like scheduling using this minimalist primitive. https://medium.com/media/0311867ab42ff4749c6db6e2653e2716/href Inside the /goal Loop: How to Build Autonomous AI Agents (repository) by GDE Alexander Amin (Germany) explores the architecture of a custom autonomous agent built with Antigravity SDK that coordinates a multi-agent squad to retrieve data and edit documents. It demonstrates how to implement human gate policies and maintain secure, production-ready agentic loops. Anatomy of a Self-Correcting Agent -- How /goal Closes the Loop in Antigravity by AI GDE Krupa Galiya (India) is a framework with a live dashboard to analyze an AI agent's self-correction process. It examines how agents respond to intentional failures through a loop of verification, diagnosis, replanning, and retrying. image source VisionOps Crew: A Multi-Agent Architecture for Computer Vision Operations Using Google ADK and the Antigravity SDK (repository) by AI GDE Henry Ruiz (US) introduces a multi-agent assistant designed to address fragmentation in computer vision engineering using ADK and Antigravity SDK. Henry leverages specialized agents and external tool integrations to coordinate model discovery, data inspection, and workflow execution. EscrowGuard: Building Approval-Gated AI Agents with the Google Antigravity SDK (repository) by AI GDE Aye Hninn Khine (Thailand) leverages Antigravity SDK to build a multi-agent architecture wi ## AI HAT vs AI Camera vs AI Kit: Which One Should You Buy? DevFeed: [AI HAT vs AI Camera vs AI Kit: Which One Should You Buy?]() Original publisher: [Read original article]() Author: Usman Qamar Published: 2026-08-15T05:00:00Z Content type: tutorial Language: en Sources: [RaspberryTips]() Topics: [Artificial Intelligence](), [Image processing](), [cpu](), [Automation]() Tags: [ai](), [computer-vision](), [cpu](), [guide](), [home-automation](), [image-processing](), [inspiration](), [raspberry-pi](), [tutorials]() ### AI overview This guide compares Raspberry Pi's AI Camera, AI HAT+ and AI Kit, explaining when faster AI processing is useful and whether an AI accessory is necessary. It focuses on the AI Camera's built-in image processing and neural processing capabilities for computer vision tasks, including how it can offload AI processing from the Raspberry Pi and support downstream actions such as home automation. ### Source excerpt I've been quite interested in Raspberry Pi's new AI accessories over the past few months, trying to understand what each one does and what makes them different. But between the AI Camera, AI HAT+ and AI Kit, it's not always obvious which one you actually need, or whether you need one at all. To be... ## How we raised mobile end-to-end test stability to 98% DevFeed: [How we raised mobile end-to-end test stability to 98%]() Original publisher: [Read original article]() Author: Michael Garfinkle Published: 2026-08-12T20:02:35Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [ci](), [React Native](), [App](), [Pull Request](), [User Experience]() Tags: [app](), [ci](), [computer-vision](), [mobile](), [pull-request](), [react-native](), [testing](), [user-experience]() ### AI overview Shopify rebuilt its mobile end-to-end testing framework with a strict builder-style API and computer vision. The approach raised test stability from 50% to 98% and restored reliable blocking checks for pull requests. ### Source excerpt We rebuilt our mobile end-to-end testing framework with a strict API and computer vision, raising test stability drastically. ## What a Raspberry Pi Can (and Can't) Do With AI DevFeed: [What a Raspberry Pi Can (and Can't) Do With AI]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-06-24T11:54:14Z Content type: article Language: en Sources: [RaspberryTips]() Topics: [Artificial Intelligence](), [Local AI](), [Large language models (LLMs)](), [object-detection](), [OpenClaw]() Tags: [ai](), [applications](), [computer-vision](), [cpu](), [gpu](), [hardware](), [large-language-models-llms](), [local](), [local-ai](), [object-detection](), [openclaw](), [quick-tips](), [raspberry-pi]() ### AI overview This article explains what Raspberry Pi devices can and cannot do with AI. They can run applications such as object detection, computer vision projects, lightweight AI agents, and some small language models, but limited computing power makes most modern LLMs slow or impractical locally. AI HAT and AI Camera products can improve computer vision workloads but do little for LLM execution. ### Source excerpt AI (artificial intelligence) is a buzzword that has been thrown around a lot these days, and the Raspberry Pi ecosystem is no exception. New use cases have been tested on it, and new products have even been released to accompany this phenomenon. So, what can your Raspberry Pi actually do with AI? A Raspberry Pi... ## MIT researchers develop a real-time spatiotemporal memory framework for robots DevFeed: [MIT researchers develop a real-time spatiotemporal memory framework for robots]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-17T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Robotics](), [Artificial Intelligence](), [Computer vision]() Tags: [3d-scene-graphs](), [aeronautical-and-astronautical-engineering](), [ai](), [algorithms](), [artificial-intelligence](), [autonomous-vehicles](), [computer-science-and-technology](), [computer-vision](), [daaam](), [describe-anything-anywhere-at-any-moment](), [electrical-engineering-and-computer-science-eecs](), [laboratory-for-information-and-decision-systems-lids](), [luca-carlone](), [machine-learning](), [map](), [memory](), [mit-schwarzman-college-of-computing](), [nicolas-gorlo](), [paper](), [real-time](), [research](), [robot-memory](), [robotic-perception](), [robotics](), [robots](), [school-of-engineering](), [spatial](), [spatiotemporal-mapping]() ### AI overview MIT researchers developed a long-term spatiotemporal memory framework that helps robots form and recall detailed models of large environments. The system combines map representations with language-based descriptions, answers environmental questions in plain language, and runs fast enough for real-time mobile-robot use. ### Source excerpt A new spatial memory system for robots efficiently captures details about the objects they see while exploring their environment. ## ESP-WHO: Get started DevFeed: [ESP-WHO: Get started]() Original publisher: [Read original article]() Author: John Lee Published: 2026-05-28T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Tutorial](), [Computer vision](), [Image processing](), [ESP32-S3](), [ESP-IDF](), [Inference](), [Hardware](), [Framework](), [Development]() Tags: [blog](), [camera](), [computer-vision](), [dl](), [esp-idf](), [esp-who](), [esp32](), [esp32-s3](), [face-recognition](), [hardware](), [image-processing](), [inference](), [led](), [tutorial]() ### AI overview This tutorial explains how to set up ESP-WHO on the ESP32-S3-EYE board, run a face recognition example, and extend it with custom detection callbacks. It covers the platform architecture, component pipeline, hardware abstraction, prerequisites, and an LED response when a face is detected. ### Source excerpt In this article, we will set up ESP-WHO on the ESP32-S3-EYE board, running a face recognition example, and extending it with custom detection callbacks. [Next page]()