Embedded World Hall 2, Booth 2-551

All-in-one camera with MediaTek Genio 510 processor
The VC EvoCam can be configured with an onboard image sensor or with one or two cable-connected remote-head cameras. Image sensors from the VC MIPI Camera portfolio are available for this purpose; the first VC EvoCam presented at embedded world is equipped with the Sony  IMX900 image sensor featuring 3.2 MP resolution and globalshutter. The MediaTek Genio 510 Edge AI processor is integrated for direct image processing. It features two ARM Cortex-A78 and four ARM Cortex-A55 cores, an ARM Mali GPU, and an NPU with a performance of 3.2 TOPS. Up to 2 GB of RAM, 16 GB of flash memory, and expandability via SD 3.0 enable the processing and storage of extensive image data. The VC EvoCam is supplied with a customized Debian Linux operating system. Common image processing functions are directly supported and
included as demo applications.

Individual adaptation with interface boards
For integration into devices and applications, the VC EvoCam features a 100-pin board-to-board connector. Signals for interfaces and processor functionalities are made available here, including I/Os, I²C, USB, Ethernet, Video DSI, and PCIe. At the start of volume production in the first half of 2026, a minimalist interface board will be available, featuring power supply, I/Os for trigger and flash, as well as USB and RJ45/LAN. A more extensive interface board will follow shortly as a development kit and for prototyping, routing all connector signals to physical interfaces. Vision Components supports customers in the development of individual interface boards as well as the design-in of the VC EvoCam.

Celebrating 30 Years of VC Smart Cameras
In 2026, Vision Components celebrates its 30th anniversary. In 1996, the company presented the first industrial-grade smart camera, developed by company founder Michael Engel. The VC EvoCam now marks another milestone, with the high computing power of the MediaTek processor, flexibly adaptable for numerous applications, and with a freely programmable Linux operating system for easy and rapid integration.

At embedded world, alongside the new VC EvoCam, Vision Components is showing the new VC MIPI IMX454 Camera Module for multispectral imaging, the VC MIPI Multiview Cam with nine image sensors for customer-specific multiview and multispectral applications, as well as the VC MIPI Portfolio of flexible and industrial-grade cameras with MIPI CSI-2 interface. The components of the VC MIPI Bricks System for plug-and-play vision integration will also be on display. It comprises various cable options and lens holders as well as ready-to-use MIPI CSI-2 Cameras.

About Vision Components
Vision Components is a leading manufacturer of embedded vision systems with over 25 years of experience. The product range extends from versatile MIPI camera modules to freely programmable cameras with ARM/Linux and OEM systems for 2D and 3D image processing. The company was founded in 1996 by Michael Engel, inventor of the first industrial-grade intelligent camera. VC operates worldwide, with sales offices in the USA, Japan, and UAE as well as local partners in over 25 countries.

Company contact:
Vision Components GmbH

Jan-Erik Schmitt

+49 7243 216 7-0
schmitt@vision-components.com
Ottostraße 2 | 76275 Ettlingen
www.vision-components.com

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Executive summary

• Ubicept’s SPAD-based system offers consistent HDR performance in nighttime driving conditions, preserving shadow and highlight detail where conventional cameras fall short.
• Unlike traditional HDR techniques which often struggle with motion artifacts, Ubicept Photon Fusion maintains clarity even when both the camera and scene are in motion.
• Watch https://www.youtube.com/watch?v=KxucJYv63pI on an HDR-capable display to compare a conventional CMOS camera with in-sensor HDR and a SPAD camera with Ubicept processing

Introduction

At Ubicept, we often talk about the “impossible triangle”—low light, fast motion, and high dynamic range—and how our technology enables perception even when all three are present. That said, it’s been a while since we’ve highlighted our HDR capabilities, so we decided to take a spin around town with our new color setup to show them off.

Before we dive in, let’s take a moment to talk about why high dynamic range matters for perception. Our world is full of extreme lighting contrasts. On sunny days, reflections from shiny surfaces can blind both humans and machines. At night, brilliant headlights and streetlamps create intense pools of light that leave surrounding areas in deep shadow. If a perception system can’t resolve detail across both the bright and the dark, it risks missing critical information. That’s why image sensors designed for applications like advanced driver assistance systems (ADAS) often emphasize their ability to handle these challenging scenarios.

Experimental setup

For this demo, we rigged up two systems side by side:

• Our prototype development kit, featuring a 1-megapixel SPAD sensor and Ubicept processing
• A 5-megapixel dash camera, featuring a low-light CMOS sensor with built-in HDR capabilities

The development kit camera was mounted outside the vehicle to capture an unobstructed view. Unfortunately, the dash camera had to remain inside due to its physical design, making it more susceptible to glare from the windshield. So, while this isn’t a perfectly fair or scientific comparison, the dramatic differences you’re about to see should still offer meaningful insight into the relative performance of the two systems in real-world scenarios.

Before you press play:

• For best results, please view this on an HDR-capable display. You can still appreciate the video on a typical SDR desktop or laptop monitor, but the results are truly stunning on an OLED smartphone or television.
• We exported the video at half speed to highlight motion detail. The dash camera only outputs at 30 fps in HDR mode, so it will look choppy when slowed down by 50%.

Key observations

We hope the comparison video speaks for itself, but we wanted to highlight a few key moments to observe if you choose to review the footage again.

First, even though the dash camera runs in HDR mode, there are plenty of situations where its dynamic range just isn’t enough. Take this frame at 3:39:

 To see this frame in full quality, see 3:39 in the video on an HDR-capable display

The outlined area is actually well-lit by the surrounding environment, but the dash camera sacrifices shadow detail to avoid overexposing the bright building. As a consequence, the trees disappear into the noise floor. In contrast, our system preserves both highlights and shadows, revealing the entire scene clearly.

We also noticed some HDR-specific artifacts in the dash camera footage. In the frame at 0:27 below, the outlined region shows a sharp window, while the bright green container (moving at the same speed relative to the car) is blurred beyond recognition:

To see this frame in full quality, see 0:27 in the video on an HDR-capable display

This is notable because, under normal conditions, motion blur reflects how much something is moving. With conventional HDR, however, that relationship becomes more complex due to how these systems operate. They blend short exposures for bright regions with longer ones for darker areas, causing motion blur to also vary by brightness. The result is frames that are harder to interpret.

These techniques can also introduce artifacts, as shown in this frame at 3:03:

To see this frame in full quality, see 3:03 in the video on an HDR-capable display

We can’t say for sure what’s happening here, since we don’t have details about the dash camera’s HDR implementation, but suffice it to say that falsely repeated objects can be confusing for downstream perception systems. The more important point, at least for this demo, is that the SPAD camera with Ubicept processing is able to deliver consistent performance across all the situations we encountered.

Please note that the still images above were mapped down to SDR for web display, so some of the shadows and highlights may appear clipped. The video itself should show the full range, so we encourage you to view it on an HDR-capable display.

Technical notes

You might be thinking, “Wow, SPADs are amazing!” And they are, but they’re not enough on their own to produce results like this. We addressed this directly in a previous blog post, as well as on our Technology and Passive Vision pages. What we’re showing here isn’t the result of a special “HDR SPAD” or a dedicated HDR algorithm. It’s all part of the same core pipeline. Put simply, HDR is just one of many challenges our system is built to handle.

With that said, achieving the best results isn’t just about the sensor and processing. As we built this demo, we came to appreciate how important it is for all parts of the system to work together. In early tests using standard machine vision lenses, we found that glare significantly reduced contrast. That led us to the Sunex DSL428—we were admittedly skeptical at first of its “HDR-optimized” marketing, but it turns out the designation was well-earned!

We also ran into some practical challenges, like condensation forming on the optical components as the night cooled (note to self: bring some microfiber cloths next time). That’s something we’ll address in future demos, but the key takeaway is that the sensor and processing weren’t the limiting factors. Either way, we’re looking forward to showing even better results here with continued refinements to the optics and housing. Of course, if you want to see how our technology performs on your most demanding perception tasks, we’d love to hear from you!

The post Pushing the Limits of HDR with Ubicept appeared first on Edge AI and Vision Alliance.

This new camera is based on a 1.2MP onsemi Hyperlux ID AF0130 global shutter depth sensor, delivering simultaneous high-resolution depth, confidence, and IR grayscale streams using Continuous-Wave indirect Time of Flight (CW-iTOF) technology. It is designed for seamless integration with NVIDIA Jetson Orin platforms.

A key differentiator of the DepthVista Helix is its dual VCSEL illumination architecture, engineered to strike the optimal balance between performance, cost, and mechanical design. To simplify deployment, e-con Systems provides the DepthVista SDK, which includes V4L2-based Linux camera drivers, Depth visualization and control tools, reference applications for Static box dimensioning and Pose estimation. This software framework significantly reduces development time and enables faster evaluation, prototyping, and production deployment.

Key Capabilities of the DepthVista Helix 3D CW iToF Camera include

• On-camera depth computation with integrated on-chip depth processing ensures exceptional depth precision with <1% deviation over 0.2m–2m and 0.5m–6m ranges.
• High-resolution depth sensing delivering 1.2MP @ 60 fps.
• IP67 rated camera design with GMSL2 cable support
• Multi-camera interference mitigation to ensure stable depth performance when multiple cameras deployed on robots or in multi-robot environments
• Compatibility with NVIDIA Jetson platforms, including Orin NX and Orin AGX.
• Dual-frequency CW iToF operation supporting long-range, high-precision depth measurement with improved multipath suppression.
• Advanced depth confidence filtering to suppress reflections, edge noise, and unstable depth pixels.
• Narrow field of view (NFOV) of the depth camera enables precise distance measurement with dense point-cloud data and reduced multipath interference
• GMSL and USB interface options to support flexible system Integration
• Optional RGB sensor support for simultaneous capture of visual and depth data.
• DepthVista SDK with Linux drivers, sample applications, and depth visualization tools.

“For industrial robotics, depth sensing must deliver metric accuracy with predictable and repeatable behavior under real operating conditions, not just favorable lab performance. With the DepthVista Helix 3D CW indirect Time-of-Flight camera, we provide 1.2MP per-pixel depth measurement based on phase-shift analysis of modulated illumination, enabling robots to reconstruct true scene geometry rather than relying on appearance-based or inferred depth cues. This system-level approach enables reliable detection of fine and low-profile obstacles, improved grasp localization accuracy, and stable navigation even in low ambient light, reflective environments, and optically complex multi-robot warehouse deployments,” said Prabu Kumar Kesavan, CTO at e-con Systems.

“onsemi’s AF0130, part of the Hyperlux ID iToF family, is engineered for precise real‑time 3D sensing in industrial environments. Its global shutter and unique pixel architecture capture and store all phases simultaneously, minimizing motion artifacts. Combined with integrated on‑chip depth processing, the sensor outputs depth, confidence, and intensity data, making it ideal for robotic applications including autonomous mobile robots, material handling systems, and access control systems,” said Steve Harris, senior director of marketing, Industrial and Commercial Sensing Division, onsemi..

Availability

To evaluate the capabilities of DepthVista Helix Camera, please visit our online web store and purchase the product.

Customization and Integration Support

e-con Systems offers customization services and end-to-end integration support for the cameras and compute box, ensuring that unique application requirements can be easily met. For customization or integration support, please contact us at camerasolutions@e-consystems.com.

About e-con Systems

e-con Systems® designs, develops, and manufactures embedded vision solutions – from custom OEM cameras to complete ODM platforms. With 20+ years of experience and expertise in embedded vision, it focuses on delivering vision and camera solutions to industries such as retail, medical, industrial, mobility, agriculture, smart city, and more. e-con Systems’ wide portfolio of products includes Time of Flight cameras, MIPI camera modules, GMSL cameras, USB cameras, stereo cameras, GigE cameras, HDR cameras, low light cameras, and more. Our cameras are currently embedded in over 350+ customer products, and we have shipped over 2 million cameras to the United States, Europe, Japan, South Korea, and many other countries.

For more information, please contact:

Mr. Harishankkar
VP – Business Development
sales@e-consystems.com
e-con Systems® Inc.,
+1 408 766 7503
Website: www.e-consystems.com

The post e-con Systems Launches DepthVista Helix 3D CW iToF Camera for Robotics and Industrial Automation appeared first on Edge AI and Vision Alliance.

The image sensor is an important component in defining the camera’s image quality. Many real-world applications pushed for smaller pixel sizes to increase resolution in compact form factors.  To address this demand, Sony has been improving its image sensor technology across generations. Over the years, this evolution has been focused on key aspects such as pixel size optimization, saturation capacity, pixel-level noise reduction, and light arrangement.

The advancements in Sony’s sensors have spanned four generations. Of these, Pregius S is the latest technology. It provides a stacked sensor architecture, optimal front illumination, and increased speed, sensitivity, and improved exposure control functionality relative to earlier generations.

Key Takeaways:

• What are the IMX264 and IMX568 sensors?
• The architectural differences between the second-generation Pregius and the fourth-generation Pregius S sensors
• Key technologies of IMX568 over IMX264 in embedded cameras

What Are the IMX264 and IMX568 Sensors?

The IMX264 sensor was the first small-pixel sensor in the industry, with a pixel size of 3.45 µm x 3.45 µm when it was introduced. Based on Sony’s “Pregius” Generation two, this sensor takes advantage of Sony’s Exmor technology.

The IMX568 sensor is a Sony Pregius S Generation Four sensor. The ‘S’ in Pregius S refers to stacked, indicating that the sensor has a stacked design, with the photodiode on top and the circuits on the bottom. This sensor is designed with an even smaller pixel size of 2.74 µm x 2.74 µm.

Comparison of key specifications:

Architectural Description: Second vs. Fourth Generation Sensors

Second-generation front-illuminated design (IMX264)
The second-generation Sony sensor uses front-illuminated technology. In front-illumination technology, the conductive elements intercept light before it reaches the light-sensitive element. As a result, some of the light might not reach the light-sensitive element. This affects the performance of the camera with small pixels.

Fourth-generation back-illuminated design (IMX568)
The Pregius S architecture revolutionizes this design by flipping the structure. The photodiode layer is positioned on top with the conductive elements beneath it. This inverted configuration allows light to reach the photodiode directly, without obstruction. It dramatically improves light-collection efficiency and enables smaller pixel sizes without sacrificing sensitivity.

The image below provides a clearer view of the difference between front- and back-illuminated technologies.

IMX264 vs. IMX568: A Detailed Comparison

Global shutter performance
IMX264 already delivers true global shutter operation, eliminating motion distortion. However, IMX568 introduces a redesigned charge storage structure that dramatically reduces parasitic light sensitivity (PLS). This ensures that stored pixel charges are not contaminated by incoming light during readout.

It results in a clear image, especially under high‑contrast or high-illumination conditions in the high-inspection system.

Frame rate and throughput
The IMX568 has a frame rate that is nearly double that of the IMX264 at full resolution. The reasons for this are faster readout circuitry and SLVS‑EC high‑speed interface. For applications such as robotic guidance, motion tracking, and high‑speed inspection, this increased throughput directly translates into higher system accuracy and productivity.

Noise performance and image quality
Pregius S sensors offer lower read noise, reduced fixed pattern noise, and better dynamic range. IMX568 produces clear images in low‑light environments and maintains higher signal fidelity across varying exposure conditions.

Such an improvement reduces reliance on aggressive ISP noise reduction, preserving fine image details critical for machine vision algorithms.

Power consumption and thermal behavior
Despite higher operating speeds, IMX568 is more power‑efficient on a per‑frame basis. Improved charge transfer efficiency and readout design result in lower heat generation, making it ideal for compact, fanless, and always‑on camera systems.

System integration considerations
IMX264 uses traditional SLVS/LVDS interfaces and integrates well with legacy ISPs and FPGA platforms. IMX568 requires support for SLVS‑EC and higher data bandwidth. While this demands a modern processing platform, it also future‑proofs the system for higher-performance vision pipelines.

What Are the Advanced Imaging Features of the IMX568 Sensor?

Short interval shutter
IMX568 can perform short-interval shutters starting at 2 μs, which helps reduce the time between frames by controlling registers. This allows the cameras to capture images of fast-moving objects for industrial automation.

Multi-exposure trigger mode
The IMX568 allows multiple exposures within a single trigger sequence. This feature allows obtaining several images of the same scene at differing exposure times, both in illuminated and dark areas of the object. This reduces dependency on complex lighting and strobe tuning.

It enables IMX568-based cameras to handle challenging lighting conditions more effectively than single-exposure sensors in vision applications such as sports analytics.

Multi-frame ROI mode
This multi-ROI sensor enables simultaneous readout of up to 64 user-defined regions from arbitrary positions on the sensor.

In the image below, you can see how data from two ROIs have been read from within a single frame. The marked areas represent the ROIs.

Full Frame

Selected Two ROIs

Cropped ROIs

e-con Systems’ recently-launched e-CAM56_CUOAGX is an IMX568-based global shutter camera capable of multi-frame Region of Interest (ROI) functionality. It supports a rate of up to 1164 fps with the multi-ROI feature.

This can be very useful in real-time embedded vision use cases, where it is necessary to focus only on a specific region of the image. e-CAM56_CUOAGX can be deployed in traffic surveillance applications where the focus should only be on car motion, facial recognition applications. That way, only the facial region of the subject can be zoomed to achieve superior security surveillance.

Short exposure mode
The IMX568 supports exposure times that can be very short while ensuring image stability and sensitivity at the same time. Exposure times for this mode may vary by up to ±500 ns depending on the sample and environmental conditions, as well as other factors such as temperature and voltage levels.

Dual trigger
The IMX568 enables dual trigger operation, allowing independent control of image capture timing and readout by dividing the screen into upper and lower areas.  This enables precise synchronization with external events, lighting, and strobes, and allows flexible capture workflows in complex inspection setups.
Read the article: Trigger Modes available in See3CAMs (USB 3.0 Cameras) – e-con Systems, to know about the trigger function in USB cameras

Gradation compression
IMX568 features gradation compression to optimize the representation of brightness levels within the output image. This preserves important image details in both bright and dark regions. With this feature, the camera can deliver more usable image data without increasing bit depth or lighting complexity.

Dual ADC
The dual-ADC architecture provides faster, more flexible signal conversion. This supports high frame rates without compromising image quality and optimizes performance across the different bit depths: 8-bit / 10-bit / 12-bit. The dual ADC operation also helps IMX568-based cameras maintain high throughput and low latency in demanding vision systems.

IMX568 Sensor-Based Cameras by e-con Systems

Since 2003, e-con Systems has been designing, developing, and manufacturing cameras. e-con Systems’ embedded cameras continue to evolve with advances in sensors to meet the growing demand for embedded vision applications.

Explore our Sony Pregius Sensor-Based Cameras.

Use our Camera Selector to check out our full portfolio.

Need help selecting the right embedded camera for your application? Talk to our experts at camerasolutions@e-consystems.com.

FAQS

1. What is Multi-ROI in image sensors?
   Multi-ROI (Multiple Regions of Interest) allows an image sensor to crop and read out multiple, user-defined areas from different locations on the sensor within a single frame, instead of reading the full frame.

1. Can multiple ROIs be read simultaneously in the same frame?
   Yes. Multiple ROIs can be read out simultaneously within the same frame, allowing spatially separated regions to be captured without increasing frame latency.

1. How many ROI regions can be configured on this sensor?
   The multi ROI image sensor supports up to 64 independent ROI areas, enabling flexible selection of multiple spatial regions based on application requirements.

1. What are the benefits of using Multi-ROI instead of full-frame readout?
   Multi-ROI reduces data bandwidth and processing load, increases effective frame rates, and enables efficient monitoring of multiple areas of interest.

1. Are all ROIs captured at the same time?
   Yes. All selected ROIs are captured within the same frame, ensuring consistent timing.

Prabu Kumar
Chief Technology Officer and Head of Camera Products, e-con Systems

The post Sony Pregius IMX264 vs. IMX568: A Detailed Sensor Comparison Guide appeared first on Edge AI and Vision Alliance.

MIPI CSI-2®, together with MIPI D-PHY and C-PHY physical layers, form the foundation of image sensor solutions across a wide range of markets, including smartphones, computing, automotive, robotics and beyond. This webinar will explore the latest CSI-2 feature developments and the continued evolution of MIPI’s low-energy, high-performance physical layer transport solutions–D-PHY and C-PHY–which leverage differential and ternary signaling, respectively.

Attendees will gain insight into recently adopted capabilities such as event-based sensing and processing, as well as D‑PHY embedded clock mode. The session will also cover near-term enhancements, including dual-PHY macro support and multi-drop bus capability, along with a forward-looking view of longer-term feature developments. By the close of the webinar, attendees will understand how MIPI imaging solutions are enabling next-generation computer and machine vision applications across a wide range of product ecosystems.

Register Now »

Featured Speakers:

Haran Thanigasalam, Chair of the MIPI Camera Working Group and Camera Interest Group

Raj Kumar Nagpal, Chair of the MIPI D-PHY Working Group

George Wiley, Chair of the MIPI C-PHY Working Group

For more information and to register, visit the event page.

The post Upcoming Webinar on CSI-2 over D-PHY & C-PHY appeared first on Edge AI and Vision Alliance.

As the need for security becomes increasingly more critical, MIPI Alliance has continued to broaden its portfolio of standardized solutions, adding two more specifications in late 2025, and continuing work on significant updates to the MIPI Camera Security Framework specifications slated for completion in mid-2026.

Read on to learn more about the newly released specifications and what lies ahead for the MIPI Camera Security Framework.

MIPI CCISE: Protecting Camera Command and Control Interfaces

The new MIPI Command and Control Interface Service Extensions (MIPI CCISE) v1.0, released in December 2025, defines a set of security service extensions that can apply data integrity protection and optional encryption to the MIPI CSI-2® camera control interface based on the I2C transport interface. The protection is provided end-to-end between the image sensor and its associated SoC or electronic control unit (ECU).

MIPI CCISE rounds out the existing MIPI Camera Security Framework, which includes MIPI Camera Security v1.0, MIPI Camera Security Profiles v1.0 and MIPI Camera Service Extensions (MIPI CSE) v2.0. Together, the specifications define a flexible approach to add end-to-end security to image sensor applications that leverage MIPI CSI-2, enabling authentication of image system components, data integrity protection, optional data encryption, and protection of image sensor command and control channels. The specifications provide implementers with a choice of protocols, cryptographic algorithms, integrity tag modes and security protection levels to offer a solution that is uniquely effective in both its security extent and implementation flexibility.

Use of MIPI camera security specifications enables an automotive system to fulfill advanced driver-assistance systems (ADAS) safety goals up to ASIL D level (per ISO 26262:2018) and supports functional safety and security mechanisms, including end-to-end protection as recommended for high diagnostic coverage of the data communication bus.

While the initial focus of the camera security framework was on securing long-reach, wired in-vehicle network connections between CSI-2 based image sensors and their related processing ECUs, the specifications are also highly relevant to non-automotive machine vision applications that leverage CSI-2-based image sensors.

A downloadable white paper, A Guide to the MIPI Camera Security Framework for Automotive Applications, provides a detailed explanation of how these specifications work together to provide application layer end-to-end data protection.

MIPI Security Specification for Debug: Enabling Remote Debug of Systems in the Field

The recently adopted MIPI Security Specification for Debug defines a standardized method for establishing secure, authenticated debug sessions between a debug and test system and a target system.

Designed to enable remote debugging in potentially hostile real-world locations outside of a test lab, the specification allows secure remote debugging of production devices without relying solely on traditional physical protections such as buried traces or restricted access to debug ports. Instead, it introduces a trusted, cryptographically protected communication path that spans end-to-end, from the physical debug tool to the target device’s package pins, through all connectors, cabling, routing and bridges.

The new speciation adds a secure messaging layer to the existing MIPI debug architecture, wrapping debug traffic in encrypted, authenticated messages while remaining interface-agnostic. Core components include a secure communications manager that is responsible for security protocol, data model processing and key generation; cryptographic message-protection functions; and secure communication management paths. To accomplish this, the specification leverages the DMTF Security Protocol and Data Model (SPDM) industry standard for platform security.

This approach ensures authenticity, confidentiality and integrity for all debug communications, regardless of the underlying transport interface, whether MIPI I3C®, USB, PCIe or others. Debugger behavior remains consistent across interfaces, simplifying implementation and validation.

The specification complements the broader MIPI debug ecosystem.

Coming in 2026: New “Fast Boot” Options for MIPI Camera Security

Enhancements to the suite of MIPI camera security specifications are being developed to enable faster boot times for imaging systems, minimizing the time taken from power-on to streaming of secure video data.

These enhancements will continue to leverage the DMTF SPDM framework and message formats, but will introduce an optional new security mode that will half the number of security handshake operations required to complete the establishment of a secure video streaming channel compared with currently defined security modes. Image sensors will be able to implement both current and new modes of operation to provide backward compatibility, and SoCs may only require software updates to implement the new mode of operation.

Both the MIPI Camera Security and the MIPI Camera Security Profiles specifications are scheduled to be updated to v1.1 in mid-2026. However, the companion specifications that will fully enable the enhancements, MIPI CSE v2.1 and the new CSE Exchange Format (EF) v1.0, will follow later this year.

All security specifications are currently available only to MIPI Alliance members.

Ian Smith
MIPI Alliance Technical Content Consultant

The post What’s New in MIPI Security: MIPI CCISE and Security for Debug appeared first on Edge AI and Vision Alliance.

Key Takeaways

• Why multi-sensor timing drift weakens edge AI perception
• How GNSS-disciplined clocks align cameras, LiDAR, radar, and IMUs
• Role of Orin NX as a central timing authority for sensor fusion
• Operational gains from unified time-stamping in autonomous vision systems

Autonomous vision systems deployed at the edge depend on seamless fusion of multiple sensor streams (cameras, LiDAR, Radar, IMU, and GNSS) to interpret dynamic environments in real time. For NVIDIA Orin NX-based platforms, the challenge lies in merging all the data types within microseconds to maintain spatial awareness and decision accuracy.

Latency from unsynchronized sensors can break perception continuity in edge AI vision deployments. For instance, a camera might capture a frame before LiDAR delivers its scan, or the IMU might record motion slightly out of phase. Such mismatches produce misaligned depth maps, unreliable object tracking, and degraded AI inference performance. A sensor fusion system anchored on the Orin NX mitigates this issue through GNSS-disciplined synchronization.

In this blog, you’ll learn everything you need to know about the sensor fusion architecture, why the unified time base matters, and how it boosts edge AI vision deployments.

What are the Different Types of Sensors and Interfaces?

Coordinating Multi-Sensor Timing with Orin NX

Edge AI systems rely on timing discipline as much as compute power. The NVIDIA Orin NX acts as the central clock, aligning every connected sensor to a single reference point through GNSS time discipline.

The GNSS receiver sends a Pulse Per Second (PPS) signal and UTC data via NMEA to the Orin NX, which aligns its internal clock with global GPS time. This disciplined clock becomes the authority across all interfaces. From there, synchronization extends through three precise routes:

1. PTP over Ethernet: The Orin NX functions as a PTP Grandmaster through its USB NIC. LiDAR and radar units operate as PTP slaves, delivering time-stamped point clouds and detections that stay aligned to the GNSS time domain.
2. PPS-derived camera triggers: Cameras linked via GMSL or MIPI CSI receive frame triggers generated from the PPS signal. This ensures frame start alignment to GNSS time with zero drift between captures.
3. Timed IMU polling: The IMU connects over I²C and is polled at consistent intervals, typically between 500 Hz and 1 kHz. Software time stamps are derived from the same GNSS-disciplined clock, keeping IMU data in sync with all other sensors.

Importance of a Unified Time Base

All sensors share the same GNSS-aligned time domain, enabling precise fusion of LiDAR, radar, camera, and IMU data.

Implementation Guidelines for Stable Sensor Fusion

• USB NIC and PTP configuration: Enable hardware time-stamping (ethtool -T ethX) so Ethernet sensors maintain nanosecond alignment.
• Camera trigger setup: Use a hardware timer or GPIO to generate PPS-derived triggers for consistent frame alignment.
• IMU polling: Maintain fixed-rate polling within Orin NX to align IMU data with the GNSS-disciplined clock.
• Clock discipline: Use both PPS and NMEA inputs to keep the Orin NX clock aligned to UTC for accurate fusion timing.

Strengths of Leveraging Sensor Fusion-Based Autonomous Vision

Direct synchronization control

Removing the intermediate MCU lets Orin NX handle timing internally, cutting latency and eliminating cross-processor jitter.

Unified global time-stamping

All sensors operate on GNSS time, ensuring every frame, scan, and motion reading aligns to a single reference.

Sub-microsecond Ethernet alignment

PTP synchronization keeps LiDAR and radar feeds locked to the same temporal window, maintaining accuracy across fast-moving scenes.

Deterministic frame capture

PPS-triggered cameras guarantee frame starts occur exactly on the GNSS second, preventing drift between visual and depth data.

Consistent IMU data

High-frequency IMU polling stays aligned with the master clock, preserving accurate motion tracking for fusion and localization.

e-con Systems Offers Custom Edge AI Vision Boxes

e-con Systems has been designing, developing, and manufacturing OEM camera solutions since 2003. We offer customizable Edge AI Vision Boxes powered by NVIDIA Orin NX and Orin Nano. It brings together multi-camera interfaces, hardware-level synchronization, and AI-ready processing into one cohesive unit for real-time vision tasks.

Our Edge AI Vision Box – Darsi simplifies the adoption of GNSS-disciplined fusion in robotics, autonomous mobility, and industrial vision. It comes with support for PPS-triggered cameras, PTP-synced Ethernet sensors, and flexible connectivity options. It also provides an end-to-end framework where developers can plug in sensors, train models, and run inference directly at the edge (without external synchronization hardware).

Know more -> e-con Systems’ Orin NX/Nano-based Edge AI Vision Box

Use our Camera Selector to find other best-fit cameras for your edge AI vision applications.

If you need expert guidance for selecting the right imaging setup, please reach out to camerasolutions@e-consystems.com.

FAQs

1. What role does sensor fusion play in edge AI vision systems?
   Sensor fusion aligns data from cameras, LiDAR, radar, and IMU sensors to a common GNSS-disciplined time base. It ensures every frame and data point corresponds to the same moment, thereby improving object detection, 3D reconstruction, and navigation accuracy in edge AI systems.

1. How does NVIDIA Orin NX handle synchronization across sensors?
   The Orin NX functions as both the compute core and timing master. It receives a PPS signal and UTC data from the GNSS receiver, disciplines its internal clock, and distributes synchronization through PTP for Ethernet sensors, PPS triggers for cameras, and fixed-rate polling for IMUs.

1. Why is a unified time base critical for reliable fusion?
   When all sensors share a single GNSS-aligned clock, the system eliminates time-stamp drift and timing mismatches. So, fusion algorithms can process coherent multi-sensor data streams, which enable the AI stack to operate with consistent depth, motion, and spatial context.

1. What are the implementation steps for achieving stable sensor fusion?
   Developers should enable hardware time-stamping for PTP sensors, use PPS-based hardware triggers for cameras, poll IMUs at fixed intervals, and feed both PPS and NMEA inputs into the Orin NX clock. These steps maintain accurate UTC alignment through long runtime cycles.

1. How does e-con Systems support developers building with Orin NX?
   e-con Systems provides customizable Edge AI Vision Boxes powered by NVIDIA Orin NX and Orin Nano. They are equipped with synchronized camera interfaces, AI-ready processing, and GNSS-disciplined timing. Hence, product developers can deploy real-time vision solutions quickly and with full temporal accuracy.

Prabu Kumar
Chief Technology Officer and Head of Camera Products, e-con Systems

The post What Sensor Fusion Architecture Offers for NVIDIA Orin NX-Based Autonomous Vision Systems appeared first on Edge AI and Vision Alliance.

Join e-con Systems and onsemi for an exclusive joint webinar on how Time-of-Flight (iToF) based 3D vision is enabling reliable perception for modern robotic applications, industrial and warehouse automation workflows.

Vision experts will discuss how industrial teams can leverage iToF sensor capabilities into deployable 3D vision solutions while addressing the perception challenges commonly faced in complex industrial environments.

Attendees will gain insights from proven customer success stories in field deployments, including parcel box dimensioning, autonomous pallet handling, obstacle detection, and collision avoidance in warehouse environments.

Register Now »

Featured Speakers:

Radhika S, Senior Project Lead, e-con Systems

Aidan Browne, Product Marketing Manager – Depth Sensing, onsemi

Key insights you’ll gain:

• Key industrial applications driving the adoption of iToF-based 3D vision
• Common perception challenges in industrial environments
• Translating sensor capability into deployable robotics vision solutions
• Proven customer success stories from field deployments

For more information and to register, visit the event page.

The post Upcoming Webinar on Industrial 3D Vision with iToF Technology appeared first on Edge AI and Vision Alliance.

The headline is OpenMV Firmware v4.8.1 paired with OpenMV IDE v4.8.1, which adds multi-sensor capabilities, expands event-camera support, and lays the groundwork for a major debugging and connectivity upgrade coming with firmware v5.

If you’re building edge-vision systems on OpenMV Cams, here are the product-focused updates worth knowing.

Firmware + IDE v4.8.1: the biggest changes

OpenMV’s latest release is OpenMV Firmware v4.8.1 with OpenMV IDE v4.8.1:

• OpenMV Firmware v4.8.1 (GitHub)
• OpenMV IDE v4.8.1 (GitHub)

New CSI module (multi-sensor support)

OpenMV introduced a new, class-based CSI module designed to support multiple camera sensors at the same time. This is now the preferred approach going forward.

The older sensor module is now deprecated. With v4.8.1, OpenMV recommends updating code to use the CSI module; no new features will be added to the legacy sensor module.

This multi-sensor work also enables official support for OpenMV’s multispectral thermal module—using an RGB camera + FLIR® Lepton® together.

OpenMV also teased what’s next in this direction: dual RGB and RGB + event-vision configurations are planned (only targeted for the N6).

GENX320: event-camera mode arrives

OpenMV added an event-vision mode for the GENX320 event camera. In this mode, the camera can deliver per-pixel event updates with microsecond timestamps—useful for applications like ultra-fast motion analysis and vibration measurement.

New USB debug protocol (foundation for firmware v5)

Firmware v4.8.1 and IDE v4.8.1 set the stage for a new USB Debug protocol planned for OpenMV firmware v5.0.0. OpenMV’s stated goals are better performance and reliability in the IDE connection—plus significantly more capability than the current link.

The new protocol introduces channels that can be registered in Python, enabling high-throughput data transfer (OpenMV cites >15MB/s over USB on some cameras). It also supports custom transports, making it possible to debug/control a camera over alternative links (UART/serial, Ethernet, Wi-Fi, CAN, SPI, I2C, etc.) depending on your implementation.

Related tooling: OpenMV Python (desktop CLI / tooling) and the OpenMV forums.

Universal TinyUSB support

OpenMV is moving “almost all” camera models to TinyUSB as part of the USB-stack standardization effort. They cite benefits including better behavior in configurations involving the N6’s NPU and Octal SPI flash.

A growing ML library (MediaPipe + YOLO family)

OpenMV says it has worked through much of its plan to support “smartphone-level” AI models on the upcoming N6 and AE3. They highlight support for running models from Google MediaPipe, YOLOv2, YOLOv5 YOLOv8 and more.

Roboflow integration for training custom models

OpenMV now has an operable workflow for training custom models using Roboflow, with an emphasis on training custom YOLOv8 models that can run onboard once the N6 and AE3 are in market.

Other notable improvements

• Frame buffer management improvements with a new queuing system.
• Embedded code profiler support in firmware + IDE (requires a profiling build to use).
• Automated unit testing in GitHub Actions; OpenMV cites testing Cortex-M7 and Cortex-M55 targets using QEMU to catch regressions (including SIMD correctness).
• Image quality improvements for the PAG7936 and PS5520 sensors, plus numerous bug fixes across the platform.

Kickstarter hardware: N6 and AE3 status

On the hardware front, OpenMV says it is now manufacturing the OpenMV N6 and OpenMV AE3, check out their Kickstarter for ongoing updates.

What to do now

• If you’re actively developing on OpenMV, consider updating to v4.8.1 and planning your code migration from the deprecated module to the new CSI module.
• If you’re exploring event-based vision, the new GENX320 event mode is the key software enablement to watch.
• Keep an eye on firmware v5 for the new debug protocol—especially if you need higher-throughput streaming, custom host/device channels, or alternative debug transports.

The post OpenMV’s Latest: Firmware v4.8.1, Multi-sensor Vision, Faster Debug, and What’s Next appeared first on Edge AI and Vision Alliance.

Training robots in the physical world is slow, expensive, and difficult to scale. Roboticists developing AI policies depend on high quality data—especially for complex tasks like picking up flexible objects or navigating cluttered environments. These tasks rely on data from sensors, motors, and other components used by the robot. Yet generating this data in the real world is time-consuming and requires extensive hardware infrastructure.

Simulation offers a scalable alternative. By running multiple robotic motion scenarios in parallel, teams can significantly reduce the time required for data collection. However, most simulations environments face a trade-off: performance or physical precision.

A model with near-perfect, real-world fidelity often requires vast amounts of computation and time. Such precise but slow simulations produce less data, reducing their usefulness. Instead, many developers choose simplifications that improve speed but result in a disconnect between training and deployment—commonly known as the sim-to-real gap. This means that robots trained solely in simulation will struggle in the real world. Their policies will be confused by actual sensor data that includes noise, interference, and flaws.

To address this challenge and accelerate simulation, Analog Devices developed a machine learning-based surrogate model. In our testing, the model simulated the behavior of an indirect time-of-flight (iToF) sensor with near-real-time performance, while preserving critical characteristics of the real sensor’s output. The model offers a true acceleration breakthrough in scalable, realistic training for robotic policies, and a path forward with complex simulation.

Simulating Sensors with Real-World Accuracy

iToF sensors, such as ADI’s ADTF3175, are common in robotic perception. These sensors emit light in a regular pattern to measure depth by calculating its reflection. In the real world, sensors exhibit readout noise, and accounting for this interference is essential for training reliable robotic policies. However, most simulation environments offer idealized sensor data. For example, NVIDIA’s Isaac Sim provides clean depth maps based on geometry, not the noisy output of real-world sensors.

To fill this gap, ADI had previously developed a physics-based simulator that modeled iToF sensor behavior at the pixel level. While accurate, the simulator was too slow for full-frame, real-time use. At just 0.008 frames per second (FPS), it was impractical for training AI policies that require thousands of scenes per second.

Using Machine Learning to Speed Up Simulation

The breakthrough came from using machine learning to emulate the high-fidelity simulator’s output. We trained a multilayer perceptron (MLP) model as a surrogate to approximate the behavior of the precise white-box simulator. Importantly, the team designed this stand-in model to learn not just the average output but also reflect the original’s variability and noise characteristics.

The surrogate model decomposes its task into three sub-tasks:

• Predict the expected depth measurement.
• Estimate the standard deviation, accounting for uncertainty.
• Predict whether a pixel’s depth measurement will be invalid or unresolved.

The surrogate model uses this probabilistic output to capture the essential stochastic behavior of the original simulator while dramatically accelerating inference. The result is a simulation that runs at 17 FPS. That’s fast enough for real-time use while maintaining approximately 1% error from the high-fidelity model.

Real-World Validation in Isaac Sim

After building the surrogate model, the team integrated it into NVIDIA’s Isaac Sim environment. Testing using a digital twin of a robot arm performing peg-insertion tasks showed that the model closely matched the original simulator’s output. The output even included the noise that was absent from standard simulations.

Real-world iToF sensors are sensitive to optical effects in the near-infrared (NIR) range, a property often ignored in standard simulations. Furthermore, iToF performance varies across different surface materials. To ensure the surrogate accounts for both behaviors, the team used fast surrogate inference and adjusted the NIR reflectivity of simulated objects to better match sensor behavior in physical experiments.

This technique helped reduce differences between simulation and real sensor data, particularly on matte surfaces. While imperfect, these adaptations made major strides to minimize the sim-to-real gap. The team is actively exploring additional improvements, including changes to the underlying physics models and

What’s Next: Improving Fidelity and Generalization

This surrogate model serves as a baseline for enabling fast, realistic simulation of iToF sensors in robotic training workflows. But it’s only the first step. New work involves physics-informed neural operator (PINO) models to improve accuracy, reduce training data needs, and generalize across different scenes and tasks.

In the future, the aim is to eliminate the need for an intermediate white-box simulator. By training models directly on real-world sensor data, simulators could adapt more readily to diverse environments without requiring manual tuning or scene-specific calibration.

These developments could dramatically reduce the time and cost required to deploy robotics systems to real-world environments. Ideally, this work will advance deployments in logistics, manufacturing, product inspection, and beyond.

Philip Sharos, Principal Engineer, Edge AI

The post Faster Sensor Simulation for Robotics Training with Machine Learning Surrogates appeared first on Edge AI and Vision Alliance.