Hidden Spy Camera Detection Using Smartphones with Sony ToF Sensor

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National University of Singapore and Korea Yonsei University present an ACM paper "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors" by Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, and Jun Han and a poster "On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras" by the same authors. The detection was tested on  Samsung Galaxy S20+, S20 Ultra 5G, and Note 10+ containing VGA Sony IMX516 ToF sensor. Although the sensor has VGA resolution, the current Android API only provides a 240 × 320 image.

"Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate."

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Hidden Spy Camera Detection Using Smartphones with Sony ToF Sensor

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National University of Singapore and Korea Yonsei University present an ACM paper "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors" by Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, and Jun Han and a poster "On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras" by the same authors. The detection was tested on  Samsung Galaxy S20+, S20 Ultra 5G, and Note 10+ containing VGA Sony IMX516 ToF sensor. Although the sensor has VGA resolution, the current Android API only provides a 240 × 320 image.

"Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate."

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Hidden Spy Camera Detection Using Smartphones with Sony iToF Sensor

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National University of Singapore and Korea Yonsei University present an ACM paper "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors" by Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, and Jun Han and a poster "On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras" by the same authors. The detection was tested on  Samsung Galaxy S20+, S20 Ultra 5G, and Note 10+ containing VGA Sony IMX516 iToF sensor. Although the sensor has VGA resolution, the current Android API only provides a 240 × 320 image.

"Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate."

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Tetromino Binning vs Regular Binning in Low Light

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Univeristat Erlangen-Nurnberg, Germany, publishes Arxiv.org paper "Image Super-Resolution Using T-Tetromino Pixels" by Simon Grosche, Andy Regensky, Jürgen Seiler, André Kaup.

"For modern high-resolution imaging sensors, pixel binning is performed in low-lighting conditions and in case high frame rates are required. To recover the original spatial resolution, single-image super-resolution techniques can be applied for upscaling. To achieve a higher image quality after upscaling, we propose a novel binning concept using tetromino-shaped pixels. In doing so, we investigate the reconstruction quality using tetromino pixels for the first time in literature. Instead of using different types of tetrominoes as proposed in the literature for a sensor layout, we show that using a small repeating cell consisting of only four T-tetrominoes is sufficient. For reconstruction, we use a locally fully connected reconstruction (LFCR) network as well as two classical reconstruction methods from the field of compressed sensing. Using the LFCR network in combination with the proposed tetromino layout, we achieve superior image quality in terms of PSNR, SSIM, and visually compared to conventional single-image super-resolution using the very deep super-resolution (VDSR) network. For the PSNR, a gain of up to +1.92 dB is achieved."

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Event-Based Imaging Thesis

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Université Grenoble Alpes publishes Maxence Bouvie's PhD thesis "Study and design of an energy efficient perception module combining event-based image sensors and spiking neural network with 3D integration technologies."

"Event-driven acquisition permits to generate sparse data, with high acquisition speed at the order of the microsecond, while conserving an exceptionally large dynamic range. Event-driven imagers are thus highly suited for deployment in situations where speed and application robustness are of high importance. However, event-based image sensors come with major drawbacks that render them nearly impracticable in embedded situations. They are noisy, poorly resolved and generate an incredible amount of data relatively to their resolution. This Ph.D. study thus focuses on understanding how they can be used, and how their drawbacks can be alleviated. The work explores bio-inspired applications for tasks where frame-based methods are already successful but present robustness flaws because classical frame-based imagers cannot be intrinsically high speed and high dynamic range. This manuscript provides leads to understand and decide why some algorithms matches more than other to their novel data type. It also tries to touch upon the reasons these sensors cannot be used as they are, but how they could be efficiently integrated into classical frame-based algorithmic pipelines and systems by deploying motion compensation of the raw data. In addition, a bio-inspired hardware-based solution to simultaneously reduce the output bandwidth and filter out noise, directly at the output of a grid of event-based pixels, is presented. It consists in the hardware implementation of a bio-inspired convolutional neural network accelerator - a neuromorphic processor – distributed near-sensor, that takes major advantages from being conceived toward a three-dimensional integration. This system was designed for minimizing its power budget, at the 28nm FDSOI node, and demonstrates a 2.86pJ per synaptic operation – or 93.0aJ per input event per pixel. On top of that, it is scalable for megapixel resolution sensors without induced overhead."

Appendix C (pp. 134-135) gives a nice comparison of Samsung, Prophesee (Sony), and Celepixel (Omnivision) event-driven sensor approaches.

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Recent Videos: Teledyne, Omron, EnliTech, Aeye, MIPI

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Teledyne publishes 2 videos: "3D ToF Imaging" and "Clarity at High Speed."


Omron presents ins optical sensing solutions, including ToF and thermal imagers:


Enli Technology has recently publishes a number of videos on its CMOS image sensor testers:



Aeye Chief R&D Officer, Hod Finkelstein, presents at IEEE Photonics Conference 2021:


Synopsys presents MIPI A-PHY system modeling:

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Smartsens Announces 8MP AI (Advanced Imaging) Sensor

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SmartSens announces the SC830AI, an 8MP CMOS sensor with a 1.5µm pixel size and a 1/2.7” optical format to enable 4K video performance for security camera applications.

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Himax Reports Top-Tier Design Win for its Low Power Sensor

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GlobeNewswire: Himax quarterly earning release updates about its WiseEye ultra-low power image sensing solution:

"Himax is pleased to report that the design-win with a top-tier name for a mainstream application that it indicated earlier is on track to enter into mass production in Q4. Equally important, the number of awarded projects is growing quickly, covering a broad range of applications, including notebook, home appliances, utility meter, automotive, battery-powered surveillance camera, panoramic video conferencing, and medical, just to name a few. Some applications are already slated for mass production at the end of this year. In addition to consumer electronics players who aim to add AI capability to their products, within just one year since Himax started sampling, Company’s WiseEye solution has also drawn much attention from cloud service providers who look for secure and low-power edge AI devices to help collect big data for their cloud-based services."

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Omnivision to Qualify Medical-Grade 8MP 60fps RGB-IR Sensor for Endoscopes

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BusinessWire: OmniVision and Diaspective Vision GmbH announce their partnership in the development of a new type of endoscopic camera, the MALYNA system, which is based on multispectral imaging technology.

The MALYNA camera uses OmniVision’s OH08B CMOS sensor. Diaspective Vision will be adding a second OH08B sensor to the MALYNA and the sensors will be synchronized to provide a 3D stereoscopy image. The OH08B is the first 8MP medical-grade image sensor to use OmniVision’s Nyxel NIR technology. The 1/1.8” optical format, 2.0µm PureCel Plus-S pixel image sensor offers 4K2K resolution at 60 fps and supports HDR.

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Infineon Forecasts ToF Market Growth

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Infineon investor presentation shows the company's view on ToF market size and its products:

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Yet Another Color Router Paper

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 InterDigital publish a MDPI paper "Optical Efficiency Enhancement of Nanojet-Based Dielectric Double-Material Color Splitters for Image Sensor Applications" by Oksana Shramkova, Valter Drazic, Bobin Varghese, Laurent Blondé, and Valerie Allié.

"We propose a new type of color splitter, which guides a selected bandwidth of incident light towards the proper photosensitive area of the image sensor by exploiting the nanojet (NJ) beam phenomenon. Such splitting can be performed as an alternative to filtering out part of the received light on each color subpixel. We propose to split the incoming light thanks to a new type of NJ-based near-field focusing double-material element with an insert. To suppress crosstalk, we use a Deep-Trench Isolation (DTI) structure. We demonstrate that the use of a dielectric insert block allows for reduction in the size of the color splitting element. By changing the position of the DTI, the functionality of separating blue, green and red light can be improved."

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Interview with CEO of Ruisi Zhixin (AlpsenTek)

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Sohu publishes an interview with Deng Jian, CEO of Ruisi Zhixin, english name AlpsenTek, a developer of hybrid of event-driven with regular sensor. Few quotes in Google translation:

"The "Hybrid Vision" technology of RISZ is independently developed by our team. This technology integrates the team's understanding and accumulation of various advanced visual sensing technologies and event camera technologies in the past 15 years, and is formed by fusing some advanced visual sensing technologies with bionic event camera technologies at the sensor pixel level. At present, our first chip ALPIX-Pilatus based on "Hybrid Vision" technology has been successfully taped out, which also verifies the feasibility of our technology. Our success at the first tapeout is also due to the team’s past experience in the field of chip design.

We currently have applied for more than 30 domestic, PCT and other countries or regions patents around the "Hybrid Vision" technology, and are still applying for it.

Our chip has three main advantages:

(1) Compared with the existing bionic event camera chip that requires a dual-camera solution with a traditional image sensor in most scenarios, one chip of the ALPIX series can replace two chip functions, which not only has a system cost advantage, but also has more data. Rich.

(2) Solve the problem of dual-camera heterogeneous registration. In the dual-camera system of event camera chip + traditional image sensor, due to the difference in viewing angles of the two cameras, real-time registration of event camera data and image data is required. Event data and image data are two completely different types of data, and spatial registration is difficult, which brings great difficulties to the realization of the underlying fusion algorithm. The ALPIX series chips naturally solve the problem of difficult spatial registration because the two types of fusion signals are generated by the same pixel.

(3) ALPIX can output image signals and is compatible with existing image processing algorithms and architectures.

Most of the current event camera field is based on the core architecture design of Professor Tobi Delbruck of ETH. It has the advantage of ultra-low latency. The chip can reach an equivalent frame rate of 100,000 frames per second. Therefore, It has advantages in some high-end special scenes such as industry. However, it is also difficult to output high-quality traditional image signals.

We mainly follow the technical route of integration. The goal is that the chip can output two channels of event stream signals and high-quality traditional image signals to meet application scenarios that require two types of signals, mostly consumer electronics. Therefore, our application goals and technical logic are quite different."

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Counterpoint: Smartphone Rear Cameras Resolution Grows, Front Resolution Stays Flat

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Counterpoint publishes a report on smartphone camera resolution trends:

"Despite supply shortages, smartphone rear cameras are increasingly adopting high-resolution image sensors, which continue to penetrate lower-end smartphone segments. According to the latest research from Counterpoint Smartphone Camera Tracker, smartphones featuring rear main cameras powered by 48MP and above megapixels accounted for 43% of total sales in Q2 2021, rising significantly from 38.7% in Q1 2021. The share of 64MP alone increased 3.5% QoQ to 14% in Q2 2021.

Commenting on this high-resolution advance, Senior Analyst Ethan Qi said, “48MP and 64MP have become the mainstream for models priced between $200 and $400, while flagship smartphones resort to large-area sensors to deliver a DSLR-like professional performance, of which 50MP is the most adopted. Although the share of 108MP fell to 3.1% in Q2 2021, the more affordable 0.7µm-based 108MP sensors continue to spread to mid-range models from OEMs such as Redmi and realme.”

On the other hand, low-resolution sensors continue to suffer from the demand-supply imbalance, with the price increasing sharply. For instance, 5MP sensors have experienced more than a 10% increase in cost since the beginning of this year.

Nevertheless, entry-level smartphones (wholesale price below $100) continue to upgrade their rear primary cameras from 8MP and below resolutions to 12MP or 13MP. Therefore, the collective share of the 8MP and below cameras shrunk to 5.9% in Q2 2021.

In front main cameras, the collective share of 20MP and above resolutions almost stayed flat QoQ in Q2 2021 due to the decline in sales of high-end models. However, we expect the resolution of the front-facing camera to continue to improve with more high-end smartphones adopting 32MP and even 48MP image sensors.

Meanwhile, the share of 8MP and below resolutions further increased to 45.2%, with 5MP and 8MP together accounting for 41.7% on strong demand for low-end smartphones in Q2.
"

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Gigajot CTO Gets EDS Early Career Award

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IEEE Electron Design Society presents 2021 Early Career Award to Gigajon CTO and co-founder Jiaju Ma:

Jiaju Ma is a pioneering inventor and entrepreneur in the field of CMOS image sensor and Quanta Image Sensor. Ma is the co-inventor and designer of the first CMOS image sensor pixel devices with deep sub-electron read noise that enables photon counting and photon-number resolution without using electron multiplication, generally referred to as a Quanta Image Sensor (QIS). 

The low-noise photon-counting pixel device that Ma co-invented and developed is being commercialized at Gigajot Technology (Pasadena, CA), a startup co-founded by Ma. Because of its superior low-light imaging capability, high resolution and high dynamic range performance, this technology is considered an enabling technology for advanced scientific, space, and defense applications.

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Black Phosphorus Vision Sensor

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University of Washington, Seattle, publishes an Arxiv.org paper "Programmable black phosphorus image sensor for broadband optoelectronic edge computing" by Seokhyeong Lee, Ruoming Peng, Changming Wu, and Mo Li.
"Image sensors with internal computing capability enable in-sensor computing that can significantly reduce the communication latency and power consumption for machine vision in distributed systems and robotics. Two-dimensional semiconductors are uniquely advantageous in realizing such intelligent visionary sensors because of their tunable electrical and optical properties and amenability for heterogeneous integration. Here, we report a multifunctional infrared image sensor based on an array of black phosphorous programmable phototransistors (bP-PPT). By controlling the stored charges in the gate dielectric layers electrically and optically, the bP-PPT's electrical conductance and photoresponsivity can be locally or remotely programmed with high precision to implement an in-sensor convolutional neural network (CNN). The sensor array can receive optical images transmitted over a broad spectral range in the infrared and perform inference computation to process and recognize the images with 92% accuracy. The demonstrated multispectral infrared imaging and in-sensor computing with the black phosphorous optoelectronic sensor array can be scaled up to build a more complex visionary neural network, which will find many promising applications for distributed and remote multispectral sensing."

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SWIR Sensor Startup Trieye Raises $74M, Total $96M in 4 Years

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EETimes-Europe, i-Micronews, PRNewsWire: Israeli CMOS-based SWIR sensor sturtup Trieye raises $74M in a new financing round rringing its total raised capital to $96M in 4 years. The new investment will be used to commercialize SEDAR (Spectrum-Enhanced Detection And Ranging) sensor for ADAS and AV. SEDAR generates a 3D point cloud using pulsed illumination methodology.

SWIR is unique in that, from a physics standpoint, most materials exhibit their spectral differences primarily in the SWIR range – in other words, SWIR cameras can sense the differences between various materials and make them visible,” says Avi Bakal, CEO and co-founder of TriEye. “This is because every material has a unique spectral response, or signature, defined by its chemical composition and physical characteristics, impacting how wavelengths are absorbed or reflected. By comparing the relative reflection of light between different materials in carefully chosen spectral bands, a distinction between materials is easily revealed. Simply put, SWIR reveals critical sensing information which simply does not exist in other wavelengths.

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Forza Reports Mastering BSI Process for its High Speed Imagers

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BusinessWire: While BSI sensors have been available for more than 10 years in many cellphones and standard digital cameras, the manufacturing process is inherently more difficult than comparable FSI sensors as it requires additional manufacturing steps. The realities of semiconductor economics have also made it difficult to transfer the technology from the high production volumes of standard cameras to the lower volumes of high-speed imaging sensors.

Overcoming these issues, Forza Silicon leveraged its Integrated Production Services (IPS) capabilities to optimize the manufacturing process for BSI sensors and achieve practical yields.

These in-house services include prototype and wafer probe development, qualification services, yield optimization, and production test and delivery,” says Loc Duc Truong, VP of Engineering, Forza Silicon. “Together, these capabilities have enabled us to overcome the manufacturing challenges associated with BSI sensors.

The high-speed BSI sensor design is the result of a collaboration between Forza Silicon and Vision Research.

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OmniVision Unveils Its First RGBC Sensor With Always-on Capability for Selfie Cameras

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BusinessWire: OmniVision announces the OV32C, a RGBC 32MP image sensor in a 1/3.2-inch optical format for front-facing mobile phone “selfie” cameras.  The OV32C’s low power modes can help enable “always-on” user experiences by facilitating AI processing to automate many of the common tasks of the camera, such as face detection, QR code scans, etc.

Image sensors for front facing ‘selfie’ cameras are expected to grow 4.7% CAGR over the next five years, with smartphone front camera image sensor revenues reaching US$4.4 billion by 2026,” says Pierre Cambou, principal analyst, Imaging at Yole Développement. “Stacking technology is giving more degree of freedom to image sensor designers to embed advanced pre-ISP algorithms; this could be a game changer for RGBC sensors by solving the difficulties of their non-Bayer arrangement. Adding ‘always-on’ smart capabilities to phone cameras will bring a new level of ergonomics and performance to the phone’s user interface.

The OV32C is our first sensor for the mobile phone market to feature RGBC―providing a 50% boost in overall sensitivity to enhance low-light image quality. Additionally, we’ve reduced design complexity for OEMs with an on-chip RGBC-to-Bayer fusion algorithm, therefore, a separate RGBC processing unit isn’t required,” says Arun Jayaseelan, staff marketing manager at OmniVision.

The OV32C is built on 0.7µm PureCel Plus-S pixel. RGBC technology uses a 4-cell color filter pattern with clear pixels and on-chip RGBC-to-Bayer fusion. It supports CPHY and DPHY interfaces and can output 15 fps at 32MP, or 30 fps at 8MP with on-chip fusion, for premium video and image quality. The OV32C also offers dual DOVDD support at 1.8 and 1.2 volts.

Samples of the OV32C are available now.

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Tower Semi Updates on its Imaging Business

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SeekingAlpha: Tower Semi Q3 earnings call gives an update on the company's imaging business:

"Our imaging business represented more than 15% of our revenues. We continue to see very strong demand in the industrial and machine vision markets, as well as the medical and dental X-ray markets. Our customers in these areas are highly interested in securing capacity for the coming years, seeing long-term market demand.

We recently partnered with the University of Southern California to announce a breakthrough development in LiDAR IC technology designed for advanced driver assistance systems, and ultimately self-driving cars.

Our stitch field sensors is very high margin. Overall the imaging margins are very strong. Even non-stitch field imaging margins are very strong..."

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It’s Official Now: TSMC-Sony Fab in Japan

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Sony and TSMC jointly announce: TSMC and Sony Semiconductor Solutions Corporation (“SSS”)  jointly announce that TSMC will establish a subsidiary, Japan Advanced Semiconductor Manufacturing, Inc. (“JASM”), in Kumamoto, Japan to provide foundry service with initial technology of 22/28-nanometer processes to address strong global market demand for specialty technologies, with SSS participating as a minority shareholder.

Construction of JASM’s fab in Japan is scheduled to begin in the 2022 calendar year with production targeted to begin by the end of 2024. The fab is expected to directly create about 1,500 high-tech professional jobs and to have a monthly production capacity of 45,000 12-inch wafers. The initial capital expenditure is estimated to be approximately US$7 billion with strong support from the Japanese government.

Under definitive agreements reached between TSMC and SSS, SSS plans to make an equity investment in JASM of approximately US$0.5 billion, which will represent a less than 20% equity stake in JASM. The closing of the transaction between TSMC and SSS is subject to customary closing conditions.

While the global semiconductor shortage is expected to be prolonged, we expect partnership with TSMC to contribute to securing a stable supply of logic wafers, not only for us but also for the overall industry. We believe that further strengthening and deepening our partnership with TSMC, which has the world’s leading semiconductor production technology, is extremely meaningful for the Sony Group,” says Terushi Shimizu, President and CEO of Sony Semiconductor Solutions.

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Pixart Expects Lower Sales

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Digitimes: PixArt Imaging expects to post sequential revenue decreases through December due mainly to uneven inventories across different chip types and tight foundry capacity supply, but is guardedly optimistic about its business prospects.

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Image Sensors Contribute 19% to Axcelis Revenue

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Axcelis reports that ion implanters for image sensor manufacturing contribute 19% to its 2020 revenue:

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Fight for Velodyne Control Continues

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BusinessWire: David Hall, Velodyne founder and the owner of 42.9% of the common stock, issued a statement:

"While we welcome the news of Brad Culkin's retirement from the Board, it does not remedy the fact that we believe Chairman Michael Dee continues to lead Velodyne Lidar down the path of financial ruin. During Mr. Dee’s tenure, Velodyne Lidar has fallen from its position as a global leader in lidar technology and innovation, and the Board has undertaken a series of anti-stockholder actions that has, in our view, resulted in insular corporate governance, troubling underperformance and strategic incompetency."

BusinessWire: Meanwhile, Velodyne announces a new CEO, Theodore “Ted” L. Tewksbury, instead of Anand Gopalan who has been heavily criticized by David Hall:

"Dr. Tewksbury is a proven technology executive with more than 30 years of leadership experience across a series of public and private companies. Dr. Tewksbury most recently served as Chief Executive Officer of Eta Compute, a leading provider of ultra-low power AI vision systems. Prior to Eta Compute, he held several chief executive and board positions at leading high-technology companies, including Entropic Communications and Integrated Device Technology. Dr. Tewksbury is an accomplished engineer, with a Ph.D. in electrical engineering from MIT. He has held a variety of engineering and management positions in his career, including introducing over 180 innovative new product families during his time at Maxim Integrated Products."


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Microsoft-China and Sony-Shanghai Partner on AI Camera Solutions

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Microsoft China and Sony Semiconductor Solutions (Shanghai) Ltd. announce a Partner Enablement Program to encourage China local qualified independent software vendors (ISV) and system integrators (SI) partners creating commercial AI camera-driven solutions, which combines Sony’s IMX500 AI-enabled sensor w