Over the last ten years, there has been a remarkable increase in industrial interest in Dynamic Vision Sensors (DVS). Renowned multinational companies in the vision sensor industry, such as Samsung, Sony, Omnivision, and Prophesee, have quickly incorporated DVS technology into their extensive product offerings.

This paper reports an architectural concept for achieving dynamic vision with Single-Photon Avalanche Diode (SPAD) detectors. This new concept, demonstrated through a dedicated proof-of-concept sensor chip and an associated camera module, addresses the challenge of large data volume due to frequent laser exposures by optimizing data processing via temporal-contrast-based event filtering. The manuscript analyzes trade-offs between contrast sensitivity, latency, and noise in the proposed discrete DVS architecture and validates the proposal through experimental results obtained with the camera module. The proposed sensor has different operation modes that show potential for augmented reality applications. Benchmarking comparison with competing SPAD-based vision sensors shows latency and power consumption advantages.

Spatial contrast (SC) detection is a fundamental task in computer vision, crucial for simplifying images at an early stage. However, this process typically demands significant power and processing resources. In this paper, we present a novel 128 × 128 pixel asynchronous vision sensor designed to compute SC, thereby eliminating the need to transmit and process intensity frames externally. The sensor is fabricated using 180 nm CMOS technology, with a pixel pitch of$28 \times 26 \mu \text {m}^{2}$. Each pixel provides temporal information relative to its four adjacent pixels to compute the temporal contrast, transmitting data asynchronously. An integrated on-chip Address-Event-Representation (AER) sequencer efficiently manages pixel events, reducing latency requirements on the processing unit. Additionally, the sensor is capable of performing standard imaging operations. Various methods for SC computation are discussed, along with their implementation within the sensor.

This paper presents a novel approach to implement discrete dynamic vision utilizing Single-Photon Avalanche Diode (SPAD) detectors. Departing from conventional continuous-time methodologies, our approach encodes photon arrival rates into frequency-based spikes, enabling asynchronous event-driven processing. The theoretical framework is validated through experimental results. Probability models for event detection and background noise are developed, demonstrating excellent agreement with theoretical predictions. The proposed probability models serve as a key tool for analyzing trade-offs between contrast sensitivity, speed, and background noise. This work not only advances the field of dynamic vision but also lays the groundwork for further innovations in SPAD-based imaging technologies.

The Address Event Representation (AER) protocol is widely used in pixelated neuromorphic systems to implement word-serial asynchronous transmission links. The data is encoded in time or frequency and transferred from the pixel to the receiver in the form of events through a shared channel. Asynchronous sensors benefit from lower data throughput, power consumption, and latency; however, they present several timing limitations to scale the size of the array, since delay in the transmission line and collisions in the readout channel corrupt the timing information. This paper analyzes the effect of increasing the number of pixels in terms of transmission delay and proposes several design guidelines to overcome scaling limitations in high-resolution pixel arrays. The results of the post-layout simulation verify the effectiveness of the design proposals, achieving a reduction of more than 70% in the time delay of the request line.

Thermography imaging has great potential for the diagnosis and follow-up of clinical entities that alter body temperature locally [1]. Infrared (IR) cameras have dropped in price during the last ten years. Nowadays, they are compatible with clinical use because they provide immediate results, do not require physical contact with the patient, and their images are easy to interpret. In particular, vascular anomalies constitute a vast family of clinical entities that alter body temperature and can exploit this emerging imaging modality [2]. Many IR systems devoted to the medical field are available in the market. However, to the best of our knowledge, there is a lack of customizable systems easily adaptable to particular medical application scenarios by incorporating different peripherals or sensors and embedding custom image processing algorithms. Commercial IR systems are limited to image acquisition.

Spiking luminance asynchronous sensors are image sensors whose pixels spike asynchronously and autonomously with a frequency proportional to illumination. They can render images with low latency, high dynamic range, and low power consumption. However, one of their drawbacks is the operation under high illumination or with large pixel arrays that can easily saturate their asynchronous readout logic. In this article, the novel Quanta Imaging acquisition technique is evaluated to alleviate this limitation. Simulation and experimental results demonstrate that the technique is easy to implement. It also reduces the data throughput, keeping acceptable image quality.

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided.

This paper reports the use of vertically stacked photodiodes as compact solid-state spectrometers for transmission scanning electron microscopy. SEM microscopes operate by illuminating the sample with accelerated electrons. They can have one or more solid-state sensors. In this work we have tested a set of stacked photodiodes fabricated in a standard 180nm HV-CMOS technology without process modifications. We have measured their sensitivity to electron irradiation in the energy range between 10keV and 30keV. We have also assessed their radiation hardness. The experiments are compared with Monte Carlo simulations to investigate their spectral sensitivity.

The optic flow (OF) detection is a complex operation made by biological systems to perform complex tasks as navigation, movement detection or object segmentation within the visual scene. To accomplish this operation with artificial bio-inspired systems is a great challenge. This demo displays a motorized platform [1] with a bio-inspired optical-flow detection sensor which tracks a selected target. The entire system is controlled through a low-cost microcontroller board that has been programmed with very low-level programming language to achieve high performance. The proposed platform can be applied to very different applications scenarios like the obstacle avoidance and the autonomous navigation. Its power consumption and the microcontroller computational load are quite reduced. A low resolution imager with only 18 ×18 pixels is employed.

This demo shows a first prototype two-axis miniaturized spiking sun sensor. The device is composed of spiking pixels, and uses a novel Time-to-First-n-Spikes with time-out readout mode to reduce bandwidth consumption and post-processing computation. Due to on-chip processing, and compressing the angle information, the sensor produces much less data and is much faster than digital sensors. Its response latency is 88 μW, and average power consumption is 6.3 μW. An integrated circuit with core electronics was fabricated in the AMS 0.35 μm CMOS image sensor process, and was integrated inside a very small QFN64 package with micro-optics on top.

We present a simple circuit to handle communication between cells of neuromorphic arrays. It allows cells to operate continuously without waiting for acknowledgement signals back from the AER (Address Event Representation) arbitration circuitry. The module also implements aging of cell petitions i.e., old petitions to access to the AER bus are automatically discarded to give priority to the more recent ones and alleviate the bus congestion. The new arbitration scheme has been implemented and tested. A particular application scenario with an image sensor with spiking pixels that sense light continuously is explained. The sensing errors per event due to discontinued pixel operation can be minimized a factor 8.1. Experimental data obtained with real visual scenes are provided.

Despite being well established in robotics, classical motor controllers have several disadvantages: they pose a high computational load, therefore requiring powerful devices, they are not easy to tune and they are not suited for neuroprosthetics. In contrast, bio-inspired controller do not transform the output of the controller therefore no delays are introduced and a smooth response is achieved; they also have a high scalability. Finally, the most important feature of bio-inspired controllers is that they could integrate learning features to make them adaptable to new tasks within the same hardware robotic platform. We present the model and simulation of a spiking neural network for low-level motor control. The proposed neural network acts as a motor controller and produces pulsed signals which can be directly interfaced with commercial DC motors. The simulated network is compatible with neuromorphic VLSI implementation and paves the way to the implementation bio-inspired motor controller which are compact, low power, scalable and compatible with neuroprosthetic. The network presented is inspired by the current knowledge about biological motor control: it comprises alpha motoneuron for driving the motor and spindle populations to provide the feedback and close the loop. The spikes from the motoneuron population are time lengthen to a fixed amount of time and supplied to the simulated motor: Pulse Frequency Modulation (PFM) modulation is used. This paper presents the software simulations using the Brian simulator for a position controller. Our controller is a first step toward a novel bio-inspired motor control approach suitable for robotics as well as neuroprosthetic.

We present a new method to sense NIR (Near Infrared Radiation) with an asynchronous pixel event AER (Address Event Representation) color vision sensor. The sensor's asynchronous output data flow can be processed by a computer to monitor oscillations of NIR illumination levels. Such variations can be indicators for fires or flames. The new sensor is an alternative to high-speed CMOS cameras with NIR filters as front-end for flame detection and characterization methods. Our sensor maintains a high temporal resolution and low latency for regions that are bright in the NIR spectrum, e.g. flames and hot-spots, at an overall low (scene dependent) data rate and power consumption. This makes it specially suitable for Wireless Sensor Networks (WSNs). A dedicated real-time Java interface was programmed to process the sensor outputs. In this paper, we describe the algorithm for sensing NIR intensity and describe experiments that show oscillations of NIR levels of a flame.

Summary form only given. We demonstrate the very first tri-color asynchronous pixel-event vision sensor, the latest addition of the growing family of bio-inspired AER (Address Event Representation) vision sensors. It is an asynchronous pulse/frequency density modulation (PDM/PFM) sensor (popularly known as octopus retina) that employs stacked photo diodes for color separation. Simple linear combination of the sensor's inherent pseudo color representation is employed to reconstruct approximate a RGB video stream. The 22×22 pixel array has been fabricated in the standard STM 90nm CMOS process.

Reported AER (Address Event Representation) contrast retinae perform a contrast computation based on the ratio between a pixel's local light intensity and a spatially weighted average of its neighbourhood. This results in compact circuits, but with the penalty of all pixels generating output signals even if they sense no contrast. In this paper we present a spatial contrast retina with bipolar output: contrast is computed as the relative normalized difference (not the ratio) between a pixel's local light and its weighted spatial average, normalized to average light. As a result, contrast includes a sign, is ambient light independent, and the output will be zero if there is no contrast. Furthermore, an adjustable thresholding mechanism has been included, such that pixels remain silent until they sense an absolute contrast above the adjustable threshold. The pixel contrast computation circuit is based on Boahen's Biharmonic operator contrast circuit, which has been improved to include mismatch calibration and adaptive current based biasing. As a result, the contrast computation circuit shows much less mismatch, is almost insensitive to ambient light illumination, and biasing is much less critical than in the original voltage biasing scheme. The retina also includes an optional TFS (Time-to-First-Spike) integration mode. A full AER retina version has been fabricated and tested. In the present paper we provide preliminary experimental results.

Address event representation (AER) is an emergent technology for assembling modular multi-blocks bio-inspired sensory and processing systems. Visual sensors (retinae) are among the first AER modules to be reported since the introduction of the technology. Spatial contrast AER retinae are of special interest since they provide highly compressed data flow without reducing the relevant information required for performing recognition. Reported AER contrast retinae perform a contrast computation based on the ratio between a pixel's local light intensity and a spatially weighted average of its neighbourhood. This resulted in compact circuits, but with the penalty of all pixels generating output signals even if they sensed no contrast. In this paper we present a spatial contrast retina with bipolar output: contrast is computed as the relative difference between a pixel's local light and its weighted spatial average. As a result, contrast includes a sign and the output will be zero if there is no contrast. Furthermore, an adjustable thresholding mechanism has been included, such that pixels remain silent until they sense an absolute contrast above the adjustable threshold. The pixel contrast computation circuit is based on Boahen's biharmonic operator contrast circuit, which has been improved to include mismatch calibration and adaptive current based biasing. As a result, the contrast computation circuit shows much less mismatch, is almost insensitive to ambient light illumination, and biasing is much less critical than in the original voltage biasing scheme. A full AER retina version has been submitted for fabrication. In the present paper we provide simulation results.

Low current applications, like neuromorphic circuits, where operating currents can be as low as few nano amps or less, suffer from huge transistor mismatches, resulting in around or less than 1-bit precision. Here we present a new calibration approach based on individually calibrated current sources made with MOS transistors of digitally adjustable length, which require only N unit transistors. The scheme includes a translinear circuit based tuning scheme, which allows to expand the operating range of the calibrated circuits with graceful precision degradation, over 4 decades of operating currents. Experimental results are provided for 5-bit resolution DACs operating at 20 nA.