Light-induced perovskite dynamic transformation enabling photodetector mimicking neuromorphic vision sensing system

As an artificial perception system, neuromorphic vision sensing system can imitate the complex image sensing and processing functions of the human visual neural network. In order to stimulate the nervous...

Floating Gate Negative Capacitance MoS2 Phototransistor with High Photosensitivity

Monolayer MoS2 exhibits interesting optoelectronic properties that have been utilized in applications such as photodetectors and light emitting diodes. For image sensing applications, improving the light sensitivity relies on achieving...

Mnemonic-Opto-Synaptic Transistor for In-Sensor Vision System

A mnemonic-opto-synaptic transistor (MOST) that has triple functions is demonstrated for an in-sensor vision system. It memorizes a photoresponsivity that corresponds to a synaptic weight as a memory cell, senses light as a photodetector, and performs weight updates as a synapse for machine vision with an artificial neural network (ANN). Herein the memory function added to a previous photodetecting device combined with a photodetector and a synapse provides a technical breakthrough for realizing in-sensor processing that is able to perform image sensing and signal processing in a sensor. A charge trap layer (CTL) was intercalated to gate dielectrics of a vertical pillar-shaped transistor for the memory function. Weight memorized in the CTL makes photoresponsivity tunable for real-time multiplication of the image with a memorized photoresponsivity matrix. Therefore, these multi-faceted features can allow in-sensor processing without external memory for the in-sensor vision system. In particular, the in-sensor vision system can enhance speed and energy efficiency compared to a conventional vision system due to the simultaneous preprocessing of massive data at sensor nodes prior to ANN nodes. Recognition of a simple pattern was demonstrated with full sets of the fabricated MOSTs. Furthermore, recognition of complex hand-written digits in the MNIST database was also demonstrated with software simulations.

Research on X-Ray Inspection of Basin Insulators and Wireless Image Sensing Technology

This paper presents an in-depth study and analysis of X-ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X-ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo-Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three-dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X-ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment.

Implementation of Multi-Exit Neural-Network Inferences for an Image-Based Sensing System with Energy Harvesting

Wireless sensor systems powered by batteries are widely used in a variety of applications. For applications with space limitation, their size was reduced, limiting battery energy capacity and memory storage size. A multi-exit neural network enables to overcome these limitations by filtering out data without objects of interest, thereby avoiding computing the entire neural network. This paper proposes to implement a multi-exit convolutional neural network on the ESP32-CAM embedded platform as an image-sensing system with an energy constraint. The multi-exit design saves energy by 42.7% compared with the single-exit condition. A simulation result, based on an exemplary natural outdoor light profile and measured energy consumption of the proposed system, shows that the system can sustain its operation with a 3.2 kJ (275 mAh @ 3.2 V) battery by scarifying the accuracy only by 2.7%.

3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing

Since single sensor and high-density point cloud data processing have certain direct processing limitations in urban traffic scenarios, this paper proposes a 3D instance segmentation and object detection framework for urban transportation scenes based on the fusion of Lidar remote sensing technology and optical image sensing technology. Firstly, multi-source and multi-mode data pre-fusion and alignment of Lidar and camera sensor data are effectively carried out, and then a unique and innovative network of stereo regional proposal selective search-driven DAGNN is constructed. Finally, using the multi-dimensional information interaction, three-dimensional point clouds with multi-features and unique concave-convex geometric characteristics are instance over-segmented and clustered by the hypervoxel storage in the remarkable octree and growing voxels. Finally, the positioning and semantic information of significant 3D object detection in this paper are visualized by multi-dimensional mapping of the boundary box. The experimental results validate the effectiveness of the proposed framework with excellent feedback for small objects, object stacking, and object occlusion. It can be a remediable or alternative plan to a single sensor and provide an essential theoretical and application basis for remote sensing, autonomous driving, environment modeling, autonomous navigation, and path planning under the V2X intelligent network space– ground integration in the future.

Digital Naturalist Using Deep Learning

A naturalist is someone who studies the patterns of nature identify different kingdom of flora and fauna in the nature. Being able to identify the flora and fauna around us often leads to an interest in protecting wild species, collecting and sharing information about the species we see on our travels is very useful for conserving groups like NCC. Deep-learning based techniques and methods are becoming popular in digital naturalist studies, as their performance is superior in image analysis fields, such as object detection, image classification, and semantic segmentation. Deep-learning techniques have achieved state of-the -art performance for automatic segmentation of digital naturalist through multi-model image sensing. Our task as naturalist has grown widely in the field of natural-historians. It has increased from identification to saviours as well. Not only identifying flora and fauna but also to know about their habits, habitats, living and grouping lead to fetching services for protection as well.