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We present a novel adaptive multimodal intensity-event algorithm to optimize an overall objective of object tracking under bit rate constraints for a host-chip architecture. The chip is a computationally resource-constrained device acquiring high-resolution intensity frames and events, while the host is capable of performing computationally expensive tasks. We develop a joint intensity-neuromorphic event rate-distortion compression framework with a quadtree (QT)-based compression of intensity and events scheme. The goal of this compression framework is to optimally allocate bits to the intensity frames and neuromorphic events based on the minimum distortion at a given communication channel capacity. The data acquisition on the chip is driven by the presence of objects of interest in the scene as detected by an object detector. The most informative intensity and event data are communicated to the host under rate constraints so that the best possible tracking performance is obtained. The detection and tracking of objects in the scene are done on the distorted data at the host. Intensity and events are jointly used in a fusion framework to enhance the quality of the distorted images, in order to improve the object detection and tracking performance. The performance assessment of the overall system is done in terms of the multiple object tracking accuracy (MOTA) score. Compared with using intensity modality only, there is an improvement in MOTA using both these modalities in different scenarios.

Room-temperature semiconductor radiation detectors (RTSD) have broad applications in medical imaging, homeland security, astrophysics and others. RTSDs such as CdZnTe, CdTe are often pixelated, and characterization of these detectors at micron level can benefit 3-D event reconstruction at sub-pixel level. Material defects alongwith electron and hole charge transport properties need to be characterized which requires several experimental setups and is labor intensive. The current state-of-art approaches characterize each detector pixel, considering the detector in bulk. In this article, we propose a new microscopic learning-based physical models of RTSD based on limited data compared to what is dictated by the physical equations. Our learning models uses a physical charge transport considering trapping centers. Our models learn these material properties in an indirect manner from the measurable signals at the electrodes and/or free and/or trapped charges distributed in the RTSD for electron–hole charge pair injections in the material. Based on the amount of data used during training our physical model, our algorithm characterizes the detector for charge drifts, trapping, detrapping and recombination coefficients considering multiple trapping centers or as a single equivalent trapping center. The RTSD is segmented into voxels spatially, and in each voxel, the material properties are modeled as learnable parameters. Depending on the amount of data, our models can characterize the RTSD either completely or in an equivalent manner.

With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dynamic range, and/or power constraint. Yet unlike image/video compression, the performance of event compression algorithm is far from satisfying and practical. The main challenge for compressing events is the unique event data form, i.e., a stream of asynchronously fired event tuples each encoding the 2D spatial location, timestamp, and polarity (denoting an increase or decrease in brightness). Since events only encode temporal variations, they lack spatial structure which is crucial for compression. To address this problem, we propose a novel event compression algorithm based on a quad tree (QT) segmentation map derived from the adjacent intensity images. The QT informs 2D spatial priority within the 3D space-time volume. In the event encoding step, events are first aggregated over time to form polarity-based event histograms. The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map. Next, differential encoding and run length encoding are employed for encoding the spatial and polarity information of the sampled events, respectively, followed by Huffman encoding to produce the final encoded events. Our Poisson Disk Sampling based Lossy Event Compression (PDS-LEC) algorithm performs rate-distortion based optimal allocation. On average, our algorithm achieves greater than 6x compression compared to the state of the art., 8 main pages

We present a novel adaptive host-chip modular architecture for video acquisition to optimize an overall objective task constrained under a given bit rate. The chip is a high resolution imaging sensor such as gigapixel focal plane array (FPA) with low computational power deployed on the field remotely, while the host is a server with high computational power. The communication channel data bandwidth between the chip and host is constrained to accommodate transfer of all captured data from the chip. The host performs objective task specific computations and also intelligently guides the chip to optimize (compress) the data sent to host. This proposed system is modular and highly versatile in terms of flexibility in re-orienting the objective task. In this work, object tracking is the objective task. While our architecture supports any form of compression/distortion, in this paper we use quadtree (QT)-segmented video frames. We use Viterbi (Dynamic Programming) algorithm to minimize the area normalized weighted rate-distortion allocation of resources. The host receives only these degraded frames for analysis. An object detector is used to detect objects, and a Kalman Filter based tracker is used to track those objects. Evaluation of system performance is done in terms of Multiple Object Tracking Accuracy (MOTA) metric. In this proposed novel architecture, performance gains in MOTA is obtained by twice training the object detector with different system generated distortions as a novel 2-step process. Additionally, object detector is assisted by tracker to upscore the region proposals in the detector to further improve the performance.

In this paper, we propose an adaptive host-chip system for video acquisition constrained under a given bit rate to optimize object tracking performance. The chip is an imaging instrument with limited computational power consisting of a very high-resolution focal plane array (FPA) that transmits quadtree (QT)-segmented video frames to the host. The host has unlimited computational power for video analysis. We find the optimal QT decomposition to minimize a weighted rate distortion equation using the Viterbi algorithm. The weights are user-defined based on the class of objects to track. Faster R-CNN and a Kalman filter are used to detect and track the objects of interest respectively. We evaluate our architecture’s performance based on the Multiple Object Tracking Accuracy (MOTA).

Controlled electrical discharge aids in conditioning of the system while uncontrolled discharges damage its electronic components. DC Accelerator being a high voltage system is no exception. It is useful to classify electrical discharges according to the severity. Experimental prototypes of the accelerator discharges are developed. Photomultiplier Tubes (PMTs) are used to detect the signals from these discharges. Time and Frequency domain characteristics of the detected discharges are used to extract features. Machine Learning approaches like Fuzzy Logic, Neural Network and Least Squares Support Vector Machine (LSSVM) are employed to classify the discharges. This aids in detecting the severity of the discharges.

S-Band standing wave (SW) linacs in the range of 1–10 MeV have many potential industrial applications world wide. In order to mitigate the industrial requirement it is required to reduce the overall size and weight of the system. On this context a 2856 M Hz, 1 Me V, bi-periodic on axis coupled self transverse focused SW linac has been designed and tested. The RF phase focusing is achieved by introducing an asymmetric field distribution in the first cell of the 1 MeV linac. The pulsed electron beam of 40 keV, 650 mA and 5 µs duration is injected from a LaB 6 thermionic gun. This paper presents the structure design, beam dynamics simulation, fabrication and experimental results of the 1 MeV auto-focusing SW linac.

Protective spark gaps are used in the high voltage multiplier column of a 3 MeV DC Accelerator to prevent excessive voltage build-ups. Precise gap of 5 mm is maintained between the electrodes in these spark gaps for obtaining 120kV±5kV in 6kg/cm2 SF 6 environment which is the dielectric medium. There are 74 such spark gaps used in the multiplier. Each spark gap has to be qualified for electrical performance before fitting in the accelerator to ensure reliable operation. As the breakdown voltage stabilizes after a large number of sparks between the electrodes, the qualification process becomes time consuming and cumbersome. For qualifying large number of spark gaps an automatic breakdown analysis setup has been developed. This setup operates in air, a dielectric medium. The setup consists of a flyback topology based high voltage power supply with maximum rating of 25 k V. This setup works in conjuction with spark detection and automated shutdown circuit. The breakdown voltage is sensed using a peak detector circuit. The voltage breakdown data is recorded and statistical distribution of the breakdown voltage has been analyzed. This paper describes details of the diagnostics and the spark gap qualification process based on the experimental data.

Detection of Electrical Discharges using chemical, electrical, acoustic and optical techniques holds key to the protection of any high voltage (HV) system. DC Accelerator, being a HV system is prone to various electrical discharges. In DC Accelerators, optical technique promises better performance than other detection techniques due to its fast response and inherent immunity to electromagnetic noises. Photomultiplier tubes (PMT) are used for collecting the discharge signatures. Experimental prototypes of the accelerator discharges are developed. PMTs are used to detect the signals from these discharges.