Exploiting Spatial Sparsity for Event Cameras with Visual Transformers

Event cameras report local changes of brightness through an asynchronous stream of output events. Events are spatially sparse at pixel locations with little brightness variation. We propose using a visual transformer (ViT) architecture to leverage its ability to process a variable-length input. The input to the ViT consists of events that are accumulated into time bins and spatially separated into non-overlapping sub-regions called patches. Patches are selected when the number of nonzero pixel locations within a sub-region is above a threshold. We show that by fine-tuning a ViT model on the selected active patches, we can reduce the average number of patches fed into the backbone during the inference by at least 50% with only a minor drop (0.34%) of the classification accuracy on the N-Caltech101 dataset. This reduction translates into a decrease of 51% in Multiply-Accumulate (MAC) operations and an increase of 46% in the inference speed using a server CPU

Person identification using deep neural networks on physiological biomarkers during exercise

Much progress has been made in wearable sensors that provide real-time continuous physiological data from non- invasive measurements including heart rate and biofluids such as sweat. This information can potentially be used to identify the health condition of a person by applying machine learning algorithms on the physiological measurements. We present a person identification task that uses machine learning algorithms on a set of biomarkers collected from 30 subjects carrying out a cycling experiment. We compared an SVM and a gated recurrent neural network (RNN) for real-time accuracy using different window sizes of the measured data. Results show that using all biomarkers gave the best results from any of the models. With all biomarkers, the gated RNN model achieved ∼90% accuracy even in a 30 s time window; and ∼92.3% accuracy in a 150 s time window. Excluding any of the biomarkers leads to at least 7.4% absolute accuracy drop for the RNN model. The RNN implementation on the Jetson Nano incurs a low latency of ∼45 ms per inference

A 128-channel real-time VPDNN stimulation system for a visual cortical neuroprosthesis

With the recent progress in developing large-scale micro-electrodes, cortical neuroprotheses supporting hundreds of electrodes will be viable in the near future. We describe work in building a visual stimulation system that receives camera input images and produces stimulation patterns for driving a large set of electrodes. The system consists of a convolutional neural network FPGA accelerator and a recording and stimulation Application-Specific Integrated Circuit (ASIC) that produces the stimulation patterns. It is aimed at restoring visual perception in visually impaired subjects. The FPGA accelerator, VPDNN, runs a visual prosthesis network that generates an output used to create stimulation patterns, which are then converted by the ASIC into current pulses to drive a multi-electrode array. The accelerator exploits spatial sparsity and the use of reduced bit precision parameters for reduced computation, memory and power for portability. Experimental results from the VPDNN show that the 94.5K parameter 14-layer CNN receiving an input of 128 × 128 has an inference frame rate of 83 frames per sec (FPS) and uses only an incremental power of 0.1 W, which is at least 10× lower than that measured from a Jetson Nano. The ASIC adds a maximum delay of 2ms, however it does not impact the FPS thanks to double-buffered memory. Index Terms—Visual prosthesis, convolutional neural network, FPGA Accelerator, stimulation and recording ASI

On the Security of Some Nonrepudiable Threshold Proxy Signature Schemes with Known Signers

A (t, n) threshold proxy signature scheme enables an original signer to delegate the signature authority to a proxy group of n member such that t or more than t proxy signers can cooperatively sign messages on behalf of the original signer. In the paper, we review the security of some nonrepudiable threshold proxy signature schemes with known signers. We show that Sun's threshold proxy scheme, Yang et al.'s threshold proxy signature scheme and Tzeng et al.'s threshold proxy signature scheme are insecure against an original signer's forgery. We also show that Hsu et al.'s threshold proxy signature scheme su#ers from the conspiracy of the original signer and the secret share dealer SA, and that Hwang et al.'s threshold proxy signature scheme is universally forgeable. In a word, none of the above-mentioned threshold proxy signature schemes can provide non-repudiation

Digital proxy blind signature schemes based on

Abstract. In a proxy signature scheme, a potential signer delegates his signing power to a proxy, who signs a message on behalf of the original signer. In a blind signature scheme, the signee cannot link the relationship between the blind message and the signature of the chosen message. That is, the signee cannot make a linkage between the blind signature and the identity of the requester. Therefore, it is very suitable for electronic commerce application. In this paper, on the basis of the Schnorr blind signature, we present two digital proxy blind signature schemes, which satisfy the security properties of both the blind signature and the proxy signature. 1

LiteEdge: Lightweight Semantic Edge Detection Network

Scene parsing is a critical component for understanding complex scenes in applications such as autonomous driving. Semantic segmentation networks are typically reported for scene parsing but semantic edge networks have also become of interest because of the sparseness of the segmented maps. This work presents an end-to-end trained lightweight deep semantic edge detection architecture called LiteEdge suitable for edge deployment. By utilizing hierarchical supervision and a new weighted multi-label loss function to balance different edge classes during training, LiteEdge predicts with high accuracy category-wise binary edges. Our LiteEdge network with only ≈ 3M parameters, has a semantic edge prediction accuracy of 52.9% mean maximum F (MF) score on the Cityscapes dataset. This accuracy was evaluated on the network trained to produce a low resolution edge map. The network can be quantized to 6-bit weights and 8-bit activations and shows only a 2% drop in the mean MF score. This quantization leads to a memory footprint savings of 6X for an edge device

Vein Texture Extraction Using the Multiscale Second-Order Differential Model

In order to analyze the back of hand vein pattern rapidly and effectively, a novel approach based on multi-scale second-order differential model is proposed to extract the vein texture from vein samples directly, which is made up of two section: one is the foundation of local second-order differential model of vein texture(VLSDM), the other is texture extraction based on the multi-scale VLSDM. This paper analyzes the vein extraction using the multi-scale VLSDM and handles the filter response using the method of multi-scale analyzed noise filtered. This new algorithm has achieved good results for the vein texture, which is fuzzy, uneven distributed and cross-adhesion. Additionally this method keeps the original form of local shape and achieves orientation and scale information of the vein texture. The experiment result getting from this new method has also compared with another method and shown its outstanding performance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.365

Fast temporal decoding from large-scale neural recordings in monkey visual cortex

With new developments in electrode and nanoscale technology, a large-scale multi-electrode cortical neural prosthesis with thousands of stimulation and recording electrodes is becoming viable. Such a system will be useful as both a neuroscience tool and a neuroprosthesis. In the context of a visual neuroprosthesis, a rudimentary form of vision can be presented to the visually impaired by stimulating the electrodes to induce phosphene patterns. Additional feedback in a closed-loop system can be provided by rapid decoding of recorded responses from relevant brain areas. This work looks at temporal decoding results from a dataset of 1024 electrode recordings collected from the V1 and V4 areas of a primate performing a visual discrimination task. By applying deep learning models, the peak decoding accuracy from the V1 data can be obtained by a moving time window of 150 ms across the 800 ms phase of stimulus presentation. The peak accuracy from the V4 data is achieved at a larger latency and by using a larger moving time window of 300 ms. Decoding using a running window of 30 ms on the V1 data showed only a 4\% drop in peak accuracy. We also determined the robustness of the decoder to electrode failure by choosing a subset of important electrodes using a previously reported algorithm for scaling the importance of inputs to a network. Results show that the accuracy of 91.1\% from a network trained on the selected subset of 256 electrodes is close to the accuracy of 91.7\% from using all 1024 electrodes