Analysis of radiation effect on the threshold voltage of flash memory device

Flash memory experiences adverse effects due to radiation. These effects can be raised in terms of doping, feature size, supply voltages, layout, shielding. The the operating point shift of the device forced to enter the logically-undefined region and cause upset and data errors under radiation exposure. In this letter, the threshold voltage shift of the floating gate transistor (FGT) is analyzed by a mathematical model

Insights into tunnel FET-based charge pumps and rectifiers for energy harvesting applications

In this paper, the electrical characteristics of tunnel field-effect transistor (TFET) devices are explored for energy harvesting front-end circuits with ultralow power consumption. Compared with conventional thermionic technologies, the improved electrical characteristics of TFET devices are expected to increase the power conversion efficiency of front-end charge pumps and rectifiers powered at sub-µW power levels. However, under reverse bias conditions the TFET device presents particular electrical characteristics due to its different carrier injection mechanism. In this paper, it is shown that reverse losses in TFET-based circuits can be attenuated by changing the gate-to-source voltage of reverse-biased TFETs. Therefore, in order to take full advantage of the TFETs in front-end energy harvesting circuits, different circuit approaches are required. In this paper, we propose and discuss different topologies for TFET-based charge pumps and rectifiers for energy harvesting applications.Peer ReviewedPostprint (author's final draft

Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks

Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform, generate most of the video semantics. Video-based HAR has applications in various domains, but one of the most important and challenging is HAR in sports videos. Some of the major issues include high inter- and intra-class variations, large class imbalance, the presence of both group actions and single player actions, and recognizing simultaneous actions, i.e., the multi-label learning problem. Keeping in mind these challenges and the recent success of CNNs in solving various computer vision problems, in this work, we implement a 3D CNN based multi-label deep HAR system for multi-label class-imbalanced action recognition in hockey videos. We test our system for two different scenarios: an ensemble of $k$ binary networks vs. a single $k$-output network, on a publicly available dataset. We also compare our results with the system that was originally designed for the chosen dataset. Experimental results show that the proposed approach performs better than the existing solution.Comment: Accepted to IEEE/ACIS SNPD 2018, 6 pages, 3 figure

Using Modified Bessel Functions for Analysis of Nonlinear Effects in a MOS Transistor Operating in Moderate Inversion

This work was supported in part by the NSERC, Canada, in part by the Portuguese Foundation for Science and Technology under Project PESTOEEEI/UI0066/2015 and foRESTER Project PCIF/SSI/0102/2017, and in part by the Academy of Finland.This paper describes analysis of nonlinear effects in a MOS transistor operating in moderate inversion and saturation. The dependence of the drain current on the gate-source and drain-source voltages is described using a modified version of the 'reconciliation' model developed by Y. Tsividis. In the new model, the current components, which correspond to the terms depending exponentially on normalized gate-source or drain-source modulating sinusoidal voltages, are presented using modified Bessel functions. This approach allows one to find the first, second, and third harmonics of the drain current caused by the gate-source or drain-source voltage sinusoidal modulation and find also the intermodulation terms produced by these two modulating voltages. The results are applied to set the requirements to the gate-source and drain-source bias voltages in design of low-distortion and/or low-voltage amplifiers. It is shown that the realization of the stage with the zero value of third-order harmonic requires extremely tight tolerances for the threshold voltage. The suppression of intermodulation terms requires increased drain-source voltage. These recommendations are confirmed by simulations.authorsversionpublishe

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation

On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis

Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system, with potential impact in its final performance. Despite its importance, text preprocessing has not received much attention in the deep learning literature. In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a standard neural text classifier. We perform an extensive evaluation on standard benchmarks from text categorization and sentiment analysis. While our experiments show that a simple tokenization of input text is generally adequate, they also highlight significant degrees of variability across preprocessing techniques. This reveals the importance of paying attention to this usually-overlooked step in the pipeline, particularly when comparing different models. Finally, our evaluation provides insights into the best preprocessing practices for training word embeddings.Comment: Blackbox EMNLP 2018. 7 page

Optimization of a Hydrodynamic Computational Reservoir through Evolution

As demand for computational resources reaches unprecedented levels, research is expanding into the use of complex material substrates for computing. In this study, we interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir and optimize its properties using an evolution in materio approach. Input data are encoded as waves applied to our shallow water reservoir, and the readout wave height is obtained at a fixed detection point. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm, with the objective of maximizing the system's ability to linearly separate observations in the training data by maximizing the readout matrix determinant. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters. We also applied our approach to a regression task and show that our approach improves out-of-sample accuracy. Results from this study will inform how we interface with the physical reservoir in future work, and we will use these methods to continue to optimize other aspects of the physical implementation of this system as a computational reservoir.Comment: Accepted at the 2023 Genetic and Evolutionary Computation Conference (GECCO 2023). 9 pages, 8 figure

Decoding-complexity-aware HEVC encoding using a complexity–rate–distortion model

The energy consumption of Consumer Electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device's the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper (i) first performs an analysis to determine the trade-off between the decoding complexity, video quality and bit rate with respect to a reference decoder implementation on a General Purpose Processor (GPP) architecture. Thereafter, (ii) a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity-rate-distortion optimized High Efficiency Video Coding (HEVC) bit streams. The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams