Abstract

Shape Memory Alloy (SMA) wire actuator contracts or expands in response to heat due to its mechanical (NiTi) property. This paper is about the control perspective of light weight artificial muscle arm based on helical SMA actuator. The Arm is structured around the idea of simplicity and modularity. The post decomposition of the arm dynamics into position and force subspaces, Sliding Mode Control is utilized with the end goal that time taken to reach the equilibrium point from any initial state is guaranteed to be finite time. This setup presents a light weight (70 g) and noiseless operation with a maximum payload of 64 g which is noteworthy when compared to the weight of SMA based artificial muscle and the arm.

Abstract

The avenue of deep learning is scarcely explored in the domain of thermal imaging. Recovering a high-resolution output from images and videos is a classical problem in many computer vision applications. In this paper, we propose an optimized pipeline for a real-time video super-resolution task using thermal camera on embedded edge device. To tackle the challenges, we make contributions in the following several aspects: 1) comparative study of selected deep learning super-resolution models; 2) constructing and optimizing an end-to-end inference pipeline; 3) using cutting edge technology to integrate the whole workflow; 4) a real-time performance was achieved using less data; 5) we have also experimented the entire pipeline on our custom thermal dataset. As a consequence, the chosen model was able to achieve a real-time speed of over 29, 36 and 45 high FPS; 32.9dB/0.889, 31.86dB/0.801 and 30.94dB/0.728 PSNR/SSIM values for 2x, 3x and 4x scaling factors respectively.

Abstract

The ramp generator is a crucial circuit component in the design of switching power supply, analog to digital converters, CMOS image sensors, and many other essential circuits. Conventionally, the ramp generator has been implemented with a CMOS comparator that works on the hysteresis principle, which leads to an inherent systemic delay. This paper aims at improving the overall circuit speed by proposing the design of a ramp generator using CMOS proteretic comparator in 180nm TSMC process. It is established from the post-layout simulation that a speedup of 25% is achieved by replacing a hysteretic comparator with a proteretic comparator, and this is done with a trade-off in power consumption and circuit area.

Abstract

The effect of Residual Mechanical Jitter (RMJ) has been analysed on the incoherent beam combination through various atmospheric turbulences. It is observed that the RMJ effect is more prominent for lower turbulence than higher turbulence.

Abstract

In this study, we characterize biosamples using a microstrip patch based Radio Frequency (RF) sensor by integrating both resonant and non-resonant parameters. In particular, the resonance frequency, the resonance amplitude, −10 dB bandwidth, range of operation, and return loss at center frequency were employed to perform the analysis. The idea was to utilize multiple parameters to build a comprehensive understanding of different bio-samples. In order to demonstrate the working of the methodology, two sets of analytes were studied-(i) aqueous solutions of biomolecules and (ii) body fluids. For both sets of analytes, the method demonstrated significant efficiency in performing qualitative characterization.

Abstract

In this article, we present a novel target classification technique by mmWave frequency modulated continuous wave (FMCW) Radars using the Machine Learning on raw data features obtained from range fast Fourier transform (FFT) plot. FFT plots are extracted from the measured raw data obtained with a Radar operating in the frequency range of 77 - 81 GHz. The features such as peak, width, area, standard deviation, and range on range FFT plot peaks are extracted and fed to a machine learning model. Two light weight classification models such as Logistic Regression, Naive Bayes are explored to assess the performance. Based on the results, we demonstrate and achieve an accuracy of 86.9% using Logistic Regression. The proposed technique will be highly useful for several applications in cost-effective and reliable ground station traffic management systems for autonomous systems. The end-to-end framework presented here, expands the capabilities of mmWave Radar beyond range detection to classification. The implications of this added functionalities will facilitate utilization of mmWave Radars in computer vision, object recognition, and towards fully autonomous traffic control and management systems.

Abstract

Around 40% of global energy produced is consumed by buildings. By using renewable energy resources we can alleviate the dependence on electrical grids. Recent trends focus on incentivizing consumers to reduce their demand consumption during peak hours for sustainable demand response. To minimize the loss, the distributor companies should target the right set of consumers and demand the right amount of electricity reductions.This paper proposes a novel bounded integer min-knapsack algorithm and shows that the algorithm, while allowing for multiple unit reduction, also optimizes the loss to the distributor company within a factor of two (multiplicative) and a problem dependent additive constant. Existing CMAB algorithms fail to work in this setting due to nonmonotonicity of reward function and time varying optimal sets. We propose a novel algorithm (TwinMinKPDR-CB) to learn these compliance probabilities efficiently. Twin-MinKPDR-CB works for non-monotone reward functions, bounded minknapsack constraints, and time-varying optimal sets. We find that Twin-MinKPDR-CB achieves sub-linear regret of O(log T) with T being the number of rounds demand response is run.

Abstract

Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. However, it comes at the expense of high computational complexity and does not constitute a rigorous privacy guarantee for optimization outcomes, as the result of the computation itself may compromise agents’ preferences. In this work, we show how to achieve privacy, specifically differential privacy, through the randomization of the solving process. In particular, we present P-Gibbs, which adapts the SD-Gibbs algorithm to obtain differential privacy guarantees with much higher computational efficiency. Experiments on graph coloring and meeting scheduling show the algorithm’s privacy-performance trade-off for varying privacy budgets, and the SD-Gibbs algorithm.

Abstract

We introduce MeronymNet, a novel hierarchical approach for con- trollable, part-based generation of multi-category objects using a single unified model. We adopt a guided coarse-to-fine strategy involving semantically conditioned generation of bounding box layouts, pixel-level part layouts and ultimately, the object depic- tions themselves. We use Graph Convolutional Networks, Deep Recurrent Networks along with custom-designed Conditional Vari- ational Autoencoders to enable flexible, diverse and category-aware generation of 2-D objects in a controlled manner. The performance scores for generated objects reflect MeronymNet’s superior perfor- mance compared to multiple strong baselines and ablative variants.

Abstract

The current study aims to analyse VR measures in correspondence with 360° virtual affective experiences to understand the underlying spatio-temporal attention processes. We selected extreme pleasant and extreme unpleasant emotional videos from the Stanford affective database and displayed to participants on HMD VR. The participants’ task was to explore the video and report their affective experience using the Self- Assessment Manikin (SAM) scale. The exploration behaviour was analysed using head-tracking parameters, such as standard deviation of all the three head-rotation axes (yaw, pitch and roll), angular speed and region-wise analysis. We observed a positive correlation between standard deviation of yaw and valence, and angular speed and valence. The result suggests that affective experience not only increases the scope of attention spatially (wide scanning), it also motivates to seek more information that leads to more head-movement during exploration. In region- wise analysis, we observed front field-of-view was primarily explored, and rear field-of-view was least explored, showing natural responses similar to real environments. In addition, we used Beck Depression Inventory-II (BDI) to assess participants’ emotional state, specifically depression symptoms. The relation between virtual affective experience and 360° exploration be- haviour is discussed in light of the BDI categories. The study findings broaden the scope for VR affective research by enabling psychomotor assessment in more ecologically valid settings. Index Terms—Emotion, Depression, Affective State, 360° Ex- ploration, Head-Tracking