Abstract

In recent times, quadrotors have become immensely applicable in scenarios such as relief operations, infrastructure maintenance, search-and-rescue missions etc. A key control design challenge arises in these applications when the quadrotor has to manoeuvre through constrained spaces such as narrow windows, pipelines in the presence of external disturbances and parametric uncertainties: such conditions necessitate the controller to guarantee predefined tracking accuracy so as to not violate the constraints and simultaneously tackle uncertainties. However, state-of-the-art controllers dealing with constrained system motion are not applicable either for an underactuated system like quadrotor or for an uncertain system dynamics. This work proposes a robust controller that enables the quadrotor to follow a trajectory with predefined tracking accuracy in constrained space as well as to tackle uncertainties stemming from imprecise system modelling and external disturbances. The closed-loop system stability is analysed via the Barrier Lyapunov approach and the effectiveness of the proposed controller is validated via simulation with state of the art.

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 question of how the human brain represents information, and if insights from this knowledge help us in formulating better deep learning algorithms, is still open. This paper attempts to address these questions by investigating the brain responses when participants viewed images of objects belonging to a variety of categories. We adopt the representational similarity analysis (RSA) framework wherein the brain responses are projected into a representational dissimilarity (RD) space so that responses from multiple subjects can be assessed on a common basis. We performed RSA of the brain responses of 15 participants on a set of 92 images (from the Algonauts challenge data set) Cichy et al. (The Algonauts project: a platform for communication between the sciences of biological and artificial intelligence 2019 [4]). The results reveal that human brain responses exhibit appropriate qualitative similarities observed in the image space. We then evaluated various deep networks such as VGGnet, AlexNet, and Inception in Siamese configuration with RD-loss function and identified hidden layers that had maximal similarity to human RD matrices (RDM). The best layers then were subjected to further analysis by looking at the image pairs corresponding to high and

Abstract

There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors. The idea is to exploit the label hierarchy (e.g., the WordNet ontology) and consider graph distances as a proxy for mistake severity. Surprisingly, on examining mistake-severity distributions of the top-1 prediction, we find that current state-of-the-art hierarchy-aware deep classifiers do not always show practical improvement over the standard cross-entropy baseline in making better mistakes. The reason for the reduction in average mistake-severity can be attributed to the increase in low-severity mistakes, which may also explain the noticeable drop in their accuracy. To this end, we use the classical Conditional Risk Minimization (CRM) framework for hierarchy-aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra hyperparameters and requires adding just a few lines of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top- predictions across datasets, with very little loss in accuracy. CRM, because of its simplicity, can be used with any off-the-shelf trained model that provides reliable likelihood estimates.

Abstract

Text simplification is the process of splitting and rephrasing a sentence to a sequence of sentences making it easier to read and un- derstand while preserving the content and ap- proximating the original meaning. Text sim- plification has been exploited in NLP appli- cations like machine translation, summariza- tion, semantic role labeling, and information extraction, opening a broad avenue for its ex- ploitation in comprehension-based question- answering downstream tasks. In this work, we investigate the effect of text simplification in the task of question-answering using a compre- hension context. We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset. Firstly, we outline each step in the dataset cre- ation pipeline, including style transfer, thresh- olding of sentences showing correct transfer, and offset finding for each answer. Secondly, we verify the quality of the transferred sen- tences through various methodologies involv- ing both automated and human evaluation. Thirdly, we benchmark the newly created cor- pus and perform an ablation study for ex- amining the effect of the simplification pro- cess in the SQuAD-based question answering task. Our experiments show that simplification

Abstract

This chapter provides a behind-the-scenes account of the birth of a naturalistic approach to the neuroscience of the musical aesthetic experience. The story starts from a lab talk giving the inspiration to translate the naturalistic paradigm initially applied to neuroimaging studies of the visual domain into music research. The circumstantial co-presence of neuroscientists and computational musicologists at the same center did the trick, permitting the identification of controlled variables for brain signal processing from the automatic extraction of the acoustic features of real music. This approach is now well accepted by the music neuroscience community while still waiting for full exploitation by aesthetic research.

Abstract

Organizational and management theories generally focus on obtaining a conceptual and operational understanding of a particular facet of enterprises. In contrast, systems engineers focus on obtaining a logical integrated understanding of the structure and functioning of the systems they design and work with. This chapter presents such an approach to capturing the nature of enterprises and their evolution over time, suitable for creating software models and analysis tools, based on systems science and systems engineering concepts and principles. Specifically, we present an understanding of enterprises in terms of a morphogenetic structure: a series of models with derivation-based consistency relationships among them. We also discuss how the epigenesis of enterprises, i.e., their change and evolution over time, can be understood relative to this structure. These models are influenced by the fact that they were developed within an information technology organization, concerned with the information, processes, and knowledge that enable the functioning of business systems. This enables all the information about the enterprise, relating to different facets and hierarchical levels, to be captured in a unified modelling structure, so that various views can be derived from it for analysis purposes. As we go through the approach, we indicate how the representation can enable the application of various management theories – the models provide the basic structure for representing the information, from which views can be

Abstract

The study focuses on the extraction of cardiac sound components using a multi-channel micro-electromechanical system (MEMS) microphone-based phonocardiography system. The proposed multi-channel phonocardiography system classifies the cardiac sound components using artificial neural networks (ANNs) and synaptic weights that are calculated using the inverse delayed (ID) function model of the neuron. The proposed ANN model was simulated in MATLABR and implemented in a field-programmable gate array (FPGA). The proposed system examined both abnormal and normal samples collected from 30 patients. Experimental

Abstract

The study presents a novel design method that improves system availability using fault-tolerant features in a non-invasive medical diagnostic system. This approach addresses the effective detection of functional faults, improves the uninterruptible system operating period with reduced false alarms, and provides an authentic measure of vital cardiac signs using diverse multimodal sensing elements like the photoplethysmogram (PPG) and the ECG. Most systems rely on a 1oo1 (one-out-of-one) design method, which inherently limits accuracy in existing practice. In this proposed approach, the quality of segregated authentic vital sign measured values could tremendously benefi

Abstract

A major problem in Acoustic Scene Classification (ASC) is a representation of an acoustic scene, which serves to be an important task for ASC. This study used Linear Prediction Cepstral Coefficients (LPCC) and Spectral Centroid Magnitude Cepstral Coefficients (SCMC) features along with log-Mel band energies for the representation of an acoustic scene. Deep Neural Networks (DNN) is being used to model the Acoustic Scene Classification (ASC). LPCCs are used to capture the changes in the auditory spectrum with time and SCMCs are used to capture the weighted average magnitude finely for a given acoustic scene subband. log-Mel band energies are used to capture the spectral envelopes of audio frame. The DNN architecture is used for audio track level classification. We have experimented on Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 development dataset and DCASE 2017 dataset. We carried out experiments with individual feature sets, and also performed decision level DNN score fusions for improving the performance.