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Fu, Peng (author)Keyword spotting (KWS) is an essential component of voice recognition services on smart devices. Its always-on characteristic requires high accuracy and real-time response. Also, low power consumption is another key demand for KWS devices. In previous research, neural networks have become popular for KWS tasks for their accuracy compared to...master thesis 2024
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Dekhovich, A. (author), Tax, D.M.J. (author), Sluiter, M.H.F. (author), Bessa, M.A. (author)Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task. The human brain, however, developed specialized regions for different tasks and performs inference with a small fraction of its neuronal connections. We propose an iterative pruning strategy introducing a simple...journal article 2024
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Sigurðsson, Snorri Þór (author)Regular maintenance of civil engineering structures is essential for their safety. Current maintenance regimes involve periodic inspections at regular time intervals. In the time between inspections, there can be a critical development in the structural integrity of a structure, which can be expensive to repair or could even lead to structural...master thesis 2023
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Joseph, G. (author)In this article, we study the conditions to be satisfied by a discrete-time linear system to ensure output controllability using sparse control inputs. A set of necessary and sufficient conditions can be directly obtained by extending the Kalman rank test for output controllability. However, the verification of these conditions is...journal article 2023
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He, Y. (author), Joseph, G. (author)This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve...conference paper 2023
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Chen, Qinyu (author), Wang, Zuowen (author), Liu, Shih Chii (author), Gao, C. (author)This paper presents a sparse Change-Based Convolutional Long Short-Term Memory (CB-ConvLSTM) model for event-based eye tracking, key for next-generation wearable healthcare technology such as AR/VR headsets. We leverage the benefits of retina-inspired event cameras, namely their low-latency response and sparse output event stream, over...conference paper 2023
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Zahedi, M.Z. (author), Custers, Geert (author), Shahroodi, Taha (author), Gaydadjiev, G. (author), Wong, J.S.S.M. (author), Hamdioui, S. (author)Performing analysis on large graph datasets in an energy-efficient manner has posed a significant challenge; not only due to excessive data movements and poor locality, but also due to the non-optimal use of high sparsity of such datasets. The latter leads to a waste of resources as the computation is also performed on zero's operands which do...conference paper 2023
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Chen, Qilin (author)Convolutional neural networks (CNNs) are often pruned to achieve faster training and inference speed while also requiring less memory. Nevertheless, during computation, most modern GPUs cannot take advantage of the sparsity automatically, especially on networks with unstructured sparsity. Therefore, many libraries that exploit sparsity, have...bachelor thesis 2022
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Wang, J. (author), Ding, Ming (author), Yarovoy, Alexander (author)In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking...journal article 2022
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Zhou, H. (author), Chahine, I. (author), Zheng, Wei Xing (author), Pan, W. (author)This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for system identification. Although DNNs show impressive approximation ability in various fields, several challenges still exist for system identification problems. First, DNNs are known to be too complex that they can easily overfit the training data. Second, the...journal article 2022
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Sriram, Chandrasekhar (author), Joseph, G. (author), Murthy, Chandra R. (author)The stabilizability of a linear dynamical system (LDS) refers to the existence of control inputs that drive the system state to zero. In this article, we analyze both the theoretical and algorithmic aspects of the stabilizability of an LDS using sparse control inputs with potentially time-varying supports. We show that an LDS is stabilizable...journal article 2022
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Suvarna, Anusha Ravish (author), Koppelaar, Arie (author), Jansen, Feike (author), Wang, J. (author), Yarovoy, Alexander (author)High angular resolution is in high demand in automotive radar. To achieve a high azimuth resolution a large aperture antenna array is required. Although MIMO technique can be used to form larger virtual apertures, a large number of transmitter-receiver channels are needed, which is still technologically challenging and costly. To circumvent this...conference paper 2022
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Joseph, G. (author), Varshney, Pramod K. (author)In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the...conference paper 2022
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Preetha Vijayan, Preetha (author)In the recent past, real-time video processing using state-of-the-art deep neural networks (DNN) has achieved human-like accuracy but at the cost of high energy consumption, making them infeasible for edge device deployment. The energy consumed by running DNNs on hardware accelerators is dominated by the number of memory read/writes and...master thesis 2021
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Vreugdenhil, Robbie (author)We propose a novel approximation hierarchy for cardinality-constrained, convex quadraticprograms that exploits the rank-dominating eigenvectors of the quadratic matrix. Each levelof approximation admits a min-max characterization whose objective function can be op-timized over the binary variables analytically, while preserving convexity in the...master thesis 2021
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van Wilsum, Max (author)Based on sparsity maximization we propose a controller for Multiple Daily Injections (MDI)therapy for Type 1 Diabetes melitus (T1DM) individuals. Based on convex relaxations onthe sparsity maximization problems and by implementing personalised linear models of pa-tient we formulate a model predictive controller to determine insulin boluses. By...master thesis 2021
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Enthoven, Maarten (author)At the Center for Ultrasound and Brain imaging at Erasmus MC in Rotterdam, a mouse's visual cortex had been imaged using the fUS technique. The mouse had been exposed to different visual stimuli. The stimuli varied in position, size, and shape. We investigate how the measured task-based fUS signals differ depending on the visual stimuli...master thesis 2021
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Moens, Roger (author)Modern imaging modalities across many application domains increasingly acquire a large number of very high-dimensional measurements, commonly collecting hundreds to millions of variables per spatial resolution element. That high-dimensional nature can severely challenge traditional (often Euclidean distance based) approaches to noise and...master thesis 2021
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Chahine, Ibrahim (author)System identification is a mature field in physical sciences and an emerging field in social sciences, with a vast range of applications. Nevertheless, it remains of great focus in academia. The main challenge is the efficient use of data to generate good model fits. System identification involves multi-disciplinary techniques from statistical,...master thesis 2021
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Dun, H. (author), Tiberius, C.C.J.M. (author), Diouf, C.E.V. (author), Janssen, G.J.M. (author)This paper presents a methodology to design a sparse multiband ranging signal with a large virtual bandwidth, from which time delay and carrier phase are estimated by a low complexity multivariate maximum likelihood (ML) method. In the estimation model for a multipath channel, not all reflected paths are considered, and time delay and carrier...journal article 2021