Negative / Null Result ReportOpen accessEngineering
MD Irteeja Kobir, Pedro Machado, Ahmad Lotfi et al. · 2025 · Sensors
Human Activity Recognition (HAR) is crucial for understanding human behaviour through sensor data, with applications in healthcare, smart environments, and surveillance. While traditional HAR often relies on ambient sensors, wearable devices or vision-based systems, these approaches can face limitations in dynamic settings and raise privacy concerns. Device-free HAR systems, utilising Wi-Fi Channel State Information (CSI) to human movements, have emerged as a promising privacy-preserving alternative for next-generation health activity monitoring and smart environments, particularly for multi-u
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J. Jithish, Nagarajan Mahalingam, Bo Wang et al. · 2025 · Cybersecurity
Abstract The forthcoming 6G technology offers significant potential for the advancement of the smart grid domain. 6G promises ultra-low latency, higher data transfer rates, native Artificial Intelligence (AI) support, enhanced connectivity, and improved security for smart grids. Smart grids are vulnerable to cyberattacks, such as Distributed Denial-of-Service (DDoS) attacks, posing a significant threat to grid functionality. To address security concerns, smart grids implement intrusion detection systems (IDS), but detecting novel attacks such as subtle multi-domain DDoS attacks through traditi
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Deepak Singla, Soham Chatterjee, Lavanya Ramapantulu et al. · 2020 · arXiv
Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area. While neuromorphic vision sensors (NVS) may offer advantages over traditional imagers in this domain, the existing NVS systems either do not meet the power constraints or have not demonstrated end-to-end system performance. To address this, we improve on a recently proposed hybrid event-frame approach by using morphological image processing algorithms for region proposal and address the low-power requirement for object detection and classification by exploring various convolut
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Luca Didaci, Sara Maria Pani, Claudio Frongia et al. · 2023 · arXiv
EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results
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Neil Irwin Bernardo, Jingge Zhu, Jamie Evans · 2021 · arXiv
We analyze the symbol error probability (SEP) of $M$-ary pulse amplitude modulation ($M$-PAM) receivers equipped with optimal low-resolution quantizers. We first show that the optimum detector can be reduced to a simple decision rule. Using this simplification, an exact SEP expression for quantized $M$-PAM receivers is obtained when Nakagami-$m$ fading channel is considered. The derived expression enables the optimization of the quantizer and/or constellation under the minimum SEP criterion. Our analysis of optimal quantization for equidistant $M$-PAM receiver reveals the existence of error fl
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Amritanshu Pandey, Marko Jereminov, Gabriela Hug et al. · 2017 · arXiv
Recent advances in power system simulation have included the use of complex rectangular current and voltage (I-V) variables for solving the power flow and three-phase power flow problems. This formulation has demonstrated superior convergence properties over conventional polar coordinate based formulations for three-phase power flow, but has failed to replicate the same advantages for power flow in general due to convergence issues with systems containing PV buses. In this paper, we demonstrate how circuit simulation techniques can provide robust convergence for any complex I-V formulation tha
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Junwei Yang, Xiao-Xin Li, Feihong Liu et al. · 2021 · arXiv
Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modalities. Most of the state-of-the-art approaches have achieved improvement through the development of network architectures for fixed under-sampling patterns, without fully exploiting the complementary information between modalities. Although existing under-sampling pattern learning algorithms can be simply modified to allow the fully-sampled T1-weighted MR image to assist the pattern learning, no significant improveme
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Rancy Chepchirchir, Jill Sunday, Raymond Confidence et al. · 2025 · arXiv
In Sub-Saharan Africa (SSA), the utilization of lower-quality Magnetic Resonance Imaging (MRI) technology raises questions about the applicability of machine learning methods for clinical tasks. This study aims to provide a robust deep learning-based brain tumor segmentation (BraTS) method tailored for the SSA population using a threefold approach. Firstly, the impact of domain shift from the SSA training data on model efficacy was examined, revealing no significant effect. Secondly, a comparative analysis of 3D and 2D full-resolution models using the nnU-Net framework indicates similar perfor
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Nina R Benway, Jonathan L Preston, Asif Salekin et al. · 2023 · arXiv
Mispronunciation detection tools could increase treatment access for speech sound disorders impacting, e.g., /r/. We show age-and-sex normalized formant estimation outperforms cepstral representation for detection of fully rhotic vs. derhotic /r/ in the PERCEPT-R Corpus. Gated recurrent neural networks trained on this feature set achieve a mean test participant-specific F1-score =.81 (σx=.10, med = .83, n = 48), with post hoc modeling showing no significant effect of child age or sex.
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Max Langtry, Vijja Wichitwechkarn, Rebecca Ward et al. · 2024 · arXiv
Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve m
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Ezra Tampubolon, Haris Ceribasic, Holger Boche · 2020 · arXiv
Congestion game is a widely used model for modern networked applications. A central issue in such applications is that the selfish behavior of the participants may result in resource overloading and negative externalities for the system participants. In this work, we propose a pricing mechanism that guarantees the sub-linear increase of the time-cumulative violation of the resource load constraints. The feature of our method is that it is resource-centric in the sense that it depends on the congestion state of the resources and not on specific characteristics of the system participants. This f
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Di Wu, Yifei Jia, Siyuan Li et al. · 2025 · arXiv
Neurophysiological decoding, fundamental to advancing brain-computer interface (BCI) technologies, has significantly benefited from recent advances in deep learning. However, existing decoding approaches largely remain constrained to single-task scenarios and individual subjects, limiting their broader applicability and generalizability. Efforts towards creating large-scale neurophysiological foundation models have shown promise, but continue to struggle with significant challenges due to pervasive data heterogeneity across subjects and decoding tasks. Simply increasing model parameters and da
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Daniele Gerosa, Thomas Eriksson · 2026 · arXiv
Motivated by recent developments in stochastic modeling of clock jitter in Analog-to-Digital Converters (ADCs) as autoregressive processes of order one (AR(1)), we study the density and stability properties of AR(1)-jittered sampling sets for Paley-Wiener signals. We show that, despite having the correct asymptotic density both on average and almost surely, such sets almost surely fail to be stable sampling sets. We complement this negative result with a finite-dimensional analysis, showing that the corresponding jittered sinc matrices are nonetheless well-conditioned with high probability.
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Ramin Babaee, Shahab Oveis Gharan, Martin Bouchard · 2025 · arXiv
We propose a novel digital-to-analog converter (DAC) weighting architecture that statistically minimizes the distortion caused by random timing mismatches among current sources. To decode the DAC input codewords into corresponding DAC switches, we present three algorithms with varying computational complexities. We perform high-level Matlab simulations to illustrate the dynamic performance improvement over the segmented structure.
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George Sterpu, Christian Saam, Naomi Harte · 2020 · arXiv
Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech. This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. Our experiments are performed on two of the largest publicly available AVSR datasets, TCD-TIMIT and
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Siqi Ye, Yong Long, Il Yong Chun · 2020 · arXiv
This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. At each layer of the proposed Momentum-Net, the model-based image reconstruction module solves the majorized penalized weighted least-square problem, and the image refining module uses a four-layer convolutional neural network (CNN). Experimental results with the NIH AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset show that the proposed Momentum-Net architecture significantly improves image reconstruction accuracy,
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Karl Strecker, Sabit Ekin, John OHara · 2021 · arXiv
A theoretical framework and numerical simulations quantifying the impact of atmospheric group velocity dispersion on wireless terahertz communication link error rate were developed based upon experimental work. We present, for the first time, predictions of symbol error rate as a function of link distance, signal bandwidth, signal-to-noise ratio, and atmospheric conditions, revealing that long-distance, broadband terahertz communication systems may be limited by inter-symbol interference stemming from group velocity dispersion, rather than attenuation. In such dispersion limited links, increas
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Nikhil Raghav, Subhajit Saha, Md Sahidullah et al. · 2024 · arXiv
In this report, we describe the speaker diarization (SD) and language diarization (LD) systems developed by our team for the Second DISPLACE Challenge, 2024. Our contributions were dedicated to Track 1 for SD and Track 2 for LD in multilingual and multi-speaker scenarios. We investigated different speech enhancement techniques, voice activity detection (VAD) techniques, unsupervised domain categorization, and neural embedding extraction architectures. We also exploited the fusion of various embedding extraction models. We implemented our system with the open-source SpeechBrain toolkit. Our fin
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Jingqi Li, Anand Siththaranjan, Somayeh Sojoudi et al. · 2024 · arXiv
Autonomous agents should coordinate effectively without prior knowledge of others' intents. While prior work has focused on intent inference, we address the inverse problem: how agents can strategically demonstrate their intents within general-sum dynamic games. We model this problem and propose an algorithm that balances intent demonstration with task performance. To handle nonlinear dynamic games with continuous state-action spaces, our method leverages iterative linear-quadratic game approximations and provides efficient intent-teaching guarantees: the uncertain agent's belief can be driven
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Arjun D. Desai, Francesco Caliva, Claudia Iriondo et al. · 2020 · arXiv
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Simil
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Kaito Hosono, Shunsuke Ono, Takamichi Miyata · 2020 · arXiv
Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank--e.g. the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank. Since the Tucker rank is nonconvex and discontinuous, many relaxations of the Tucker rank have been proposed, e.g., the tensor nuclear norm, weighted tensor nuclear norm, and weighted tensor Schatten-$p$ norm. In particular, the weighted tensor Schatten-p norm has two parameters, the weight and $p$, and the tensor nuclear norm and
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Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg et al. · 2020 · arXiv
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme
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Huy Kinh Phan, Viet Lam Phung, Tuan Anh Dinh et al. · 2020 · arXiv
In recent years, statistical parametric speech synthesis (SPSS) systems have been widely utilized in many interactive speech-based systems (e.g.~Amazon's Alexa, Bose's headphones). To select a suitable SPSS system, both speech quality and performance efficiency (e.g.~decoding time) must be taken into account. In the paper, we compared four popular Vietnamese SPSS techniques using: 1) hidden Markov models (HMM), 2) deep neural networks (DNN), 3) generative adversarial networks (GAN), and 4) end-to-end (E2E) architectures, which consists of Tacontron~2 and WaveGlow vocoder in terms of speech qua
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Yanyu Cheng, Chongjun Ouyang, Yuanwei Liu et al. · 2025 · arXiv
This paper conducts a comprehensive performance analysis for pinching-antenna systems (PASS) under both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission. Given the cost of waveguides, we consider a scenario where the waveguide is not deployed in all rooms, i.e., some users are beyond the line-of-sight (LoS) link service area of the PASS. Specifically, we consider a PASS where a pinching antenna in one room serves two users located in separate rooms. The wireless transmissions between the pinching antenna and the users are performed via LoS and non-line-of
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Haoyang Li, Yuchen Hu, Chen Chen et al. · 2024 · arXiv
Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures needed for high-fidelity SE. Group-Rational KAN (GR-KAN), a variant of Kolmogorov-Arnold Networks (KAN), retains KAN's expressiveness while improving scalability on complex tasks. We adapt GR-KAN to existing DNN-based SE by replacing dense layers with GR-KAN layers in the time-frequency (T-F) domain MP-SENet and adapting GR-KAN's activations into the 1D CNN layers in the time-domain Demucs. Results on Voicebank-DEMAND sho
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Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit et al. · 2024 · arXiv
Generative Error Correction (GEC) has emerged as a powerful post-processing method to enhance the performance of Automatic Speech Recognition (ASR) systems. However, we show that GEC models struggle to generalize beyond the specific types of errors encountered during training, limiting their ability to correct new, unseen errors at test time, particularly in out-of-domain (OOD) scenarios. This phenomenon amplifies with named entities (NEs), where, in addition to insufficient contextual information or knowledge about the NEs, novel NEs keep emerging. To address these issues, we propose DARAG (D
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Mengqi Wang, Zhan Liu, Zengrui Jin et al. · 2025 · arXiv
Diffusion-based large language models (DLLMs) have recently attracted growing interest as an alternative to autoregressive decoders. In this work, we present an empirical study on using the diffusion-based large language model LLaDA for automatic speech recognition (ASR). We first investigate its use as an external deliberation-based processing module for Whisper-LLaMA transcripts. By leveraging the bidirectional attention and denoising capabilities of LLaDA, we explore random masking, low-confidence masking, and semi-autoregressive strategies, showing that Whisper-LLaDA substantially reduces
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Jiaqi Wu, Jingyi Yuan, Yang Weng et al. · 2025 · arXiv
Power system voltage regulation is crucial to maintain power quality while integrating intermittent renewable resources in distribution grids. However, the system model on the grid edge is often unknown, making it difficult to model physical equations for optimal control. Therefore, previous work proposes structured data-driven methods like input convex neural networks (ICNN) for "optimal" control without relying on a physical model. While ICNNs offer theoretical guarantees based on restrictive assumptions of non-negative neural network parameters, can one improve the approximation power with
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Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong et al. · 2023 · arXiv
Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted systems that have complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve legibility since the intricate motions still retain complex characteristics even after being magnified, which may di
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Kushal Mehta, Arshita Jain, Jayalakshmi Mangalagiri et al. · 2020 · arXiv
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features and finally the combination of imagery + biomarkers + volumetric features in orde
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