pwc-archive/papers-with-abstracts huggingface dataset:
Robust Target Localization in 2D: A Value-at-Risk Approach
Authors: João Domingos, João Xavier
Date: 2023-07-02 00:00:00
Tasks: Portfolio Optimization

This paper consider considers the problem of locating a two dimensional target from range-measurements containing outliers. Assuming that the number of outlier is known, we formulate the problem of minimizing inlier losses while ignoring outliers. This leads to a combinatorial, non-convex, non-smooth problem involving the percentile function. Using the framework of risk analysis from Rockafellar et al., we start by interpreting this formulation as a Value-at-risk (VaR) problem from portfolio optimization. To the best of our knowledge, this is the first time that a localization problem was formulated using risk analysis theory. To study the VaR formulation, we start by designing a majorizer set that contains any solution of a general percentile problem. This set is useful because, when applied to a localization scenario in 2D, it allows to majorize the solution set in terms of singletons, circumferences, ellipses and hyperbolas. Using know parametrization of these curves, we propose a grid method for the original non-convex problem. So we reduce the task of optimizing the VaR objective to that of efficiently sampling the proposed majorizer set. We compare our algorithm with four benchmarks in target localization. Numerical simulations show that our method is fast while, on average, improving the accuracy of the best benchmarks by at least 100m in a 1 Km$^2$ area.
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm
Authors: Jiang Zhu, Hansheng Zhang, Ning Zhang, Jun Fang, Fengzhong Qu
Date: 2023-07-02 00:00:00
Tasks: Quantization, Super-Resolution

As radar systems will be equipped with thousands of antenna elements and wide bandwidth, the associated costs and power consumption become exceedingly high, and a potential solution is to adopt low-resolution quantization technology, which not only reduces data storage needs but also lowers power and hardware costs. This paper focuses on line spectral estimation and detection (LSE\&D) with few-bit ADCs (typically 1-4 bits) by investigating the signal-to-noise ratio (SNR) loss, establishing a framework to understand the impact of intersinusoidal interference, the bit-depth of the quantizer, and the noise variance on weak signal detection in scenarios involving multiple sinusoids under low-resolution quantization. Additionally, a low-complexity, super-resolution, and constant false alarm rate (CFAR) algorithm, named generalized Newtonized orthogonal matching pursuit (GNOMP), is proposed. Extensive numerical simulations are conducted to validate the theoretical findings, particularly in terms of the detection probability bound. The effectiveness of GNOMP is demonstrated through comparisons with state-of-the-art algorithms, the Cram\'{e}r Rao bound, and the detection probability bound. Real data acquired by mmWave radar further substantiates the effectiveness of GNOMP in practical applications.
Connecting the Dots: A Comprehensive Literature Review on Low and Medium-Voltage Cables, Fault Types, and Digital Signal Processing Techniques for Fault Location
Authors: Shankar Ramharack, Sanjay Bahadoorsingh
Date: 2023-07-02 00:00:00
Tasks: Fault Diagnosis, Management

The review begins with an exploration of acceptable cable types guided by local standards. It then investigates typical cable faults, including insulation degradation, conductor faults, and ground faults, providing insights into their characteristics, causes, and detection methods. Furthermore, the manuscript surveys the latest publications and standards on DSP techniques in fault location spanning various algorithms used. This review provides a comprehensive understanding of low and medium-voltage cables, fault types, and DSP techniques. The findings contribute to improved fault diagnosis and localization methods, facilitating more accurate and efficient cable fault management strategies
Digital Filters for Instantaneous Frequency Estimation
Authors: Hugh Lachlan Kennedy
Date: 2023-07-02 00:00:00

This technical note is on digital filters for the high-fidelity estimation of a sinusoidal signal's frequency in the presence of additive noise. The complex noise is assumed to be white (i.e. uncorrelated) however it need not be Gaussian. The complex signal is assumed to be of (approximately) constant magnitude and (approximately) polynomial phase such as the chirps emitted by bats, whale songs, pulse-compression radars, and frequency-modulated (FM) radios, over sufficiently short timescales. Such digital signals may be found at the end of a sequence of analogue heterodyning (i.e. mixing and low-pass filtering), down to a bandwidth that is matched to an analogue-to-digital converter (ADC), followed by digital heterodyning and sample rate reduction (optional) to match the clock frequency of the processor. The spacing of the discrete frequency bins (in cycles per sample) produced by the Fast Fourier Transform (FFT) is equal to the reciprocal of the window length (in samples). However, a long FFT (for fine frequency resolution) has a high complexity and a long latency, which may be prohibitive in embedded closed-loop systems, and unnecessary when the channel only contains a single sinusoid. In such cases, and for signals of constant frequency, the conventional approach involves the (weighted) average of instantaneous phase differences. General, naive, optimal, and pragmatic (recursive), filtering solutions are discussed and analysed here using Monte-Carlo (MC) simulations.
A System for Differentiation of Schizophrenia and Bipolar Disorder based on rsfMRI
Authors: Daniela Janeva, Stefan Krsteski, Matea Tashkovska, Nikola Jovanovski, Tomislav Kartalov, Dimitar Taskovski, Zoran Ivanovski, Branislav Gerazov
Date: 2023-07-01 00:00:00
Tasks: Diagnostic

Schizophrenia and bipolar disorder are debilitating psychiatric illnesses that can be challenging to diagnose accurately. The similarities between the diseases make it difficult to differentiate between them using traditional diagnostic tools. Recently, resting-state functional magnetic resonance imaging (rsfMRI) has emerged as a promising tool for the diagnosis of psychiatric disorders. This paper presents several methods for differentiating schizophrenia and bipolar disorder based on features extracted from rsfMRI data. The system that achieved the best results, uses 1D Convolutional Neural Networks to analyze patterns of Intrinsic Connectivity time courses obtained from rsfMRI and potentially identify biomarkers that distinguish between the two disorders. We evaluate the system's performance on a large dataset of patients with schizophrenia and bipolar disorder and demonstrate that the system achieves a 0.7078 Area Under Curve (AUC) score in differentiating patients with these disorders. Our results suggest that rsfMRI-based classification systems have great potential for improving the accuracy of psychiatric diagnoses and may ultimately lead to more effective treatments for patients with this disorder.
Metric Learning-Based Timing Synchronization by Using Lightweight Neural Network
Authors: Chaojin Qing, Na Yang, Shuhai Tang, Chuangui Rao, Jiafan Wang, Hui Lin
Date: 2023-07-01 00:00:00
Tasks: Metric Learning

Timing synchronization (TS) is one of the key tasks in orthogonal frequency division multiplexing (OFDM) systems. However, multi-path uncertainty corrupts the TS correctness, making OFDM systems suffer from a severe inter-symbol-interference (ISI). To tackle this issue, we propose a timing-metric learning-based TS method assisted by a lightweight one-dimensional convolutional neural network (1-D CNN). Specifically, the receptive field of 1-D CNN is specifically designed to extract the metric features from the classic synchronizer. Then, to combat the multi-path uncertainty, we employ the varying delays and gains of multi-path (the characteristics of multi-path uncertainty) to design the timing-metric objective, and thus form the training labels. This is typically different from the existing timing-metric objectives with respect to the timing synchronization point. Our method substantively increases the completeness of training data against the multi-path uncertainty due to the complete preservation of metric information. By this mean, the TS correctness is improved against the multi-path uncertainty. Numerical results demonstrate the effectiveness and generalization of the proposed TS method against the multi-path uncertainty.
Learning to Localize with Attention: from sparse mmWave channel estimates from a single BS to high accuracy 3D location
Authors: Yun Chen, Nuria González-Prelcic, Takayuki Shimizu, HongSheng Lu
Date: 2023-06-30 00:00:00
Tasks: Position

One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the channel parameters and the user position. These relationships can be built from the line-of-sight (LOS) path and first-order reflections, or purely first-order reflections, requiring high resolution channel estimates to ensure centimeter level accuracy. In this paper, we consider a mmWave multiple-input multiple-output (MIMO) system employing a hybrid architecture, and develop a low complexity two-stage multidimensional orthogonal matching pursuit (MOMP) algorithm suitable for accurate estimation of high dimensional channels. Then, a deep neural network (DNN) called PathNet is designed to classify the order of the estimated channel paths, so that only the LOS path and first-order reflections are selected for localization. Next, a 3D localization strategy exploiting the geometry of the environment is developed to operate in both LOS and non-line-of-sight (NLOS) conditions, while considering the unknown clock offset between the transmitter (TX) and the receiver (RX). Finally, a Transformer based network exploiting attention mechanisms called ChanFormer is proposed to refine the initial position estimate obtained from geometric localization. Simulation results obtained with realistic vehicular channels indicate that localization errors below 28 cm can be achieved for 80% of the users when the LOS path is present, while sub-meter accuracy can be achieved for 55% of the users in NLOS conditions.
RIS-Aided Kinematic Analysis for Remote Rehabilitation
Authors: Don-Roberts Emenonye, Anik Sarker, Alan T. Asbeck, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2023-06-30 00:00:00

This paper is the first to introduce the idea of using reconfigurable intelligent surfaces (RISs) as passive devices that measure the position and orientation of certain human body parts over time. In this paper, we investigate the possibility of utilizing the available geometric information provided by on-body RISs that reflect signals from an off-body transmitter to an off-body receiver for stroke rehabilitation. More specifically, we investigate the possibility of using on-body RISs to estimate the location information over time of upper limbs that may have been impaired due to stroke. This location information can help medical professionals to estimate the possibly time varying pose and obtain progress on the rehabilitation of the upper limbs. Our analysis is focused on two scenarios: i) after assessment exercises for stroke rehabilitation when the upper limbs are resting at predefined points in the rehabilitation center, and ii) during the assessment exercises. In the first scenario, we explore the possibility of upper limb orientation estimation by deriving the Fisher information matrix (FIM) under near-field and far-field propagation conditions. It is noteworthy that the FIM quantifies how accurately we can estimate location information from a signal, and any subsequent algorithm is bounded by a function of the FIM. Coming to our propagation assumptions, the difference between the near-field and far-field regimes lies in the curvature of the wavefront. In the near-field, a receiver experiences a spherical wavefront, whereas in the far-field, the wavefront is approximately linear. The threshold to be within the near-field can be on the order of $10 \text{ m}.$
Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs
Authors: Mengyuan Ma, Nhan Thanh Nguyen, Italo Atzeni, Markku Juntti
Date: 2023-06-30 00:00:00
Tasks: Quantization

Low-resolution analog-to-digital converters (ADCs) have emerged as an efficient solution for massive multiple-input multiple-output (MIMO) systems to reap high data rates with reasonable power consumption and hardware complexity. In this paper, we analyze the performance of oversampling in uplink massive MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems with low-resolution ADCs. Considering both the temporal and spatial correlation of the quantization distortion, we derive an approximate closed-form expression of an achievable sum rate, which reveals how the oversampling ratio (OSR), the ADC resolution, and the signal-to-noise ratio (SNR) jointly affect the system performance. In particular, we demonstrate that oversampling can effectively improve the sum rate by mitigating the impact of the quantization distortion, especially at high SNR and with very low ADC resolution. Furthermore, we show that the considered low-resolution massive MIMO-OFDM system can achieve the same performance as the unquantized one when both the SNR and the OSR are sufficiently high. Numerical simulations confirm our analysis.
Unlimited Sampling Radar: a Real-Time End-to-End Demonstrator
Authors: Thomas Feuillen, Bhavani Shankar MRR, Ayush Bhandari
Date: 2023-06-30 00:00:00
Tasks: Quantization

In this paper, the trade-off between the quantization noise and the dynamic range of ADCs used to acquire radar signals is revisited using the Unlimited Sensing Framework (USF) in a practical setting. Trade-offs between saturation and resolution arise in many applications, like radar, where sensors acquire signals which exhibit a high degree of variability in amplitude. To solve this issue, we propose the use of the co-design approach of the USF which acquires folded version of the signal of interest and leverages its structure to reconstruct it after its acquisition. We demonstrate that this method outperforms other standard acquisition methods for Doppler radars. Taking our theory all the way to practice, we develop a prototype USF-enabled Doppler Radar and show the clear benefits of our method. In each experiment, we show that using the USF increases sensitivity compared to a classic acquisition approach.
Accurate 2D Reconstruction for PET Scanners based on the Analytical White Image Model
Authors: Tomislav Matulić, Damir Seršić
Date: 2023-06-30 00:00:00

In this paper, we provide a precise mathematical model of crystal-to-crystal response which is used to generate the white image - a necessary compensation model needed to overcome the physical limitations of the PET scanner. We present a closed-form solution, as well as several accurate approximations, due to the complexity of the exact mathematical expressions. We prove, experimentally and analytically, that the difference between the best approximations and real crystal-to-crystal response is insignificant. The obtained responses are used to generate the white image compensation model. It can be written as a single closed-form expression making it easy to implement in known reconstruction methods. The maximum likelihood expectation maximization (MLEM) algorithm is modified and our white image model is integrated into it. The modified MLEM algorithm is not based on the system matrix, rather it is based on ray-driven projections and back-projections. The compensation model provides all necessary information about the system. Finally, we check our approach on synthetic and real data. For the real-world acquisition, we use the Raytest ClearPET camera for small animals and the NEMA NU 4-2008 phantom. The proposed approach overperforms competitive, non-compensated reconstruction methods.
Sensing of Side Lobes Interference for Blockage Prediction in Dense mmWave Networks
Authors: Mohamed Sana, Hiba Dakdouk, Benoit Denis
Date: 2023-06-30 00:00:00
Tasks: Management

The integration of sensing capability in the design of wireless communication systems is foreseen as a key enabler for efficient radio resource management in next-generation networks. This paper focuses on millimeter-wave communications, which are subject to severe attenuation due to blockages, ultimately detrimental to system performance. In this context, the sensing functionality can allow measuring or even imaging the wireless environment allowing anticipation of possible link failures, thus enabling proactive resource reallocation such as handover. This work proposes a novel mechanism for opportunistic environment sensing, which leverages existing network infrastructure with low complexity. More specifically, our approach exploits the fluctuations of interference, perceived in antenna side lobes, to detect local activity due to a moving blocker around the reference communication link. Numerical evaluations show that the proposed method is promising as it allows effective assessment of the blocker direction, trajectory and possibly, its location, speed, and size.
Enhancing Feature Extraction for Indoor Fingerprint Localization Using Diversified Data
Authors: Jiyu Jiao, Xiaojun Wang, Chenlin He
Date: 2023-06-30 00:00:00

Given the rapid advancements in wireless communication and terminal devices, high-speed and convenient WiFi has permeated various aspects of people's lives, and attention has been drawn to the location services that WiFi can provide. Fingerprint-based methods, as an excellent approach for localization, have gradually become a hot research topic. However, in practical localization, fingerprint features of traditional methods suffer from low reliability and lacking robustness in complex indoor environments. To overcome these limitations, this paper proposes a innovative feature extraction-enhanced intelligent localization scheme named Secci, based on diversified channel state information (CSI). By modifying the device driver, diversified CSI data are extracted and transformed into RGB CSI images, which serve as input to a deep convolutional neural network (DCNN) with SE attention mechanism-assisted training in the offline stage. Employing a greedy probabilistic approach, rapid prediction of the estimated location is performed in the online stage using test RGB CSI images. The Secci system is implemented using off-the-shelf WiFi devices, and comprehensive experiments are carried out in two representative indoor environments to showcase the superior performance of Secci compared to four existing algorithms.
ELM-based Timing Synchronization for OFDM Systems by Exploiting Computer-aided Training Strategy
Authors: Mintao Zhang, Shuhai Tang, Chaojin Qing, Na Yang, Xi Cai, Jiafan Wang
Date: 2023-06-30 00:00:00

Due to the implementation bottleneck of training data collection in realistic wireless communications systems, supervised learning-based timing synchronization (TS) is challenged by the incompleteness of training data. To tackle this bottleneck, we extend the computer-aided approach, with which the local device can generate the training data instead of generating learning labels from the received samples collected in realistic systems, and then construct an extreme learning machine (ELM)-based TS network in orthogonal frequency division multiplexing (OFDM) systems. Specifically, by leveraging the rough information of channel impulse responses (CIRs), i.e., root-mean-square (r.m.s) delay, we propose the loose constraint-based and flexible constraint-based training strategies for the learning-label design against the maximum multi-path delay. The underlying mechanism is to improve the completeness of multi-path delays that may appear in the realistic wireless channels and thus increase the statistical efficiency of the designed TS learner. By this means, the proposed ELM-based TS network can alleviate the degradation of generalization performance. Numerical results reveal the robustness and generalization of the proposed scheme against varying parameters.
Radar Cross Section Reduction of Microstrip Patch Antenna using Metamaterial Techniques
Authors: Syamly S. B, Job Chunkath
Date: 2023-06-30 00:00:00
Tasks: Band Gap

Radar cross section (RCS) reduction has become one of the critical research areas in recent years. The RCS of the target should be small to avoid detection. Different methods are used to reduce RCS, but the major challenge with many RCS minimization methodologies is that, it may deteriorate some antenna parameters. When antenna mode RCS is considered; structural mode RCS, and antenna parameters are critical, as the structure should be an antenna and a RCS reducing structure simultaneously. The techniques like applying Radar Absorption Material (RAM) entirely over the target, deployment of Energy Band Gap (EBG) structures, the use of passive, active cancellation, and polarization conversion are prevalent methods to reduce RCS. The manifestation of metamaterial property in an antenna results in the antenna's electromagnetic characteristics becoming negative for a particular bandwidth. Thus the RCS of the antenna can be reduced to a minimum range by loading the metamaterial structures. This paper discusses the application of polarization conversion method (PCM), L-structured and Square-structured fractal metamaterial antenna for RCS reduction. This paper reports the simulation, fabrication, and testing of the above antennas with their performance comparison. The antennas are designed for 4.3GHz frequency with a total dimension of 80mmx80mmx1.6mm. Antenna parameters like return loss, gain, radiation pattern, and bandwidth are analyzed along with the RCS. The L-structured metamaterial antenna implemented has a 29.37% larger bandwidth than the reference patch antenna with a gain of 2.94dB with a return loss of -28.28dB.
Compressed Sensing Radar Detectors based on Weighted LASSO
Authors: Siqi Na, Yoshiyuki Kabashima, Takashi Takahashi, Tianyao Huang, Yimin Liu, Xiqin Wang
Date: 2023-06-30 00:00:00
Tasks: compressed sensing

The compressed sensing (CS) model can represent the signal recovery process of a large number of radar systems. The detection problem of such radar systems has been studied in many pieces of literature through the technology of debiased least absolute shrinkage and selection operator (LASSO). While naive LASSO treats all the entries equally, there are many applications in which prior information varies depending on each entry. Weighted LASSO, in which the weights of the regularization terms are tuned depending on the entry-dependent prior, is proven to be more effective with the prior information by many researchers. In the present paper, existing results obtained by methods of statistical mechanics are utilized to derive the debiased weighted LASSO estimator for randomly constructed row-orthogonal measurement matrices. Based on this estimator, we construct a detector, termed the debiased weighted LASSO detector (DWLD), for CS radar systems and prove its advantages. The threshold of this detector can be calculated by false alarm rate, which yields better detection performance than the naive weighted LASSO detector (NWLD) under the Neyman-Pearson principle. The improvement of the detection performance brought by tuning weights is demonstrated by numerical experiments. With the same false alarm rate, the detection probability of DWLD is obviously higher than those of NWLD and the debiased (non-weighted) LASSO detector (DLD).