pwc-archive/papers-with-abstracts huggingface dataset:
Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps
Authors: Ross Gardiner, Sareh Rowands, Benno I. Simmons
Date: 2024-11-18 00:00:00
Tasks: Specificity

Camera traps, combined with AI, have emerged as a way to achieve automated, scalable biodiversity monitoring. However, the passive infrared (PIR) sensors that trigger camera traps are poorly suited for detecting small, fast-moving ectotherms such as insects. Insects comprise over half of all animal species and are key components of ecosystems and agriculture. The need for an appropriate and scalable insect camera trap is critical in the wake of concerning reports of declines in insect populations. This study proposes an alternative to the PIR trigger: ultra-lightweight convolutional neural networks running on low-powered hardware to detect insects in a continuous stream of captured images. We train a suite of models to distinguish insect images from backgrounds. Our design achieves zero latency between trigger and image capture. Our models are rigorously tested and achieve high accuracy ranging from 91.8% to 96.4% AUC on validation data and >87% AUC on data from distributions unseen during training. The high specificity of our models ensures minimal saving of false positive images, maximising deployment storage efficiency. High recall scores indicate a minimal false negative rate, maximising insect detection. Further analysis with saliency maps shows the learned representation of our models to be robust, with low reliance on spurious background features. Our system is also shown to operate deployed on off-the-shelf, low-powered microcontroller units, consuming a maximum power draw of less than 300mW. This enables longer deployment times using cheap and readily available battery components. Overall we offer a step change in the cost, efficiency and scope of insect monitoring. Solving the challenging trigger problem, we demonstrate a system which can be deployed for far longer than existing designs and budgets power and bandwidth effectively, moving towards a generic insect camera trap.
Improving the Test-retest Reliability of Quantitative Susceptibility Mapping
Authors: Shuai Huang, Thomas Denney, Deqiang Qiu
Date: 2024-11-15 00:00:00

Motivation - The test-retest reliability of quantitative susceptibility mapping (QSM) is affected by parameters of the acquisition protocol such as the angulation of acquisition plane with respect to the B0 field direction and spatial resolution. Goal - We aim to reduce the protocol-dependent biases/errors that might overshadow subtle changes of the susceptibility values due to pathology in the brain. Approach - The magnitude and phase images used for QSM are registered according to a standard reference protocol in the k-space through nonuniform fast Fourier transform (NUFFT). Results - With the help of our proposed approach, the test-retest reliability of scans acquired from different protocols is notably improved. Impact - The improved test-retest reliability of QSM makes it easier to detect subtle susceptibility changes in the early stages of neurodegeneration such as Alzheimer's disease and Parkinson's disease.
Using Spatial Diffusions for Optoacoustic Tomography Image Reconstruction
Authors: Martin G. Gonzalez, Matias Vera, Leonardo Rey Vega
Date: 2024-11-08 00:00:00
Tasks: Image Reconstruction, SSIM

Optoacoustic tomography image reconstruction has been a problem of interest in recent years. By exploiting the exceptional generative power of the recently proposed diffusion models we consider a scheme which is based on a conditional diffusion process. Using a simple initial image reconstruction method such as Delay and Sum, we consider a specially designed autoencoder architecture which generates a latent representation which is used as conditional information in the generative diffusion process. Numerical results show the merits of our proposal in terms of quality metrics such as PSNR and SSIM, showing that the conditional information generated in terms of the initial reconstructed image is able to bias the generative process of the diffusion model in order to enhance the image, correct artifacts and even recover some finer details that the initial reconstruction method is not able to obtain.
Fast Hyperspectral Reconstruction for Neutron Computed Tomography Using Subspace Extraction
Authors: Mohammad Samin Nur Chowdhury, Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan, Andrew W. Needham, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman
Date: 2024-11-05 00:00:00

Hyperspectral neutron computed tomography enables 3D non-destructive imaging of the spectral characteristics of materials. In traditional hyperspectral reconstruction, the data for each neutron wavelength bin is reconstructed separately. This per-bin reconstruction is extremely time-consuming due to the typically large number of wavelength bins. Furthermore, these reconstructions may suffer from severe artifacts due to the low signal-to-noise ratio in each wavelength bin. We present a novel fast hyperspectral reconstruction algorithm for computationally efficient and accurate reconstruction of hyperspectral neutron data. Our algorithm uses a subspace extraction procedure that transforms hyperspectral data into low-dimensional data within an intermediate subspace. This step effectively reduces data dimensionality and spectral noise. High-quality reconstructions are then performed within this low-dimensional subspace. Finally, the algorithm expands the subspace reconstructions into hyperspectral reconstructions. We apply our algorithm to measured neutron data and demonstrate that it reduces computation and improves reconstruction quality compared to the conventional approach.
Advancements in Data Processing and Calibration for the Hyperspectral Imaging Satellite (HySIS)
Authors: Ankur Garg, Abhishek Patil, Meenakshi Sarkar, S. Manthira Moorthi, Debajyoti Dhar
Date: 2024-11-01 00:00:00
Tasks: Earth Observation, Image Generation

Hyperspectral imaging is a powerful tool for Earth exploration, allowing for detailed analysis of spectral features. India has launched a dedicated hyperspectral Earth observation satellite capable of capturing data across a wide electromagnetic spectrum from 400 nm to 2500 nm, with an impressive spatial resolution of 30 meters. This paper details the comprehensive calibration procedures performed before launch to ensure data accuracy and instrument optimization for Hysis payload. Performance metrics demonstrate the satellite's ability to deliver high-quality imagery of the Earth's surface. In-orbit radiometric and geometric calibration further validated the satellite's functionality, while observations and anomalies encountered during this phase led to the development of specialized algorithms for operational enhancement. The paper outlines the operational data processing pipeline, covering stages from raw data acquisition to final image generation. A thorough assessment of the satellite's radiometric and geometric quality reinforces its reliability and effectiveness, highlighting its contribution to advancing Earth exploration through hyperspectral imaging technology.
Adaptive Detection of On-Orbit Jamming for Securing GEO Satellite Links
Authors: Anouar Boumeftah, Olfa Ben Yahia, Jean-François Frigon, Gregory Falco, Gunes Karabulut Kurt
Date: 2024-11-25 00:00:00

This paper introduces a scenario where a maneuverable satellite in geostationary orbit (GEO) conducts on-orbit attacks, targeting communication between a GEO satellite and a ground station, with the ability to switch between stationary and time-variant jamming modes. We propose a machine learning-based detection approach, employing the random forest algorithm with principal component analysis (PCA) to enhance detection accuracy in the stationary model. At the same time, an adaptive threshold-based technique is implemented for the time-variant model to detect dynamic jamming events effectively. Our methodology emphasizes the need for the use of orbital dynamics in integrating physical constraints from satellite dynamics to improve model robustness and detection accuracy. Simulation results highlight the effectiveness of PCA in enhancing the performance of the stationary model, while the adaptive thresholding method achieves high accuracy in detecting jamming in the time-variant scenario. This approach provides a robust solution for mitigating the evolving threats to satellite communication in GEO environments.
Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios
Authors: Thodoris Spanos, Fran Fabra, José A. López-Salcedo, Gonzalo Seco-Granados, Nikos Kanistras, Ivan Lapin, Vassilis Paliouras
Date: 2024-11-25 00:00:00

This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and with actual measurements. Leveraging 5G capabilities, we investigate AoA algorithms for single-base station positioning. The study includes simulations and practical tests on a developed dedicated testbed featuring a base station equipped with a three-element Uniform Linear Array (ULA), considering Line of Sight conditions in an open environment. The testbed, employing Ettus E312 as the transmitter and Ettus N310 as the receiver, details waveform structures and reception processes. Additionally, our study examines the performance of Angle of Arrival (AoA) estimation algorithms, such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT), and Joint Angle and Delay Estimation (JADE) ESPRIT. A MATLAB ray tracing propagation model of the environment where the measurements are conducted, has been constructed. Simulation results using this model are presented, along with the actual measurements. The obtained results affirm the effectiveness of our implementation.
Shapes of direct cortical responses vs. short-range axono-cortical evoked potentials: The effects of direct electrical stimulation applied to the human brain
Authors: Clotilde Turpin, Olivier Rossel, Félix Schlosser-Perrin, Sam Ng, Riki Matsumoto, Emmanuel Mandonnet, Hugues Duffau, François Bonnetblanc
Date: 2024-11-25 00:00:00

Objective: Direct cortical responses (DCR) and axono-cortical evoked potentials (ACEP) are generated by electrically stimulating the cortex either directly or indirectly through white matter pathways, potentially leading to different electrogenic processes. For ACEP, the slow conduction velocity of axons (median around 4 m.s$^{-1}$) is anticipated to induce a delay. For DCR, direct electrical stimulation (DES) of the cortex is expected to elicit additional cortical activity involving smaller and slower non-myelinated axons. We tried to validate these hypotheses. Methods: DES was administered either directly on the cortex or to white matter fascicles within the resection cavity, while recording DCR or ACEP at the cortical level in nine patients. Results: Short but significant delays (around 2 ms) were measurable for ACEP immediately following the initial component (around 7 ms). Subsequent activities (around 40 ms) exhibited notable differences between DCR and ACEP, suggesting the presence of additional cortical activities for DCR. Conclusion: Distinctions between ACEPs and DCRs can be made based on a delay at the onset of early components and the dissimilarity in the shape of the later components >40 ms after the DES artifact). Significance: The comparison of different types of evoked potentials allows to better understand the effects of DES.
Approaching Maximum Likelihood Performance via End-to-End Learning in MU-MIMO Systems
Authors: Hao Chang, Hoang Triet Vo, Alva Kosasih, Branka Vucetic, Wibowo Hardjawana
Date: 2024-11-25 00:00:00

Multi-user multiple-input multiple-output (MU-MIMO) systems allow multiple users to share the same wireless spectrum. Each user transmits one symbol drawn from an M-ary quadrature amplitude modulation (M-QAM) constellation set, and the resulting multi-user interference (MUI) is cancelled at the receiver. End-to-End (E2E) learning has recently been proposed to jointly design the constellation set for a modulator and symbol detector for a single user under an additive white Gaussian noise (AWGN) channel by using deep neural networks (DNN) with a symbol-error-rate (SER) performance, exceeding that of Maximum likelihood (ML) M-QAM detectors. In this paper, we extend the E2E concept to the MU-MIMO systems, where a DNN-based modulator that generates learned M constellation points and graph expectation propagation network (GEPNet) detector that cancels MUI are jointly optimised with respect to SER performance loss. Simulation results demonstrate that the proposed E2E with learned constellation outperforms GEPNet with 16-QAM by around 5 dB in terms of SER in a high MUI environment and even surpasses ML with 16-QAM in a low MUI condition, both with no additional computational complexity.
A General Sensing-assisted Channel Estimation Framework in Distributed MIMO Network
Authors: Hui Zhou, Xiaolan Liu, Sangarapillai Lambotharan
Date: 2024-11-24 00:00:00

In 6G communications, it is envisioned to equip the traditional access point (AP) with sensing capability to fully benefit the existing wireless communication infrastructures. Thus, sensing-assisted communication has attracted significant attention from both industry and academia. However, most existing works focused on sensing-assisted communication in line-of-sight (LoS) scenarios due to sensing limitations, where the sensing target (ST) and communication user equipment (UE) remain the same. In this paper, we propose a general sensing-assisted channel estimation framework in the distributed multiple-input and multiple-output (DMIMO) network and consider a scenario where the ST and UE are different entities. In addition, ST is a moving target (e.g. a robot) which causes channels between APs and UEs to vary due to changes in the reflection paths of the indoor environment. Therefore, we let multiple APs to jointly sense the position of the ST, which will be incorporated in a Ray tracing model to obtain a more accurate estimate of the channels from APs to UEs for both the LoS and non-line-of-sight (NLoS) scenarios. Simulation results demonstrate that our proposed sensing-assisted communication framework achieves a much higher channel estimation accuracy and downlink throughput compared to the traditional least-square (LS) channel estimation. More importantly, the feasibility of the proposed framework has been validated to guarantee the stringent channel estimation accuracy requirement in the DMIMO network.
Phase Selection and Analysis for Multi-frequency Multi-user RIS Systems Employing Subsurfaces in Correlated Ricean and Rayleigh Environments
Authors: Amy S. Inwood, Peter J. Smith, Philippa A. Martin, Graeme K. Woodward
Date: 2024-11-24 00:00:00

We analyse the performance of a reconfigurable intelligent surface (RIS) aided system where the RIS is divided into subsurfaces. Each subsurface is designed specifically for one user, who is served on their own frequency band. The other subsurfaces (those not designed for this user) provide additional uncontrolled scattering. We derive the exact closed-form expression for the mean signal-to-noise ratio (SNR) of the subsurface design (SD) when all channels experience correlated Ricean fading. We simplify this to find the mean SNR for line-of-sight (LoS) channels and channels experiencing correlated Rayleigh fading. An iterative SD (ISD) process is proposed, where subsurfaces are designed sequentially, and the phases that are already set are used to enhance the design of the remaining subsurfaces. This is extended to a converged ISD (CISD), where the ISD process is repeated multiple times until the SNR increases by less than a specified tolerance. The ISD and CISD both provide a performance improvement over SD, which increases as the number of RIS elements (N) increases. The SD is significantly simpler than the lowest complexity multi-user (MU) method we know of, and despite each user having less bandwidth, the SD outperforms the existing method in some key scenarios. The SD is more robust to strongly LoS channels and clustered users, as it does not rely on spatial multiplexing like other MU methods. Combined with the complexity reduction, this makes the SD an attractive phase selection method.
Near-Range Environmental Perception for Inland Waterway Vessels: A Comparative Study of LiDAR and Automotive FMCW RADAR Sensors
Authors: R. Herrmann, S. Bose, I. Filip, D. Medina, R. Ziebold, S. Gehrig, T. Lenhard, M. Gardill
Date: 2024-11-24 00:00:00
Tasks: Simultaneous Localization and Mapping

Advancing towards high automation and autonomous operations is crucial for the future of inland waterway transport (IWT) systems. These systems necessitate robust and precise onboard sensory technologies that can perceive the environment under all weather conditions, including static features for local positioning techniques such as Simultaneous Localization and Mapping (SLAM). Traditional marine RADAR, mandatory on vessels and operating in the 9300-9500 MHz frequency band, can cover ranges from 15 to 1200 meters but are inadequate for detecting closer objects, making them unsuitable for automated docking maneuvers, lock entry, or bridge undercrossings. This necessitates the development of reliable close-range sensor technology that functions effectively in all weather conditions. In present research works on vessel automation, LiDAR sensors, operating in the nearinfrared range, are used predominantly to detect the immediate surroundings of vessels but suffer significant degradation in poor visibility. Conversely, automotive RADAR sensors, utilizing the 76-81 GHz frequency band, can detect objects from a few centimeters to up to 200 meters, even in adverse conditions. These sensors are commonly used in advanced autonomous road traffic systems and are evaluated in this study for their suitability in inland navigation and maneuvering. This paper discusses a distributed sensor network of four compact automotive frequencymodulated continuous-wave (FMCW) radars mounted on a cabin boat as a test platform. Initial field experiments demonstrate the RADAR network's ability to perceive closerange static environmental features around the boat in inland waters. The paper also provides a comparative analysis of the environmental detection capabilities of automotive RADAR and LiDAR sensors.
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing
Authors: Yuxiao Wu, Huizhi Wang, Yong Zeng
Date: 2024-11-23 00:00:00
Tasks: Super-Resolution

In this letter, a fast Fourier transform (FFT)-enhanced low-complexity super-resolution sensing algorithm for near-field source localization with both angle and range estimation is proposed. Most traditional near-field source localization algorithms suffer from excessive computational complexity or incompatibility with existing array architectures. To address such issues, this letter proposes a novel near-field sensing algorithm that combines coarse and fine granularity of spectrum peak search. Specifically, a spectral pattern in the angle domain is first constructed using FFT to identify potential angles where sources are present. Afterwards, a 1D beamforming is performed in the distance domain to obtain potential distance regions. Finally, a refined 2D multiple signal classification (MUSIC) is conducted within each narrowed angle-distance region to estimate the precise location of the sources. Numerical results demonstrate that the proposed algorithm can significantly reduce the computational complexity of 2D spectrum peak searches and achieve target localization with high-resolution.
Covertness in the Near Field: Maximizing the Covert Region with FDA
Authors: Fatemeh Lotfi, Stefan Roth, Anas Chaaban, Aydin Sezgin
Date: 2024-11-22 00:00:00

Covert communication in wireless networks ensures that transmissions remain undetectable to adversaries, making it a potential enabler for privacy and security in sensitive applications. However, to meet the high performance and connectivity demands of sixth-generation (6G) networks, future wireless systems will require larger antenna arrays, higher operating frequencies, and advanced antenna architectures. This shift changes the propagation model from far-field planar-wave to near-field spherical-wave which necessitates a redesign of existing covert communication systems. Unlike far-field beamforming, which relies only on direction, near-field beamforming depends on both distance and direction, providing additional degrees of freedom for system design. In this paper, we aim to utilize those freedoms by proposing near-field Frequency Diverse Array (FDA)-based transmission strategies that manipulate the beampattern in both distance and angle, thereby establishing a non-covert region around the legitimate user. Our approach takes advantage of near-field properties and FDA technology to significantly reduce the area vulnerable to detection by adversaries while maintaining covert communication with the legitimate receiver. Numerical simulations show that our methods outperform conventional phased arrays by shrinking the non-covert region and allowing the covert region to expand as the number of antennas increases.
Noise-Aware Ensemble Learning for Efficient Radar Modulation Recognition
Authors: Do-Hyun Park, Min-Wook Jeon, Jinwoo Jeong, Isaac Sim, Sangbom Yun, Junghyun Seo, Hyoung-Nam Kim
Date: 2024-11-22 00:00:00
Tasks: Computational Efficiency, Ensemble Learning

Electronic warfare support (ES) systems intercept adversary radar signals and estimate various types of signal information, including modulation schemes. The accurate and rapid identification of modulation schemes under conditions of very low signal power remains a significant challenge for ES systems. This paper proposes a recognition model based on a noise-aware ensemble learning (NAEL) framework to efficiently recognize radar modulation schemes in noisy environments. The NAEL framework evaluates the influence of noise on recognition and adaptively selects an appropriate neural network structure, offering significant advantages in terms of computational efficiency and recognition performance. We present the analysis results of the recognition performance of the proposed model based on experimental data. Our recognition model demonstrates superior recognition accuracy with low computational complexity compared to conventional classification models.
Resolution-Adaptive Micro-Doppler Spectrogram for Human Activity Recognition
Authors: Do-Hyun Park, Min-Wook Jeon, Hyoung-Nam Kim
Date: 2024-11-22 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

The rising demand for remote-sensing systems for detecting hazardous situations has led to increased interest in radar-based human activity recognition (HAR). Conventional radar-based HAR methods predominantly rely on micro-Doppler spectrograms for recognition tasks. However, spectrograms frequently fail to effectively capture micro-Doppler signatures because of their limited linear resolution. To address this limitation, we propose a time--frequency domain representation method that adaptively adjusts the resolution based on activity characteristics. This approach nonlinearly transforms the resolution to focus on the most relevant frequency range for micro-Doppler signatures. We validate the proposed method by training deep-learning-based HAR models on datasets generated using the adaptive representation method. Experimental results demonstrate that the models trained using the proposed method achieve superior recognition accuracy compared with those trained using conventional methods.