pwc-archive/papers-with-abstracts huggingface dataset:
Networked ISAC for Low-Altitude Economy: Coordinated Transmit Beamforming and UAV Trajectory Design
Authors: Gaoyuan Cheng, Xianxin Song, Zhonghao Lyu, Jie Xu
Date: 2024-06-19 00:00:00
Tasks: ISAC

This paper exploits the networked integrated sensing and communications (ISAC) to support low-altitude economy (LAE), in which a set of networked ground base stations (GBSs) cooperatively transmit joint information and sensing signals to communicate with multiple authorized unmanned aerial vehicles (UAVs) and concurrently detect unauthorized objects over the interested region in the three-dimensional (3D) space. We assume that each GBS is equipped with uniform linear array (ULA) antennas, which are deployed either horizontally or vertically to the ground. We also consider two types of UAV receivers, which have and do not have the capability of canceling the interference caused by dedicated sensing signals, respectively. Under each setup, we jointly design the coordinated transmit beamforming at multiple GBSs together with the authorized UAVs' trajectory control and their GBS associations, for enhancing the authorized UAVs' communication performance while ensuring the sensing requirements. In particular, we aim to maximize the average sum rate of authorized UAVs over a given flight period, subject to the minimum illumination power constraints toward the interested 3D sensing region, the maximum transmit power constraints at individual GBSs, and the flight constraints of UAVs. These problems are highly non-convex and challenging to solve, due to the involvement of binary UAV-GBS association variables as well as the coupling of beamforming and trajectory variables. To solve these non-convex problems, we propose efficient algorithms by using the techniques of alternating optimization, successive convex approximation, and semi-definite relaxation. Numerical results show that the proposed joint coordinated transmit beamforming and UAV trajectory designs efficiently balance the sensing-communication performance tradeoffs and significantly outperform various benchmarks.
Sub-mW 30GHz Variable-Gain LNA in 22nm FDSOI CMOS for Low-Power Tapered mm-Wave 5G/6G Phased-Array Receivers
Authors: M. Spasaro, D. Zito
Date: 2024-06-17 00:00:00

Next-generation cellular systems require low-power mm-wave phased-array ICs. Variable-gain LNAs (VG-LNAs) are key blocks enabling reduced hardware complexity, performance improvement and added functionalities. This paper reports a low-power 30GHz VG-LNA for mm-wave 5G/6G phased-array ICs, with a gain control of 8 dB for 18dB Taylor taper in a 30GHz 8x8 antenna array. The VG-LNA exhibits a peak gain of 16 dB in the high-gain state, consumes less than 1 mW and occupies an area of 0.20 x 0.22 mm2.
Unmixing Noise from Hawkes Process to Model Learned Physiological Events
Authors: Guillaume Staerman, Virginie Loison, Thomas Moreau
Date: 2024-06-17 00:00:00
Tasks: Dictionary Learning, Event Detection

Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Traditional methods rely on handcrafted procedures or supervised learning, presenting challenges such as expert dependence, lack of robustness, and the need for extensive labeled data. Data-driven methods like Convolutional Dictionary Learning (CDL) offer an alternative but tend to produce spurious detections. This work introduces UNHaP (Unmix Noise from Hawkes Processes), a novel approach addressing the joint learning of temporal structures in events and the removal of spurious detections. Leveraging marked Hawkes processes, UNHaP distinguishes between events of interest and spurious ones. By treating the event detection output as a mixture of structured and unstructured events, UNHaP efficiently unmixes these processes and estimates their parameters. This approach significantly enhances the understanding of event distributions while minimizing false detection rates.
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex
Authors: Spandan Madan, Will Xiao, Mingran Cao, Hanspeter Pfister, Margaret Livingstone, Gabriel Kreiman
Date: 2024-06-16 00:00:00
Tasks: Benchmarking, Object Recognition, Out-of-Distribution Generalization

We characterized the generalization capabilities of DNN-based encoding models when predicting neuronal responses from the visual cortex. We collected \textit{MacaqueITBench}, a large-scale dataset of neural population responses from the macaque inferior temporal (IT) cortex to over $300,000$ images, comprising $8,233$ unique natural images presented to seven monkeys over $109$ sessions. Using \textit{MacaqueITBench}, we investigated the impact of distribution shifts on models predicting neural activity by dividing the images into Out-Of-Distribution (OOD) train and test splits. The OOD splits included several different image-computable types including image contrast, hue, intensity, temperature, and saturation. Compared to the performance on in-distribution test images -- the conventional way these models have been evaluated -- models performed worse at predicting neuronal responses to out-of-distribution images, retaining as little as $20\%$ of the performance on in-distribution test images. The generalization performance under OOD shifts can be well accounted by a simple image similarity metric -- the cosine distance between image representations extracted from a pre-trained object recognition model is a strong predictor of neural predictivity under different distribution shifts. The dataset of images, neuronal firing rate recordings, and computational benchmarks are hosted publicly at: https://bit.ly/3zeutVd.
Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems using DRL and PSO
Authors: Marwan Dhuheir, Aiman Erbad, Ala Al-Fuqaha, Mohsen Guizani
Date: 2024-06-16 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users in diverse scenarios like festivals, large sporting events, natural and man-made disasters due to their advantages in terms of versatility and maneuverability. However, the limited resources of UAVs (e.g., energy budget and different service requirements) can pose challenges for adopting UAVs for such applications. Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAVs. In this work, we introduce an optimization model with the aim of maximizing the throughput and UAVs coverage through optimal path planning of UAVs and multi-RIS phase configurations. The formulated optimization is challenging to solve using standard linear programming techniques, limiting its applicability in real-time decision-making. Therefore, we introduce a two-step solution using deep reinforcement learning and particle swarm optimization. We conduct extensive simulations and compare our approach to two competitive solutions presented in the recent literature. Our simulation results demonstrate that our adopted approach is 20 \% better than the brute-force approach and 30\% better than the baseline solution in terms of QoS.
SGSM: A Foundation-model-like Semi-generalist Sensing Model
Authors: Tianjian Yang, Hao Zhou, Shuo Liu, Kaiwen Guo, Yiwen Hou, Haohua Du, Zhi Liu, Xiang-Yang Li
Date: 2024-06-15 00:00:00
Tasks: model

The significance of intelligent sensing systems is growing in the realm of smart services. These systems extract relevant signal features and generate informative representations for particular tasks. However, building the feature extraction component for such systems requires extensive domain-specific expertise or data. The exceptionally rapid development of foundation models is likely to usher in newfound abilities in such intelligent sensing. We propose a new scheme for sensing model, which we refer to as semi-generalist sensing model (SGSM). SGSM is able to semiautomatically solve various tasks using relatively less task-specific labeled data compared to traditional systems. Built through the analysis of the common theoretical model, SGSM can depict different modalities, such as the acoustic and Wi-Fi signal. Experimental results on such two heterogeneous sensors illustrate that SGSM functions across a wide range of scenarios, thereby establishing its broad applicability. In some cases, SGSM even achieves better performance than sensor-specific specialized solutions. Wi-Fi evaluations indicate a 20\% accuracy improvement when applying SGSM to an existing sensing model.
Xi-Net: Transformer Based Seismic Waveform Reconstructor
Authors: Anshuman Gaharwar, Parth Parag Kulkarni, Joshua Dickey, Mubarak Shah
Date: 2024-06-14 00:00:00
Tasks: Decoder

Missing/erroneous data is a major problem in today's world. Collected seismic data sometimes contain gaps due to multitude of reasons like interference and sensor malfunction. Gaps in seismic waveforms hamper further signal processing to gain valuable information. Plethora of techniques are used for data reconstruction in other domains like image, video, audio, but translation of those methods to address seismic waveforms demands adapting them to lengthy sequence inputs, which is practically complex. Even if that is accomplished, high computational costs and inefficiency would still persist in these predominantly convolution-based reconstruction models. In this paper, we present a transformer-based deep learning model, Xi-Net, which utilizes multi-faceted time and frequency domain inputs for accurate waveform reconstruction. Xi-Net converts the input waveform to frequency domain, employs separate encoders for time and frequency domains, and one decoder for getting reconstructed output waveform from the fused features. 1D shifted-window transformer blocks form the elementary units of all parts of the model. To the best of our knowledge, this is the first transformer-based deep learning model for seismic waveform reconstruction. We demonstrate this model's prowess by filling 0.5-1s random gaps in 120s waveforms, resembling the original waveform quite closely. The code, models can be found at: https://github.com/Anshuman04/waveformReconstructor.
CubeSat-Enabled Free-Space Optics: Joint Data Communication and Fine Beam Tracking
Authors: Hossein Safi, Mohammad Taghi Dabiri, Julian Cheng, Iman Tavakkolnia, Harald Haas
Date: 2024-06-13 00:00:00
Tasks: Benchmarking

The integration of CubeSats with Free Space Optical (FSO) links accelerates a major advancement in high-throughput, low-Earth orbit communication systems. However, CubeSats face challenges such as size, weight, and power (SWaP) limitations, as well as vibrations that cause fluctuations in the angle-of-arrival (AoA) of the optical beam at the receiver. These practical challenges make establishing CubeSat-assisted FSO links complicated. To mitigate AoA fluctuations, we expand the receiver's field of view and track the location of the focused beam spot using an array of avalanche photodiodes at the receiver. Initially, we model the optical channel between the transmitter and the detector array. Furthermore, to reduce the computational load of maximum likelihood sequence detection, which is infeasible for CubeSats due to SWaP constraints, we propose a sub-optimal blind sequence data detection approach that relies on the generalized likelihood ratio test (GLRT) criterion. We also utilize combining methods such as equal gain combining (EGC) and maximal ratio combining (MRC) for data detection, benchmarking their performance against the GLRT-based method. Numerical results demonstrate that the proposed low-complexity GLRT-based method outperforms the combining methods, achieving performance close to that of the ideal receiver.
Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
Authors: TingWei Chen, Yantao Wang, Hanzhi Chen, Zijian Zhao, Xinhao Li, Nicola Piovesan, Guangxu Zhu, Qingjiang Shi
Date: 2024-06-13 00:00:00
Tasks: All

The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications. However, this technological leap comes with a substantial increase in energy consumption, presenting a significant challenge. To improve the energy efficiency of 5G networks, it is imperative to develop sophisticated models that accurately reflect the influence of base station (BS) attributes and operational conditions on energy usage.Importantly, addressing the complexity and interdependencies of these diverse features is particularly challenging, both in terms of data processing and model architecture design. This paper proposes a novel 5G base stations energy consumption modelling method by learning from a real-world dataset used in the ITU 5G Base Station Energy Consumption Modelling Challenge in which our model ranked second. Unlike existing methods that omit the Base Station Identifier (BSID) information and thus fail to capture the unique energy fingerprint in different base stations, we incorporate the BSID into the input features and encoding it with an embedding layer for precise representation. Additionally, we introduce a novel masked training method alongside an attention mechanism to further boost the model's generalization capabilities and accuracy. After evaluation, our method demonstrates significant improvements over existing models, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, leading to a performance gain of more than 60%.
A Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification
Authors: Wei Huang, Ning Wang, Panpan Feng, HaiYan Wang, Zongmin Wang, Bing Zhou
Date: 2024-06-12 00:00:00
Tasks: ECG Classification, Rhythm

Electrocardiograms (ECG), which record the electrophysiological activity of the heart, have become a crucial tool for diagnosing these diseases. In recent years, the application of deep learning techniques has significantly improved the performance of ECG signal classification. Multi-resolution feature analysis, which captures and processes information at different time scales, can extract subtle changes and overall trends in ECG signals, showing unique advantages. However, common multi-resolution analysis methods based on simple feature addition or concatenation may lead to the neglect of low-resolution features, affecting model performance. To address this issue, this paper proposes the Multi-Resolution Mutual Learning Network (MRM-Net). MRM-Net includes a dual-resolution attention architecture and a feature complementary mechanism. The dual-resolution attention architecture processes high-resolution and low-resolution features in parallel. Through the attention mechanism, the high-resolution and low-resolution branches can focus on subtle waveform changes and overall rhythm patterns, enhancing the ability to capture critical features in ECG signals. Meanwhile, the feature complementary mechanism introduces mutual feature learning after each layer of the feature extractor. This allows features at different resolutions to reinforce each other, thereby reducing information loss and improving model performance and robustness. Experiments on the PTB-XL and CPSC2018 datasets demonstrate that MRM-Net significantly outperforms existing methods in multi-label ECG classification performance. The code for our framework will be publicly available at https://github.com/wxhdf/MRM.
Anomaly Detection Utilizing a Riemann Metric for Robust Myoelectric Pattern Recognition
Authors: ZongYe Hu, Ge Gao, Xiang Chen, Xu Zhang
Date: 2024-06-12 00:00:00
Tasks: Anomaly Detection, Motion Detection

Traditional myoelectric pattern recognition (MPR) systems excel within controlled laboratory environments but they are interfered when confronted with anomaly or novel motions not encountered during the training phase. Utilizing metric ways to distinguish the target and novel motions based on extractors compared to training set is a prevalent idea to alleviate such interference. An innovative method for anomaly motion detection was proposed based on simplified log-Euclidean distance (SLED) of symmetric positive definite manifolds. The SLED enhances the discrimination between target and novel motions. Moreover, it generates a more flexible shaping of motion boundaries to segregate target and novel motions, therefore effectively detecting the novel ones. The proposed method was evaluated using surface-electromyographic (sEMG) armband data recorded while performing 6 target and 8 novel hand motions. Based on linear discriminate analysis (LDA) and convolution prototype network (CPN) feature extractors, the proposed method achieved accuracies of 89.7% and 93.9% in novel motion detection respectively, while maintaining a target motion classification accuracy of 90%, outperforming the existing ones with statistical significance (p<0.05). This study provided a valuable solution for improving the robustness of MPR systems against anomaly motion interference.
Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning
Authors: Yidong Zhu, Nadia B Aimandi, Mohammad Arif Ul Alam
Date: 2024-06-12 00:00:00
Tasks: Representation Learning

In the U.S., over a third of adults are pre-diabetic, with 80\% unaware of their status. This underlines the need for better glucose monitoring to prevent type 2 diabetes and related heart diseases. Existing wearable glucose monitors are limited by the lack of models trained on small datasets, as collecting extensive glucose data is often costly and impractical. Our study introduces a novel machine learning method using modified recurrence plots in the frequency domain to improve glucose level prediction accuracy from wearable device data, even with limited datasets. This technique combines advanced signal processing with machine learning to extract more meaningful features. We tested our method against existing models using historical data, showing that our approach surpasses the current 87\% accuracy benchmark in predicting real-time interstitial glucose levels.
Quantifying the Impact of Energy System Model Resolution on Siting, Cost, Reliability, and Emissions
Authors: Anna F. Jacobson, Denise L. Mauzerall, Jesse D. Jenkins
Date: 2024-06-11 00:00:00

Energy systems models, critical for power sector decision support, incur non-linear memory and runtime penalties when scaling up under typical formulations. Even hardware improvements cannot make large models tractable, requiring omission of detail which affects siting, cost, and emission outputs to an unknown degree. Recent algorithmic innovations have enabled large scale, high resolution modeling. Newly tractable, granular systems can be compared with coarse ones for better understanding of inaccuracies from low resolution. Here we use a state of the art model to quantify the impact of resolution on results salient to policymakers and planners, affording confidence in decision quality. We find more realistic siting in recommendations from high resolution energy systems models, improving emissions, reliability, and price outcomes. Errors are generally stronger from low spatial resolution. When models have low resolution in multiple dimensions, errors are introduced by the coarser of temporal or spatial resolution. We see no diminishing returns in accuracy for several key metrics when increasing resolution. We recommend using computationally efficient techniques to maximize granularity and allocating resolution without leaving any aspect (spatial, temporal, operational) of systems unduly coarse.
GreenShield: CNN-Based Real-Time Forest Monitoring and Response
Authors: Avishek Bhattacharjee, Swarup Samanta, Jagadish Bhattacharya, Manish Kumar Singh
Date: 2024-06-10 00:00:00

This research introduces an innovative forest monitoring system designed to detect and mitigate the threats of forest fires. The proposed system leverages Arduino-based technology integrated with state-of-the-art sensors, including DHT11 for temperature and humidity detection and Flame sensor along with GSM module for gas and smoke detection. The integration of these sensors enables real-time data acquisition and analysis, providing a comprehensive and accurate assessment of environmental conditions within the forest ecosystem. The Arduino platform serves as the central processing unit, orchestrating the communication and synchronization of the sensor data. The DHT11 sensor monitors ambient temperature and humidity levels, crucial indicators for assessing fire risk and identifying potential deforestation activities. Simultaneously, the Flame sensor module detects the occurrence of fire flames nearby thus indicating it by a buzzer. The collected data is processed through an intelligent algorithm that employs machine learning techniques to discern patterns indicative of potential threats. The system is equipped with an adaptive threshold mechanism, allowing it to dynamically adjust to changing environmental conditions. In the event of abnormal readings or anomalies, the system triggers immediate alerts, notifying forest rangers and relevant authorities to facilitate timely response and intervention. The integration of low-cost, easily deployable Arduino-based devices makes this solution scalable and accessible for implementation across diverse forest environments. The proposed system represents a significant step towards leveraging technology to address environmental challenges and protect our forests.
On the Coexistence of OTFS Modulation with OFDM-based Communication Systems
Authors: Akram Shafie, Jinhong Yuan, Paul Fitzpatrick, Taka Sakurai, Yuting Fang
Date: 2024-06-08 00:00:00

We investigate the coexistence of orthogonal time-frequency space (OTFS) modulation with current fourth- and fifth-generation (4G/5G) communication systems that primarily use orthogonal frequency-division multiplexing (OFDM) waveforms. We first derive the input-output-relation of OTFS in the considered coexisting system. In this derivation, we consider (i) the inclusion of multiple cyclic prefixes (CPs) with unequal lengths to the OTFS signal and (ii) edge carrier unloading (ECU), to account for the impacts of CP length, frame structure, and subcarrier arrangement described in 3GPP standards for 4G/5G systems. Our analysis reveals that the inclusion of multiple CPs to the OTFS signal and ECU lead to the channel response exhibiting spreading effects/leakage along the Doppler and delay dimensions, respectively. Consequently, the effective sampled delay-Doppler (DD) domain channel model for OTFS in coexisting systems may exhibit reduced sparsity. We also show that the effective DD domain channel coefficients for OTFS in coexisting systems are influenced by the unequal lengths of CPs. Subsequently, we propose an interference cancellation-based channel estimation (CE) technique for OTFS in coexisting systems. Through numerical results, we validate our analysis, highlight the importance of not ignoring the unequal lengths of CPs during signal detection, and show the significance of the proposed CE technique.