Systems and Control
Gas Fire Power Plant Management Through Numerical Approximation of Spark Spread Options
Babacar Seck and Anas Abdullah
Cross-commodity valuation approaches to value gas fire power plants are well studied in the literature. Hence, the value of the gas fire power plant is identical to the value of a spark spread option wherein the underlying are electricity and gas with a strike price assimilated to operating and maintenance costs. Power and fuels spot prices account for uncertain futures cash-flows for power-plant generator owners. For instance, for gas-fired turbine plant, spot prices of electricity and gas determine the random cash-flows of the power-plant. Other than the spot prices, the valuation of such plant involves among other deterministic cost the plant heat rate and operating costs. Recently, the cost of emissions is considered into the valuation to tackle environmental issues. Given some simplifications in the plant cash-flow modelling, the value of such plant can either be expressed as the price of i) a cross-commodity option or ii) the price of a real option. Here, we focus on cross-commodity option valuation approach where the value of the power plant is approached as the value of a spark spread option. When spot prices of the underlying commodities are log-normal, closed formulae or approximations can be obtained using Kirk's approximation. Naturally, the spot price of electricity and gas present spikes due to seasonality among other factors. However, in that case it is not possible to get a closed formula for the spark spread option. In this paper we explore possibilities to approximate spark spread options when spot prices fall into a class of jump
diffusion processes.
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Signal Processing
Affordable EEG, Actionable Insights: An Open Dataset and Evaluation Framework for Epilepsy Patient Stratification
HM Shadman Tabib, Md. Hasnaen Adil, Ayesha Rahman et al. (+4 more)
Access to clinical multi-channel
EEG remains limited in many regions worldwide. We present NEUROSKY-EPI, the first open
dataset of single-channel, consumer-grade
EEG for epilepsy, collected in a South Asian clinical setting along with rich contextual metadata. To explore its utility, we introduce EmbedCluster, a patient-stratification pipeline that transfers representations from EEGNet models trained on clinical data and enriches them with contextual autoencoder
embeddings, followed by unsupervised clustering of patients based on
EEG patterns. Results show that low-cost, single-channel data can support meaningful stratification. Beyond algorithmic performance, we emphasize human-centered concerns such as deployability in resource-constrained environments, interpretability for non-specialists, and safeguards for
privacy, inclusivity, and bias. By releasing the
dataset and code, we aim to catalyze interdisciplinary research across health technology, human-computer interaction, and machine learning, advancing the goal of affordable and actionable
EEG-based epilepsy care.
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All authors HM Shadman Tabib, Md. Hasnaen Adil, Ayesha Rahman, Ahmmad Nur Swapnil, Maoyejatun Hasana, Ahmed Hossain Chowdhury, A. B. M. Alim Al Islam
Image and Video Processing · Cross-listed from physics.optics
A Calibration Method for Indirect Time-of-Flight Cameras to Eliminate Internal Scattering Interference
Yansong Du, Jingtong Yao, Yuting Zhou et al. (+3 more)
In-camera light scattering is a typical form of non-systematic interference in indirect Time-of-Flight (iToF) cameras, primarily caused by multiple reflections and optical path variations within the camera body. This effect can significantly reduce the accuracy of background depth measurements. To address this issue, this paper proposes a calibration-based model derived from real measurement data, introducing three physically interpretable calibration parameters: a normal-exposure amplitude influence coefficient, an overexposure amplitude influence coefficient, and a scattering phase shift coefficient. These parameters are used to describe the effects of foreground size, exposure conditions, and optical path differences on scattering interference. Experimental results show that the depth values calculated using the calibrated parameters can effectively compensate for scattering-induced errors, significantly improving background depth recovery in scenarios with complex foreground geometries and varying illumination conditions. This approach provides a practical, low-cost solution for iToF systems, requiring no complex hardware modifications, and can substantially enhance measurement accuracy and robustness across a wide range of real-world applications.
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All authors Yansong Du, Jingtong Yao, Yuting Zhou, Feiyu Jiao, Zhaoxiang Jiang, Xun Guan
Audio and Speech Processing
Data-Centric Lessons To Improve Speech-Language Pretraining
Vishaal Udandarao, Zhiyun Lu, Xuankai Chang et al. (+6 more)
Spoken Question-Answering (SQA) is a core capability for useful and interactive artificial intelligence systems. Recently, several
speech-
language models (SpeechLMs) have been released with a specific focus on improving their SQA performance. However, a lack of controlled ablations of pretraining data processing and curation makes it challenging to understand what factors account for performance, despite substantial gains from similar studies in other data modalities. In this work, we address this gap by conducting a data-centric exploration for pretraining SpeechLMs. We focus on three research questions fundamental to
speech-
language pretraining data: (1) how to process raw web-crawled audio content for
speech-text pretraining, (2) how to construct synthetic pretraining
datasets to augment web-crawled data and (3) how to interleave (text, audio) segments into training sequences. We apply the insights from our controlled data-centric ablations to pretrain a 3.8B-parameter SpeechLM, called SpeLangy, that outperforms models that are up to 3x larger by 10.2% absolute performance. We hope our findings highlight the impact of effective data curation for
speech-
language pretraining and guide future data-centric exploration in SpeechLMs.
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All authors Vishaal Udandarao, Zhiyun Lu, Xuankai Chang, Yongqiang Wang, Violet Z. Yao, Albin Madapally Jose, Fartash Faghri, Josh Gardner, Chung-Cheng Chiu
Image and Video Processing
Lightweight Classifier for Detecting Intracranial Hemorrhage in Ultrasound Data
Phat Tran, Enbai Kuang and Fred Xu
Intracranial hemorrhage (ICH) secondary to Traumatic
Brain Injury (TBI) represents a critical diagnostic challenge, with approximately 64,000 TBI-related deaths annually in the United States. Current diagnostic modalities including Computed
Tomography (CT) and
Magnetic Resonance Imaging (MRI) have significant limitations: high cost, limited availability, and infrastructure dependence, particularly in resource-constrained environments. This study investigates machine learning approaches for automated ICH detection using Ultrasound Tissue Pulsatility Imaging (TPI), a portable technique measuring tissue displacement from hemodynamic forces during cardiac cycles. We analyze ultrasound TPI signals comprising 30 temporal frames per cardiac cycle with recording angle information, collected from TBI patients with CT-confirmed ground truth labels. Our preprocessing pipeline employs z-score normalization and Principal Component Analysis (PCA) for dimensionality reduction, retaining components explaining 95% of cumulative variance. We systematically evaluate multiple classification
algorithms spanning probabilistic, kernel-based, neural network, and ensemble learning approaches across three feature representations: original 31-dimensional space, reduced subset, and PCA-transformed space. Results demonstrate that PCA transformation substantially improves classifier performance, with ensemble methods achieving 98.0% accuracy and F1-score of 0.890, effectively balancing precision and recall despite class imbalance. These findings establish the feasibility of machine learning-based ICH detection in TBI patients using portable ultrasound devices, with applications in emergency medicine, rural healthcare, and military settings where traditional imaging is unavailable.
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Audio and Speech Processing
Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization
Hyungjun Yoon, Seungjoo Lee, Yu Yvonne Wu et al. (+11 more)
Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyday tasks remains challenging due to two key limitations: (i)~insufficient data diversity, as most ExG recordings are collected in controlled labs with bulky, expensive devices; and (ii)~task-specific model designs that require tailored processing (i.e., targeted frequency filters) and architectures, which limit generalization across tasks. To address these challenges, we introduce an approach for scalable, task-agnostic ExG monitoring in the wild. We collected 50 hours of unobtrusive free-living ExG data with an earphone-based hardware prototype to narrow the data diversity gap. At the core of our approach is Physiology-informed Multi-band Tokenization (PiMT), which decomposes ExG signals into 12 physiology-informed
tokens, followed by a reconstruction task to learn robust representations. This enables adaptive feature recognition across the full frequency spectrum while capturing task-relevant information. Experiments on our new DailySense
dataset, the first to enable ExG-based analysis across five human senses, together with four public ExG
benchmarks, demonstrate that PiMT consistently outperforms state-of-the-art methods across diverse tasks.
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All authors Hyungjun Yoon, Seungjoo Lee, Yu Yvonne Wu, Xiaomeng Chen, Taiting Lu, Freddy Yifei Liu, Taeckyung Lee, Hyeongheon Cha, Haochen Zhao, Gaoteng Zhao, Dongyao Chen, Cecilia Mascolo, Sung-Ju Lee, Lili Qiu
Systems and Control
Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems
Tochukwu E. Ogri, Muzaffar Qureshi, Zachary I. Bell et al. (+2 more)
In this paper, we develop a safe control synthesis method that integrates state estimation and parameter estimation within an adaptive optimal control (AOC) and control barrier function (CBF)-based control architecture. The developed approach decouples safety objectives from the learning objectives using a CBF-based guarding controller where the CBFs are robustified to account for the lack of full-state measurements. The coupling of this guarding controller with the AOC-based stabilizing control guarantees safety and regulation despite the lack of full state measurement. The paper leverages recent advancements in deep neural network-based adaptive observers to ensure safety in the presence of state estimation errors. Safety and convergence guarantees are provided using a Lyapunov-based analysis, and the effectiveness of the developed controller is demonstrated through simulation under mild excitation conditions.
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All authors Tochukwu E. Ogri, Muzaffar Qureshi, Zachary I. Bell, Wanjiku A. Makumi, Rushikesh Kamalapurkar
Signal Processing
Semantic Communication for Task Execution and Data Reconstruction in Multi-View Scenarios
Maximilian H. V. Tillmann, Avinash Kankari, Carsten Bockelmann et al. (+1 more)
Semantic communication has gained significant
attention with the advances in machine learning. Most semantic communication works focus on either task execution or data reconstruction, with some recent works combining the two. In this work, we propose a semantic communication system for concurrent task execution and data reconstruction for a multi-view scenario, which we formulate as the maximization of mutual information. To investigate the trade-off between the two objectives, we formulate a joint objective as a convex combination of task execution and data reconstruction. We show that under specific assumptions, the \ac{SSIM} loss can be obtained from the mutual information maximization objective for data reconstruction, which takes human visual perception into account. Furthermore, for constant resource use, we show that by increasing the weight of the reconstruction objective up to a certain point, the task execution performance can be kept nearly constant, while the data reconstruction can be significantly improved.
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All authors Maximilian H. V. Tillmann, Avinash Kankari, Carsten Bockelmann, Armin Dekorsy
Systems and Control · Cross-listed from cs.CR
Ultra-Fast Wireless Power Hacking
Hui Wang, Hans D. Schotten and Stefan M. Goetz
The rapid growth of electric vehicles (EVs) has driven the development of roadway wireless charging technology, effectively extending EV driving range. However, wireless charging introduces significant cybersecurity challenges. Any receiver within the
magnetic field can potentially extract energy, and previous research demonstrated that a hacker could detect the operating frequency and steal substantial power. However, our approach required time to track new frequencies or precise adjustments of inductance and capacitance, which would be less effective against potential rapid transmitter frequency changes or capacitance drift. As a solution, we enhanced the interceptor and enabled it to intrude as well as steal energy within just three cycles of the high-frequency signal. Moreover, it can work without any
circuit parameters or look-up tables. The key innovation is synchronizing the receiver current with the phase of the
magnetic sensor voltage. Through MATLAB / Simulink simulations, finite-element analysis, and experimental validation, we demonstrated that our improved method can steal over 76% of the power received by a fully resonant receiver under identical conditions. This attack demonstrates that simple frequency-changing power encryption offers limited protection against such threats.
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Systems and Control
Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning
Changrui Liu, Shengling Shi, Anil Alan et al. (+2 more)
Efficient energy management is essential for reliable and sustainable microgrid operation amid increasing renewable integration. In this paper, an imitation learning-based framework to approximate mixed-integer Economic Model Predictive Control (EMPC) is proposed for microgrid energy management, considering fuel generators, renewable energy resources, a unified energy storage unit, and curtailable loads. Within the proposed framework, a neural network is trained to imitate expert EMPC control actions from offline trajectories, thereby enabling fast real-time decision making without solving online mixed-integer optimization problems, which often exhibit highly variable solution times across instances and do not scale well to large problem sizes; in particular, worst-case solve times can be excessively large and therefore unsuitable for real-time deployment. In contrast, the learned policy provides predictable and consistently low computation times. To enhance robustness and generalization, the learning process incorporates noise injection during training to mitigate distribution shift and explicitly accounts for forecast uncertainty in renewable generation and demand. Furthermore, a constraint-tightening approach combined with a projection layer is proposed to ensure recursive feasibility and constraint satisfaction of the learned controller. Simulation results demonstrate that the learned policy achieves economic performance comparable to EMPC, while reducing computation time by approximately one order of magnitude relative to the optimization-based EMPC.
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All authors Changrui Liu, Shengling Shi, Anil Alan, Ganesh Kumar Venayagamoorthy, Bart De Schutter
Signal Processing
NanoHydra: Energy-Efficient Time-Series Classification at the Edge
Cristian Cioflan, Jose Fonseca, Xiaying Wang et al. (+1 more)
Time series classification (TSC) on extreme edge devices represents a stepping stone towards intelligent sensor nodes that preserve user
privacy and offer real-time predictions. Resource-constrained devices require efficient TinyML
algorithms that prolong the device lifetime of battery-operated devices without compromising the classification accuracy. We introduce NanoHydra, a TinyML TSC methodology relying on lightweight binary random convolutional kernels to extract meaningful features from data streams. We demonstrate our system on the ultra-low-power GAP9 microcontroller, exploiting its eight-core cluster for the parallel execution of computationally intensive tasks. We achieve a classification accuracy of up to 94.47% on ECG5000
dataset, comparable with state-of-the-art works. Our efficient NanoHydra requires only 0.33 ms to accurately classify a 1-second long ECG signal. With a modest energy consumption of 7.69 uJ per inference, 18x more efficient than the state-of-the-art, NanoHydra is suitable for smart wearable devices, enabling a device lifetime of over four years.
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All authors Cristian Cioflan, Jose Fonseca, Xiaying Wang, Luca Benini
Systems and Control
IMAS$^2$: Joint Agent Selection and Information-Theoretic Coordinated Perception In Dec-POMDPs
Chongyang Shi, Wesley A. Suttle, Michael Dorothy et al. (+1 more)
We study the problem of jointly selecting sensing
agents and synthesizing decentralized active perception policies for the chosen subset of
agents within a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework. Our approach employs a two-layer optimization structure. In the inner layer, we introduce information-theoretic metrics, defined by the mutual information between the unknown trajectories or some hidden property in the environment and the collective partial observations in the multi-
agent system, as a unified objective for active perception problems. We employ various optimization methods to obtain optimal sensor policies that maximize mutual information for distinct active perception tasks. In the outer layer, we prove that under certain conditions, the information-theoretic objectives are monotone and submodular with respect to the subset of observations collected from multiple
agents. We then exploit this property to design an IMAS$^2$ (Information-theoretic Multi-
Agent Selection and Sensing)
algorithm for joint sensing
agent selection and sensing policy synthesis. However, since the policy search space is infinite, we adapt the classical Nemhauser-Wolsey argument to prove that the proposed IMAS$^2$
algorithm can provide a tight $(1 - 1/e)$-guarantee on the performance. Finally, we demonstrate the effectiveness of our approach in a multi-
agent cooperative perception in a grid-world environment.
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All authors Chongyang Shi, Wesley A. Suttle, Michael Dorothy, Jie Fu
Signal Processing
MATLAB-Simulated Dataset for Automatic Modulation Classification in Wireless Fading Channels
M.M. Sadman Shafi, Tasnia Siddiqua Ahona and Ashraful Islam Mridha
Accurate modulation classification is a core challenge in cognitive
radio, adaptive communications, spectrum analysis, and related domains, especially under dynamic channels without transmitter knowledge. To address this need, this article presents a labeled synthetic
dataset designed for wireless modulation classification under realistic propagation scenarios. The signals were generated in MATLAB by modulating randomly generated bitstreams using five digital modulation schemes: BPSK, QPSK, 16-QAM, 64-QAM, and 256-QAM. These signals were then transmitted through Rayleigh and Rician fading channels with standardized parameters, along with additional impairments to enhance realism and diversity. Each modulated signal contains 1000 symbols. A comprehensive set of features was extracted from the signals, encompassing statistical, time-domain, frequency-domain, spectrogram-based, spectral correlation-based, and image-processing-based descriptors such as BRISK, MSER, and GLCM. The
dataset is organized into 10 CSV files covering two channel types (Rayleigh and Rician) across five sampling frequencies: 1 MHz, 10 MHz, 100 MHz, 500 MHz, and 1 GHz. To facilitate reproducibility and encourage further experimentation, the MATLAB scripts used for signal generation and feature extraction are also provided. This
dataset serves as a valuable
benchmark for developing and evaluating machine learning models in modulation classification, signal identification, and wireless communication research.
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Image and Video Processing
Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets
Jiashi Feng, Xiu Li, Jing Lin et al. (+25 more)
Developing
embodied AI
agents requires scalable training environments that balance content diversity with physics accuracy. World simulators provide such environments but face distinct limitations:
video-based methods generate diverse content but lack real-time physics feedback for interactive learning, while physics-based engines provide accurate dynamics but face scalability limitations from costly manual asset creation. We present Seed3D 1.0, a foundation model that generates simulation-ready 3D assets from single images, addressing the scalability challenge while maintaining physics rigor. Unlike existing 3D generation models, our system produces assets with accurate geometry, well-aligned textures, and realistic physically-based materials. These assets can be directly integrated into physics engines with minimal configuration, enabling deployment in robotic manipulation and simulation training. Beyond individual objects, the system scales to complete scene generation through assembling objects into coherent environments. By enabling scalable simulation-ready content creation, Seed3D 1.0 provides a foundation for advancing physics-based world simulators. Seed3D 1.0 is now available on https://console.volcengine.com/ark/region:ark+cn-beijing/experience/
vision?modelId=doubao-seed3d-1-0-250928&tab=Gen3D
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All authors Jiashi Feng, Xiu Li, Jing Lin, Jiahang Liu, Gaohong Liu, Weiqiang Lou, Su Ma, Guang Shi, Qinlong Wang, Jun Wang, Zhongcong Xu, Xuanyu Yi, Zihao Yu, Jianfeng Zhang, Yifan Zhu, Rui Chen, Jinxin Chi, Zixian Du, Li Han, Lixin Huang, Kaihua Jiang, Yuhan Li, Guan Luo, Shuguang Wang, Qianyi Wu, Fan Yang, Junyang Zhang, Xuanmeng Zhang
Image and Video Processing
Visible Iris Area as a Quality Metric for Reliable Iris Recognition Under Pupil Dilation and Eyelid Occlusion
Jack Pessaud, Eric Moran, John Nguyen et al. (+1 more)
With the increasing adoption of iris recognition systems and the expansion of large-scale enrollment databases, there is a growing need to efficiently assess iris image quality at the time of acquisition, particularly to model user non-compliance in real time. Image quality may degrade due to eyelid occlusion or pupil dilation. Although previous studies have shown that occlusion and changes in the pupil-to-iris ratio negatively impact recognition performance, these investigations were typically limited by small sample sizes and did not examine the combined effects of eyelid and pupil variations. In this study, we analyze both dilation and eyelid occlusion using a large
dataset of 555 distinct irises and demonstrate a strong correlation between probe image visible iris area and the Hamming distance of iris code pairs. These results suggest that visible iris area is a robust indicator of probe image quality and could be efficiently incorporated into the iris acquisition process to improve confidence in match predictions.
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All authors Jack Pessaud, Eric Moran, John Nguyen, Joel Palko
Systems and Control · Cross-listed from math.OC
Nodal Capacity Expansion Planning with Flexible Large-Scale Load Siting
Tomas Valencia Zuluaga, Simon Pang and Jean-Paul Watson
We propose explicitly incorporating large-scale load siting into a
stochastic nodal power system capacity expansion planning model that concurrently co-optimizes generation, transmission and storage expansion. The potential operational flexibility of some of these large loads is also taken into account by considering them as consisting of a set of tranches with different reliability requirements, which are modeled as a constraint on expected served energy across operational scenarios. We implement our model as a two-stage
stochastic mixed-integer optimization problem with cross-scenario expectation constraints. To overcome the challenge of scalability, we build upon existing work to implement this model on a high performance computing platform and exploit scenario parallelization using an augmented Progressive Hedging
Algorithm. The
algorithm is implemented using the bounding features of mpisppy, which have shown to provide satisfactory provable optimality gaps despite the absence of theoretical guarantees of convergence. We test our approach to assess the value of this proactive planning framework on total system cost and reliability metrics using realistic testcases geographically assigned to San Diego and South Carolina, with datacenter and direct air capture facilities as large loads.
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Signal Processing
Interpretable machine learning for identifying individual-specific cardiogram signatures
Ilija Tanasković, Ljiljana B. Lazarević, Goran Knežević et al. (+4 more)
This study investigates cardiogram-based biometric identification as a physiological authentication problem, focusing on the discriminative capacity of electrocardiogram (ECG) and impedance cardiogram (ICG) features and their robustness to emotional variability. A total of 29 features spanning four domains (temporal, amplitude, slope, and morphological) are evaluated using random forest (RF) models combined with multiple interpretability methods. Features derived from ECG and ICG rank consistently among the top 10 most important by Gini importance, permutation importance, and Shapley additive explanations (SHAP) values, with ECG-derived QRS descriptors occupying the highest positions. In parallel, BCX features derived from ICG provide complementary information, although their stability across methods is lower. Correlation analysis reveals substantial multicollinearity, where the RF distributes and diminishes importance across highly correlated pairs, confirming reduced independent contributions. Statistical analysis identifies 14 features that differ significantly between baseline and anger, without a clear pattern by domain. Feature selection with recursive feature elimination and genetic
algorithms converges on a subset (12 features) that attains accuracy within 1% of the full set (99%), improving efficiency in storage and computation. Complementary analyses indicate that individual differences are primarily encoded in ECG features describing QRS amplitude, slope, morphology, and duration, with the first three remaining stable between baseline and anger, and duration being the only selected QRS feature that changes significantly. BCX amplitude features provide supportive but less stable discriminatory cues. More broadly, the proposed framework may help distinguish stable person-related characteristics, such as identity and personality-related traits, from transient affective changes.
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All authors Ilija Tanasković, Ljiljana B. Lazarević, Goran Knežević, Nikola Milosavljević, Olga Dubljević, Bojana Bjegojević, Nadica Miljković
Systems and Control
Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks
G. Svistunov, A. Akhtarshenas, D. López-Pérez et al. (+3 more)
HAPS are emerging as key enablers in the
evolution of 6G wireless networks, bridging terrestrial and non-terrestrial infrastructures. Operating in the stratosphere, HAPS can provide wide-area coverage, low-latency, energy-efficient broadband communications with flexible deployment options for diverse applications. This survey delivers a comprehensive overview of HAPS use cases, technologies, and integration strategies within the 6G ecosystem. The roles of HAPS in extending connectivity to underserved regions, supporting dynamic backhauling, enabling massive IoT, and delivering reliable low-latency communications for autonomous and immersive services are discussed. The paper reviews state-of-the-art architectures for terrestrial and non-terrestrial network integration, highlights recent field trials. Furthermore, key enabling technologies such as channel modeling, AI-driven resource allocation, interference control, mobility management, and energy-efficient communications are examined. The paper also outlines open research challenges. By addressing existing gaps in the literature, this survey positions HAPS as a foundational component of globally integrated, resilient, and sustainable 6G networks.
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All authors G. Svistunov, A. Akhtarshenas, D. López-Pérez, M. Giordani, G. Geraci, H. Yanikomeroglu
Signal Processing
Micro-Doppler Energy-Based Robust Multi-Target Vital Signs Monitoring Using 77-GHz FMCW Radar with Spatiotemporal Adaptive Processing
Chenxing Tan, Yuguan Hou, Hao Wang et al. (+1 more)
This paper presents a novel micro-Doppler energy-based framework for robust multi-target vital signs monitoring using 77-GHz Frequency-Modulated Continuous-Wave (FMCW) radar. Unlike conventional phase-based methods that are susceptible to environmental noise, random body movements, and stringent calibration requirements, our approach exploits the energy variations in radar returns induced by cardiopulmonary activities. The proposed system integrates a comprehensive processing pipeline including space-time adaptive processing (STAP) for target detection and tracking, MUSIC
algorithm for high-resolution angle estimation, and an innovative adaptive spectral filtering technique for vital signs extraction. We establish a rigorous mathematical framework that formalizes the relationship between micro-Doppler energy variations and physiological activities, enabling robust separation of closely spaced targets. The key innovation lies in the micro-Doppler energy extraction methodology that provides inherent robustness to phase noise and motion artifacts. Experimental results using millimeter-wave radar
datasets demonstrate that the system can accurately detect and separate vital signs of up to four targets within \SI{5}{\meter} range, achieving mean absolute errors of \SI{1.2}beats per minute and \SI{2.3} beats per minute for respiration and heart rates, respectively. The proposed approach demonstrates superior performance compared to traditional phase-based methods, particularly in challenging multi-target scenarios with environmental noise and subject movement.
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All authors Chenxing Tan, Yuguan Hou, Hao Wang, Zhonghao Yuan
Signal Processing
Multilayer Perceptron Neural Network Model: A Novel Approach for LFP Contrast Sensitivity Tuning
Sahar Maleki, Reza Lashgari, Mahdi Aliyari Shoorehdeli et al. (+1 more)
Local field potentials (LFPs) have been demonstrated to be an important measurement to study the activity of a local population of
neurons. The response tunings of LFPs have been mostly reported as weaker and broader than spike tunings. Therefore, selecting optimized tuning methods is essential for appropriately evaluating the LFP responses and comparing them with neighboring spiking activity. In this paper, new models for tuning of the contrast response functions (CRFs) are proposed. To this end, luminance contrast-evoked LFP responses recorded in primate primary visual cortex (V1) are first analyzed. Then, supersaturating CRFs are distinguished from linear and saturating CRFs by using monotonicity index (MI). The supersaturated recording data are then identified through static identification methods including multilayer perceptron (MLP) neural network, radial basis function (RBF) neural network, fuzzy model, neuro-fuzzy model, and the local linear model tree (LOLIMOT)
algorithm. Our results demonstrate that the MLP neural network, compared to traditional and modified
hyperbolic Naka-Rushton functions, exhibits superior performance in tuning the local field potential responses to luminance contrast stimuli, resulting in successful tuning of a significantly higher number of neural recordings of all three types. These results suggest that the MLP neural network model can be used as a novel approach to measure a better fitted contrast sensitivity tuning curve of a population of
neurons than other currently used models.
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All authors Sahar Maleki, Reza Lashgari, Mahdi Aliyari Shoorehdeli, Mohammad Komareji