arXiv subjects

Diep N. Nguyen

Publications and source records attributed to Diep N. Nguyen.

At least 19 recordsLinked to original sources

arXiv2026

Secrecy Sum-Rate Maximization in Finite Blocklength IRS-aided Systems With Perfect and Imperfect CSI

The rapid growth of the Internet of Things (IoT) requires efficient and secure communication technologies to enable ultra-reliable low-latency (URLLC) communication applications. Intelligent reflecting surfaces (IRS) have emerged as a promising solution to enhance IoT network secrecy performance by improving signal quality for legitimate devices (Bob), while thwarting the eavesdropper (Eve) interception. However, ensuring secrecy across multiple users, given their diverse channels and locations, is challenging, especially under finite blocklength (FBR) constraints, which are common in IoT networks. This paper investigates the secrecy performance of IRS-aided URLLC systems under three channel state information (CSI) scenarios: perfect CSI, imperfect CSI, and unknown eavesdropper's CSI. We first formulate a non-convex optimization problem to maximize the system's sum secrecy rate (SSR) by jointly optimizing the transmitter's beamforming and the IRS's passive reflective elements, while maintaining FBR-related latency and transmission duration constraints. For imperfect CSI, the semi-infinite uncertainty constraints are transformed into finite linear matrix inequalities (LMIs) via a successive convex approximation (SCA)-based approach, and the proposed algorithm is proven to converge to a locally optimal solution with low computational complexity.

eess.SP↗

arXiv2026

Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular environments. Meanwhile, the growing availability of heterogeneous sensors, such as cameras and LiDAR, offers new opportunities to improve beam prediction through richer environmental perception. Motivated by this, this paper proposes a multi-modal beam prediction framework for V2X networks. Specifically, we develop BeamTransFuser, a hierarchical Transformer-based architecture that progressively fuses camera, LiDAR, radar, and GPS observations for robust beam prediction. In addition, to handle possible missing modalities in practical deployment, we introduce a generative module that reconstructs missing modality features from the available observations. Experimental results on a real-world multi-modal V2X dataset show that the proposed framework consistently outperforms representative baselines, while the generative module further improves robustness under incomplete sensing conditions.

cs.LG↗

arXiv2026

Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection

Knowledge distillation (KD) has been widely used to improve lightweight AI models by transferring soft-label knowledge from a large teacher model to a student model. However, existing KD methods are primarily designed for the image domain rather than lightweight IoT devices, and they often struggle to maintain well-separated feature representations for different attack types, especially as the number of classes increases and attack behaviors become more diverse. This paper proposes a novel \textit{teacher-free latent self-distillation framework based on a Twin Autoencoder (TAE)}. Instead of relying on an external teacher, TAE self-learns intrinsic class-wise latent representations, which act as soft labels, similar to KD, but without requiring a teacher model. The decoder then projects the input back into soft labels, enforcing class separation in the decoder output. The resulting decoder representations are used for classification, improving the discrimination between benign and malicious traffic while maintaining a lightweight design suitable for IoT deployment. We theoretically derive conditions for perfect class separation and show that lower empirical risk yields better representations, identifying regimes where TAE achieves strictly lower empirical risk than models using fixed class centers. Extensive experiments on 13 cybersecurity datasets, covering IoT botnets, network intrusion detection, malware, cloud DDoS, and synthetic multi-class data, show that TAE achieves up to 96.1% average accuracy for IoT attack detection and 98.7% for cloud intrusion detection. With a compact model size (1 MB) and ultra-fast inference (0.26 μs per sample), TAE offers a practical and scalable solution for real-world cybersecurity and IoT systems.

cs.CR↗

arXiv2026

FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks

Personalized Federated Learning (PFL) has emerged as a promising solution for intrusion detection in heterogeneous IoT environments, as it can improve local adaptation under highly Non-Independent and Identically Distributed (non-IID) data distributions. However, existing PFL methods often rely on client-side self-adjustment, which may lead to over-personalization and substantial degradation in out-of-distribution (OOD) attack detection. In this paper, we propose Federated Bandit Intrusion Detection (FBID), a novel adaptive PFL framework to address this limitation through server-side personalization control. In particular, FBID employs a contextual multi-armed bandit at the server to dynamically regulate each client's local training intensity according to its observed behavior and update quality. Moreover, FBID introduces a trust-based blending mechanism to derive client-specific interpolation coefficients between the global and local models, thereby preserving global attack-detection knowledge while still allowing beneficial local specialization. Through extensive experiments on the CICIoT2023 dataset under heterogeneous client distributions and OOD stress-test settings, we show that FBID improves individual client OOD Detection Rate (DR) by up to 7.66% and F1-Score (F1) by up to 5.08% (relative) over the strongest stable baseline, while also improving robustness to previously unseen attack classes.

cs.CR↗

arXiv2026

Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning

Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently impair the performance of the global model. However, existing incentive mechanisms typically assume temporal homogeneity, treating all training rounds as equally important, thereby failing to prioritize and attract high-quality contributions during CLPs. This inefficiency is compounded by information asymmetry due to privacy regulations, where the cloud lacks knowledge of client training capabilities, leading to adverse selection and moral hazard. Thus, in this article, we propose a time-aware contract-theoretic incentive framework, named Right Reward Right Time (R3T), to encourage client involvement, especially during CLPs, to maximize the utility of the cloud. We formulate a cloud utility function that captures the trade-off between the achieved model performance and rewards allocated for clients' contributions, explicitly accounting for client heterogeneity in system capabilities, effort, and joining time. Then, we devise a CLP-aware incentive mechanism deriving an optimal contract design that satisfies individual rationality, incentive compatibility, and budget feasibility constraints, motivating rational clients to participate early and contribute efforts. By providing the right reward at the right time, our approach can attract the highest-quality contributions during CLPs. Simulation studies show that R3T mitigates information asymmetry, increases cloud utility, and yields superior economic efficiency compared to conventional incentive mechanisms. Our proof-of-concept yields a 2-3x training speedup, reduces the required client pool by 5.2-47.6% to reach target performance, and improves final accuracy by up to 9%. The source code can be found at https://github.com/linhnt31/R3T.

cs.LG↗

arXiv2026

Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention

WiFi-based human pose estimation (HPE) enables the detection and interpretation of human body positions and movements without the need for wearable devices while preserving individual privacy concerns. Implementing this solution requires enhancing model performance and maintaining efficiency, especially on resource-constrained devices. This paper introduces a novel framework, WiLHPE, for lightweight and efficient human pose estimation using WiFi CSI signals. Empowered by a camera-based model during training, WiLHPE processes raw WiFi signals directly to estimate human poses in the testing phase. It employs a novel neural network architecture to dynamically learn convolutional kernels and apply attention mechanisms across channel and frequency spaces. This innovative method diversifies the kernels to improve the recognition capabilities of WiFi signals without adding complexity, ensuring efficiency. Additionally, the Tree-Structured Parzen Estimator algorithm is employed to optimize the critical hyperparameters of the neural network efficiently, minimizing the time required for optimal hyperparameter search compared to heuristic methods. Results from experiments on both the MM-Fi and WiPose datasets highlight the superiority of WiLHPE over state-of-the-art approaches, achieving 85.96% and 94.27% at PCK50, respectively, with minimal computational overhead. Notably, WiLHPE performs impressively even under challenging conditions, maintaining around 80% at PCK50 under AWGN noise with an error variance of 0.5.

cs.CV↗

arXiv2026

Securing SIM-Assisted Wireless Networks via Quantum Reinforcement Learning

Stacked intelligent metasurfaces (SIMs) have recently emerged as a powerful wave-domain technology that enables multi-stage manipulation of electromagnetic signals through multilayer programmable architectures. While SIMs offer unprecedented degrees of freedom for enhancing physical-layer security, their extremely large number of meta-atoms leads to a high-dimensional and strongly coupled optimization space, making conventional design approaches inefficient and difficult to scale. Moreover, existing deep reinforcement learning (DRL) techniques suffer from slow convergence and performance degradation in dynamic wireless environments with imperfect knowledge of passive eavesdroppers. To address these challenges, we propose a hybrid quantum proximal policy optimization (QPPO) framework for SIM-assisted secure communications that jointly optimizes transmit power allocation and SIM phase shifts to maximize the average secrecy rate under power and quality-of-service constraints. Specifically, a parameterized quantum circuit is embedded into the actor network, forming a hybrid classical-quantum policy architecture that enhances policy representation capability and exploration efficiency in high-dimensional continuous action spaces. Extensive simulations demonstrate that the proposed Q-PPO scheme consistently outperforms DRL baselines, achieving approximately 15% higher secrecy rates and 30% faster convergence under imperfect eavesdropper channel state information. These results establish Q-PPO as a powerful optimization paradigm for SIM-enabled secure wireless networks.

cs.NI↗

arXiv2026

Joint Service Placement and Resource Optimization in Hierarchical Edge-Cloud Networks

Hierarchical edge-cloud computing-aided Internet of Things (IoT) networks offer low-latency and cost-efficient services to a growing number of data-intensive IoT devices. However, optimizing service placement, which involves determining the most suitable locations within a network to deploy various services, is critical to balancing workloads dynamically and ensuring efficient resource utilization. In this paper, we jointly optimize service placement, edge/cloud cooperation, task offloading, and bandwidth allocation to enhance processing efficiency and response times. The main objective is to minimize both the overall end-to-end latency and the system cost, including service deployment and operational costs. The formulated problem belongs to the class of non-convex mixed-integer nonlinear programming, where finding a feasible solution is already challenging. Towards a stable system, we first transform the original problem into a more tractable form and then decompose it into sub-problems which are solved at different timescales. Combining tools from relaxation and the successive convex approximation method, we develop iterative algorithms to solve these problems efficiently. With an appropriate penalty parameter, the proposed algorithms guarantee convergence to at least a local optimum. We produce extensive numerical results to demonstrate the superior performance of the proposed algorithms over benchmark schemes as well as emphasize the significance of the joint service placement and resource allocation in enhancing system performance and efficiency.

cs.IT↗

arXiv2026

Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication

This work proposes a novel immersive communication framework that leverages brain-computer interface (BCI) to acquire brain signals for inferring user-centric states (e.g., intention and perception-related discomfort), thereby enabling more personalized and robust immersive adaptation under strong individual variability. Specifically, we develop a personalized federated learning (PFL) model to analyze and process the collected brain signals, which not only accommodates neurodiverse brain-signal data but also prevents the leakage of sensitive brain-signal information. To address the energy bottleneck of continual on-device learning and inference on energy-limited immersive terminals (e.g., head-mounted display), we further embed spiking neural networks (SNNs) into the PFL. By exploiting sparse, event-driven spike computation, the SNN-enabled PFL reduces the computation and energy cost of training and inference while maintaining competitive personalization performance. Experiments on real brain-signal dataset demonstrate that our method achieves the best overall identification accuracy while reducing inference energy by 6.46$\times$ compared with conventional artificial neural network-based personalized baselines.

cs.LG↗

arXiv2026

Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty

Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eavesdropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve's CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve's information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramer-Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, underscoring the effectiveness of the proposed deep learning-driven friendly jamming framework under practical ISAC impairments.

cs.LG↗

arXiv2025

Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems

While intrusion detection systems (IDSs) benefit from the diversity and generalization of IoT data features, the data diversity (e.g., the heterogeneity and high dimensions of data) also makes it difficult to train effective machine learning models in IoT IDSs. This also leads to potentially redundant/noisy features that may decrease the accuracy of the detection engine in IDSs. This paper first introduces a novel neural network architecture called Multiple-Input Auto-Encoder (MIAE). MIAE consists of multiple sub-encoders that can process inputs from different sources with different characteristics. The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks. To distil and retain more relevant features but remove less important/redundant ones during the training process, we further design and embed a feature selection layer right after the representation layer of MIAE resulting in a new model called MIAEFS. This layer learns the importance of features in the representation vector, facilitating the selection of informative features from the representation vector. The results on three IDS datasets, i.e., NSLKDD, UNSW-NB15, and IDS2017, show the superior performance of MIAE and MIAEFS compared to other methods, e.g., conventional classifiers, dimensionality reduction models, unsupervised representation learning methods with different input dimensions, and unsupervised feature selection models. Moreover, MIAE and MIAEFS combined with the Random Forest (RF) classifier achieve accuracy of 96.5% in detecting sophisticated attacks, e.g., Slowloris. The average running time for detecting an attack sample using RF with the representation of MIAE and MIAEFS is approximate 1.7E-6 seconds, whilst the model size is lower than 1 MB.

cs.LG↗

arXiv2025

Efficient STAR-RIS Mode for Energy Minimization in WPT-FL Networks with NOMA

With the massive deployment of IoT devices in 6G networks, several critical challenges have emerged, such as large communication overhead, coverage limitations, and limited battery lifespan. FL, WPT, multi-antenna AP, and RIS can mitigate these challenges by reducing the need for large data transmissions, enabling sustainable energy harvesting, and optimizing the propagation environment. Compared to conventional RIS, STAR-RIS not only extends coverage from half-space to full-space but also improves energy saving through appropriate mode selection. Motivated by the need for sustainable, low-latency, and energy-efficient communication in large-scale IoT networks, this paper investigates the efficient STAR-RIS mode in the uplink and downlink phases of a WPT-FL multi-antenna AP network with non-orthogonal multiple access to minimize energy consumption, a joint optimization that remains largely unexplored in existing works on RIS or STAR-RIS. We formulate a non-convex energy minimization problem for different STAR-RIS modes, i.e., energy splitting (ES) and time switching (TS), in both uplink and downlink transmission phases, where STAR-RIS phase shift vectors, beamforming matrices, time and power for harvesting, uplink transmission, and downlink transmission, local processing time, and computation frequency for each user are jointly optimized. To tackle the non-convexity, the problem is decoupled into two subproblems: the first subproblem optimizes STAR-RIS phase shift vectors and beamforming matrices across all WPT-FL phases using block coordinate descent over either semi-definite programming or Rayleigh quotient problems, while the second one allocates time, power, and computation frequency via the one-dimensional search algorithms or the bisection algorithm.

cs.IT↗

arXiv2025

Secure Communications for All Users in Low-Resolution IRS-aided Systems Under Imperfect and Unknown CSI

Provisioning secrecy for all users, given the heterogeneity and uncertainty of their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. This work takes the first step to guarantee secrecy for all users where a low resolution intelligent reflecting surfaces (IRS) is used to enhance legitimate users' reception and thwart the potential eavesdropper (Eve) from intercepting. In real-life scenarios, due to hardware limitations of the IRS' passive reflective elements (PREs), the use of a full-resolution (continuous) phase shift (CPS) is impractical. In this paper, we thus consider a more practical case where the phase shift (PS) is modeled by a low-resolution (quantized) phase shift (QPS) while addressing the phase shift error (PSE) induced by the imperfect channel state information (CSI). To that end, we aim to maximize the minimum secrecy rate (SR) among all users by jointly optimizing the transmitter's beamforming vector and the IRS's passive reflective elements (PREs) under perfect/imperfect/unknown CSI. The resulting optimization problem is non-convex and even more complicated under imperfect/unknown CSI. The resulting optimization problem is non-convex and even more complicated under imperfect/unknown CSI. To tackle it, we linearize the objective function and decompose the problem into sequential subproblems. When the perfect CSI is not available, we use the successive convex approximation (SCA) approach to transform imperfect CSI related semi-infinite constraints into finite linear matrix inequalities (LMI). We prove that our proposed algorithm converges to a locally optimal solution with low computational complexity. Extensive simulations with practical settings show that our approach can ensure secure communication for all users while the IRS's PREs are quantized and are affected by the PSE.

eess.SP↗

arXiv2025

"Security for Everyone" in Finite Blocklength IRS-aided Systems With Perfect and Imperfect CSI

Provisioning secrecy for all users, given the heterogeneity in their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. The problem is even more difficult under finite blocklength constraints that are popular in ultra-reliable low-latency communication (URLLC) and massive machine-type communications (mMTC). This work takes the first step to guarantee secrecy for all URLLC/mMTC users in the finite blocklength regime (FBR) where intelligent reflecting surfaces (IRS) are used to enhance legitimate users' reception and thwart the potential eavesdropper (Eve) from intercepting. To that end, we aim to maximize the minimum secrecy rate (SR) among all users by jointly optimizing the transmitter's beamforming and IRS's passive reflective elements (PREs) under the FBR latency constraints. The resulting optimization problem is non-convex and even more complicated under imperfect channel state information (CSI). To tackle it, we linearize the objective function, and decompose the problem into sequential subproblems. When perfect CSI is not available, we use the successive convex approximation (SCA) approach to transform imperfect CSI-related semi-infinite constraints into finite linear matrix inequalities (LMI). We prove that our proposed algorithm converges to a locally optimal solution with low computational complexity thanks to our closed-form linearization approach. This makes the solution scalable for large IRS deployments. Extensive simulations with practical settings show that our approach can ensure secure communication for all users while satisfying FBR constraints even with only imperfect CSI.

eess.SP↗

arXiv2025

Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning

Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. To address this problem, this paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals to transmit their data when the shared spectrum is occupied by mobile users. To obtain the optimal spectrum access policy, i.e., stay idle or access the shared spectrum and perform active transmissions or backscattering ambient RF signals for transmissions, to maximize the average throughput for D2D users, deep reinforcement learning (DRL) can be adopted. However, DRL-based solutions may require long training time due to the curse of dimensionality issue as well as complex deep neural network architectures. For that, we develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters compared to DRL thanks to the quantum superposition and quantum entanglement principles. Specifically, instead of using conventional deep neural networks, the proposed quantum RL algorithm uses a parametrized quantum circuit to approximate an optimal policy. Extensive simulations then demonstrate that the proposed solution not only can significantly improve the average throughput of D2D devices when the shared spectrum is busy but also can achieve much better performance in terms of convergence rate and learning complexity compared to existing DRL-based methods.

cs.NI↗

arXiv2025

Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control

An intelligent Real-Time Sensing (RTS) system must continuously acquire, update, integrate, and apply knowledge to adapt to real-world dynamics. Managing distributed intelligence in this context requires Federated Continual Learning (FCL). However, effectively capturing the diverse characteristics of RTS data in FCL systems poses significant challenges, including severely impacting computational and communication resources, escalating energy costs, and ultimately degrading overall system performance. To overcome these challenges, we investigate how the data distribution shift from ideal to practical RTS scenarios affects Artificial Intelligence (AI) model performance by leveraging the \textit{generalization gap} concept. In this way, we can analyze how sampling time in RTS correlates with the decline in AI performance, computation cost, and communication efficiency. Based on this observation, we develop a novel Sample-driven Control for Federated Continual Learning (SCFL) technique, specifically designed for mobile edge networks with RTS capabilities. In particular, SCFL is an optimization problem that harnesses the sampling process to concurrently minimize the generalization gap and improve overall accuracy while upholding the energy efficiency of the FCL framework. To solve the highly complex and time-varying optimization problem, we introduce a new soft actor-critic algorithm with explicit and implicit constraints (A2C-EI). Our empirical experiments reveal that we can achieve higher efficiency compared to other DRL baselines. Notably, SCFL can significantly reduce energy consumption up to $85\%$ while maintaining FL convergence and timely data transmission.

cs.LG↗

arXiv2025

Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing

This paper aims to propose a novel machine learning (ML) approach incorporating Homomorphic Encryption (HE) to address privacy limitations in Unmanned Aerial Vehicles (UAV)-based face detection. Due to challenges related to distance, altitude, and face orientation, high-resolution imagery and sophisticated neural networks enable accurate face recognition in dynamic environments. However, privacy concerns arise from the extensive surveillance capabilities of UAVs. To resolve this issue, we propose a novel framework that integrates HE with advanced neural networks to secure facial data throughout the inference phase. This method ensures that facial data remains secure with minimal impact on detection accuracy. Specifically, the proposed system leverages the Cheon-Kim-Kim-Song (CKKS) scheme to perform computations directly on encrypted data, optimizing computational efficiency and security. Furthermore, we develop an effective data encoding method specifically designed to preprocess the raw facial data into CKKS form in a Single-Instruction-Multiple-Data (SIMD) manner. Building on this, we design a secure inference algorithm to compute on ciphertext without needing decryption. This approach not only protects data privacy during the processing of facial data but also enhances the efficiency of UAV-based face detection systems. Experimental results demonstrate that our method effectively balances privacy protection and detection performance, making it a viable solution for UAV-based secure face detection. Significantly, our approach (while maintaining data confidentially with HE encryption) can still achieve an accuracy of less than 1% compared to the benchmark without using encryption.

cs.CR↗

arXiv2025

End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at $10^{-4}$ BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of $5\times10^{-4}$ for reconstructed sensor signals, and reducing joint angular error by 37\% for the reconstructed human poses compared to the baseline.

cs.CV↗