Search Results (19)

We study the reconciliation of several biased, partially-overlapping estimators of a latent distribution on the probability simplex (the set of non-negative credit shares summing to one) when no ground truth is available to adjudicate their disagreement—the situation faced when conversion credit is reported by independent commercial attribution sources. We cast this as non-additive information fusion and develop a four-layer fuzzy framework: a reliability-weighted fuzzy representation, an interaction-corrected conflict measure, a Choquet integral with respect to an elicited fuzzy measure (capacity) followed by an $L_1$-closure onto the simplex (a per-cell renormalization returning a valid distribution), and a Mamdani layer producing a conformally calibrated uncertainty band. Our theoretical contributions are (i) a simplex-consistency result—channel-wise Choquet aggregation with $L_1$-closure always returns a valid distribution and reduces to the weighted mean under an additive capacity; (ii) variational and axiomatic characterizations of the operator; (iii) an exact Shapley–disagreement decomposition that quantifies how non-additivity reweights redundant sources; and (iv) a Lipschitz stability bound that guarantees robustness to noisy inputs. The capacity is elicited from observable signals alone, so the method requires no labels. On a firewalled, ten-seed benchmark across eight regimes the framework attains the lowest MAE in five of eight conditions and the lowest KL in six, improving on Dempster–Shafer, correlation-clustering, and reliability-weighted baselines by 1.6–2.3% (MAE) and 3.1–4.4% (KL)—significant in four of its five targeted redundancy- and bias-dominated regimes—with the conformal step attaining nominal coverage. This advantage stems from the elicited capacity rather than the Choquet operator (a simpler ordinal integral matches it on point error); Choquet is retained for its cardinal, calibratable output. An illustrative two-source real-data study exposes a 13.7% cost-per-install distortion in the single-source view, with the (uncalibrated) band widest where the sources disagree most. Full article

A reconfigurable intelligent surface (RIS) is a promising technology for beyond-fifth-generation (B5G) and sixth-generation (6G) wireless communications, but its passive reflection and two-hop double-fading structure make cascaded channel estimation challenging. Conventional convolutional neural network (CNN) estimators require many trainable parameters, while a single shallow parameterized quantum circuit (PQC) may have limited feature representation. Deep quantum circuits can also suffer from noise and barren-plateau effects on noisy intermediate-scale quantum (NISQ) devices. To address these issues, this paper proposes a multi-filter quantum convolutional neural network (MF-QCNN) for cascaded channel estimation in RIS-assisted multi-user uplink systems. The proposed model uses multiple independent shallow PQC filters in parallel, concatenates their measured features, and estimates the cascaded channel through a compact classical dense head, with the total trainable-parameter count scaling as $182F+696$ for F parallel filters. Simulation results, compared with a single-filter quantum convolutional neural network (QCNN), CNN, and multilayer perceptron (MLP) baselines, show that at a signal-to-noise ratio (SNR) of 20 dB, the 3-filter MF-QCNN reduces the normalized mean squared error (NMSE) by approximately 22.9, 8.1, and 4.6 dB relative to the single-filter QCNN, CNN, and MLP baselines, respectively, while using only about 19.3% of the CNN trainable parameters. Under zero-forcing (ZF) precoding, it achieves the highest achievable sum rate among the learning-based estimators; at SNR = 30 dB, it improves the achievable sum rate by approximately 17.4% and 12.8% over the CNN and MLP baselines, respectively. These simulation results suggest that the parallel shallow-PQC design can serve as a compact quantum-aided estimator for RIS channel estimation and may provide a useful basis for future studies on AI-native transceiver design in B5G/6G networks. Full article

Android malware detection is increasingly important for mobile and edge security because malicious applications may compromise user privacy, device reliability, and sensitive service transactions. However, single-view static detection methods often provide limited semantic coverage and are sensitive to noisy or obfuscated code, while many deep learning models remain too heavy for resource-constrained deployment. To address these challenges, this paper proposes a lightweight Android malware detection framework based on SDAE-guided multi-view static feature fusion. The framework extracts three complementary static views, namely API calls, permission requests, and system components, from AndroidManifest.xml and classes.dex. These views are independently denoised and compressed by stacked denoising autoencoders, then aligned as the R, G, and B channels of a pseudo-RGB representation. A compact MicroNet-SE classifier with squeeze-and-excitation blocks is used to recalibrate the fused semantic channels and perform malware classification. Experiments on the CICMalDroid 2020 and CIC-AndMal2017 datasets show that the proposed framework achieves 99.01% accuracy, 99.15% precision, 98.99% recall, and 99.07% F1-score, with only 99.6 k parameters and a model size of 1.26 MB. After conversion to TensorFlow Lite, the MicroNet-SE classifier achieves average on-device inference latencies ranging from 1.85 ms to 2.16 ms on two real mobile devices. The model also maintains stable performance under synthetic feature perturbations and practical APK-level obfuscation settings. These findings suggest that combining multi-view static semantics with denoising-based representation learning can improve both detection robustness and deployment efficiency. Overall, the results indicate that the proposed framework provides an effective and lightweight static screening component for Android malware detection in resource-constrained mobile and edge environments. Full article

Functional brain networks in schizophrenia (SZ) are often characterized by covariance-based measures, yet covariance matrices live on a curved geometric structure rather than in ordinary Euclidean space, complicating noise-robust inference from scalp EEG. We develop a Riemannian Geometry-based Adaptive Nonlinear Coupling Analysis (RGA-NCA) framework that integrates the affine-invariant Riemannian metric (AIRM), tangent space mapping (TSM), and an anatomically adaptive artifact rejection (AAAR) strategy accounting for regional signal-to-noise heterogeneity. The framework is grounded in the observation that Euclidean summaries of symmetric positive definite matrices are sensitive to noise-driven volume inflation, whereas geodesic distances on the manifold emphasize shape deformation. RGA-NCA was evaluated on four benchmark dynamical systems, a supplementary multichannel EEG-like sample covariance simulation, and a public button-tone SZ/HC EEG dataset associated with the auditory feedback paradigm described by Ford et al. (81 subjects; 49 SZ, 32 healthy controls). Compared with Euclidean and linear baselines, RGA-NCA showed lower sensitivity to noise-driven distance distortion and yielded clearer group-level contrasts in the tested ROI analyses; all four pre-specified frontotemporal and parietal channel pairs remained significant after Benjamini–Hochberg FDR correction. The resulting patterns are consistent with reduced long-range connectivity together with localized hyper-synchronization-like effects in SZ. Quantitatively, the Riemannian structural sensitivity index $(\mathrm{sim}=\exp (-d^2/4))$ remained high across all tested SNR levels (−20 to +10 dB; 50 Monte Carlo trials per level; range 0.936–0.964), with only a 0.026 endpoint change between +10 and −20 dB, whereas the Euclidean metric fell from 0.922 at +10 dB to 0.000 at −20 dB. These findings support Riemannian modeling as a candidate strategy for noisy covariance-based neural data, pending validation in larger independent cohorts. Full article

This paper proposes a Q-Learning-Based Distributed Energy-Efficient Routing (QDER) protocol for underwater acoustic sensor networks (UASNs). The routing problem is formulated as a Markov Decision Process (MDP) and a distributed Q-learning approach is proposed. Each sensor node is treated as an agent that independently selects its next-hop node based on a Q-table. The rewards function is designed that jointly considers node residual energy and depth information, enabling each node to learn an effective routing policy through distributed decision-making. Unlike centralized routing approaches that rely on extensive global information exchange, the proposed scheme allows nodes to make local decisions, thereby reducing communication overhead and energy consumption while maintaining efficient routing paths. In addition, link quality is designed in the reward to account for channel conditions, which improves the robustness of the routing strategy under noisy underwater acoustic environments. Simulation results demonstrate that the QDER achieves better system performance compared with Depth-Based Routing (DBR) and Deep Q-Network-Based Intelligent Routing (DQIR). Considering channel attenuation and noise, the proposed method with the link quality metric achieves improved network lifetime and energy efficiency. It also shows good robustness and adaptability under different signal-to-noise ratio (SNR) conditions. Full article

With the rapid development of emerging applications such as the Internet of Things (IoT) and distributed visual perception, massive amounts of correlated image data require efficient transmission under constrained bandwidth and noisy channel conditions. Although Shannon’s separation theorem provides a theoretically optimal basis for independent source-channel design, end-to-end joint optimization methods demonstrate higher performance potential in finite block length scenarios. This paper addresses the distributed image transmission problem with source correlation by proposing a Deep Joint Source-Channel Coding (DeepJSCC)-based framework. The scheme introduces a correlation feature extraction module at the receiver to uncover similarities among multiple sources and assist image reconstruction. Experimental results demonstrate that this method significantly improves reconstruction quality across various signal-to-noise ratios (SNRs), particularly excelling under small bandwidth ratios. Full article

We present a detailed analysis of the effect of quantum loss and decoherence in the Bell-CHSH scenario. Adopting a device-independent approach, we study the change in the bipartite conditional probability distribution, i.e., the behavior of the realized nonlocal box pair when the elements of the entangled qubit pair subjected to independent noisy quantum channels modeled by completely positive maps. As the verification of Bell inequalities is crucial in device-independent quantum cryptography, our considerations are instructive from the perspective of quantum realizations of nonlocal box pairs. We find that the impact of quantum channels cannot be described by an equivalent classical noise channel. Full article

The objective of this research was to develop a coded language, similarly to Morse or Braille, via a haptic thermal interface. The method is based on the human thermal sense to receive and decode the messages, and is to be used as an alternative or complementary channel for various scenarios in which conventional channels are not applicable or not sufficient (e.g., communication with the handicapped or in noisy/silent environments). For the method to be effective, it must include a large variety of short recognizable cues. Hence, we designed twenty-two temporally short (<3 s) cues, each composed of a sequence of thermal pulses, meaning a combination of warm and/or cool pulses with several levels of intensity. The thermal cues were generated using specially designed equipment in a laboratory environment and displayed in random order to eleven independent participants. The participants identified all 22 cues with 95% accuracy, and 16 of them with 98.3% accuracy. These results reflect extraordinary reliability, indicating that this method can be used to create an effective innovative capability. It has many potential implications and is applicable immediately in the development of a new communication capability, either as a single-modality thermal interface, or combined with tactile sensing to form a full haptic multisensory interface. This report presents the testing and evaluating process of the proposed set of thermal cues and lays out directions for possible implementation and further investigations. Full article

A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to n independent and identically distributed samples, denoted by $\mathbf{U}$ and $\mathbf{V}$, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of $(\mathbf{U},\mathbf{V})$ based on $\mathbf{V}$ and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off. Full article

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r² cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r² = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data. Full article

An MHD vibration sensor, as a new type of sensor used for vibration measurements, meets the technical requirements for the low-noisy measurement of acceleration, velocity, and micro-vibration in spacecraft during their development, launch, and orbit operations. A linear vibration sensor with a runway type based on MHD was independently developed by a laboratory. In a practical test, its output signal was mixed with a large amount of noise, in which the continuous narrowband interference was particularly prominent, resulting in the inability to efficiently carry out the real-time detection of micro-vibration. Considering the high interference of narrowband noise in linear vibration signals, a single-channel blind signal separation method based on SSA and FastICA is proposed in this study, which provides a new strategy for linear vibration signals. Firstly, the singular spectrum of the linear vibration signal with noise was analyzed to suppress the narrowband interference in the collected signal. Then, a FastICA algorithm was used to separate the independent signal source. The experimental results show that the proposed method can effectively separate the useful linear vibration signals from the collected signals with low SNR, which is suitable for the separation of the MHD linear vibration sensor and other vibration measurement sensors. Compared with EEMD, VMD, and wavelet threshold denoising, the SNR of the separated signal is increased by 10 times on average. Through the verification of the actual acquisition of the linear vibration signal, this method has a good denoising effect. Full article

We address minimization of information leakage from continuous-variable quantum channels. It is known, that regime of minimum leakage can be accessible for the modulated signal states with variance equivalent to a shot noise, i.e., vacuum fluctuations, in the case of collective attacks. Here we derive the same condition for the individual attacks and analytically study the properties of the mutual information quantities in and out of this regime. We show that in such regime a joint measurement on the modes of a two-mode entangling cloner, being the optimal individual eavesdropping attack in a noisy Gaussian channel, is no more effective that independent measurements on the modes. Varying variance of the signal out of this regime, we observe the nontrivial statistical effects of either redundancy or synergy between the measurements of two modes of the entangling cloner. The result reveals the non-optimality of entangling cloner individual attack for sub-shot-noise modulated signals. Considering the communication between the cloner modes, we show the advantage of knowing the residual noise after its interaction with the cloner and extend the result to a two-cloner scheme. Full article

In this work, we study complex-valued data detection performance in massive multiple-input multiple-output (MIMO) systems. We focus on the problem of recovering an n-dimensional signal whose entries are drawn from an arbitrary constellation $\mathcal{K}\subset \mathbb{C}$ from m noisy linear measurements, with an independent and identically distributed (i.i.d.) complex Gaussian channel. Since the optimal maximum likelihood (ML) detector is computationally prohibitive for large dimensions, many convex relaxation heuristic methods have been proposed to solve the detection problem. In this paper, we consider a regularized version of this convex relaxation that we call the regularized convex relaxation (RCR) detector and sharply derive asymptotic expressions for its mean square error and symbol error probability. Monte-Carlo simulations are provided to validate the derived analytical results. Full article

We address the problem of entanglement protection against surrounding noise by a procedure suitably exploiting spatial indistinguishability of identical subsystems. To this purpose, we take two initially separated and entangled identical qubits interacting with two independent noisy environments. Three typical models of environments are considered: amplitude damping channel, phase damping channel and depolarizing channel. After the interaction, we deform the wave functions of the two qubits to make them spatially overlap before performing spatially localized operations and classical communication (sLOCC) and eventually computing the entanglement of the resulting state. This way, we show that spatial indistinguishability of identical qubits can be utilized within the sLOCC operational framework to partially recover the quantum correlations spoiled by the environment. A general behavior emerges: the higher the spatial indistinguishability achieved via deformation, the larger the amount of recovered entanglement. Full article

This paper asks what is the value of transforming the kitchen into a sonic performative work and public site for art and social practice. A Public Kitchen is formed by recreating the private and domestic space of a kitchen into a public space through a sonic performance artwork. The kitchen table is a platform for exploring, repositioning and amplifying kitchen tools as material phenomena through electronic and manual manipulation into an immersive sonic performance installation. This platform becomes a collaborative social space, where somatic movement and sensory, sonic power of the repositioned kitchen tools are built on a relational architecture of iterative sound performances that position the art historical and the sociopolitical, transforming disciplinary interpretations of the body and technology as something that is not specifically exclusively human but post-human. A Public Kitchen represents a pedagogical strategy for organizing and responding collectively to the local, operating as an independent nomadic event that speaks through a creative practice that is an unfolding process. (Re)imagining the social in a Public Kitchen produces noisy affects in a sonic intra-face that can contribute to transforming our social imaginations, forming daring dissonant narratives that feed post-human ethical practices and feminist genealogies. This paper reveals what matters—a feminist struggle invaluable in channeling the intra-personal; through the entanglement of the self, where language, meaning and subjectivity are relational to human difference and to what is felt from the social, what informs from a multi-cultural nomadic existence and diffractive perspective. The labored body is entangled with post-human contingencies of food preparation, family and social history, ritual, tradition, social geography, local politics, and women’s oppression; and is resonant and communicates as a site where new sonic techniques of existence are created and experiences shared. Full article