The asserted violations of Bell's Inequality arise from the use of mixed frames of reference that violate the derivation of the inequality. Accordingly, entangled pairs may still follow local physics.

Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches. A wellknown optimization-based method, the Variational Mode Decomposition (VMD), relies on the formulation of an optimization problem, utilizing constant bandwidth Wiener filters. However, this poses limitations in constant bandwidth and the need for constituent count. In this paper, a new method, namely Dynamic Bandwidth VMD (DB-VMD), is proposed to generalize VMD by addressing the Wiener filter limitations through enhancement of the optimization problem with an additional constraint. Experiments in synthetic signals highlight DB-VMDâ\euro™s noise robustness and adaptability in comparison to VMD, paving the way for many applications, especially when the analyzed signals are contaminated with noise.

Thermal feedback has been proven to enhance user experience in human-machine interactions. Yet state-of the-art thermal technology has focused on the single finger or palm in static contact, overlooking dynamic and multi-finger interactions. The underlying challenges include incompatible designs of conventional interfaces for providing salient thermal stimuli for such interactions and, thereby, a lack of knowledge on human thermal perception for relevant conditions. Here we present the ThermoSurf, a new thermal display technology that can deliver temperature patterns on a large interface suitable for dynamic and multi-finger interactions. We also investigate how user exploration affects the perception of the generated temperature distributions. Twenty-three human participants interacted with the device following three exploration conditions: static-single finger, dynamic-single finger, and static-multi finger. In these experiments, the individuals evaluated 15 temperature differences ranging from -7.5 °C to +1.5 °C with an initial temperature of 38 °C. Our results showed that human sensitivity against thermal stimuli is significantly greater for static-single finger contact compared to the other tested conditions. In addition, this interaction type resulted in higher thermal discrimination thresholds than the ones reported in the literature. Our findings offer new perspectives on providing salient and consistent thermal feedback for future tactile interfaces.

This is the pre-review draft of the second of two papers submitted to Journal of Lightwave Technology, where the effect on confinement loss of thickness variations along the perimeter of the tubes composing the cladding of inhibited-coupling guiding Tubular Lattice hollow-core fibers is investigated by using the Azimuthal Fourier Decomposition technique (developed in Part I) for the description of the cladding modal dynamics and their interaction with fundamental core mode. The results show that the thickness inhomogeneity affects the confinement loss spectrum through confinement loss increase and frequency red- and blue-shift of the high loss spectral regions. The magnitudes of the confinement loss increase and the high-loss region frequency shift strongly depend on the spatial distribution of the thickness inhomogeneity. The study provides insight into the loss mechanism of non-ideal tube lattice fibers, it allows quantifying the impact of such kind of structural deformations, identifying the route to make fibers more resilient to such fabrication imperfections, and highlighting once again the importance played in inhibited-coupling fibers by the interaction between core modes and the intricate set of cladding modes.

The present work introduces a framework for learning and selecting semi-universal adversarial perturbations. It relies on a joint estimation of multiple universal adversarial perturbations which are chosen in an unsupervised manner depending on the sample to attack. Two algorithmic solutions, with convergence guarantees under Lipschitz continuity assumptions, are proposed to handle either small scale or large scale datasets. Numerical experiments, conducted on benchmark datasets, support its unifying aspect between universal and specific attacks as the number of perturbations grows. In addition, the learned perturbations display strong patterns indicative of the existing similarities between the training instances of different classes.

The popularity of cloud technology is expanding day-by-day not only to the enterprises but also to the smart phone users due to its various service models available at lower cost. The storage service is one of the widely used service to outsource massive amount of data. With the growth of internet facility and availability, the massive amount of data is being outsourced to cloud storage server with duplicate entries. The duplicate data at server may leads to the consumption of more computational resources.It is essential to prevent the redundant data at cloud storage server to improve efficiency of storage service. These outsourced data contains sensitive information like health related information and can be compromised by inner and outer adversaries. Therefore, safety and security of sensitive information is essential in the cloud computing, but conventional crytosystem and deduplication contradicts to each other due to private keys used for encoding and decoding process, and also current cryptosystems are suffers from content distribution attack, fake key and fake ownership claiming attacks during data deduplication process. Therefore, This paper presents Finite State Automata based one-key cryptosystem and Hash-based indexing technique to achieve secure data deduplication process. The statistical analysis is carried out on system and results shows the strength of the proposed algorithms. The paper also presents Proof of Ownership and Data Integrity Verification protocol to protect the data from unauthorized access and modification. The overall analysis and results shows the strength of proposed approach as compared to existing system in reducing the utilization of computational resources.

Incident response tabletop simulation exercises allow training people in skills related to reactions and processes in crisis situations. This paper analyzes several experiences of tabletop simulations that resulted in learning of practical utility for the participants. After applying the traditional approach based on conversational interaction, and the modern approach based on interaction through virtual platforms, a new, more accessible and scalable modality was proposed, developed in free software, which allows taking this practice to any environment. In addition, it was found that the exercises carried out in educational environments improve the learning of the topics for both participants and observers.

The micro-expression recognition (MER) has gained high attention in real-world applications: human-computer interaction, depression estimation, virtual reality, etc. However, MER systems still face difficulty in capturing the subtle information with spatial appearance of micro expressions. In this paper, an efficient and robust motion flow-guided MicroNet framework is proposed for MER. The proposed MicroNet consists: motion flow generator (MFGen) and avalanche feature (AFeat) block. The MFGen is introduced to extract the temporal changes at expressive regions by analyzing the motion intensity of pixels among the frames. Further, the AFeat block is designed to capture the spatiotemporal feature using a multi-lateral complementary feature (MCFeat) block. The MCFeat block is proposed to elicit the coarse and deep edge responses from multi-scale receptive fields. Thus, the proposed MicroNet estimates not only momentary variations but also learns affective appearance features of micro expressions. Furthermore, the efficacy of the proposed MicroNet is examined by conducting experiments on two experimental setups using six comprehensive datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC and COMPOSITE, with two validation schemes: leave-one-subject-out and cross-domain to demonstrate the generalization capacity and robustness of the proposed framework. Also, eight ablation experiments have been executed to validate each module’s role in the MicroNet framework.

The development of an ambient radio frequency (RF) powered Internet of Things (IoT) wireless sensor system is reported. The system integrates all major components including multiple antennas, rectifiers, a power management unit (PMU), a microcontroller unit (MCU) with RF transceiver module and sensing unit. A novelty of this applied engineering innovation is the design and use of multi-band dual-polarized multiple antennas to handle the low ambient RF power density found in the everyday environment. Our prototype has demonstrated that it is able to sense temperature, pressure and humidity and wirelessly transmit these signals to a central server with a duty cycle of approximately 15 minutes, when the background ambient RF signals are as low as −28.6 dBm at 950 MHz. The proposed system can address the important issue of energy supply in the development of IoT providing an alternative energy source to batteries or solar cells. Ambient RF powered IoT devices do not require periodic replacement of their batteries or to be placed visible regions which makes them suitable for wall, ceiling and floor applications. Our ambient RF powered IoT wireless sensor system provides concrete proof, through demonstration, that harnessing ambient RF energy for powering IoT sensor systems is feasible.

In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labeling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UFQGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme

Due to the increasing demand for real-time observation data, remote-sensing satellites can be considered as users to access mega communication constellations, which can constitute a Low Earth Orbit (LEO) Double-Layered Satellite Network (DLSN) composed of Remote-sensing Layer Satellites (RLSs) and Communication Layer Satellites (CLSs). In this LEO DLSN, a Distributed Inter-Layer Topology Optimization (DITO) method is proposed based on time-space relationships between two layers, which can maximize task-aware observation benefits with limited onboard resources. For practical purposes, we take link switching interval constraints into account for the recapture and track of laser transceivers. Besides, we put forward a new time-slot division method based on the link switching interval for the linear modeling of the time-sequential coupling problem. With the simulation results in four constellation systems, the proposed distributed interactive method can obtain an optimal solution close to the centralized global solution with much less time. Simulation results indicate that the proposed method can support onboard optimization for future space networks.

This work introduced an auto-encoder-based deep learning architecture consisting of a self-expressive layer for the deep subspace clustering task

Heating is a common problem in electric machines and cooling of small size machines is very challenging. Nowadays, the application of these machines in the actuation of weight-critical mechatronics systems is increasing. In this paper, a cooling mechanism for such electric machines with surface permanent magnet (SPM) is proposed. The mechanism is based on the fan-cooled air conditioning system except that no external impeller is needed. It has an air-inlet structure which comprises a set of six holes shrouded within a hollow cylindrical ventilation duct at the rear end of the rotor. The cooling mechanism is evaluated in a high-power-density SPM motor. The airflow rate is analyzed by developing a theoretical model using flow resistance network technique. Further verification of the airflow rate was performed with numerical computational fluid dynamics (CFD) simulation. With a discrepancy margin within 5.0%, both flow resistance network and CFD models show that the system generates a total flow rate of 118.0 L/min at maximum rated speed of the motor. Also, steady state thermal analysis of the motor using lumped parameter thermal network (LPTN) is verified in comparison with CFD simulation, which shows the cooling system maintains the motor temperature within operating range during high-speed operations.

The obtained results demonstrate a significantly enhanced detection performance under heavy interference conditions when employing spiking-based and deep learning detection techniques, as opposed to traditional baseline matched-filter methods. Although spiking-based detection approaches exhibit error rates comparable to those of deep learning techniques, they consume considerably less power -- several orders of magnitude lower, in fact. Owing to their power efficiency, spiking-based detection networks emerge as the optimal choice for signal detection in resource-limited systems, such as low-Earth orbit satellites.

In various permanent magnet synchronous motor (PMSM) drive applications the torque dynamics are an important performance criterion. Here, time-optimal control (TOC) methods can be utilized to achieve highest control dynamics. Applying state-of-the-art TOC methods leads to unintended overcurrents and torque over- and undershoots during transient operation. To prevent these unintended control characteristics while still achieving TOC performance the time-optimal model predictive control (TO-MPC) is proposed in this work. The TO-MPC contains a reference pre-rotation (RPR) and a continuous control set model predictive flux control (CCS-MPFC). By applying Pontryagin’s maximum principle, the TOC solution trajectories for states and inputs of the PMSM are determined neglecting current and torque limits. With the TOC solution, a flux linkage reference for the CCS-MPFC is calculated that corresponds to a pre-rotation of the operating point in the stator-fixed coordinate system. This pre-rotated flux linkage reference is reached in minimum time without overcurrents and torque over- as well as undershoots by incorporating current and torque limits as time-varying softened state constraints into the CCS-MPFC. Simulative and experimental investigations for linearly and nonlinearly magnetized PMSMs in the whole speed and torque range show that, compared to state-of-the-art TOC methods, overcurrents and torque over- as well as undershoots are prevented by the proposed TO-MPC.

Objective: Quantitative analysis of spindle dynamics in mitosis through fluorescence microscopy requires tracking spindles elongation in noisy image sequences. Deterministic methods, which use typical microtubule detection and tracking methods, perform poorly in the case of the sophisticated background of spindles. Methods: In this paper, we present SpindlesTracker, a fully automatic and extensible workflow that can efficiently analyze time-lapse images’ dynamic spindle mechanism. First, we design a novel deep neural network: YOLOX-SP (YOLOX for spindle). It consists of double branches responsible for spindle bounding boxes and endpoints detection. Then an improved SORT algorithm is used to link the same identity in different frames. Subsequently, we pair endpoints that fall into the same spindle bounding box as the spindle poles. Finally, we introduce the minimal cost path (MCP) algorithm to extract the continuous, single-pixel spindle skeleton. Result: SpindlesTracker is evaluated in all aspects of detection, tracking, and skeleton extraction through a fission yeast dataset. It achieves 84.1% mAP in bounding box detection and over 90% accuracy in endpoint detection. And for tracking, the comparison results show that the improved SORT algorithm increases by 1.3% in multiple object tracking accuracy (MOTA) and by 6.5% in multiple object tracking precision (MOTP). In addition, the statistical result shows that the mean error of spindle length is within 1 ?m. Conclude: SpindlesTracker provides a new baseline for multiple spindles analysis. Significance: This workflow could be easily extended to other microtubule or filamentous structures. The code is released on GitHub.

Quantum computing is a rapidly developing field with the potential to revolutionize our ability to solve certain computational problems . While classical computers operate using classical bits, quantum computers use quantum bits (qubits), which can exist in a superposition of states, allowing for parallel computations. The existence of quantum algorithms, which can solve certain problems exponentially faster than classical algorithms, is one of the most exciting aspects of quantum computing. These exotic algorithms are designed to take advantage of quantum mechanical properties such as entanglement and interference, and have the potential to transform a range of fields, including cryptography, optimization, and machine learning.

As the bit rate of the optical transmission system gets higher and higher, 40 Gb/s to 100 Gb/s, the modeling of proposed modulation techniques is very important so as to avoid costly practical demonstration. A Polarity Multiplexed-QPSK transmission system is modelled in MATLAB and simulated with Additive White Gaussian Noise (AWGN) added through the channel with Chromatic Dispersion (CD). Output is filtered and performance is measured and presented in this article.