The precision of micro-force measurement is determined by the sensitivity of force sensors and the magnitude of environmental disturbances. Damping, a process that converts vibrational energy into heat, is one of the most effective methods of suppressing disturbances. Inspired by the shadow formed at a pond when water striders walked on the water, a bionic viscoelastic-polymer micro-force (VPMF) sensor with a high damping ratio based on the shadow method was developed. In the VPMF sensor, the surface of the polymer was deformed by the contact of a cylindrical flat punch when the sensor was subjected to a normal force. A shadow with a bright edge was formed due to the refraction that parallel light went through the deformed surface. The force was in proportion to the change of the shadow diameter. The sensor optimal sensitivity was 2.15 μN/pixel and the measurement range was 0.981 mN. The damping ratio of the VPMF sensor was 0.22 on account of viscoelasticity, which could suppress disturbances effectively. The VPMF sensor could reduce the influence of disturbances by about 96.23% compared to the cantilever. The present study suggests that the VPMF sensor is hopefully applied to the reliable measurement of micro force under complex environments.

In order to preserve data privacy while fully utilizing data from different owners, federated learning is believed to be a promising approach in recent years. However, aiming at federated learning in the image domain, gradient inversion techniques can reconstruct the input images on pixel-level only by leaked gradients, without accessing the raw data, which makes federated learning vulnerable to the attacks. In this paper, we review the latest advances of image gradient inversion techniques and evaluate the impact of them to federated learning from the attack perspective. We use eight models and four datasets to evaluate the current gradient inversion techniques, comparing the attack performance as well as the time consumption. Furthermore, we shed light on some important and interesting directions of gradient inversion against federated learning.

In this letter, through TCAD simulations, we show that the introduction of a thin paraelectric (PE) layer between the ferroelectric (FE) and dielectric (DE) layers in an MFIS structure, expands the design space for the FE layer enabling hysteresis-free and steep subthreshold behavior, even with a thicker FE layer. This can be explained by analyzing the FE-PE stack from a capacitance perspective where the thickness of the PE layer in the FE-PE stack has the effect of reducing the FE layer thickness, while also reducing the remnant polarization. Finally, for the same FE-PE-DE stack, analog performance parameters such as $\frac{g_{m}} g_{ds}}$ and $\frac{g_{m}}{I_{d}}$ are analyzed, showing good characteristics over a wide range of gate lengths, at low drain voltages, thus demonstrating applicability for low power applications.

We propose a neuromimetic architecture that can perform always-on pattern recognition. To achieve this, we have extended an existing event-based algorithm (Lagorce et al., 2017), which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events captured by a neuromorphic camera, these time surfaces allow to encode the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we have extended this method to improve its performance. First, we add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns (Grimaldi et al., 2021). We also provide a new mathematical formalism that allows an analogy to be drawn between the HOTS algorithm and Spiking Neural Networks (SNN). Following this analogy, we transform the offline pattern categorization method into an online and event-driven layer. This classifier uses the spiking output of the network to define new time surfaces and we then perform the online classification with a neuromimetic implementation of a multinomial logistic regression. These improvements not only consistently increase the performance of the network, but also bring this event-driven pattern recognition algorithm fully online. The results have been validated on different datasets: PokerDVS (Serrano-Gotarredona and Linares-Barranco, 2015), N-MNIST (Orchard et al., 2015a) and DVS Gesture (Amir et al., 2017). This demonstrates the efficiency of this bio-realistic SNN for ultra-fast object categorization through an event-by-event decision making process.

The paper presents algorithms for parallel and vectorized full-word addition of big unsigned integers with carry propagation. Because of the propagation, software parallelization and vectorization of non-polynomial addition of big integers have long been considered impractical due to data dependencies between digits of the operands. The presented algorithms are based upon parallel and vectorized detection of carry origins within elements of vector operands, masking bits which correspond to those elements and subsequent scalar addition of the resulting integers. The acquired bits can consequently be taken into account to adjust the sum using the Kogge-Stone method. Essentially, the paper formalizes and experimentally verifies parallel and vectorized implementation of carry-lookahead adders applied at arbitrary granularity of data. This approach is noticeably beneficial for manycore, CUDA and vectorized implementation using AVX-512 with masked instructions.

this article is aimed to design two impedances matching network to let the source degeneration and common gate amplifier to achieve the ideal characteristic. In the source degeneration case as the resistance is so small, so we just use down converting matching network. As the source degeneration gives us a high resistance , we just match with a up converting network.

There exist several approaches to design the optimal control strategy to harvest wave energy with a point absorber. However they are generally based on the assumption that the WEC and the PTO dynamics are well-known. In the practical WEC control implementation, this is generally not the case. The objective of this paper is to design a robust optimal control strategy that can take into account the uncertain WEC and PTO dynamics. Our choice is a robust adaptive PI control law. The proposed controller is validated and compared through simulation for irregular sea states.

The Terahertz (THz) band (0.1-3.0 THz) spans a great portion of the Radio Frequency (RF) spectrum that is mostly unoccupied and unregulated. It is a potential candidate for application in Sixth-Generation (6G) wireless networks as it has the capabilities of satisfying the high data rate and capacity requirements of future wireless communication systems. Profound knowledge of the propagation channel is crucial in communication systems design which nonetheless, is still at its infancy as channel modeling at THz frequencies has been mostly limited to characterizing fixed Point-to-Point (P2P) scenarios up to 300 GHz. Provided the technology matures enough and models adapt to the distinctive characteristics of the THz wave, future wireless communications systems will enable a plethora of new use cases and applications to be realized in addition to delivering higher spectral efficiencies that would ultimately enhance the Quality-of-Service (QoS) to the end user. In this paper, we provide an insight into THz channel propagation characteristics, measurement capabilities and modeling methods along with recommendations that will aid in the development of future models in the THz band. We survey the most recent and important measurement campaigns and modeling efforts found in literature based on the use cases and system requirements identified. Finally, we discuss the challenges and limitations of measurements and modeling at such high frequencies and contemplate the future research outlook toward realizing the 6G vision.

selected active device is essential to design an RF power amplifier for optimum gain and power added efficiency. As they are obtained, these impedances may not be realizable network functions over the desired frequency band to yield the input and the output matching networks for the amplifier. Therefore, in this paper, first, we introduce a new method to test if a given impedance is realizable. Then, a novel “Real Frequency Line Segment Technique” based numerical procedure is introduced to assess the gain-bandwidth limitations of the given source and load impedances, which in turn results in the ultimate RF-power intake/ delivering performance of the amplifier. During the numerical performance assessments process, a robust tool called “Virtual Gain Optimization” is presented. Finally, a new definition called “Power-Performance-Product” is introduced to measure the quality of an active device. Examples are presented to test the realizability of the given source/load pull data and to assess the gain-bandwidth limitations of the given source/load pull impedances for a 45W-GaN power transistor, namely “Cree CG2H40045”, over 0.8 -3.8 GHz bandwidth.

We investigated the effects of mechanical sensor cover damages like scratches, cracks, and holes on point clouds of automotive lidar.

In this paper, we comprehensively present new advancements towards the working-principle, algorithm, prototype setup and experimental characterization and feasibility demonstration of a new aircraft helmet mounted display aiming system. More specifically, we describe detailed design methodology and algorithm of an eye tracking based HMD aiming system. We also present a proof-of-concept prototype and conduct experiment tests in lab environment to characterize the performance of the proposed system. Lastly, through a comparison with related works, we demonstrate the advantages of the proposed HMD system for free head movement and no additional light source required.

In this paper, we comprehensively present and derive two INDI principle based guidance laws for fixed wing aircrafts. More specifically, two control methods are mathematically derived in detail, where the first decouples the lateral and the longitudinal channels while the second takes the interactions into account. The cumbersome mathematical operations involved in the derivation process aim at reaching a more concise control method and also at providing the community with clearer physical concepts behind this formula. The reason for manipulating transformation matrices is to find a univariate function and to isolate the variable as a virtual input. Efficient and modular guidance control law is then permitted. Lastly, the proposed guidance methods are applied to a 6 dof nonlinear platform under various flight modes to demonstrate the feasibility and advantages.

Full duplex (FD) is an auspicious wireless technology that holds the potential to double data rates through simultaneous transmission and reception. This paper proposes two innovative designs of hybrid beamforming (HYBF) for a multicell massive multiple-input-multiple-output (mMIMO) millimeter wave (mmWave) FD system. Initially, we introduce a novel centralized HYBF (C-HYBF) scheme, which employs the minorization-maximization (MM) method. However, while centralized beamforming designs offer superior performance, they suffer from high computational complexity, substantial communication overhead, and demand expensive computational resources. To surmount these challenges, we present a framework that facilitates per-link parallel and distributed HYBF (P$\&$D-HYBF) in the mmWave frequency band. This cooperative approach enables each base station (BS) to independently solve its local, low-complexity sub-problems in parallel, resulting in a substantial reduction in communication overhead and computational complexity. Simulation results demonstrate that P$\&$D-HYBF achieves comparable performance to C-HYBF, and with only a few radio-frequency (RF) chains, both designs surpass the capabilities of fully digital half duplex (HD) systems.

Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. Many techniques have been proposed to ensure user privacy, such as visible distortions to the images, manipulation of the original image with new face attributes, face swapping etc. Though these techniques achieve the goal of user privacy by fooling face recognition models, they don't help the user when they want to upload original images without visible distortions or manipulation. The objective of this work is to implement techniques to ensure the privacy of user's sensitive or personal data in face images by creating minimum pixel level distortions using white-box and black-box perturbation algorithms to fool AI models while maintaining the integrity of the image, so as to appear the same to a human eye.

In view of the development status of the security of LEO satellite communication system, a comprehensive review, induction, and summary is carried out.

In this paper, we develop a Verilog-A implementable compact model for the dynamic switching of ferroelectric Fin-FETs (Fe-FinFETs) for asymmetric non-periodic input signals. We use the multi-domain Preisach Model to capture the saturated P-E loop of the ferroelectric capacitors. In addition to the saturation loop, we model the history dependent minor loop paths in the P-E by tracing input signals’ turning points. To capture the input signals’ turning points, we propose an R-C circuit based approach in this work. We calibrate our proposed model with the experimental data, and it accurately captures the history effect and minor loop paths of the ferroelectric capacitor. Furthermore, the elimination of storage of each turning point makes the proposed model computationally efficient compared with the previous implementations. We also demonstrate the unique electrical characteristics of Fe-FinFETs by integrating the developed compact model of Fe-Cap with the BSIM-CMG model of 7nm FinFET.

Industry 4.0 (or 4th industrial revolution) facilitates horizontal and vertical digital information flow along value chains up to the end-customer and is highly relevant in a broad variety of industries. Augmented reality (AR) is a key technology in Industry 4.0, which connects the virtual and real-world environments using such digital information flows. In doing so, the technology relies upon the systems that includes hardware and software components. Particularly, optics and photonics are of much importance in the display and processing of information in these systems. However, a particular challenge is that the AR-based systems have not been adopted in the industry as much as other technologies even after several decades of their existence. Based on review of academic literature, an industrial survey and experiments conducted in the industry, this article aims to identify success factors and challenges of AR systems and metrics of photonic components that can form the basis of an AR* framework for photonics-based system design for future research. @font-face {font-family:“Cambria Math”; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:“”; margin:0cm; mso-pagination:widow-orphan; font-size:10.0pt; font-family:“Times New Roman”,serif; mso-fareast-font-family:“Times New Roman”; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}div.WordSection1 {page:WordSection1;}

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles of PIs’ bounds. It relies on the online learning ability of reinforcement learning (RL) to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve PIs’ quality. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay (PER) strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.