Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Face Recognition Technology

Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. People collect the face images, and the recognition equipment automatically processes the images. The paper introduces the related researches of face recognition from different perspectives. The paper describes the development stages and the related technologies of face recognition. We introduce the research of face recognition for real conditions, and we introduce the general evaluation standards and the general databases of face recognition. We give a forward-looking view of face recognition. Face recognition has become the future development direction and has many potential application prospects.
Assistance Quality Analysis and Robust Control of Electric Vehicle With Differential Drive Assisted Steering System

As a novel power steering technology, Differential Drive Assisted Steering (DDAS) technology for the independent-wheel-drive electric vehicle has gradually appealed to researcher’s attention. However, the previous experimental results show that its assistance quality cannot be fully accepted due to its caused sensitive steering wheel torque fluctuation in actual work environment. According to the working principle of the DDAS system, it is founded that the road roughness, the front wheel alignment parameters and sensor noise are the main factors that influence the quality of assisted steering and driver’s road feel. Hence the three factors are added as interference into the ideal vehicle model. The simulation results and its comparison with the previous real vehicle tests confirm this causality between these factors considered and the steering wheel torque fluctuation of the DDAS system. Then a robust $H_{\infty }$ loop-shaping controller is designed to solve the issue caused by these inner interferences and outer noises. Simulations results validate the proposed controller and show better steering wheel torque performance than the traditional anti-windup PID controller published in the literature earlier. The proposed robust controller is further verified via real vehicle tests and the results are similar to the simulation results which can effectively suppress the steering wheel torque ripple, improve the anti-interference ability of DDAS system and greatly improve the driver’s road feel.
Effects of Quercetin on Acute Toxicity of Rat Spleen and Chromosome Aberrations in Bone Marrow Induced by Hexavalent Chromium

Quercetin, in some fruits and vegetables, is a potent oxygen free radical scavenger and a metal chelator. This study was planned to investigate the possible protective effects of quercetin on hexavalent chromium in form of potassium dichromate (K ₂ Cr ₂ O ₇ ) induced chromosome aberrations in bone marrow and histopathological changes of spleen at the light microscopic level. Male Wistar rats were administered hexavalent chromium at doses of 5, 10 and 20 mg/kg BW and quercetin at dose of 100 mg/kg BW before administration of hexavalent chromium 20 mg/kg BW for one hour. All rats were treated by single gavage and killed 24 hours after each treatment. The results demonstrated that the histology of spleen was not changed in all groups of treatment. However, a significant increase in percentage of chromosome aberrations was observed in hexavalent chromium at doses of 5, 10 and 20 mg/kg BW as compared to the control group (p < 0.01 and p < 0.001). The increase of chromosome aberrations was dose-dependent manner of hexavalent chromium. Interestingly, the percentage of chromosome aberrations induced by hexavalent chromium 20 mg/kg BW could be reduced significantly (p < 0.001) by quercetin pretreatment. These results indicated that the antioxidant quercetin might have a protective effect against hexavalent chromium induced chromosome aberrations in rat bone marrow. This study suggested that quercetin might involve at least in part neutralizing free radicals.
Effect of a Backing Material on the Bendability of Flexible Substrates with Passive SMD components

Bendability of printed circuits on flexible substrates has created a significant interest in the wearables and IoT industry. Flexible circuits on plastic substrates with SMD components that can be bent sharply (small radius of curvature) and show little or no change in electrical and mechanical properties are useful for wearable applications. Component adhesion of the flexible circuits can be low if there is a mismatch in Youngs moduli and thickness ratios of the trace, substrate, component, and bonding adhesive. We demonstrate a method that can be used to increase the adhesion of passive SMD components on flexible substrates through the addition of a backing layer. Analysis of the shear strength of bonds were used to characterize the adhesion of the component with and without the backing layer. COMSOL and MATLAB were used to develop an understanding of the difference in adhesion between different material stacks. Bending stress reduction through incorporation of a backing layer can be beneficial for multiple solder pastes and substrate combinations.
A Longitudinal Investigation of the Efficacy of Supported In-Home Post-Stroke Rehabilitation

Owing to the population aging and a change in life patterns, the number of stroke incidents in China has increased dramatically with three million new occurrences every year. While the demand for rehabilitation services in China is enormous, the existing rehabilitation resources are not only disparately distributed but also unable to cope with the ever-increasing number of patients. In this paper, we present a study that focuses on a long-term limb function recovery program that uses a wearable body area network (WBAN) for in-home people with stroke discharged from hospital. The investigation lasted for 60 weeks and involved 12 people with stroke sparsely located in Jiaxing, Zhejiang province, China. These patients, who were all within their first 3 months from stroke event, were evenly and randomly split into the experiment group and the control group. The Brunnstrom Stage (BS) and the Mobility Index (MI) were used to monitor the patients’ recovery processes. The results demonstrated that the patients from the experimental group had experienced a steady increase in MI throughout the program. They also had improved BS by at least one stage, which outperformed the control group in average. This longitudinal efficacy investigation indicates that the supported in-home rehabilitation system has the potential to reduce the cost and the effort for patients and doctors while still maintaining the quality and effectiveness of rehabilitation; hence could be a possible solution in alleviating the great stress that the healthcare system is currently experiencing in China nationwide.
A Low-Cost Conflict-Free NoC Architecture for Heterogeneous Multicore Systems

Heterogeneous multi-core systems integrate general purpose CPUs and data-parallel GPUs on a single chip. However, the integration between CPUs and GPUs causes severe interference between CPU and GPU data traffic. Because the CPUs and GPUs have diverse sensitivity to network performance regarding latency and throughput, the Networkon-Chip (NoC) needs to be carefully designed to avoid performance degradation caused by the conflicts between the CPUs and GPUs. In this paper, we propose an interference-free NoC architecture to solve this problem. Specifically, our proposed scheme can reduce the network interference effectively through memory controller (MC) partition, specially designed routing algorithm, and a bypass scheme to reduce the cost. Our simulation results show that the proposed scheme can reduce over 17% of energy consumption on the average compared with the baseline heterogeneous architecture. In addition, the average performance of CPUs can be improved by as much as 30% and the GPU performance can be increased over 9% on the average.
Novel Piezoelectric Force Sensor Array for Investigation of Ultrasonic Wire Bonding

In this paper, the process chain of a novel 4×3 force sensor array prototype for in-situ detection of the stress distribution during ultrasonic (US) wire bonding process is demonstrated. The sensor is based on d15 lead zirconate titanate (PZT) 255 ceramic, which is cut into individual columns by precision dicing. Polyimide is then filled among the columns to constitute a new PZT/polyimide hybrid sensor platform. Each PZT column is responsible for the measurement of the local tangential force at the wire/substrate interface during US wire bonding.
Combat Information Overload Problem in Social Networks With Intelligent Information-Sharing and Response Mechanisms

Increasingly popular social networks have become fast-growing platforms for information sharing, job searching, and product marketing. Information propagates rapidly in social network and may reach a very large population within a very short period of time. An excessive amount of information shared in social networks not only costs computational and communicational resources but also causes the information overload problem, which results in the delay and difficulty of making decisions and may lead to physical and psychological strain. We used computer technologies to attack this information overload problem. First, we developed automatic decision-making mechanisms to help each individual effectively share information. Second, we built a simulation test bed and proposed an evaluation matrix and then conducted an experimental evaluation of six different information-sharing strategies in terms of interest degrees, reachability, appreciation degrees, and communication cost. We also implemented two intelligent response mechanisms. The first one allows users to order information pieces according to the learned ratings of the information sources. The second mechanism dynamically adjusts the network structure based on machine-learning results. The simulation results show that such mechanisms would be very useful to motivate social-network users to adopt more selective information-sharing strategies.
Broadside FD Antenna Topologies for Nanosat Intersatellite Link

This paper presents and compares four different antenna topologies dedicated to Full-Duplex (FD) applications. The proposed architectures are able to operate simultaneously for transmitting TX and receiving RX radiating elements along broadside direction, contrarily to most of state of the art FD RF front-ends that also rely on destructive wave's concept to get Self-Interference Cancellation (SIC). The four prototypes make use of three SIC stages and the common first level is obtained using orthogonal linear polarization between TX and RX. Step-by-step improvements are introduced from one to another topology in order to increase the SIC level and to reduce both side lobe levels and antenna size. Simulated and experimental results are provided, compared and discussed at the center frequency of 2.4 GHz as a proof of concept. The isolation obtained between TX and RX paths is higher than 80 dB in simulation and 60 dB in measurement. These results pave the way to an implementation of a high gain Full-Duplex Ka-band antenna for Nanosat Intersatellite Link
Machine-Learning Based DDOS Attack Classifier in Software Defined Network

Due to centralized control and programmable capability of the SDN architecture, network administrators can easily manage and control the whole network through the centralized controller. According to the SDN architecture, the SDN controller is vulnerable to distributed denial of service (DDOS) attacks. Thus, a failure of SDN controller is a major leak for security concern. The objectives of paper is therefore to detect the DDOS attacks and classify the normal or attack traffic in SDN network using machine learning algorithms. In this proposed system, polynomial SVM is applied to compare to existing linear SVM by using scapy, which is packet generation tool and RYU SDN controller. According to the experimental result, polynomial SVM achieves 3% better accuracy and 34% lower false alarm rate compared to Linear SVM.
The Influence of Carbon Dioxide and Daily Photon-flux Density on Optimal Leaf Nitrogen Concentration and Root: Shoot Ratio

Using a cost-benefit model, the leaf nitrogen concentration and root:shoot ratio that maximize whole-plant relative growth rate are determined as a function of the above-ground environment (integrated daily photon flux density and the concentration of carbon dioxide at the site of fixation with the leaf). The major advantage of this approach is that it determines the adaptive significance of leaf physiology by considering the functional integration of leaves and roots. The predicted response to increasing daily photon flux densities is an increase in optimal leaf N concentration (N opt ) and a concomitant increase in root:shoot ratio (.)
When FTM Discovered MUSIC: Accurate WiFi-based Ranging in the Presence of Multipath

The recent standardization by IEEE of Fine Timing Measurement (FTM), a time-of-flight based approach for ranging has the potential to be a turning point in bridging the gap between the rich literature on indoor localization and the so-far tepid market adoption. However, experiments with the first WiFi cards supporting FTM show that while it offers meter-level ranging in clear line-of-sight settings (LOS), its accuracy can collapse in non-line-of-sight (NLOS) scenarios.We present FUSIC, the first approach that extends FTM’s LOS accuracy to NLOS settings, without requiring any changes to the standard. To accomplish this, FUSIC leverages the results from FTM and MUSIC – both erroneous in NLOS – into solving the double challenge of 1) detecting when FTM returns an inaccurate value and 2) correcting the errors as necessary. Experiments in 4 different physical locations reveal that a) FUSIC extends FTM’s LOS ranging accuracy to NLOS settings – hence, achieving its stated goal; b) it significantly improves FTM’s capability to offer room-level indoor positioning.
Development of Dry EEG Electrodes and Dry EEG Cap for Neuromonitoring

Monitoring and recording of electroencephalogram (EEG) is a non-invasive procedure that is increasingly used in neuromonitoring. Patient management is assessed and tailored based on the acquired brain signals that indicates the neurological state of the patient. Conventional EEG systems are wet systems. EEG recording in itself is not painful, but scalp preparation for the electrode attachment and washing afterwards may be uncomfortable. In this paper, a soft flexible gel-free dry electroencephalogram (EEG) electrodes and dry EEG head cap is developed and proposed. The dry EEG electrodes is a hybrid packaging rigid metal snap connectors, soft and flexible polymer material, and Silver-Silver Chloride (Ag- AgCl) coating. The sensor body/ structure does not use conductive polymer and there is no peeling off of the structure that could lead to concern in signal degradation. The dry EEG head cap is a fully-adjustable one-piece stretchable material integrated with reference and ground electrodes. Impedance reading of wet system is <5 KOhms, mainly due to the use of electrolytic/ conductive paste with such system. The proposed dry electrodes have impedance readings measured at <65 KOhms, which is lower compared to commercially available dry EEG electrodes that have impedance readings between 100 KOhms and 2000 KOhms. Despite the difference in impedance readings between the conventional wet system and the proposed dry electrodes, the captured brain signals / brain wave patterns are comparable. These brain signals are important especially when utilized for brain computer interfaces (BCI) that power and drive limb orthotic for rehabilitation following a stroke. The dry electrodes passed biocompatibility tests, specifically cytotoxicity test per ISO-10993-5 and primary skin irritation tests per ISO- 10993-10.
Cross Domain Mean Approximation for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled source domain to help the task of target domain with the unlabeled data. It is a key step for UDA to minimize the cross-domain distribution divergence. In this paper, we firstly propose a novel discrepancy metric, referred to as Cross Domain Mean Approximation (CDMA) discrepancy, to evaluate the distribution differences between source and target domains, which calculate the sum of the squares of the distances from the source and target domains to the mean of the other domain. Secondly, Joint Distribution Adaptation based on Cross Domain Mean Approximation (JDA-CDMA) is developed on the basis of CDMA to extract shared feature and simultaneously reduce the marginal and conditional distribution discrepancy between domains during the label refinement process. Thirdly, we construct a classifier utilizing CDMA metric and neighbor information. Finally, the proposed feature extraction approach and classifier are combined to realize transfer learning. Results from extensive experiments on five visual benchmarks including object, face, and digit images, show the proposed methods outperform the state-of-the-art unsupervised domain adaptation.
Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human–machine interactions.
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe

Intelligent agents are continuously faced with the challenge of optimizing a policy based on what they can observe (see) and which actions they can take (do) in the environment where they are deployed. Most policy can be parametrized in terms of these two dimensions, i.e., as a function of what can be seen and done given a certain situation, which we call a mixed policy . In this paper, we investigate several properties of the class of mixed policies and provide an efﬁcient and effective characterization, including optimality and non-redundancy. Speciﬁcally, we introduce a graphical criterion to identify unnecessary contexts for a set of actions, leading to a natural characterization of non-redundancy of mixed policies. We then derive sufﬁcient conditions under which one strategy can dominate the other with respect to their maximum achievable expected rewards (optimality). This characterization leads to a fundamental understanding of the space of mixed policies and a possible reﬁnement of the agent’s strategy so that it converges to the optimum faster and more robustly. One surprising result of the causal characterization is that the agent following a more standard approach—intervening on all intervenable variables and observing all available contexts—may be hurting itself, and will never achieve an optimal performance.
Robust RFID-based Respiration Monitoring in Dynamic Environments

Respiration monitoring (RM) is essential for diagnosing and tracking respiratory diseases. Recently, RFID technology has enabled RM in a lightweight and cost-effective way by only attaching the tiny and cheap RFID tag on the monitored person’s chest. However, current systems are mostly designed for static environments with no surrounding people’s movements. In reality, dynamic environments where people could move nearby the monitored person are quite common. In such environments, respiration signals would be disturbed by the dynamic multipath signals from ambient movements, which may lead to inaccurate RM results. In this paper, we study how to realize robust RFID-based RM in dynamic environments with accurate respiration rate estimation and apnea detection. We find that the dynamic multipath signals can cause not only high-frequency noises but also fake and distorted respiration cycles, which cannot be simply removed by the low-pass filter. Thus, we need a new method to eliminate the effect of multipath signals. Inspired by the intrinsic features of human respiration pattern, we propose to transform the respiration pattern into a matched filter, which can extract the real respiration cycles out of noisy RFID signals. We then estimate the respiration rate by counting the respiration cycles via multi-scale peak detection. For apnea detection, the problem from multipath signals is that the fake respiration cycles can result in the missing detection of apnea when the monitored person stops breathing. To address this issue, we define a new indicator which measures the dominance of respiration components in the signal’s spectrum to identify apnea from multipath signals. Experimental results show that our system achieves an average error of 0.5 bpm for respiration rate estimation and a 5.3% error for apnea detection in dynamic environments.
Characterization of Atrial Fibrillation Episodes Using a Point Process Model

The purpose of the present study is to introduce a point process model for characterizing the pattern of atrial fibrillation (AF) episodes. A variant of the bivariate Hawkes process is proposed, accounting for clustered episodes. The model parameters are inferred by the maximum likelihood method. The goodness-of-fit analysis show that model fits the data in most of the recordings (27 out of 32). The information provided by this approach is complementary to AF burden.
Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

Cardiovascular variability is the result of the activity of several physiological control mechanisms, which involve different variables and operate across multiple time scales encompassing short term dynamics and long range correlations. This study presents a new approach to assess the multiscale complexity of multivariate time series, based on linear parametric models incorporating autoregressive coefficients and fractional integration. The approach extends to the multivariate case recent works introducing a linear parametric representation of multiscale entropy, and is exploited to assess the complexity of cardiovascular and respiratory time series in healthy subjects studied during postural and mental stress.