Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical 3-D Monitoring System for Human-Robot Collaboration

Three-dimensional monitoring systems, based on sensor-fusion of Microsoft Kinect 3-D cameras, can create a safe workspace for human-robot collaboration. Cameras fields of view overlap to create a higher frame rate within the workspace which allows for more accurate and quicker responses to humans in an industrial environment. An experiment was also conducted to determine the computational cost of a Kinect monitoring system under various loads, focusing mainly on CPU and GPU utilization. The experimental results showed that 3-D image processing can be processed in parallel benefitting from the higher core count found on modern processor.
An Unsupervised Analysis of an Alzheimer’s Disease Patient Population Using Subspace Search and Hierarchical Density-Based Clustering

Syndromes that affect the mental health of individuals, such as Dementia and Mild Cognitive Impairment, are characterized by degradation of cognitive functions. The consequences affect not only the individual’s life but also family and caregivers. Alzheimer’s disease is the most common cause of these disorders. Cognitive assessments (screening tests) are used worldwide, particularly in poor regions of the world, to diagnose such diseases. However, those assessments are subject of individual analysis by clinicians, and so can be challenging to diagnose given the subtle aspects and symptoms of the illness. In this work, we present an unsupervised analysis using subspace search to find subspaces of highly correlated features and a hierarchical density-based algorithm to find patterns in a dataset of an Alzheimer’s disease patient population. We show that the subspace search can help discover correlation relationships between features and that subspaces can be used to find meaningful clusters of individuals. We also show that structurally well-formed clusters may not lead to good models when the features are not relevant to the domain.
A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

A new fusion strategy is introduced in this article to estimate state for multi-rate multi-sensor systems with missing measurements. $N$ sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology.
Development of an Non-Speech Audio Event Detection System

This paper describes a process of acoustic event detection system development. It includes sound gathering and preprocessing, detector training and testing. The system can recognize thirteen types of sound events, such as baby crying, a person screaming or asking for help and so on. It has extremely low false positive rate and can be used for automated continuous monitoring.
Android Malware Detection Based on Logistic Regression and XGBoost

The proliferation of Android malicious applications dangerously injure users’ information, property, and privacy. Aiming at the problem that the characteristics of malware dynamic analysis and detection aren’t excellent, and the detection efficiency and classifier performance are insufficient, this paper proposes a multi-dimensional feature fusion malicious application detection method based on Logistic Regression and XGBoost. The method provided non-invasively extract the framework layer Application Programming Interface(API) call information of the Android application, apply the Logistic Regression to train the N-gram modeled API call sequence, fuse the obtained probability feature with the basic statistical feature, and input it into the XGBoost for Android malicious application detection. The experimental results show that the method effectively improves the accuracy of Android malicious application detection and decreases the time expense of it.
A Lightweight Two-factor Mutual Authentication Scheme for Cloud-based IoT

Internet of Things (IoT) is a platform to provide the connection between IoT devices and servers for performing exchange of information. IoT devices play a major role in our everyday life, like homes, offices, agriculture, healthcare, transportation, wearables, industries, etc. Due to limited processing power and memory, IoT devices connect to large resource pools like cloud servers. However, authentication is a major security concern while connecting IoT and Cloud. To address these issues, we propose a lightweight two-factor mutual authentication scheme for Cloud-based IoT. The proposed scheme achieves security requirements such as mutual authentication, the security of a secret key, session key agreement, resists forgery attack, resists an insider attack, resists replay attack and resists impersonation attack. The security analysis proves that our scheme is secure against various known attacks. Performance analysis demonstrates that our scheme is lightweight and suitable for the resource-constraint IoT devices.
A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines

Accurate remaining useful life prognosis of bearings in wind turbines can effectively help to schedule maintenance strategy and reduce operational costs at wind farms. Unscented particle filter is good at state tracking in nonlinear problem. A robust model-based approach based on improved unscented particle filter is presented to deal with bearing life prognosis in wind turbines, which involves: (1) The mean of sigma points after unscented Kalman transform is regarded as the particles in particle filter to guarantee the particles aggregation; (2) Several past measurements are utilized to estimate the likelihood function of current step; (3) Uniform distribution is adopted for resampling particles to make them diversity. The presented remaining useful life prognosis approach depends more on the measurement, rather than the initial parameters of degradation model, which makes it practicable for the on-site wind turbines. Three life-cycle bearings from wind turbine high-speed shafts demonstrate the effectiveness of the proposed approach.
Immunohistochemical expression of Ki-67 in invasive breast carcinoma and its correlation with prognostic parameters

Background: Ki-67 expression is strictly associated with cellular proliferation and is an excellent marker for determining the growth fraction of a given cell population. It may be considered as alternative to mitotic counts in grading schemes and as a single parameter that can be used in fine needle aspirates and small biopsies. The aim of this study was to evaluate proliferative fraction of Ki67 expression in invasive breast carcinoma and to find out the correlation between Ki67 expression and clinico-pathological prognostic factors such as tumor size, lymph node status, histological grade and Nottingham prognostic index. Materials and Methods: Seventy cases of infiltrating ductal carcinoma breast were studied. Immunohistochemistry using the anti-Ki-67 antibody [MIB-1] was performed on all cases and Ki-67 staining was categorized as low and high grade. Result: High expression of Ki-67 was found in 71.05% cases of breast tumor with positive lymph node metastasis and 92.31% cases of Grade III tumor. Ki-67 expression was significantly associated with nodal status (p<0.0001) and poorly differentiated tumor (p<0.0001). Although, Ki-67 expression was associated with tumor size, but their correlation was not statistically significant (p= 0.065). Conclusion: High Ki-67 expression was associated with a higher grade of malignancy. Conclusively, proliferative activity determined by Ki-67 expression may reflect the aggressive behavior of breast carcinoma. Ki-67 detection provides valuable information about proliferative activity of cells so, it is necessary to combine this with other parameters for better prognosis.
Processing and Analysis of the Results of Long-Term Continuous Monitoring of the Health Status of Patients with Arterial Hypertension and Cardiac Arrhythmias

The article deals with processing and analysis methods of the results of long-term continuous monitoring of the health status of patients with arterial hypertension and cardiac arrhythmias. The developed system of indicators is presented, which must be taken into account when processing and analyzing the results of long-term continuous monitoring of the patient’s health, as well as algorithms for supporting decision-making by a doctor in the treatment of patients with arterial hypertension and cardiac arrhythmias.
A Compact mmWave Butler Matrix for Wireless Power Transfer

A compact $4\times 4$ butler matrix at 30 GHz is designed, fabricated and measured for wireless power transfer applications in microstrip configuration. The 90°hybrid coupler is designed with two cross slots of different lengths. The crossover is realized using a ring and cross junction. The coupler and crossover are designed and analyzed. The butler matrix is verified by both the simulated and measured results.
A 26-GHz-band High Efficiency Stacked-FET Power Amplifier IC with Adaptively Controlled Load and Bias Circuits in 40-nm SOI CMOS

This paper presents a 26 GHz band high efficiency power amplifier (PA) IC with adaptively controlled load and bias conditions. An adaptively controlled load matching circuit cooperating with an adaptive bias circuit is proposed to achieve a high efficiency PA IC in a broad range of input power. The chip of PA IC is fabricated using 40-nm silicon on insulator (SOI) CMOS process and evaluated on wafer. The fabricated PA IC has shown a peak PAE of 40.2% and a saturation output power (Psat) of 20.5 dBm with a 4.0 V applied voltage on Vdd at 26GHz. A PAE of over 35% at an input power between −2 dBm and 7 dBm has been realized.
An End-to-End Model Based on TDNN-BiGRU for Keyword Spotting

In this paper, we proposed a neural network architecture based on Time-Delay Neural Network (TDNN)Bidirectional Gated Recurrent Unit (BiGRU) for small-footprint keyWord spotting. Our model consists of three parts: TDNN, BiGRU and Attention Mechanism. TDNN models the time information and BiGRU extracts the hidden layer features of the audio. The attention mechanism generates a vector of fixed length with hidden layer features. The system generates the final score through vector linear transformation and softmax function. We explored the step size and unit size of TDNN and two attention mechanisms. Our model has achieved a true positive rate of 99.63% at a 5% false positive rate.
A Compact Low-Pass Filter Based on Spoof Surface Plasmon Polaritons Using Defected Ground Structure

In this work, defected grounded structure (DGS) is employed to a spoof surface plasmon polaritons (SSPPs) unit. Dispersion properties of SSPPs and normalized impedance the unit are analyzed. Based on a smooth transition with simple structure and small size, a compact low-pass filter based on SSPPs is designed. Excellent simulated results (return loss lower than −15.8dB up to 10.1GHz) shows a good balance between broadband features and compact size. This work can find significant potential applications in microwave devices.
Intelligent Elderly-Care in the Era of Internet and Big Data

In the 21st century, all the advanced countries in the world are facing serious problems of aging and great trouble caused by increasing demands for long-term care. However, with the development of computer technology as well as popularization of the internet and smart phone, human beings have stepped into the era of digitalization, when the internet and big data develop fast and are applied extensively in almost all of people's activities. Even, it has overturned the old provision patterns for the aged completely and brought new chance of “intelligent elderly-care” for the future visions of Chinese elderly-care policies.
5G Retro-Directive Quasi-Optical Lens Beamforming MIMO

This paper presents a beamspace MIMO dictated by the proposed quasi-optical lens with transceiver MMICs distributed on the focal plane in close proximity within the point spread of the lens, followed by a switching matrix module arbitrarily connecting p baseband units and n sets of transceivers. Ray-tracing diagrams explain the retro-directive channel propagation characteristics of the proposed quasi-optical beamforming MIMO, demonstrating that the returned signals from a remote terminal under interrogation will reach the nearby receiver in the focal plane, which points to a promising solution to green communications at more than one order of magnitude improvement in energy consumption for a particular case study.
Ultra-thin HfAlO Nanofilms on Graphene Directly Deposited by Atomic Layer Deposition

In order to realize graphene-based devices, ultra-thin, uniform-coverage and pinhole-free dielectric films with high permittivity on top of graphene are required. Here we report the direct growth of HfAlO onto graphene by H<inf>2</inf>O-based atomic layer deposition (ALD). Al2O<inf>3</inf>-onto- HfO<inf>2</inf>stacks benefited the doping of Al<inf>2</inf>O<inf>3</inf>into HFO<inf>2</inf> matrixes more than HfO<inf>2</inf>-onto-Al<inf>2</inf>O<inf>3</inf>stacks did due to the micro-molecular property of Al<inf>2</inf>O<inf>3</inf>and high chemical activity of TMA. The capacitance and the relative permittivity of HfAlO was up to 1.18 µF/cm<sup>2</sup> and 12, respectively, indicating high quality of high-K films on graphene.
Post-Route Performance Estimation Method for Reconfigurable Systems-on-a-Chip

The well-known methods for evaluating the performance of digital circuits designed using the Field-Programmable Gate Array (FPGA) or Reconfigurable System on a Chip (RSoC) use simplified elements delay model or the simulation of the final post-routing SPICE-netlist. SPICE simulation is highly dependent on the circuit size and the amount of used routing resources and can be time-consuming. Therefore, it’s necessary to quickly evaluate the circuit performance before the verification stage using SPICE simulation. Performance evaluation is required after routing as well as at the other design stages.This article describes the developed method for the quick post-routing performance estimation. We show its necessity and present a step-by-step process of adding it in a whole RSoC design flow. This method uses software that performs static timing analysis using a characterized logic elements library in Liberty format and nets capacitive characteristics. The article also considers the advantages, limitations, problems and features of the proposed method.
Android Malware Detection Based on Naive Bayes

Android phones are one of the most popular mobile intelligent terminals in the world. The open source nature of Android platform brings convenience to the development of thirdparty Android applications, but also provides conditions for Android malwares. In addition to Google's official app store, there are many mixed third-party Android app stores. Android users may download and install malware due to the large number of Android apps and the management omission of app stores, resulting in privacy disclosure, malicious fee deductions and other adverse consequences. In this paper, we propose an Android malware static detection method base on Naive Bayes. We extract requested permissions, system API calls, and the proportion of Activity among the four Android major components through Android packages. We use these three types of information as features to characterize each application, and perform classification model training and malware detection through Naive Bayes classifier. Our approach does not run Android programs, which reduces the cost of the experiment and completes the detection of malware before users’ installation. The sample features we selected are common features that can be extracted from each Android application, so this detection method can be extended to all Android applications. We verify the feasibility of our method and achieve good results through the experiments on 6120 Android malwares and 6032 benign applications.
Wave Excitation Force Estimation Using an Electrical-Based Extended Kalman Filter for Point Absorber Wave Energy Converters

Accurate real-time knowledge of the wave excitation force affecting a wave energy converter (WEC) – either through measurement or by estimation – is crucial for implementing effective control strategies that ensure optimum power absorption, system reliability, and durability. The estimation of the excitation force using other readily available measurements is deemed a cost-effective solution given the technical difficulties associated with directly measuring the excitation force on the WEC’s floater hull. In this study, an electrical-based extended Kalman filter (E-EKF) estimator for estimating the wave excitation force, floater’s heave displacement, and velocity is proposed. The estimator is derived using a holistic nonlinear wave-to-wire model of a direct-drive heaving WEC. A continuous and differentiable approximation of the well-known Tustin friction model is utilized to incorporate the friction force model into the estimator. The proposed E-EKF estimator requires only the measurement of the three-phase permanent magnet linear generator stator currents using current transducers. A practical approach is provided to overcome the need for measuring the wave surface elevation and velocity. Simulations are conducted to assess the goodness of the proposed E-EKF under various sea-state conditions, modeling mismatches, and electric loading scenarios. For the sake of comparison, the performance of the E-EKF estimator is measured against mechanical-based extended Kalman filter and linearized mechanical Kalman filter estimators. The E-EKF estimator exhibits superior performance in terms of nearly all performance metrics, with an excitation energy percentage error score not exceeding 9 %, while being immune to measurement noise.
Investigation of Nanostructured Matrices of Porous Semiconductors Modified by Zno/Ag Nanoparticles

Purpose. In this work, we investigated the possibility of modifying porous silicon with silver and ZnO nanoparticles. For the synthesis of substrates was used the method of electrochemical etching in hydrofluoric acid solutions. According to SEM data, it was established that the pore size is d = 50-100 nm, the layer thickness is 25 μm. By means of EMP analysis, a composition of porous nanostructures has been investigated, confirming the presence of ZnO nanoparticles with size of 1 µm on its surface. As a result of research work demonstrated the effectiveness of the electro-thermal methods for introducing nanoparticles into the porous silicon matrix.
Hand vein-based biometric authentication using two-channel similarity measure networks

In this paper the feasibility of convolutional neural networks (CNNs) for the purpose of authenticating an individual based on near infra-red (NIR) images of his/her dorsal hand vein patterns is investigated. In particular, a subset of CNNs called two-channel similarity measure networks (2CH-SMNs) is considered, since the problem at hand involves authentication (verification), instead of recognition (classification). All hand vein images are cropped and binarised before presented to the 2CH-SMN, in order to ensure that the focus of the network is on the structure of the hand veins, and not on the shape of the hand or on the grey-scale background variations surrounding the hand vein structure. A tailor-made 2CH-SMN is trained for each client enrolled into the system by considering authentic hand vein images from the client in question, as well as fraudulent images from other enrolled clients. Different images from the client in question and images from other non-training clients are employed for validation purposes, as well as for selecting optimal client-specific probability thresholds. Another distinct subset of authentic images, as well as images from imposters (non-clients) are used for testing. The results are encouraging.
A Compact Model of MoS2 Field-Effect Transistors From Drift-Diffusion to Ballistic Carrier Transport Regimes

In this letter, a compact model for charge and drain current in molybdenum disulfide (MoS2) field-effect transistors (FETs) is developed, which is valid from ballistic to quasi-ballistic to drift-diffusion electronic transport regimes. Considering the influence of trap charges in MoS2 transistors, a physical-based and analytical charge model is derived. Based on the virtual source model which applies to both ballistic and quasi-ballistic transports, the carrier number density and current expressions are combined to yield the current-voltage (I-V) characteristics. Furthermore, the presented model is validated by experimental data as well as recently reported simulations for MoS2 FETs with different gate lengths. It shows that our model is accurate, straight-forward, scalable and compatible for short- and long-channel devices.
Reverse recovery current in virtual diodes

The switching of a diode from ON to OFF state causes a temporarily high reverse current. We show that such reverse recovery current also occurs in electrostatically doped diodes fabricated in ultrathin FD-SOI film. Since the HP diode features adaptable doping-level controlled by the front and back gates, the recovery current varies accordingly. Virtual P-N or P-I-N diodes are emulated and measured. The impact of the switching speed is investigated and used to extract the parasitic capacitance that undermines the carrier lifetime extraction needed for 1T-DRAM cells.
Commodity Sensors, Physiological Signals, Research Opportunities, and Practical Issues

We discuss selected emerging technologies in physiological signal processing, low-cost pervasive sensors, diagnostic pattern recognition, and some research issues they entail. Serious practical issues remain for signal acquisition from users in their own environments using these commodity sensors. To illustrate the technical issues, we describe a novel robust processing algorithm for mobile ECG sensors (i.e. non-contact capacitive sensors with low signal-to-noise ratios). We describe the detection techniques designed to function effectively with such noisy ECG data.
Possibilities of methods for IDS testing

The aim of this paper is to present what devices IDS and IPS are and ways of possibilities of testing these devices. In the individual chapters we present the key features of IDS/IPS systems, its differences, principle of detection, possible ways and methodology of testing parameters. Based on analysis there is chosen an optimal way to prepare a test for architecture that is shown before the conclusion. There are several software tools mentioned that can be used while process of testing on the chosen device or overall architecture.