Miracle-dz/UniFAR-FTUs huggingface dataset:
28/38 GHz Single-feed Stacked Patch Antenna for 5G Communication

This paper proposes a simple design of single-feed dual-band stacked patch antenna operating at 28 GHz and 38 GHz for 5G communication. The stacked structure, consisting of an E-shaped patch and a rectangular patch, is fed by a probe. The bandwidths $(\vert \mathrm{S}_{11}\vert < -10\ \text{dB})$ at 28 and 38 GHz bands achieve 15.9% and 8.8%, respectively. The peak gains of the two bands are 6.7 and 6.8 dBi. The advantages of the antenna are simple structure, stable radiation patterns and flexible frequency tuning.
A Review on ECG Signal Analysis for Mental Stress Assessment

In current scenario, the mental stress is becoming an unavoidable part of our daily life because of various social and professional responsibilities. In literature, various biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) together with other biomedical parameters such as body temperature, the pulse rate and heartrate variability (HRV) have been used for the detection and classification of mental stress levels in a patient. In the present work, a comprehensive review of various methods used in literature for the assessment and classification of mental stress is presented. The work done in this area mainly during the last decade is studied considering the important aspects of ECG signal analysis such as data acquisition, feature extraction, feature selection and classification. The basic steps required for the detection of mental stress using different features of ECG signals are discussed. Popularly used classification algorithms for mental stress are considered in the work and their results are compared.
Quotation Model of Energy Storage participating in Electric Power Day-Ahead Market based on Deep Learning Surrogate Model

Energy storage has been paid more and more attention in China and will become an important part of future power system and power market. How to incorporate the energy storages in the day-ahead market so as to maximize the economic benefits of both energy storages and the whole market has become an urgent problem to be studied. In this paper, the bidding model of energy storages under the current electric power day-ahead market rules (taking Guangdong’s rules as an example) is established firstly. Then a new trading mode of energy storage participating in day-ahead market based on surrogate model is suggested, and a both model-driven and data-driven surrogate modeling method for energy storage participating in day-ahead market quotation is proposed. And further, a multi-objective optimization based market clearing model with the proposed surrogate-model is built and solved by NSGAII method. Case studies are accomplished to verify the advantages of the proposed trading mode and the feasibility and effectiveness of both surrogate modeling method and the market clearing model.
A 1-V Supply OTA for Low-power VLSI and its Applications

The paper presents a linear, low voltage and low power operational transconductance amplifier (OTA). The transconductance can be controlled electronically with the help of an external bias voltage. The basic OTA cell presented in previos work has been modified in order to reduce the power consumption and voltage. It is observed that modified OTA has an increase in the linear range of operation. The characteristics of the proposed OTA have been compared with the conventional one. The proposed circuit has also been used to implement basic active filters. All simulations have been carried out using 130nm technology parameter in LTspice.
Accurate and Robust RGB-D Dense Mapping with Inertial Fusion and Deformation-Graph Optimization

RGB-D dense mapping has become more and more popular, however, when encountering rapid movement or shake, the robustness and accuracy of most RGB-D dense mapping methods are degraded and the generated maps are overlapped or distorted, due to the drift of pose estimation. In this paper, we present a novel RGB-D dense mapping method, which can obtain accurate, robust and global consistency map even in the above complex conditions. Firstly, the improved ORBSLAM method, which tightly-couples RGB-D information and inertial information to estimate the current pose of robot, is firstly introduced for accurate pose estimation rather than traditional frame-to-frame method in most RGB-D dense mapping methods. Besides, the TSDF (Truncated Signed Distance Function) method is used to effectively fuse depth frame into a global model, and to keep the global consistency of the generated map. Furthermore, since the drift error is inevitable, a deformation graph is constructed to minimize the consistent error in global model, to further improve the mapping performance. The performance of the proposed RGB-D dense mapping method was validated by extensive localization and mapping experiments on public datasets and real scene datasets, and it showed strongly accuracy and robustness over other state-of-the-art methods. What's more, the proposed method can achieve real-time performance implemented on GPU.
Dynamic process fault detection based on D-SVDD

There are dynamic and non-linear characteristics in industrial processes. These features make the faults difficult to be detected. To improve the situation, the dynamic support vector domain description (D-SVDD) is proposed. This method is based on dynamic expanding matrix and the support vector domain description (SVDD). Compared with the traditional support vector domain description method, the new method takes the sequence correlation between variables into account. So it is more suitable for dynamic process data than traditional method. And it builds a more accurate model. First of all, the appropriate number of dynamic steps are chosen and the dynamic augmented matrices are constructed. Then the dynamic augmented matrices are used to calculate the boundary of the minimum hypersphere by means of SVDD. In this way, the distribution range of the data is described. In the end, the monitoring statistics of new samples are calculated. And the monitoring statistics are compared with the control limit obtained in the previous step to determine whether there are faults or not. The proposed method is applied to the Tennessee Eastman Process. The results show that the fault detection effect of D-SVDD has been significantly improved.
Map Reduce Framework for Network Motif Finding

Network motifs are recurring patterns of interconnections that are significantly over represented. The study of network motifs can provide significant new insights into the functionality of these motifs in the network. Previously developed algorithms for discovering network motifs have been limited to motifs of 6 to 8 nodes. New methods are necessary to identify larger network substructures and functional motifs. The aim here is to apply Map-Reduce concept for mining large motifs from networks. The performance evaluation shows our algorithm can detect larger motifs in large size networks and has good scalability.
dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

Comprehensive identification and cataloging of copy number variation (CNVs) are essential to providing a complete view of human genetic variation and to finding diseased genes. Due to the large-scale sequencing and cost control whole-genome sequencing (WGS) data, low-coverage data is favorably disposed towards CNV identification. However, such low-coverage data is sensitive to noise and sequencing biases, which results in low resolution of CNV detection in past experimental designs for WGS datasets. In this paper, we present a control-free Dirichlet process Gaussian mixture model (dpGMM) based approach, to analyze the read depth (RD) of low-coverage WGS datasets for CNV discovery. First, noise and biases of the RD signals are corrected through the preprocessing step of dpGMM. Then we assume that RD signals across genomic regions follow a Gaussian mixture model (GMM) in which each Gaussian distribution is followed by a copy number state. Without requiring the number of Gaussian distributions, dpGMM builds a Dirichlet process (DP) GMM for RD signals and further uses a DP prior to infer the number of Gaussian models. After that, we apply dpGMM to simulation datasets with different coverages and individual datasets, and compare ours to three widely used RD-based pipelines, CNVnator, GROM-RD, and BIC-seq2. Simulation results demonstrate that our approach, dpGMM, has a high F1 score in both low- and high- coverage sequences. Also, the number of overlaps between CNVs detected in real data by ours and the standard benchmark is twice as much as that detected by other tools such as CNVnator and GROM-RD.
Technology on-the-go: understanding the risks of mobile phone use during walking

Technology increasingly forces individuals to attend to multiple concurrent stimuli including virtual stimuli such as from the smartphone. The current work compared dual-task costs (DTCs) of concurrent reading/writing during walking among N=30 healthy individuals. Task performance was correlated with visual scanning and executive function. Results demonstrated that writing while walking is associated with increased gait variability and larger gait DTCs compared with reading. Similar costs to reading/writing speed were identified, but text comprehension was more impaired by writing than by reading and was related with attention and executive function. These results emphasize that greater interference in dual-task mobile phone use occurs when concurrent tasks share resources. This should be taken into consideration when developing mobile applications.
A Regularized DBN Based on Fault Diagnosis Model for Inductively Coupled Plasma System

The data-driven approach is an ideal method for modern system fault diagnosis. However, due to the complexity and comprehensiveness of inductively coupled plasma (ICP) generating systems, it is necessary to monitor the parameters of each sensor and the movement of each position, then, how to extract features effectively from massive monitoring data is the key to fault diagnosis of the system. This paper proposes a fault diagnosis model for Deep Belief Networks (DBN) based on regularized constraint. The regularized constraint is imposed on the Restricted Boltzmann Machine (RBM) to preserve the feature manifold structure in the data while ensuring the sparsity of the learning model. Finally, the model is trained for fault classification of inductively coupled plasma generating systems. The research results show that our method has a more accurate fault diagnosis rate than the existing fault diagnosis methods.
A System for Measurement of the Asymmetry between Left and Right Shoulders

The study presents the development of a system for measurement of the asymmetry between the human shoulders. Such measurements are necessary in order to diagnose potential muscular imbalance and when possible, take corrective actions in the form of daily exercises. The system is based on two infrared proximity sensors, installed above the shoulders of the investigated human shoulders. A calibration procedure of the sensors and a following calibration model selection reduced the absolute error for the shoulders difference below 1.49 mm. Yet, if the measurement distance is kept within the range (40…110) mm, the accuracy is below 1 mm. The experiments also showed that the calibration is individual for each reflection surface, which should be considered during experimental measurements.
An Efficient MQ-Signature Scheme Based on Sparse Polynomials

Multivariate quadratic (MQ) equations-based cryptography is one of the most promising alternatives for currently used public-key cryptographic algorithms in the post-quantum era. It is important to design practical public-key signature schemes on embedded processors and resource-constrained devices for emerging applications in Internet of Things. The MQ-signature schemes are suitable for low-cost constrained devices since they require only modest computational resources. In this paper, we propose an efficient MQ-signature scheme, SOV, using sparse polynomials with a shorter secret key and give its security analysis against known algebraic attacks. Compared to Rainbow, the secret key of SOV has reduced by a factor of 90% without increasing the public key size. In particular, SOV requires signatures of 52 bytes, while ECDSA-256 requires signatures of 64 bytes.
A Deterministic Multi-layered Partitioning Tool for Wire-Length Reduction of Monolithic 3D-IC

As and when Moore's law started to falter, threedimensional integrated circuits (3D-ICs) emerged as a natural alternative to tackle the problem. Monolithic 3D-IC is one of the recent technology that was introduced in the 3D-IC technology, where MIVs (Monolithic Inter-tier Vias) are used for connecting modules that spread over multiple layers. In this paper, we focus on designing the multi layer partitioning tool before placement of cells in such a way that cost of wires is minimized along with balanced area of tiers. We have not put any constraint on the number of MIVs as it does not suffer from any disadvantages like TSV (Through Silicon Via). We implement our algorithm on a set of 3D-GSRC benchmark circuits present in the URL "3D GSRC benchmark http://cadlab.cs. ucla.edu/three d/3dic.html".
The Analysis on Under-Damped Response for Short-Circuit Fault of Electric Vehicle Charging Facility

The short-circuit fault of electric vehicle charging facility based on the DC distribution network and energy router will lead a R-L-C resonant under-damped response. The theoretical deduction on R-L-C resonant under-damped response about fault voltage and current is made to analyze the effects of key influence factors and relevant system parameters. The simulation verified the validity of fault characteristics on resonant under-damped short-circuit current and capacitor voltage, which also indicated the relationship between fault characteristics and basic parameters of energy router employed in electric vehicle charging facility.
Distributed Area Coverage Control for Multi-Agent Based on Artificial Potential Field

We present a distributed coverage control for multi-agent in any convex polygon area. The distributed coverage control is based on artificial potential field. The dynamical model of multi-agent is established according to the collision avoidance, area attraction and boundary repulsion rules. The model is used to control the swarm to move to the target region according to the preset formation. After entering the area, the swarm is evenly distributed within the area under the rules. We have demonstrated the effectiveness of the distributed coverage control and it is suitable for large scale swarm formation control.
Inter-destination Synchronization : A Comparison between Master-Slave and Synchronization-Manager Techniques

With advancements in communication and the internet, Synchronous shared experience has emerged as a field of interest for researchers and scientists. The inter-destination synchronization mechanism is a fundamental requirement of synchronous shared experience. Inter-destination synchronization has been studied in the perspective of stream-based media, which does not work well with the group of participants with varied bandwidth and computing resources. Document-based media provides functionality of alternative content selection on bases of available resource, which made it a potential solution for inter-destination synchronization when participants of synchronous shared experience have varied bandwidth and computing resources.In this research work, we adopted two of the stream based inter-destination synchronization techniques to work with document-based media. We implemented, evaluated and compared the results of master-slave and synchronization-manager techniques. For both techniques, the play-out differences are within an acceptable range. However, the synchronization-manager technique performed better than the master-slave technique. Finally, we extended inter-destination synchronization techniques to synchronize user interaction actions across distributed participants
Comparison of Optimal Integer and Fractional Order PID Controllers on a Stabilized Real-Time System

In this paper, optimal fractional and integer order PID controllers are compared on a stabilized Ball Balancing Table system. The real-time system is restricted on Y-axis and stabilized by using PV controller. Firstly, fractional and integer order system models are obtained from stabilized real-time system. Then, Big Bang-Big Crunch optimization algorithm is used to design optimal fractional and integer order PID controller with ISE performance index as a cost function. In order to avoid from system aggressiveness and high control signal values, maximum sensitivity constraint is utilized. Finally, the performance of the control systems are analyzed via simulations and real-time system applications.
A Hierarchical Buffer Management Approach to Rate Adaptation for 360-Degree Video Streaming

For the 360-degree virtual reality (VR) videos, to achieve the same user-perceived playout quality with traditional video, the overall video bitrate has to be much higher, which significantly increases the bandwidth requirement. To overcome this issue, most of rate adaptation schemes are proposed based on the users’ Field of View (FoV), since users only view part of the entire videos when watching 360-degree videos. However, the FoV prediction is only valid in short terms which incurs low robustness against network dynamics and results in frozen screen events. Since the playback buffer can effectively avoid re-buffering by storing videos in advance, we propose a two-layered hierarchical buffer-based rate adaptation scheme for 360-degree VR videos in this paper. The tiles will be updated to enhance the video quality in the short buffer layer based on FoV prediction, meanwhile the long buffer layer takes charge of downloading new chunks to guarantee the buffer occupancy. To better improve the quality of experience (QoE), we jointly consider the users’ FoV, channel conditions and buffer status. Our method achieves a good trade-off between video quality and buffer occupancy. Besides, the maximum buffer level selection is optimized to further improve the performance. The simulation results show that our proposed scheme and algorithms outperform other methods by about 5%-30% of QoE improvement.
The Renal Vascular Resistance and Renal Blood Flow of Hypothermic Machine Perfusion and Cold Storage in porcine slaughterhouse kidneys

Two commonly used organ preservation techniques are Hypothermic machine perfusion (HMP) and Cold storage (CS). This report presents a partial study of comparison of injured functional unit between Cold Storage and Hypothermic Machine Perfusion in porcine kidney by evaluated the renal vascular resistance (RVR) and histopathology report from hematoxylin and eosin (H&E) stain between two techniques. Therefore, six porcine kidneys from three pigs were allocated each pair into two groups; either HMP or CS, underwent 24 hours of preservation at 2°C. The data of RVR and RBF were retrieved after preservation hours, then histopathological examination was performed. Results, two out of three samples, HMP1 and HMP2, had higher RVR value more than at time zero (65.76 mmHg/mL/min and 828.60 mmHg/mL/min higher than 4.04 mmHg/mL/min and 6.97 mmHg/mL/min, respectively). While CS1 and CS2 had RVR value at time zero higher than after 24 hours (10.82 mmHg/mL/min and 9.11 mmHg/mL/min higher than 7.53 mmHg/mL/min and 3.22 mmHg/mL/min, respectively). Furthermore, the majority histopathology reports suggested a prominent effect of CS treatments on the cellular changes of the renal tubules than HMP treatments. Which concluded the HMP treatment was superior of the CS treatment, in term of reducing kidney injury, acute kidney injury (AKI) risk development and delayed graft function (DGF).
BSite-pro: A Novel Approach for Binding Site Prediction in Protein Sequences

Accurate protein binding site annotations are vital for a profound understanding of biological processes and protein interactions. Due to less supporting information a large number of proteins remain uncharacterised. For large sets of unchar- acterised proteins, only amino acid information is available. In this paper, we proposed BSite-pro - a traditional approach - which makes use of protein sequence data for classification of binding sites. The classification procedure requires hand-crafted features from protein sequences. Our results stipulate noteworthy enhancements concerning predicted accuracy and recall upon comparison with previously proposed sequence-based techniques. BSite-pro achieves an overall validation accuracy of 85.06% and recall of 82.17%. Finally, we discuss that using the same feature extraction methods and model, we profitably dealt with two contrasting types of problem i.e. protein active and conserved sites prediction.
LTCC Wideband Bandstop Filter With Controllable Bandwidth Using Internal Coupling

A compact low temperature co-fired ceramic (LTCC) bandstop filter (BSF) with controllable bandwidth is presented in this paper. Compared with the conventional method, the internal coupling between resonators is introduced in the structure to split one zero into two, namely transmission zeros pair (TZP). Then the bandwidth is flexibly controlled by adjusting the internal coupling strength. Besides, multiple transmission poles (TPs) are generated to enhance the roll off while maintaining the stopband performance. For demonstration, a LTCC wideband BSF is implemented and measured. Both TZP and multiple TPs are observed with compact circuit size. Good agreement between the simulated and measured results verifies the proposed method.
Strip Ground based Slotted Microstrip Antenna for Wireless Communication

The proposed micro strip patch antenna consists of a U-slot in the rectangular patch to optimize gain and bandwidth deployed on FR4 material with thickness 1. 6mm and dielectric constant of 4.4. In this paper the antenna performance in term of various parameters like resonance frequency, return loss, radiation pattern, antenna gain and bandwidth is observed. The simulation results are given and parametric studies by gradually changing the ground and feed were presented in detail. The simulated frequency bandwidth of this antenna is 2.2-2.8 GHz at 2.47 GHz with the peak gain of 2. 23dBi, which can be utilized for the applications in global WiFi and 4G communication.
Analysis and Identification of Rice Adulteration Using Terahertz Spectroscopy and Pattern Recognition Algorithms

Rice adulteration is a severe problem in agro-products and food regulatory agencies, suppliers, and consumers. In this study, to effectively distinguish whether high-quality rice is mixed with low-quality rice, detection and analysis of adulterated rice in five levels with different mixing proportions was conducted via terahertz spectroscopy and pattern recognition algorithms. Initially, samples were prepared and spectral data were acquired by using the terahertz transmission mode, and a principal component analysis (PCA) algorithm was applied to extract features from original spectrum information and reduce data dimensions. Subsequently, partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and a back propagation neural network (BPNN) combined with the absorption spectra after different pretreatments, including standard normal variate (SNV) transformation, baseline correction (BC), and first derivative (1st derivative), were applied to establish the classification models. Results indicate that an SVM model employing the absorption spectra with a 1st derivative pretreatment exhibits the best discrimination ability, with an accuracy up to 97.33% in the prediction set. This result proves that terahertz spectroscopy combined with chemometric methods can be an effective tool to identify rice adulteration levels.