Miracle-dz/UniFAR-FTUs huggingface dataset:
Fighting Detection Based on Pedestrian Pose Estimation

Recently, fighting detection in videos is almost based on motion optical flow. Motion optical flow can quickly locate the target of violent movement, but it will bring errors due to the rapid movement of pedestrians or the rapid change of attitude. In this paper, we add pedestrian key nodes to determine the behavior posture based on the optical detection method. Firstly, in the convolution structure, the response graph of the image is obtained, and the key nodes of the pedestrian body are obtained through the extreme of the response graph. Secondly, the limb of a pedestrian is represented by connecting the key nodes. Finally, the optical flow of the key nodes is utilized to determine the posture of the pedestrian and abnormal behavior. Some experiments can demonstrate that our method is more effective to detect the fighting behavior in videos.
Tunable Q-Switched Fiber Laser Based on a Graphene Saturable Absorber Without Additional Tuning Element

A tunable passively <italic>Q</italic>-switched fiber laser is fabricated using a graphene saturable absorber. The graphene saturable absorber is composed of a tapered fiber coated with graphene nanosheets. It combines the abilities of a saturable absorber and power-dependent tunable filter, thereby simplifying the structure of the tunable passively <italic>Q</italic>-switched fiber laser. The transmission peak of the graphene saturable absorber is linearly dependent on the input power and temperature, with slopes of approximately −0.35 nm/mW and −3.63 nm/°C, respectively. The <italic>Q</italic>-switched fiber laser exhibits a tunable wavelength range of 7 nm (from 1558 to 1565 nm). The output pulse offers the shortest pulse duration of 2.22 μs with a repetition rate of 39.1 kHz and the widest pulse duration is 5.9 μs that corresponds to a repetition rate of 21.3 kHz. The maximum pulse energy is 251.9 nJ, corresponding to an average output power of 8.6 mW.
A Comprehensive Parts of Speech Tagger for Automatically Checked Valid Bengali Sentences

This paper constructs a method for corroborating Bengali sentences whether they are equitable syntactically, semantically and pragmatically; then a method is devised for detecting parts of speech (POS) from the valid sentences. In our approach, we have analyzed several techniques to check whether construction of inputted Bengali sentence is valid, and finally, we have chosen the best technique among them. Moreover, after analyzing the construction of several Bengali sentences we have designed a rule-based algorithm for detecting POS with a significant accuracy. The idea of ignoring sentences with grammatical mistakes helped significantly to achieve higher accuracy and to reduce execution time. Moreover, our projected method achieved an accuracy of 91.45% which is the highest among similar POS tagger.
Classification of White Blood Cells by Convolution Neural Network in Lens-Free Imaging System

Over the past decade, the lens-free imaging technique has been considered a good way to reduce the volume and the cost of cell analysis tools. However, limited by lens-free optical amplification, the cell imaging not only has low resolution but also has diffraction phenomenon in lens-free system. Therefore, there is a major problem, which traditional methods can hardly classify diffracted cell images in the system. At present, the state-of-the-art algorithm in image classification is to use the convolution neural network (CNN). Fortunately, the training of CNN method is fully accordant with the application requirements of classification of white blood cells (WBCs). In this paper, we proposed a technique for WBCs classification based on CNN in the lens-free imaging system. According to the test, the accuracy of this method for WBCs classification can reach to 90%, and it has a very broad application prospect in point-of-care testing.
Performance Evaluation of Wi-Fi Finger Printing based Indoor Positioning System

Indoor Positioning System (IPS) is a technology that provides precise and accurate location information in indoor environments. Since Global Positioning system (GPS) relies on line of sight communications with a satellite transceiver, it does not work within buildings. To overcome this challenge, IPS technology could use RF based communication and locate humans and objects indoors. The key advantage of RF based communication is its high coverage range and non-line of sight communications. Wi-Fi finger printing is an efficient RF based technique for IPS which utilizes signal strength of wireless LAN to identify different indoor locations. In this paper, we develop an experimental testbed to implement Wi-Fi fingerprinting based IPS. We analyse the performance of Wi-Fi fingerprinting based IPS for three different indoor scenarios. Results show that Wi-Fi finger printing based IPS accurately predicts user location when the multi-path fading is low and access points are closer to the user.
SMART CITY REAL-TIME DATA-DRIVEN TRANSPORTATION SIMULATION

This study assesses feasibility aspects of using a real-time data-driven transportation simulation model to evaluate and visualize network performance indices to provide dynamic operational feedback in a real world environment, in a big data context. A hybrid traffic simulation model, consisting of a mix of preset and real-time data-driven intersections, is developed. The hybrid model represents a traffic corridor partially equipped with smart devices generating high velocity, high volume datasets with limited shelf-life. The model used in this study emulates seventeen consecutive intersections on a corridor. Signal controls and vehicle volumes at two of the intersections are driven by real-time data while the remaining intersections are driven by preset data. An optimized architecture is developed to enable control of the signals and the vehicle volumes using real-time data from in-field detectors, and real-time processing of the vehicle trajectories from the simulation output to generate travel-time, energy, and emissions performance indices.
Hybrid Compression of Medical Images with Wavelets

Scientific studies have extended the Medical Big Data (MBD)collection from numerous sources. Huge MBD needs more storage space. The traditional database management systems were obsolete and they cannot support reliable MBD processing. Usually two types of compression schemes were used to reduce the MBD size. This paper presents a hybrid compression approach for medical big data for size reduction. This also guarantees MBD quality and image reliability.
Clustering-Oriented Multiple Convolutional Neural Networks for Optical Coherence Tomography Image Denoising

The speckle noise is an inherent coproduct of OCT imaging that is a significant direct influence factor of image quality, thus OCT image denoising is needed. Most existing OCT image denoising methods usually use only part of the priori information of the OCT image, but neglect the change of the texture, structure and other features of the OCT image. To address this, we introduce a framework for OCT image denoising by multiple CNNs based on clustering and residual learning. Our proposed method not only utilizes the automatic feature learning ability of CNNs but also adapts them to depict diversity of noise characteristics in different areas of noisy input. Our framework achieves great visual and quantitative performance.
Highly Sensitive and Flexible Tactile Sensor Based on Porous Graphene Sponges for Distributed Tactile Sensing in Monitoring Human Motions

This paper presents a highly sensitive and flexible tactile sensor based on 3D microstructure graphene sponges. The graphene sponges are fabricated in a dip-coating process that stacks the graphene layers onto the polyimide scaffolds in a homogeneous graphene solution with graphene oxide serving as the dispersant. The tactile sensor has <inline-formula> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> sensing units and is fabricated through photoetching, magnetron sputtering, and screen-printing processes. The graphene sponge has relatively low Young’s modulus of 16 kPa and is capable of enduring large strains over 60%. Furthermore, the sensor unit with the graphene sponge has a high sensitivity of 0.046 kPa<inline-formula> <tex-math notation="LaTeX">$^{-1}$ </tex-math></inline-formula> at 0.3 ~ 10 kPa, a lower sensitivity of 0.007 kPa<inline-formula> <tex-math notation="LaTeX">$^{-1}$ </tex-math></inline-formula> at 10 ~ 40 kPa, and shows good reproducibility and dynamic response. The increase in the contact areas of the graphene cell walls inside the graphene sponge and the overlapping areas in the laminated graphene flakes synergistically contribute to the high piezoresistive sensitivity. Body wearing experiments to detect wrist bending and muscle vibration demonstrate that the tactile sensor has potential for monitoring body motion and other biomedical applications. [2018–0189]
Combined Influence of Classifiers, Window Lengths and Number of Channels on EMG Pattern Recognition for Upper Limb Movement Classification

Electromyogram pattern recognition (EMG-PR) is a very important method for upper limb movement classification. However, there are many factors such as classifiers, window lengths and number of channels which can make an influence on EMG-PR efficiency. Previous studies examined the effects of three different factors on EMG-PR separately. However, the combinations of three different factors (classifiers, window lengths and number of channels) may also affect the classification accuracy of EMG-PR. In present study, we discussed the effects of combinations of three different factors including classifiers, window lengths and number of channels on EMG-PR. We analyzed the different combinations of three factors. Four healthy subjects participated in this study, and they played five motions of hand and wrist in this experiment. We found that these three factors had a significant effect on EMG-PRe The performance of linear discriminant analysis (LDA) of EMG-PR outperformed the performance of back propagation neural network (BPNN) (p < 10−3). The classification accuracy of LDA is higher than support vector machine (SVM) (p < 10−3). In addition, 200 ms window length had enough data to classify the different motions. Furthermore, we also found that five channels has a significant increase when compared to three channels (p < 0.05). The proposed method can increase the performance of EMG-PR.
Cross-Correlation Aided Ensemble of Classifiers for BCI Oriented EEG Study

Recently, Brain–computer interface (BCI) oriented electroencephalographic (EEG) studies have received due attention for decoding human brain signals corresponding to a specific mental state and providing an alternate solution to the disabled or paralyzed persons for communicating with the computer, robotic arm, or various neural prostheses. In this paper, we propose a two-phase approach to distinguish EEG signals of different mental tasks. The first phase combines the cross-correlation features and slow cortical potentials mean extracted from the most significant channels to form feature vectors. The second phase performs a classification of these feature vectors using SVM and KNN classifiers. It further boosts the classification performance by creating an ensemble of SVM classifiers trained with complementary feature sets extracted during the first phase. EEG signals generated for the same mental task are similar in shape to each other and dissimilar to other activities. The basic principle of cross-correlation is to measure the similarity in shape between two signals which makes it suitable for the EEG analysis. We test the performance of the proposed approach on the BCI competition II dataset Ia representing the cursor movement EEG data for a healthy subject. Experimental results on this dataset demonstrate a significant improvement in the classification accuracy compared to other reported results. Moreover, the proposed work requires fewer channels and features compared to the recent study, which uses all six channels and 42 features, manifesting the efficacy of the proposed work.
The RPL Load Balancing in IoT Network with Burst Traffic Scenarios

In Low Power and Lossy Networks (LLNs) sensor nodes are deployed in various traffic load conditions such as, regular and heavy traffic load. The adoption of Internet-of-Things enabled devices in the form of wearables and ubiquitous sensors and actuators has demanded LLNs to handle burst traffic load, which is an event required by myriad IoT devices in a shared LLN. In the large events, burst traffic load requires a new radical approach of load balancing, this scenario causes congestion increases and packet drops relatively when frequent traffic burst load rises in comparison with regular and heavy loads. In this paper, we introduced a new efficient load balance mechanism for traffic congestion in IPv6 Routing Protocol for Low Power and Lossy Network (RPL). To measure the communication quality and optimize the lifetime of the network, we have chosen packet delivery ratio (PDR) and power consumption (PC) as our metrics. We proposed a traffic-aware metric that utilizes ETX and parent count metrics (ETXPC), where communication quality for LLNs with RPL routing protocol are playing an important role in traffic engineering. In addition, we provided analytical results to quantify the impact of Minimum Rank with Hysteresis Objective on Function (MRHOF) and Objective Function zero (OF0) to the packet delivery, reliability and power consumption in LLNs. The simulation results pragmatically show that the proposed load balancing approach has increased packet delivery ratio with less power consumption.
A Single Switch Quadratic Boost High Step Up DC–DC Converter

In this paper, a high step up converter consisting of an integrated quadratic-boost converter and a voltage doubler is proposed. The integration of the quadratic-boost converter makes the system easier to lift up its voltage gain through slightly increasing the duty ratio of the single switch. The voltage doubler further increases the voltage gain of the system as the turn ratio rises. The voltage stresses on the switch and the diodes are decreased for such cascaded topology. Different operation modes are analyzed and mathematical analysis of the converter is presented in detail. The leakage inductance contributes to realizing zero current switching of the diodes in the second boost stage and the doubler and the energy can be recycled to the load. A 38-W prototype is built to work as a vehicle LED driver. Experiments are conducted to verify the advantages of the proposed converter and the efficiency is 90% at the nominal operating point.
A Relaxed ADMM Algorithm for WLS Design of Linear-Phase 2D FIR Filters

One of the main challenges for optimal designs of two-dimensional (2D) finite impulse response (FIR) filters is their heavy computational load due to the large number of filter coefficients and the high dimensions of the data for model fitting. The alternating direction method of multipliers (ADMM) is a powerful technique appropriate for optimization for big data. In this paper, a relaxed ADMM is presented and then applied in the weighted least-squares (WLS) design of linear-phase 2D FIR filters. It is shown that the relaxed ADMM algorithm converges much faster than the standard ADMM algorithm. In addition, a salient feature of the relaxed ADMM is its highly parallel structure which makes it very efficient if implemented in parallel. Simulation examples and comparisons demonstrate the fast convergence and high efficiency of the relaxed ADMM algorithm.
A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups

In linguistic large-scale group decision making (LSGDM), it is often necessary to achieve a consensus. Particularly, when computing with words and linguistic decision, we must keep in mind that words mean different things to different people. Therefore, to represent the specific semantics of each individual, we need to consider the personalized individual semantics (PIS) model in linguistic LSGDM. In this paper, we propose a consensus model based on PIS for LSGDM. Specifically, a PIS process to obtain the individual semantics of linguistic terms with linguistic preference relations is introduced. A consensus process based on PIS, including the consensus measure and feedback recommendation phases, is proposed to improve the willingness of decision makers who follow the suggestions to revise their preferences in order to achieve a consensus in linguistic LSGDM problems. The consensus measure defines two opposing consensus groups with respective acceptable and unacceptable consensus. In the feedback recommendation phase, a PIS-based clustering method to get decision makers with similar individual semantics is proposed. Recommendation rules design a feedback for decision makers with unacceptable consensus, finding suitable moderators from the decision makers with acceptable consensus based on cluster proximity.
Music Information Retrieval Techniques for Determining the Place of Origin of a Music Interpretation

Determining the place of origin of the musical compositions is a modern area of research in the field of music information retrieval (MIR). The musical interpretation of one piece carries a variety of author's intentions that influence the musical character of the resulting composition. These aspects may include rhythm, dynamics, timbre, or tonality. This paper introduces a novel methodology for determining the place of origin of a music interpretation based on advanced signal processing and machine learning techniques. For this purpose, we collected a database of 35 different interpretations of Leos Janacek's String Quartet No.1, “Kreutzer Sonat”: IV. Con Moto-Adagio. Employing random forests classifier, we achieved classification accuracy over 97 % using features derived from Mel-frequency cepstral coefficients. This paper proves it is possible to use MRI for determining the origin of a music interpretation with very high accuracy.
Multicomponent Model of Objects Attributive Signatures on Color Pictures

A technique for multicomponent model synthesis aimed at description of color, geometric and structural attributes of objects images on pictures is suggested. In terms of the model an object is represented as a hierarchical set of embedded contours, wherein the upper level of the object topological representation is the description of the external boundary of that object image and the lower levels of the topology detailing correspond to the internal structure elements. For the contours detection color information of the picture statistically homogeneous regions is used. To select the external contour points an algorithm for forming the contour circular sweep with the uniform angle step as well as the methods for binding contour points, which are based on the criterion of distance between the neighboring points of the contour have been developed. The presented results testify the possibility of using the suggested technique in the procedures of recognition of graphical objects.
Detection of Mycobacterium Tuberculosis in Ziehl-Neelsen Sputum Smear Images

Tuberculosis is one of the top 10 causes of death worldwide. In order to reduce the workload of doctors and the probability of human error, this paper presents an automatic detection algorithm for Mycobacterium tuberculosis in Ziehl-Neelsen Sputum Smear Images. The algorithm uses color feature and three morphological characters, which are aspect ratio, circularity and area. Background Equalization algorithm is proposed to utilize color feature sufficiently. This algorithm takes advantage of the watershed algorithm and the channel “a” in Lab color space. Experimental results confirmed the high accuracy of the proposed algorithm.
A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation

Selecting residual generators for detecting and isolating faults in a system is an important step when designing model-based diagnosis systems. However, finding a suitable set of residual generators to fulfill performance requirements is complicated by model uncertainties and measurement noise that have negative impact on fault detection performance. The main contribution is an algorithm for residual selection that combines model-based and data-driven methods to find a set of residual generators that maximizes fault detection and isolation performance. Based on the solution from the residual selection algorithm, a generalized diagnosis system design is proposed where test quantities are designed using multivariate residual information to improve detection performance. To illustrate the usefulness of the proposed residual selection algorithm, it is applied to find a set of residual generators to monitor the air path through an internal combustion engine.
Relaying via Cooperative Jamming in Covert Wireless Communications

In this work, a covert communication scheme under block-fading channels is proposed where the relay, apart from forwarding source's information to the destination, also acts as a cooperative jammer. Stating precisely, the relay amplifies source's signal when required to relay the signal to the destination, otherwise it transmits artificial noise with random power, causing errors in the received signal statistics of the adversary, Willie, who employs a radiometer for detection of covert transmissions. Stating the condition under which Willie makes detection errors, we first find the optimal detection threshold of Willie's detector and his minimum detection error probability and then derive the expected value of this error probability, establishing Willies detection performance from the perspective of the covert communication pair. Furthermore, for a given maximum transmit power at the relay, we derive a closed-form expression for the optimal transmit power of covert communication. Our numerical results illustrate the advantage cooperative jamming offers for achieving covertness, and demonstrate how the choice of transmit power used for covert transmissions is affected by Willie's error performance and required level of covertness.
An OFDMA-based New MAC Mechanism for IEEE 802.11ax

Two types of MAC mechanisms namely, random access and reservation can be employed for OFDMA-based wireless LANs. As OFDMA-based wireless networks promise to support heterogeneous applications, researchers assume that applications with and without traffic specifications will coexist. Thus, both reservation-based and random access schemes are required for an OFDMA-based WLAN. Eventually, OFDMA-based wireless LAN will deploy hybrid MAC mechanisms inheriting traits from random access and reservation. In this paper we propose a new MAC protocol which employs one kind of hybrid mechanism that will provide high throughput of data as well as maintains improved fair access policy to the medium among the stations. The protocol works in two steps, where in step 1 sub-channels are approximately evenly distributed to the stations and in step 2 stations within in a sub-channel will contend for medium randomly if the total number of stations of the system are larger than the number of sub-channels. The details of the protocol is illustrated in the paper and we evaluate the performance of the proposed OFDMA-based multi-channel hybrid protocol through comprehensive computer simulations. Simulation results validate that the performance of our designed protocol far better than the conventional CSMA/CA protocol.