Miracle-dz/UniFAR-FTUs huggingface dataset:
A Spontaneous Visible and Thermal Facial Expression of Human Emotion Database

In the last twenty years, analyzing facial expression and human emotion has received significant efforts among computer vision researches. Most of the works typically focus on categorizing emotions, but only few works address the interests in non-basic human emotion or dynamic facial expressions. Besides, existing databases are mostly based on visible images, which requires to be obtained under stable illumination condition. Therefore, in this study, a spontaneous human emotion database with various intensity in both visible and thermal spectrum is established. The database contains visible and thermal videos of 10 subjects generating 30 videos of facial behavior affected naturally to stimulus video clips with different intensities. A proposed analysis framework, then, is applied to assess the usability of our spontaneous database for nonbasic human emotion estimation. Firstly, a feature descriptor that represents well the relation between a single frame in thermal space and the dataset without losing discriminating abilities for subtle emotion variations is applied. Secondly, a ranking method that pays attention to the relative order information and takes a single thermal frame as input is employed in our assessment tool. The proposed database is available for research in computer vision, psychophysiology science and related fields
Direct visual servoing of UAVs using onboard monocular camera

Most current unmanned aerial vehicles (UAVs) rely on the global positioning system (GPS) for positioning. When the GPS signal is blocked, sensor-fusion-based methods are primarily used for UAV localization. However, heavy computational processing is required in most existing methods for reliably estimating the position. Therefore, in this paper, we propose a scheme involving direct visual feedback control of UAVs by using an onboard monocular camera. The feature of this scheme is that relative position information between the observed environment and the UAV is transmitted to a flight controller directly without any sensor fusion process, which will contribute to improved recognition of changing environments and quicker responses. This work is an extension of our previous work [1], with extra experiments.
A Compact 5G Millimeter -wave MIMO Antenna fo r Ultra-Wide Band Wireless Applications

The purpose of this paper is the study of a compact ultra-wide band (UWB) millimeter-wave antenna to be used for fifth generation wireless communications. The suggested antenna consists of a microstrip patch antenna printed on the top layer of Rogers RT5880 substrate, and the bottom layer of substrate contains a partial ground plane to operate at one of the prospective frequency band for the upcoming 5G mobile applications which is the band from 24.5 to 32.2GHz. The linear phased array antenna used ten antennas printed on the top side of the Rogers RT5880 substrate to achieve high gain and high beam steering properties. The fundamental properties as S-coefficients, gain, efficiency and beam-steering.
The Quench Recovery Analysis of the JT-60SA Superconducting Magnet

A CS module is composed of the 52 layers pancake coils. The 26 helium flowing paths are in a one module. The refrigerator supplies helium at 4.4 K to each flowing paths in nominal operation. In case of a quench, the refrigerator stops helium supply in order to shut out large heat load from the quenched magnet. The temperature distribution of the quenched CS will be smoothed by a heat conduction between each pancake coils while helium is stopped. Helium will be supplied again when the magnet pressure become low enough. The temperature distribution changes are calculated by using SuperMagnet.
Simulation of Full Responses of Triaxial Induction Logging in 1D Layered Arbitrarily Anisotropic Formations

Triaxial induction tools can be used to evaluate thinly laminated sand-shale sequences and fractured beds. This type of reservoirs exhibit transversely isotropy (TI) or arbitrarily anisotropy (also called as biaxial anisotropy (BA)). There have been several papers to study the responses in the TI model or the simplified BA model whose conductivity principal coordinate is always consistent with the formation coordinate. However, little work covers the most general biaxial anisotropic model whose conductivity tensor’s orientation is arbitrary. We introduce the Euler angles, then the general biaxial anisotropic conductivity tensor can be determined by three principal components and three ordered Euler angles. To derive the electromagnetic (EM) fields in arbitrarily anisotropic medium, we first convert the Maxwell’s equation of frequency-spatial domain into frequency-wavenumber domain by 2D Fourier transform, and obtain an ordinary differential system about horizontal components of EM fields. Using eigenvalue decomposition of the system matrix, this system can be decomposed into two group of equations associated with upward and downward eigen-waves respectively. We derive the solutions of EM fields in frequency-wavenumber domain by introducing transmission matrix, both local and generalized reflection matrix and propagator matrix After that, we use 2D Gauss-Legendre quadrature to calculate inverse Fourier transformation and obtain Green’s function for simulation of the tri-axial induction responses. The numerical results are compared with 3D numerical method in both vertical and deviated wells and the agreement is satisfactory. Finally, we investigate the response characteristics in several formations with different Euler angles The results show that triaxial induction responses are remarkably influenced by Euler angles even if the values of three principal components of conductivity tensor remain unchanged. Compare to the responses of the simplified BA model, those of general BA model are more complex and contain more nonzero components. The results indicate that using TI model or the simplified BA model in complex environment may cause large errors. Our algorithm are more practical than algorithms based on the simplified model because the real depositional environments are usually complicated.
Training Data Reduction using Support Vectors for Neural Networks

In the field of machine learning, deep learning is widely used to improve versatility and accuracy. Deep learning has a higher expression ability compared with conventional models but requires large amounts of data and time for training. To tackle this issue, we propose a training data reduction method using support vectors that are the closest data to the classification boundary obtained by support vector machine. The proposed method chooses a subset of training data consisting of support vectors and uses them for training neurak networks. Experimental evaluation shows that it is possible to reduce the number of training data by 12% and reduce the learning time of neural network by 9.5% in a test case of ResNet with CIFAR-10 dataset.
Thermal resistance analysis for a high power 3D integrated RF Module based on TSV interposer

This paper proposed a three-dimensional integrated RF transceiver module based on high resistivity Si interposer embedded with microchannel for on-site heat dissipation. It's composed of three stacked layers of TSV interposer, including the bottom one embedded with microchannels for on-site cooling which has four high power chips assembled on its top side, the middle one having four large cavities to accommodate the chips assembled on the bottom one which is utilized as a supporting layer, the top one having a IC driver chip mounted on its top side. In order to study its thermal property, a simplified thermal resistance network is built and analysis is done. To validate this method, finite element simulation is conducted and the results show that maximum deviation of the temperature rise of each high power chip mounted on the bottom Si interposer referring to coolant is 9.04% and minimum deviation is 4.85% in the range from 0.1m/s to 1m/s for DI coolant.
Classification of Sinhala Songs based on Emotions

Classification of Sinhala Songs has received less attention from researchers in the Sri Lankan context. The purpose of the current study is to shed light on emotion based classification based on Music Emotion Recognition (MER) which is a sub domain of Music Information Retrieval (MIR). In achieving this the authors followed three steps namely; selection of an appropriate emotion model, extraction and selection of low level music feature and training of the classifier utilizing supervised machine learning algorithms. 18 experiments were conducted to analyze the level of accuracy in each feature selection algorithm in combination with the supervised machine learning algorithms. Results of the current study suggest that the combination of ReliefF based feature selection algorithm and Random Forest supervised machine learning algorithm yields highest accuracy classification of Sinhala songs based emotions. The highest accuracy level of 91.98% in the current study is identified as the highest result achieved within the domain of Sinhala songs classification according to literature review conducted.
Numerical Decoupling Study of EMAT Testing Signal for Ferromagnetic Materials

To improve the efficiency and robustness of electromagnetic ultrasonic testing, magneto-acoustic compound inspection for ferromagnetic materials is proposed. Both ultrasonic waves and pulsed eddy current (PEC) could be obtained by the single detection of an electromagnetic acoustic transducer (EMAT), where ultrasonic waves were generated based on the coupling of the magnetostriction effect and the Lorentz force mechanism, when the EMAT was used to detect ferromagnetic materials. In this study, three simulation models of the EMAT were built by coupling different physics fields while keeping the geometry of the models unchanged and the other parameters of the EMAT constant. Transverse wave could be generated by magnetostriction force, and longitudinal wave could be generated by Lorentz force. Based on time-frequency analysis of the simulation results, the energies of ultrasonic waves and PEC were concentrated in different frequency ranges. Through the filtering strategy and by separating the time-domain signal, magnetostriction force ultrasonic wave (MFUW), Lorentz force ultrasonic wave (LFUW), and PEC could be decoupled from the EMAT testing signal. Multiple parameters can be extracted by a single detection of the EMAT, and the ability of the electromagnetic ultrasonic testing can be improved.
Automated Optical Flow Based Registration for Adaptive Optics Scanning Laser Ophthalmoscope

This study presents an automated registration method based on optical flow for an adaptive optics scanning laser ophthalmoscope. The method was designed to align and average images to obtain a higher signal-to-noise ratio image. A correlation based optical flow image registration method, which has large registration degrees of freedom, is adopted as a local registration method. By comparing the images before and after image registration, we show the effectiveness of our method. Furthermore, the advantage of our method, which is the containment of large registration degrees of freedom, is confirmed.
Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks

Hyperspectral imaging has become a mature technology which brings exciting possibilities in various domains, including satellite image analysis. However, the high dimensionality and volume of such imagery is a serious problem which needs to be faced in Earth Observation applications, where efficient acquisition, transfer and storage of hyperspectral images are key factors. To reduce the time (and ultimately cost) of transferring hyperspectral data from a satellite back to Earth, various band selection algorithms have been proposed. They are built upon the observation that for a vast number of applications only a subset of all bands convey the important information about the underlying material, hence we can safely decrease the data dimensionality without deteriorating the performance of hyperspectral classification and segmentation techniques. In this paper, we introduce a novel algorithm for hyperspectral band selection that couples new attention-based convolutional neural networks used to weight the bands according to their importance with an anomaly detection technique which is exploited for selecting the most important bands. The proposed attention-based approach is data-driven, re-uses convolutional activations at different depths of a deep architecture, identifying the most informative regions of the spectrum. Also, it is modular, easy to implement, seamlessly applicable to any convolutional network, and can be trained end-to-end using gradient descent. Our rigorous experiments, performed over benchmark sets and backed up with statistical tests, showed that the deep models equipped with the attention mechanism are competitive with the state-of-the-art band selection techniques and can work orders or magnitude faster, they deliver high-quality classification, and consistently identify significant bands in the training data, permitting the creation of refined and extremely compact sets that retain the most meaningful features. Also, the attention modules do not deteriorate the classification abilities, and slow down neither training nor inference of the deep models.
Performance Analysis of Students Based on Data Mining Techniques: A Literature Review

Data mining techniques have been applied to various sectors in other to get information and knowledge to support major decision-making. With the emergence of new technologies that can harness data associated with education and the rapid growth in educational data, Data mining can be applied to a wide variety of applications in the educational sector for the purpose of improving the performance of students as well as the status of the educational institutions. Educational Data Mining (EDM) is a major research field that uses data mining techniques, machine learning algorithms and statistical techniques to basically helps the tutors modify their teaching strategies if the results with the current teaching model are not satisfactory. The potential influence of data mining analytics on the students’ learning processes and outcomes has been realized Hence, a comprehensive review of educational data mining (EDM) and learning analytics (LA) in education was conducted. In this paper, we conducted an in-depth literature review on the use of EDM for analyzing the performance of students.
Design and Implementation of Configurable Convolutional Neural Network on FPGA

Convolutional neural network (CNN) is successful in the Computer Vision applications such as object detection, image segmentation, image classification. Nowadays, most of the CNN applications are implemented on the server due to the large data inputting, which takes time for transferring data from clients to server and vice versa. In recent years, there has been an increasing interest in implementing CNN on Field-Programmable Gate Arrays (FPGA) due to its' high performance, low power usage, and reconfigurability. In this paper, we design a synthesizable HDL-based CNN Core Generator; and it can configure these parameters (input size, number of layers, filter size, number of classes). For the accuracy and minimizing the loss of calculating, our design bases on Single Precision Floating-Point Arithmetic. For verification, we create the HDL-based CNN module of 2 layers, and it is applied on MNIST dataset. On the other hand, we build the exact model on Keras framework, extract weights and load the weights to the CNN module; and then two results will be compared. The maximum error of the two results is 2.056360244750977x10-6. This design is synthesized on the Virtex-7 VC707 Evaluation Board (XC7VX485T-2).
SBA-Based Mobile Edge Computing

5G is becoming open, service-oriented, and software- oriented by introducing Service Based Architecture (SBA) into its core network. In consequence, we propose SBA-based mobile edge computing (MEC) in this paper. Firstly, we present a service-based MEC platform architecture, composed of network functions (NFs) and service based interface. We design several specific NFs that are different from SBA in 5G core network. Secondly, MEC platform architecture is enhanced by introducing templates for dedicated MEC services. Thirdly, we present a MEC orchestration scheme, which contains parameter configuration and instantiation. We establish a testbed based on an open source software, and experiment results demonstrate that our proposed SBA-based MEC can provide users with customized services, e.g., low latency computing and high speed caching.
Energy Efficiency in Millimeter Wave based Cellular Networks with DUDe and Dynamic TDD

Millimeter wave (mmwave) based small cell base stations (SCBSs) in 5G standard proposed by 3GPP are expected to support increased data rates and capacity of the network at the expense of increased energy consumption. Dynamic time division duplexing (TDD) has also been proposed in 5G to further improve the throughput of these SCBSs by choosing their individual TDD time fractions. These mmwave based SCBSs are expected to coexist along with microwave based macro base stations (MBSs). Hence, downlink/uplink decoupling (DUDe) has been proposed by 3GPP to compensate for the transmit power imbalance between MBSs and SCBSs. In this work, the joint problem of energy efficiency (achieved through SCBS sleeping) and resource allocation for dynamic TDD with DUDe is formulated as an optimization problem in presence of a generalized a-fair scheduler. The tradeoff between throughput and energy efficiency is analyzed for static and dynamic TDD schemes, with and without DUDe. Further, we show a throughput gain of 28.4% with marginal or no loss in energy efficiency for a dynamic TDD system with DUDe as compared to a static TDD system.
Prior Based Grid Selection Algorithm for Compressed Sensing Based Direction of Arrival Estimation Methods

To reduce the number of processing channels, analog combination of large sensor arrays prior to digitization can be used. If there are few targets in the scene, compressed sensing techniques enable reliable detection of targets. In this study, when prior information on the target scene is available, a grid selection algorithm exploiting this prior information is proposed within a compressed sensing framework and performance increase is demonstrated over a set of simulations.
Cooperative Computing Anytime, Anywhere: Ubiquitous Fog Services

IoT provides ubiquitous connectivity and pervasive intelligence. Challenges arise from the data-intensive applications pertaining to IoT. The major contribution of this article is a new asymptotically optimal, fully decentralized, real-time framework which seamlessly integrates wireless computation offloading and fog computing in IoT networks with random traffic variations over space and time. Other enabling techniques are discussed to address practical challenges, such as outdated incentive for the cooperation of multiple service providers, massive access requests within limited system bandwidth, and computing acceleration. The new framework is able to provide ubiquitous computing for continuously increasing IoT services. Numerical experiments indicate that ubiquitous fog computing can substantially improve throughput, reduce computing latency, and cut off signaling overhead, without compromising the optimality of network operations.
Bias Allocation and Precoding for Tricolor Visible Light Communications with Signal-dependent Noise

Wavelength-domain multiplexing enables near parallel transmission for visible light communications (VLC). In this paper, we investigate an optimal bias allocation problem for the tri-color VLC channel when there is no color cross-talk (CoC), and propose a joint precoding and bias allocation algorithm when CoC exists. The optimization problems are formulated considering both lighting and communication requirements. The color constraint is depicted using the MacAdam ellipses on the chromaticity diagram, as an effective relaxation from a fixed point restriction. Also, for practical concerns, the addressed system is affected by signal-dependent noise (SDN). The bias allocation problem under SDN and related constraints is shown to be non-convex, thus a convex-concave procedure is utilized in this paper for convexification. The joint design problem is also non-convex, and an iterative optimization procedure is adopted, where the decoder is of Wiener filter form. Simulation results are given to show the impacts of SDN, CoC, and steps of MacAdam ellipses on system performance.
Quantum mechanical current-to-voltage conversion with quantum Hall resistance array

Accurate measurement of the electric current requires a stable and calculable resistor for an ideal current-to-voltage conversion. However, the temporal resistance drift of a physical resistor is unavoidable, unlike the quantum Hall resistance directly linked to the Planck constant h and the elementary charge e. Lack of an invariant high-resistance leads to a challenge in making small-current measurements below 1 µA with an uncertainty better than one part in 106. In this work, we demonstrate a current-to-voltage conversion in the range from a few nano amps to one microamp with an invariant quantized Hall array resistance. The converted voltage is directly compared with the Josephson voltage reference in the framework of Ohm’s law. Markedly distinct from the classical conversion, which relies on an artifact resistance reference, this current-to-voltage conversion does not demand timely resistance calibrations. It improves the precision of current measurement down to 8 × 10−8 at 1 µA.
An Improved Mobile Charging Algorithm for Wireless Sensor Networks

Recent years, Wireless Sensor Networks (WSNs) have been widely used in many applications, including intelligent transportation, health care and environmental monitoring. Since the sensors are usually powered by batteries with limited energy, the charging problem of sensor has attracted much attention of scholars. Most of the existing solutions require the mobile recharger to visit each sensor and perform the recharging task, which cause the problems of long charging path and low charging efficiency. This paper proposed an Improved Mobile Charging Algorithm, called IMCA, which recharges the sensor node in the WSNs based on multiple mobile charging vehicles. In such an algorithm, we first construct the energy consumption model of sensor node, then the charging requirement of each sensor is calculated and the charging path is designed based on the constructed model, finally the cooperative charging mechanism of multiple mobile charging vehicles is designed for complex network environment. Experiment results reveal that the proposed IMCA outperforms existing works in terms of charging efficiency, stability as well as the reliability.