Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of Manifold Microchannel Heat Sink inside Interposer

In the past 60 years, integrated circuits have been developing in the direction of smaller size and higher integration under the guidance of Moore's law. However, the heat dissipation problem of the chip becomes prominent as the chip size decreases and the integration degree increases. Therefore, this paper presents a manifold microchannel interposer based on advanced packaging technology. Compared with traditional heat sink, the interposer has high heat dissipation performance, low flow resistance, strong compatibility with IC and small thickness. The micro-channel structure of the heat sink optimized by COMSOL™ simulation software and fabricated by etching, photolithography, silicon-silicon direct bonding, thin polishing and other microelectromechanical systems (MEMS). A stable and reliable thermal testing system is designed and tested for the thermal testing of heat sink. The test results show that when the input flow rate is 40 ml/min, the heat dissipation capacity can reaches 550 W/cm2, and the chip surface temperature is only 128 □.
A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms

One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as hallucinations, functional deterioration, disorganized speech and hearing sounds and speeches that are non-existent. In this paper, a computerized approach based on optimization and classification is done to analyze the classification of schizophrenia from Electroencephalography (EEG) signals. As EEG can analyze a lot of brain disorders and is used to study the diseases of the brain in an in-depth manner, it can be used to analyze the schizophrenia EEG signals. In this paper, three feature extraction techniques are employed such as Partial Least Squares (PLS) Non linear Regression technique, Expectation Maximization based Principal Component Analysis (EM-PCA) technique and Isometric Mapping (Isomap) technique. The extracted features are further optimized with four optimization algorithms such as Flower Pollination algorithm, Eagle strategy using different evolution algorithm, Backtracking search optimization algorithm and Group search optimization algorithm. The optimized values are then classified with varied versions of both Adaboost classifier and Naïve Bayesian Classifier. The individual results show that for normal cases, Isomap features when optimized with Backtracking search optimization algorithm and classified with Modest Adaboost classifier, a classification accuracy of 98.77% is obtained. The individual results show that for schizophrenia case, when Isomap features are optimized with Flower Pollination optimization algorithm and classified with Real Adaboost classifier, a classification accuracy of 98.77% is obtained.
Accelerated Software Implementation of Authenticated Encryption Stream Ciphers for High Speed Applications

In this paper software implementation performance of CAESAR's competition round 3 Authenticated Encryption (AE) stream cipher algorithms is improved using Xilinx SDSoC tool. Xilinx SDSoC (Software Defined System-on-Chip) tool accelerates designs running on Zynq 7000 devices by implement heterogeneous co-design run on FPGA-CPU platform. AE schemes are providing both confidentiality and integrity of data which have a major role in wide number of applications such as IoT, Automotive, Medical devices, Sensors and real world protocols like TLS, SSH or IPsec. AE can be implemented either in software or in hardware. This paper will show that using SW-HW co-design improves the speed of the software implementation for ACORN, AEGIS, MORUS and Tiaoxin AE stream cipher algorithms.
End-to-End Speech Enhancement Using Fully Convolutional Networks with Skip Connections

The purpose of speech enhancement is to extract useful speech signal from noisy speech. The performance of speech enhancement has been improved greatly in recent years with fast development of the deep learning. However, these studies mainly focus on the frequency domain, which needs to complete time-frequency transformation and the phase information of speech is ignored. Therefore, the end-to-end (i.e. waveform-in and waveform-out) speech enhancement was investigated, which not only avoids fixed time-frequency transformation but also allows modelling phase information. In this paper, a fully convolutional network with skip connections (SC-FCN) for end-to-end speech enhancement is proposed. Without the fully connected layers, this network can effectively characterize local information of speech signal, and better restore high frequency components of waveform using lesser number of the parameters. Meanwhile, because of existence of skip connections in different layers, it is easier to train deep networks and the problem of gradient vanishing can also be tackled. In addition, these skip connections can obtain more details of speech signal in different convolutional layers, which is beneficial for recovering the original speech signal. According to our experimental results, the proposed method can recover the waveform better.
Parallel Implementation of K-Means Algorithm on FPGA

The K-means algorithm is widely used to find correlations between data in different application domains. However, given the massive amount of data stored, known as Big Data, the need for high-speed processing to analyze data has become even more critical, especially for real-time applications. A solution that has been adopted to increase the processing speed is the use of parallel implementations on FPGA, which has proved to be more efficient than sequential systems. Hence, this paper proposes a fully parallel implementation of the K-means algorithm on FPGA to optimize the system’s processing time, thus enabling real-time applications. This proposal, unlike most implementations proposed in the literature, even parallel ones, do not have sequential steps, a limiting factor of processing speed. Results related to processing time (or throughput) and FPGA area occupancy (or hardware resources) were analyzed for different parameters, reaching performances higher than 53 millions of data points processed per second. Comparisons to the state of the art are also presented, showing speedups of more than $15573\times $ over a partially serial implementation.
A mathematical Model for Selecting Features of Flooding Attacks Detection Methods based on Stability Criterion

In this era, the Internet represents the main tool of data searching and exchanging. By using Internet many apps in different locations, can be connected to each other remotely via web server connectivity. This connectivity is exposed to the internet vulnerabilities such as flooding attacks. Flooding attack is an example of an attack that causes harmful action against the web server resources or network bandwidth. Multivariate Correlation Analysis based Detection approach (MADM) is one of the statistical based detection (NIDS) approaches used to detect such attacks. However, MADM use predefined features that contain some intercorrelated features that have direct impact on classification performance. In this paper, a new extension on MADM architecture is proposed by adding SVM based feature selection method. So the mathematical formulation is explained. The proposed features selection method will be contributing in minimizing the false positive alarm rate and classification error rate.
A Two-Loop Hybrid Method for Optimal Placement and Scheduling of Switched Capacitors in Distribution Networks

This paper presents a method to find the optimal size and place of the switched capacitors using a hybrid optimization algorithm. The objective function includes the active and reactive power of power plants, the capital and maintenance costs of capacitor banks, and the cost of active and reactive power losses in distribution lines and transformers. The impact of the load model on the optimal sizing and placement of switched capacitors is studied using three different scenarios: In the first scenario, all loads are voltage-dependent; in the second scenario, only a portion of loads are voltage-dependent; in the third scenario, all loads are voltage-independent. The proposed hybrid algorithm incorporates an outer and two inner optimization layers. The outer layer is executed by a genetic algorithm (GA), while the inner layer is performed by a GA, an exchange market algorithm (EMA), or a particle swarm optimization (PSO). The performance of GA-GA, GA-EMA, and GA-PSO hybrid schemes are compared on an IEEE 33-bus test system. Moreover, IEEE 33-bus and 69-bus networks are used to verify the effectiveness of proposed hybrid scheme against the gravitational search algorithm (GSA), a combination of PSO and GSA (PSOGSA), cuckoo search algorithm (CSA), teaching learning-based optimization (TLBO), and flower pollination algorithm (FPA). The results highlight the advantage of the proposed hybrid optimization scheme over the other optimization algorithms.
A Compact Multi-band mm-Wave Franklin Array With Frequency Scanning Capability

The following paper proposes a novel multiband series-fed microstrip array based on Franklin's theory of collinear arrays (CoA). Conventional dipole elements have been replaced by rectangular patch elements (W / L > 1) to increase the gain with a lower number of radiating elements. C-shaped Coupled line sections are used as delay line structures to compensate the phase differences among the radiating elements while eliminating the undesired radiation of conventional λ /2 delay line structures. The proposed delay line structures are also shortened compared to conventional transmission line feed networks. Radiating patches have been loaded with U slots for multi banding and decreasing the physical size of each patch to less than λg /4. The ground plane is patterned for optimized matching. Results indicate a frequency beam scanning capability in range of 21rv26 GHz, which makes it suitable for various mmwave applications, including drone detection radars. Radiation is broadside at 26 GHz, and the simulated gain is 10.2 dB at the same frequency. Simulation results have been processed by CST software and multiple computational methods is done to prove the validity of Franklin CoA theory on the proposed work.
A Review of Control Methods on Suppression of 2ω Ripple for Single-Phase Quasi-Z-Source Inverter

One of the disadvantages of the charging systems and voltage regulation systems in alternative energy systems is that when the direct current (DC)/alternative current (AC) or AC/DC conversion is made from the energy source, the energy flow at <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> frequency between the direct current side and the alternative current side causes ripples in the DC, inductors, and capacitors. The ripples that occur in <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> frequency increase the harmonic distortion and reduce the efficiency in the photovoltaic energy system. For addressing this problem, different control methods are evaluated in this paper for the elimination of ripples that occur at <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> frequency, both on the perspective of DC control side and AC control side with a thorough comparative analysis with existing results in the literature. In the present study, the model of Single-Phase Quasi-Z Source (QZS) inverter and closed-loop control methods of the inverter are examined and compared with pulse width modulation (PWM) methods. In addition, the control methods are used to eliminate the ripples at <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> frequency in the use of inverters, and the advantages and disadvantages of these methods are presented.
Divide-by-2 Injection-Locked Frequency Dividers Using the Electric-Field Coupling Dual-Resonance Resonator

This article designs and analyzes <italic>LC</italic> injection-locked frequency dividers (ILFDs) using dual-resonance <italic>LC</italic> resonator. The ILFD consists of two single-resonance <italic>LC</italic>-tank capacitive cross-coupled sub-ILFDs operating at 3.7 and 5.1 GHz, respectively, and the two sub-ILFDs are coupled by the electric field through a pair of metal–insulator–metal (MIM) capacitors. The die area in the Taiwan Semiconductor Manufacturing Corporation (TSMC) 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS is <inline-formula> <tex-math notation="LaTeX">$0.65\times1.023$ </tex-math></inline-formula> mm<sup>2</sup>. By controlling the gate voltages of the switching transistors, the ILFD has three different operational modes—high-band dominant mode, low-band dominant mode, and concurrent oscillation mode. Overlapped locking range is demonstrated for the first time in the designed electric-field coupling resonator ILFD. Non-overlapped locking range is found for the ILFD manifesting an effect of the dual-resonance resonator.
A Study on UE Clustering and Antenna Selection for Distributed MIMO Cooperative Transmission using Multi-user MMSE-SVD

Distributed multiple-input multiple-output (MIMO) is a type of the centralized radio access network (C-RAN) to achieve a high link capacity uniformly over the service area. A large-scale distributed MIMO requires prohibitively high computational complexity. Clustering of user equipments (UEs) can reduce the computational complexity, however, this produces the inter-cluster interference (ICI). In this paper, we propose the UE clustering and antenna selection beyond the cluster boundary assuming multi-user minimum mean square error filtering combined with singular value decomposition (MMSE-SVD). We exploit the multiplexing-diversity tradeoff in MMSE-SVD to suppress effectively the ICI while keeping the multi-user multiplexing capability. It is shown that the proposed UE clustering and antenna selection can achieve 6 times higher sum capacity and 1.6 times higher UE capacity, compared to the without clustering.
Spherical Position Dependent Rate-Distortion Optimization for 360-degree Video Coding

360-degree video in spherical format cannot be well handled by the conventional video coding tools. Currently, most of the 360-degree video coding methods first project the spherical video content onto a 2-dimensional plane and then compress the projected video using a conventional video codec. However, the projection conversion process will cause an irreversible conversion error, which indicates that the reconstruction quality of the projected video cannot fully represent that of the spherical video. In view of this, this paper proposes a spherical position dependent rate-distortion optimization (RDO) approach for 360-degree video coding. During the RDO process, spherical reconstruction quality is taken into consideration and calculated according to the spherical position of the pixels in each coding unit (CU). Furthermore, the Lagrangian multiplier and quantization parameter are adjusted accordingly. The proposed method is implemented on HEVC reference software HM-16.7. Experimental results show that the proposed method can achieve better coding performance, compared with HM-16.7.
A Perspective of Thai Government information Service

Information is an important and vital thing for a citizen to raise awareness and understanding. The increasing growth factor of information technology has made information become an indispensable part of supporting citizen’s lives. The role of the government sector defines the purpose of the form of government information services. The service is the needs of the government service providers rather than the needs of the citizen who are the service recipients. Therefore, the development of data services should focus on increasing the quality of the information, choosing the type of service through a partner that the public can access easily, including presenting the information that the people want.
G3M: A heuristic for low-latency multicast back-pressure forwarding

It has been previously proven that optimal solutions to mulitcast routing and scheduling can be achieved using back-pressure together with network coding. However, this optimality comes at the cost of increased latency and complexity, which cannot be afforded in tactical military operations. In this work, we present the Greatest Group Gradient for Multicast over back-pressure (G3M), a heuristic that greatly reduces complexity, state and latency when compared to solutions that use network coding, while approaching the same optimal solutions. Unlike most prior approaches, G3M does not rely on the construction of explicit multicast trees. Instead, it uses only queue information from the 1-hop neighborhood to make distributed forwarding decisions, that are globally optimal. Additionally, we present updates to the admission control algorithm that promotes fairness between multicast flows with different characteristics. With this in place G3M can use the same forwarding algorithm can be used with both unicast and multicast traffic. We also introduce an algorithmic enhancement to back-pressure forwarding, which we call opportunistic forwarding, to reduce the replication of multicast packets, allowing the heuristic to operate closer to the optimal solution during transient network conditions. Our results show that the G3M heuristic converges to the provable optimal solution.
Man-Machine Interaction Reliability Modeling Method Based on Markov Model

The development of man-machine system poses a challenge to human’s information processing ability. Therefore, the human cognitive characteristics and the dynamic man-machine interaction (MMI) become the focus of the MMI research. This study takes the MMI process of complex system as the research object. According to multi-task and time-pressure scenarios, two kinds of MMI fault modes such as cognitive overload and cognitive confusion are proposed. In addition, this paper studies their failure mechanism and the uncertainty of MMI logic. And then a modeling method of the two faults based on Markov model are proposed. The corresponding quantitative calculation methods to complete the modeling and prediction of MMI reliability are introduced. At last, a case application proves the rationality and feasibility of the method.
3-D Printed Wideband Millimeter-Wave Horn Antenna With Conical Radiation Pattern

A wideband millimeter-wave (mmW) circular aperture horn antenna with a conical radiation pattern realized by the metallic three-dimensional (3-D) printing technology is presented in this letter. The proposed structure consists of a coaxial waveguide and a quadruple-ridged conical horn. The horn antenna is fabricated using a direct metal laser sintering (DMLS) technology, and both manual polishing and yellow-gold coating are employed to realize a surface roughness of Ra = 1.6161 μm. The overall size of the fabricated antenna is 20 mm × 20 mm × 15.8 mm. Experimental measurements show that a wide relative impedance bandwidth of 60.7% and a peak gain of 11.4 dBi can be achieved within the frequency band of 24.9–46.6 GHz.
Optimization-Based Posture Generation for Whole-Body Contact Motion by Contact Point Search on the Body Surface

Whole-body contact is an effective strategy for improving the stability and efficiency of the motion of robots. For robots to automatically perform such motions, we propose a posture generation method that employs all available surfaces of the robot links. By representing the contact point on the body surface by two-dimensional configuration variables, the joint positions and contact points are simultaneously determined through a gradient-based optimization. By generating motions with the proposed method, we present experiments in which robots manipulate objects effectively utilizing whole-body contact.
Combining Model Predictive Path Integral with Kalman Variational Auto-encoder for Robot Control from Raw Images

In this paper, we propose a framework for model learning and control of complex non-linear systems from such high-dimensional and redundant sensor observations as raw pixel images. Our framework combines Kalman variational auto-encoder (KVAE) and model predictive path integral (MPPI) to exploit both advantages of the efficient learning and inference algorithms thanks for the simple structure of latent dynamics in KVAE and sampling-based derivative-free computation of control in MPPI. We applied our framework for an air hockey task in simulations, and simulation results verified its effectiveness for model predictive control from the raw images capturing the task environment as observations without directly observing the states in the controller.
On the Evaluation of the Machine Learning Based Hybrid Approach for Android Malware Detection

Over the past few years, Android Application is deemed as one of the fastest-growing technology areas. On the other hand, the rapid growth of android applications also increases the security threats for Android users in the form of malware. Malware hacks the personal information of a user and exploits it in different criminal activities. To date, various studies have been conducted for the detection of android malware. Some authors have preferred static analysis while others have performed dynamic analysis for android malware detection. In static analysis, researchers have only employed one feature of android application either intents or permissions. As best of our knowledge, there does not exist any technique that combines both intents and permissions. In this paper, we have performed static analysis for the detection of Android malware using android intents (both implicit and explicit), android permissions and combination of intents and permissions. Furthermore, the classification is performed to analyze the effectiveness of different machine learning algorithms for malware detection and to identify the best performing feature. Our experiment results show that combination of intents and permissions play a key role in the detection of android malware.
Multi-Channel SAR System Design for HRWS and GMTI in Low PRF

High-resolution and wide-swath (HRWS) is an important challenge in synthetic aperture radar (SAR). Combined SAR with array, the azimuth dual-channel SAR configuration achieves high azimuth resolution and HRWS in low Pulse repetition frequency (PRF) by doppler band synthesizing technology. However, low PRF introduces ambiguity to ground moving target indication (GMTI). Interferometry is performed between two look data in the range frequency domain to resolve the ambiguity by decreasing the interferometry phase. As a result, the phase becomes insensitive to the radial velocity of moving targets. This condition brings difficultly in moving target detection. Two channels together with two equivalent channels are exploited fully to solve these problems. The four-channel interferometry is also conducted to improve the GMTI performance. Parameters, including system and motion parameters, are estimated unambiguously to complete HRWS imaging and GMTI. System parameter design is also considered to satisfy the requirements of HRWS and GMTI simultaneously. Simulations demonstrate the validity of the proposed methods.