Miracle-dz/UniFAR-FTUs huggingface dataset:
Deterministic Modeling for mmWave Outdoor Street Canyon Channel

The millimeter wave (mmWave) bands, a promising resource for 5G systems, suffer considerable attenuation over distance which can be mitigated by employing directional antennas with narrow beams. As 5G awaits standardization, it is vital to develop models that effectively describe the unique mmWave channels. In this paper, a custom ray tracing based directional deterministic mmWave outdoor channel model is presented. The bit error rate performance is analyzed and compared with those for both AWGN and independent and identically distributed (iid) Rayleigh fading channels. It is observed that the channel impacts differently for vertical and horizontal polarizations when the receiver is located at a closer distance from the transmitter, whereas the performance is comparable for large separation distances. This study is significantly important for effective system level simulations.
Forecast-Based Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs

Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, most of previous works assumed that mixing-then-splitting would get two identical output droplets, which is not always true due to unbalanced splitting. As a consequence, those works may fail to provide correct solutions at the presence of unbalanced splitting. Several methods have been proposed to deal with this issue. Nevertheless, some of them rely on hypotheses that may not be practical, while the others demand extra reactants or special hardware. In this paper, we propose a new probability-based sample preparation algorithm for unbalanced splitting correction. Our new algorithm not only guarantees a correct solution, but requires neither extra reactants nor on-chip special hardware. Experimental results show that the effect of unbalanced splitting can be eliminated only at the cost of 20% more operation steps. That is, the proposed algorithm is both reliable and efficient.
A Parallel Approach of Weighted Edit Distance Calculation for Log Parsing

For modern software systems, larger numbers of log massages have been generated every day. By analyzing these log messages with vital information such as exception reports, developers can manage and monitor software systems efficiently. Each log message in the log file consists of a fixed part (template) and a variable part, and the fixed parts of log messages with one event type are the same, while the variable part are different. LKE (Log Key Extraction), a widely used log parser for analyzing log messages, can find the fixed parts efficiently, due to the cluster strategy base on the calculation of weighted edit distance between log messages. However, it is time-consuming to calculate the weighted edit distance for large scale log files. In this paper, we proposed a parallel approach using a unique hierarchical index structure to calculate the weighted edit distance on GPU (Graph Processing Unit). GPU has an advantage of high parallelism and is suitable for intensive computing, therefore, the time required to process large-scale logs could be reduced by this approach. Experiments show that LKE parser using GPU to calculate the weighted edit distance has high efficiency and accuracy in the HDFS data set and the marine information data set.
Multilayer Perceptron and Long Short-Term Memory for Predicting Indonesian Composite Stock Price Index Using Macroeconomic Factors

Capital markets are complex and dynamic, so they have risks and uncertainties. The main activity in the capital market is investment. There are several instruments for investing, namely gold, land, savings, deposits, bonds and stocks. Investors need a variety of information to help determine the right momentum to invest, one of which is the stock price in the future. By making predictions, investors can minimize the risk of loss in investing. The purpose of this study is to compare the Multilayer Perceptron and Long Short-Term Memory algorithm in predicting Indonesian Composite Stock Price Index in the future based on macroeconomic factors. This study uses Dow Jones Industrial Average, Shanghai Stock Exchange, world gold prices, world oil prices, USD to IDR exchange rate, inflation, BI Rate, and money supply as macroeconomic factors to predict the price of the Indonesian Composite Stock Price Index. The time series used for the research data is monthly, from January 1991 to December 2018. Based on the results of the evaluations that have been conducted, this study found that modeling using the Multilayer Perceptron algorithm has a better performance than Long Short-Term Memory. Evaluation using Root Mean Squared Error and R-squared each produces a value 86.86% and 3.19% for Multilayer Perceptron; 74.24% and 3.94% for Long Short- Term Memory.
Influence of Microwave Pulse Power on The Burnout Effect of The AlGaN/GaN HEMT in a LNA

The influence of microwave pulse power on the burnout effect of the AlGaN/GaN high electron mobility transistor (HEMT) in a low noise amplifier (LNA) is studied. The LNA constructed by an AlGaN/GaN HEMT is modeled by using our independent-developed device-circuit joint simulator. The correctness of our simulator is verified by comparing the simulated DC characteristics of the studied HEMT by using our simulator and two kinds of commercial software. The simulated gains obtained by using our simulator and the commercial software ADS are in agreement with the measured results. The results show that the relationship between the injected power and the actual absorbed power satisfies a linear relationship, and the burnout time decreases with the increase of the injected power.
Path Planning and Robot Localization using Internet of Things and Machine Intelligence

The aim of this research is to build an autonomous robot by integrating hardware components such as PIC microcontroller, APR voice module, RSSI module, ultrasonic sensors, GSM module, and DC motor. The proposed robot represents a boat node that is integrated with the Zigbee technology. The ZigBee technology measures the received signal strength (RSSI), which is used to determine the location of the boat node. Furthermore, the autonomous robot node also utilizes ultrasonic sensors in detecting obstacles and avoiding them. The robot is also integrated with computational logic and artificial intelligence that help the robot node in its autonomous path identification. Our proposed work integrates the concepts of embedded systems, internet of things and artificial intelligence to provide a continuous monitoring system that can be useful for maritime purposes.
Design and Analysis of High Gain Interleaved Switched Capacitor DC-DC Converter

A DC-DC converter is one of the converters broadly used in power electronics applications and finds its place in designing of Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs), subway cars etc. A Switched Capacitor (SC) DC-DC converter consists of four switches especially MOSFET along with two capacitors that smoothens the output voltage ripples and current spikes gets reduced by the stray inductor. Furthermore, interleaving of the switched capacitor is done by connecting the circuits in series and in parallel with a certain phase displacement of $2\ast \pi/\mathrm{Z}$, where Z is the number of parallel branches and increases the voltage gain and current gain respectively. Interleaving is a technique that reduces the number of passive filter components as the branches are increased thus reducing the current stress. In this paper simulation results of high gain interleaved switched capacitor DC-DC converter are presented.
Enhancing the Performance of Underwater Optical Wireless Communication using Post Nonlinear Equalization

We experimentally demonstrate a long-reach underwater optical wireless transmission system using post nonlinear equalization. Compared with linear equalization, 2.52dB/4.0dB SNR gain and 0.95Gbps/1.26Gbps capacity improvement are achieved by FFE/DFE through 45-m underwater transmission distance.
Low-Complexity Maximum Likelihood (ML) Decoder for Space-Time Block Coded Spatial Permutation Modulation (STBC-SPM)

Space-time block coded spatial permutation modulation (STBC-SPM) is a new MIMO technique that integraes the STBC-spatial modulation (STBC-SM) and the recently-proposed spatial permutation modulation (SPM). Specifically, SPM exploits the permutation vector to achieve diversity gain by dispersing the signal along the time coordinate. In this work, we examine the STBC-SPM in more details and propose a low-complexity decoder for STBC-SPM by utilizing the orthogonality of the STBC Alamouti code. The numerical simulations demonstrate the superior performance of the STBC-SPM from various aspects. Moreover, the proposed decoder structure achieves up to 90.6% ~99. 9 % complexity saving without sacrificing the error rate performance, compared with the optimal maximum likelihood (ML) decoder implemented by the exhaustive search.
Free Space Optical Systems: Full-Duplex Versus Half-Duplex Buffer-Aided Relaying

In this paper, we analyze dual-hop buffer-aided (BA) relaying in the context of Free Space Optical (FSO) communications. Based on a Markov chain analysis, we study the impact of full-duplexity (FD) and half-duplexity (HD) on the outage and delay performance of the three-node network. Results highlight that FD relaying concurrently reduces the outage and delay of the network while max-link HD relaying does not enhance the diversity order compared to buffer-free systems. Our analysis also highlights on the impact of the buffer size and relay position on the achievable performance levels.
On the Impact of Different Fabrication Materials on Fingerprint Presentation Attack Detection

Presentation Attack Detection (PAD) is the task of determining whether a sample stems from a live subject (bona fide presentation) or from an artificial replica (Presentation Attack Instrument, PAI). Several PAD approaches have shown high effectiveness to successfully detect PAIs when the materials used for the fabrication of these PAIs are known a priori. However, most of these PAD methods do not take into account the characteristics of PAIs’ species in order to generalise to new, realistic and more challenging scenarios, where materials might be unknown. Based on that fact, in this work, we explore the impact of different PAI species, fabricated with different materials, on several local-based descriptors combined with the Fisher Vector feature encoding, in order to increase the robustness to unknown attacks. The experimental results over the well-established benchmarks of the LivDet 2011, LivDet 2013 and LivDet 2015 competitions reported error rates outperforming the top state-of-the-art in the presence of unknown attacks. Moreover, the evaluation revealed the differences in the detection performance due to the variability between the PAI species.
Fast Root Finding for Interpolation-Based Decoding of Interleaved Gabidulin Codes

We show that the root-finding step in interpolation-based decoding of interleaved Gabidulin codes can be solved by finding a so-called minimal approximant basis of a matrix over a linearized polynomial ring. Based on existing fast algorithms for computing such bases over ordinary polynomial rings, we develop fast algorithms for computing them over linearized polynomials. As a result, root finding costs $O^{\sim}(\ell^{\omega}\mathcal{M}(n))$ operations in $F_{q^{m}}$, where ℓ is the interleaving degree, n the code length, $F_{q^{m}}$ the base field of the code, $2 \leq \omega \leq 3$ the matrix multiplication exponent, and $\mathcal{M}(n) \in O(n^{1635})$ is the complexity of multiplying two linearized polynomials of degree at most n. This is an asymptotic improvement upon the previously fastest algorithm of complexity $O(\ell^{3}n^{2})$, in some cases $O(\ell^{2}n^{2})$.
Evaluating the Broad Range Performance of a Radial-Flux PMSG

This paper presents an exhaustive parametric study for evaluating the broad range performance of a Radial-Flux Permanent Magnet Synchronous Generator (RF-PMSG) which is typically used in direct-driven low-power wind turbines. To accomplish this aim, it is proposed also in this paper a general methodology for designing a 10 kW RF-PMSG. In this study, a numerous set of test cases are carried out for analyzing the power quality, power losses, efficiency, cogging torque and ripple torque. The results of this parametric study can help designers to make fast decisions for choosing the optimal design of a RF-PMSG. Moreover, the results provided can also be used to design other components or subsystems of a wind turbine such as the power converter and the wind rotor.
Analysis and optimization of electromagnetic compatibility for electric vehicles

Due to the changes of energy storage sources, driving systems, vehicle control units, etc., the electromagnetic compatibility (EMC) of electric vehicles is facing greater challenges than that of conventional oil-fueled vehicles. On the one hand, the use of high-power and high-voltage electrical components will generate high electromagnetic interference (EMI) energies in actual operation. On the other hand, automotive electronics with high integration and sensitivity is more susceptible to EMI, which is directly related to the safety of vehicles. In this paper, the EMI mechanism and suppression measures of motor driving systems, charging systems and other low-voltage systems are investigated. The results show that the main sources of EMI in electric vehicles are the quick switching of power switches, the operation of motor windings and the issue of signal coupling between high voltage cables and low voltage cables, and the EMI can be suppressed effectively by shielding, filtering and optimization of system principles.
Short Cavity Single-Mode DBR Lasers Based on High-Order Slotted Surface-Gratings using Narrow Slot-Width

We present re-growth free DBR lasers based on high-order slotted surface-gratings using narrow slot widths ∼100 nm through standard photolithography. The fabricated single mode laser worked with a short cavity length of 230 μm. The threshold is about 27 mA with side-mode suppression-ratio (SMSR) > 40 dB and slope-efficiency ∼0.1 mW/mA.
Cross Conditional Network for Speech Enhancement

In the signal processing field, there is a growing interest in speech enhancement. Recently, a lot of speech enhancement methods based on the deep neural network have been proposed. Mostly, these networks, such as SEGAN, Wave-U-Net, adopt the autoencoder structure. In this paper, we propose the cross conditional network for speech enhancement based on SEGAN architecture. The proposed network has two Auto-Encoder, where the mutual latent vector is composed of the concatenated vector of these encoder outputs. In the experiments, we show that the proposed method exceeds SEGAN in terms of the objective evaluation measure by PESQ.
Algorithmic Analysis of Distance-based Monarch Butterfly oriented Deep Belief Network for Diabetic Retinopathy

This paper proposes a newDiabetic Retinopathy detection model with four phases including (i) Preprocessing, (ii) Blood Vessel Segmentation, (iii) Feature Extractionand (iv) Classification. At first, pre-processing is performed using the Contrast Limited AHE (CLAHE) and median filtering methods. To segment the blood vessels, Fuzzy C-Mean (FCM) thresholding is worked well for making rough clustering of pixels. Further, features like local features and morphological transformation-based features are extracted from the segmented blood vessels. Finally, Deep Belief Network (DBN) classifier is used to classify the extracted features into a healthy and affected image. Optimization logic is undertaken in this work, where the number of hidden neurons in DBN is optimized using modified Monarch Butterfly Optimization (MBO) termed as Distance-based MBO (D-MBO). Finally, Algorithmic analysisis performed under different performance measures like positive and negative measures: Accuracy, Sensitivity, Specificity, Precision, FPR, FNR, NPV, FDR, $\mathbf{F}_{\mathbf{1}}$ Score and MCC.
Pilot Allocation for Iteration Reduction of Channel Estimation Using MF-Based Interpolation

This paper studies an iteration reduction of channel estimation method using matrix factorization (MF) based interpolation. It is important to set the initial value close to the optimal solution to reduce the iteration. To obtain an appropriate initial value, exploiting the existing methods with low computational complexity such as linear interpolation is reasonable. While MF-based interpolation can achieve high accuracy when randomly determined pilot symbol allocation, it is not suitable for the existing method. This paper proposes a pilot allocation where fixed position and the randomly determined position are mixed to perform both the linear interpolation and MF-based interpolation. Computational simulation result shows the iteration reduction by the proposed pilot symbol allocation.
Advances of Study on Angiogenesis and Antiangiogenic Therapy in Hepatocellular Carcinoma

Objective To introduce the possible effects and significances of angiogenesis and antiangiogenic in the development and treatment of hepatocellular carcinoma(HCC). Methods Recently relevant literatures were reviewed.Results Angiogenesis played a significant role in the development and therapy of HCC,and the development and metastasis of HCC could be effectively suppressed by antiangiogenic therapy.This might provide a new approach for the treatment of HCC.Conclusion Comprehending the molecular mechanism of angiogenesis and applying antiangiogenic therapy will contribute a lot for the prevention and treatment of HCC.
A Supervised Learning Method for the Design of Linear Phase FIR Digital Filter Using Keras

In this paper, a supervised learning method for the design of linear phase FIR digital filter using Keras is presented. First, the design problem of the linear phase finite impulse response (FIR) digital filter is transformed to a supervised learning problem. Then, the optimizers in Keras framework are used to determine the filter coefficients by minimizing the mean squared error (MSE) loss function. The widely-used optimizers include adaptive moments (Adam) algorithm and stochastic gradient descent (SGD) with momentum algorithm. Finally, the numerical design examples of low-pass and high-pass FIR digital filters are demonstrated to show the usefulness of the supervised learning method with Keras framework.
Equity in the Who, How and What of Computer Science Education: K12 School District Conceptualizations of Equity in ‘CS for All’ Initiatives

Equity is arguably an agreed upon value within the Computer Science education (CSed) community, and perhaps even more so within efforts to universalize access to CSed within K12 settings through emerging ‘CS for All’ initiatives. However, stakeholders often mean different things when referring to equity, with important implications for what CS teaching and learning looks like in schools. In this paper, we explore the question of how K12 school district actors’ conceptualizations of equity manifest within their planning and implementation of district-wide CSed initiatives. Based on a research-practice partnership aimed at supporting and researching district-wide CSed initiatives, data presented - interviews with district faculty, district planning documents, meeting transcripts and field observations - were drawn from five participating school districts as they made decisions and enacted activities over 11 months in areas including vision-setting, curriculum, professional development, leadership efforts and use of formative data about implementation. Analyzing these data through equity frameworks found in CSed literature, we highlight three distinct but interconnected ways that district actors conceptualized equity within their CSed initiatives: (1) equity in who Computer Science is for, (2) equity in how Computer Science is taught, and (3) equity in what Computer Science is taught. Data show that these varied conceptualizations resulted in different kinds of decisions about CSed in districts. We discuss the implications of these findings in terms of their relevance to equity-oriented CS education researchers, and what lessons they hold for policy-makers and education leaders engaged in their own efforts to support equitable computer science education.
A Low-profile Broadband Balun Fed Circular Patch Antenna

A low-profile broadband circular patch antenna that can excite TM11and TM12 modes is proposed. The proposed antenna mainly consists of an annular slotted circular radiation patch, a metal ground, a set of shorting pins, and a vertical grounded balun feeding structure. One side of the vertical feeding substrate is slotted ground and the other side is a microstrip feeding line. A cross-shaped slot is introduced on the circular radiation patch, it improves the impedance matching of the antenna significantly. With a profile of 0.063 λ0 (λ0 is the center operating wavelength in the free space), the −10 dB impedance operating band is 3.49-5.5 GHz and the relative bandwidth is 44.7%.
Wagon Number Recognition Based on the YOLOv3 Detector

The automatic recognition of wagon number plays an important role in the railway transportation system, but high-precision recognition is still challenging due to many disturbing factors, such as uneven illumination, complex background, image fouling and various type of wagon number. To reduce the complexity of the problem, we design a two-stage approach for wagon number recognition based on the state-of-the-art object detector YOLOv3. Firstly, the region of wagon number is detected by one detector and cropped according to the predicted bounding box, then the digits that make up the wagon number are detected and recognized in the cropped image by another detector. During the whole process, there is no need for image preprocessing and character segmentation, which reduce the accumulation of intermediate error and improve the speed of recognition. Experimental results show that the approach achieves a recognition rate of 96.08% and speed of 70ms per image on 1072 test images.
Improved Step Detection and Step Length Estimation Based on Pedestrian Dead Reckoning

In the indoor environment, the Global Positioning System (GPS) cannot provide accurate location information due to the complexity of the internal structure of buildings and the interference of indoor equipments. Pedestrian Dead Reckoning (PDR) is an indoor positioning method that relies on smartphone carrying sensors (such as accelerometer, gyroscope, and magnetometer) without requiring additional equipments. The intense electromagnetic interferences caused by indoor electronic equipments have serious influences on the accuracies of indoor location. This paper presents an improved step detection and counting method based on PDR. There is continuity in the walking process. In order to make full use of this feature, this paper firstly filters and differential processes the acceleration data obtained from the smartphone, then uses the improved peak detection algorithm to detect and count the steps. This paper compares two algorithms of step length estimation, and presents an improved step length estimation algorithm based on differential acceleration data. As a result, this paper improves the accuracies of both step number detection and step length estimation.