Miracle-dz/UniFAR-FTUs huggingface dataset:
A 30Gb/s 2x Half-Baud-Rate CDR

This paper presents a 2x half-baud-rate clock and data recovery technique that locks to the edge by performing 2x oversampling at half-baud-rate (every other UI). A test-chip was fabricated in TSMC 28nm HPC CMOS technology demonstrating a 30 Gb/s 2x half-baud-rate CDR with a Tyco 5” channel with 13.06 dB loss at Nyquist. The total power consumption is measured to be 79.2 mW (FOM of 2.64 pJ/bit) for 30 Gb/s PRBS31 input data.
The Influence of a Rotating Magnetic Field on the Intensity of Methane Formation in a Bioreactor

The analysis of the physicochemical processes of anaerobic fermentation under the influence of a magnetic field of low frequency is carried out. The results of modeling and laboratory studies of the effect of a rotating magnetic field on a water substrate of animal and vegetable waste are given. Magnetic treatment contributes to the dissolution of carbon dioxide in water, which contributes to an increase in the share of biomentane through the reduction of carbon dioxide with fatty acids. The acceleration of the onset of the maximum yield of biomethane for two days in comparison with the control sample and a significant increase in intensity in the initial period of fermentation were noted.
6G Wireless Communications: Vision and Potential Techniques

With the fast development of smart terminals and emerging new applications (e.g., real-time and interactive services), wireless data traffic has drastically increased, and current cellular networks (even the forthcoming 5G) cannot completely match the quickly rising technical requirements. To meet the coming challenges, the sixth generation (6G) mobile network is expected to cast the high technical standard of new spectrum and energy-efficient transmission techniques. In this article, we sketch the potential requirements and present an overview of the latest research on the promising techniques evolving to 6G, which have recently attracted considerable attention. Moreover, we outline a number of key technical challenges as well as the potential solutions associated with 6G, including physical-layer transmission techniques, network designs, security approaches, and testbed developments.
Efficiency Evaluation of a Single Phase And a Three Phase Dual Active Bridge Isolated DC-DC Converter

This paper presents the efficiency evaluation of the DC to DC Dual Active Bridge converter. Both single phase as well as three phase converters topologies have been considered in this study. Based on Dual Active Bridge DAB theoretical equations and the loss model of power semiconductor devices, full analysis of system efficiency considering various control parameters such as duty cycles, output power, and phase shifts. Simulation results are presented to validate the performance of three-phase DAB model for high power transfer considering loss model of the power converter and efficiency computation.
A Deep Convolutional Neural Network for Bangla Handwritten Numeral Recognition

Despite being one of the major languages in the world, research regarding Bengali handwritten numeral recognition (BHNR) isn't enough in comparison with the other prominent languages. Existing methods mostly rely on feature extraction and some older machine learning algorithms. Recent bloom in machine learning due to deep neural network especially using Convolutional Neural Network (CNN) showing promising results in this field with better accuracy. Some recent works show very good accuracy only in recognizing plain simple digits but perform poor in challenging scenario because of lack of large and versatile training dataset. In this work, we propose a method where our proposed CNN model which recognizes numerals with high degree of accuracy beyond 96%, even in most challenging noisy conditions. Initially 72000+ specimens were used from NumtaDB (85000+) dataset for training and 1700+ specimens were used as test dataset. The improvement in performance in challenging scenarios is observed, when training specimens are augmented to create a training dataset of size about 114000 specimens. The performance of our proposed model also compared with other existing works and presented here. These findings are based on Computer Vision Challenge on Bengali HandWritten Digit Recognition (2018) competition submissions.
A Click Fraud Detection Scheme based on Cost sensitive BPNN and ABC in Mobile Advertising

Click fraud happens in cost per click ad networks where publishers charge advertisers for each click. Click fraud is posing the huge loss to the mobile advertising industry. The conventional technologies use ensemble machine learning methods, neglecting the cost of incorrect classification for a fraud publisher is higher than a normal publisher. An effective classification model for variable click fraud is proposed in this paper. Cost-sensitive Back Propagation Neural Network is combined with the novel Artificial Bee Colony algorithm in this research (CSBPNN-ABC). Feature selection is synchronously optimized with BPNN connection weights by ABC to reduce the interaction between features and weights. Cost Parameters are added to BPNN by correcting the error function. Experiments on real world click data in mobile advertising show that its superior classification performance compared with the state-of-the-art technology.
Multi-focus Image Fusion Framework Using Total Variation and Phase Congruency

Multi-scale decomposition (MSD) strategy is wildly applied to extract characteristics at different scales in image fusion field, and then the different image components will stay in different layers. To avoid the negatively impact that using the same fusion rule on different scales, a Gaussian filter MSD-based of fusion method with total variation (TV) and phase congruency (PC) is proposed in this paper. This framework consists of three parts: First, decompose the source images into base layer and detail layer through Gaussian filter; Then, TV and PC are set as the representations of base and detail layers, respectively; Finally, combine the fusing two layers to reconstruct the fused image. Some fusion methods and several quantitative analyses are displayed, and the experimental results show that the proposed method has a superior performance compared with some other popular fusion methods, both in human vision and objective evaluations.
Multi-Agent Systems in ICT Enabled Smart Grid: A Status Update on Technology Framework and Applications

Multi-agent-based smart grid applications have gained much attention in recent times. At the same time, information and communication technology (ICT) has become a crucial part of the smart grid infrastructure. The key intention of this work is to present a comprehensive review of the literature and technological frameworks for the application of multi-agent system (MAS) and ICT infrastructure usages in smart grid implementations. In the smart grid, agents are defined as intelligent entities with the ability to take decisions and acting flexibly and autonomously according to their built-in intelligence utilizing previous experiences. Whereas, ICT enables conventional grid turned into the smart grid through data and information exchange. This paper summarizes the multi-agent concept of smart grid highlighting their applications through a detailed and extensive literature survey on the related topics. In addition to the above, a particular focus has been put on the ICT standards, including IEC 61850 incorporating ICT with MAS. Finally, a laboratory framework concepts have been added highlighting the implementation of IEC 61850.
Resource Allocation for Downlink SCMA System Based on Coalitional Game

Sparse code multiple access (SCMA), a newly-developed non-orthogonal multiple access technology, will play a pivotal role in future wireless communication networks. Although SCMA has drawn much attention, some problems still need to be addressed, especially the problem of wireless resource management. This paper analyzes the resource allocation problem of SCMA based downlink system and decomposes the resource allocation process into two steps: codebook combination and codebook allocation. A novel Maximization of High-Quality CodeBook (M-HQCB) algorithm for codebook combination and a coalitional game based Maximization of Group Proportional Fairness (M-GPF) algorithm for codebook allocation are proposed to improve the system throughput. System-level simulation results demonstrate that the combination of M-HQCB and M-GPF has up to 20% throughput gain comparing with the combination of traditional Random Codebook Combination (RCC) and Proportional Fair (PF) codebook allocation scheme at certain expense of fairness.
Stereo Matching Based on Improved Census Transform

In order to reduce the mismatch rate of binocular stereo matching algorithm, a stereo matching algorithm based on improved Census transform is proposed in this paper. Firstly, the classical Census transform is improved, the mean value rather than the center pixel is used as the reference value, and the three states based on the pixels' distances are used to describe the relative relationship between each pixel in the transform window and the reference value; Secondly, the sparse transform window is used to reduce the computational complexity of the algorithm, and the transformed Hamming distance is calculated as the matching cost; Finally, in the maximum disparity range, the points with the least matching cost are selected as the matching points by WTA algorithm, and the disparity is refined by sub-pixel fitting. Experimental results show that the improved Census transformed image has more detailed information in the depth discontinuous region. Compared with the algorithm that directly uses the gray feature as the matching base elements, the proposed algorithm has better operation speed and matching accuracy.
Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines

Permanent magnet synchronous machines (PMSMs) are a popular choice in many traction drive applications due to their high energy and power density and moderate assembly costs. However, electric motor thermal robustness in general is harmed by the lack of accurate temperature monitoring capabilities such that safe operation is ensured through oversized materials at the cost of its effective utilization. Classic thermal modeling is conducted through lumped-parameter thermal networks (LPTNs), which help to estimate internal component temperatures rather precisely but also require expertise in choosing model parameters and lack physical interpretability as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this work, it is shown that, as an alternative to LPTNs, linear regression achieves similar predictive performance with low computational complexity as long as input representations are preprocessed with exponentially weighted moving averages. Thus, domain knowledge becomes neglectable, and estimation performance depends entirely on collected data and considered input representations. Furthermore, dependence on data quantity and data diversification is examined in order to assess the minimal mandatory amount of test bench measurements.
Interleaver-Aided Successive Cancellation Flip Decoding Algorithm for Polar Codes

When the code length is finite, the successive cancellation list (SCL) decoding of polar codes shows as good error-correction performance as low-density parity-check codes and turbo codes and it will be used in high-speed communications. However, since the hardware complexity of the SCL increases linearly as the size of the list increases, it is difficult to use the high-complexity SCL decoding for devices with limited resource such as IoT devices. The successive cancellation flip (SCF) decoding algorithm has been proposed to solve the problem of high hardware complexity and to enhance the error-correction performance of successive cancellation decoding. However, the conventional SCF decoding has high computational complexity. In this paper, we propose the interleaver-aided successive cancellation flip (ISCF) decoding to reduce the computational complexity. The computational complexity of the ISCF decoding is less by at least 32% than the conventional SCF decoding for error frame at $\mathrm{E}_{\mathrm{b}}/\mathrm{N}_{0}=3.5\mathrm{dB}$.
A Survey on Data Mining and Image Processing Techniques Used in Agriculture

: Data mining is used to extract the information from the large dataset and used to predict patterns and behavior of an application. Data mining plays a chief role in the fields of e-commerce, healthcare sector, and agricultural sector. Agriculture is the prime occupation in India. Crop productivity mainly depends on weather conditions. Data mining is used in agriculture to predict crop productivity, water management, crop disease management, pesticide recommendation by using different algorithms. Image processing techniques are also widely used to identify crop diseases using images of affected crops. The purpose of this survey is to study data mining and image processing techniques used in crop disease prediction and identification .
Magnetic Barkhausen Noise Spectral Emission of Grain Oriented Steel Under Ultra Low Frequency Magnetization

The Barkhausen noise spectrograms for ultra low frequency triangular excitation signal are presented. The current source is used for sample excitation to ensure the exact waveform of the field strength. The measurement was performed at 4.2 mHz. Only an anti aliasing filter was used. The frequency range is, therefore, 0.39 Hz–25 kHz.
Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey

Cognitive radio (CR)-based Internet of Things (IoT) system is an effective step toward a world of smart technology. Many frameworks have been proposed to build CR-based IoT systems. The CR-based IoT frameworks are the key points on which this survey focuses. Efficient spectrum sensing and sharing are the main functional components of the CR-based IoT. Reviews of recent SS and sharing approaches are presented in this survey. This survey classifies the SS and sharing approaches and discusses the merits and limitations of those approaches. Moreover, this survey discusses the design factors of the CR-based IoT and the criteria by which the proper SS and access approaches are selected. Furthermore, the survey explores the integration of newly emerging technologies with the CR-based IoT systems. Finally, the survey highlights some emerging challenges and concludes with suggesting future research directions and open issues.
Efficient Bandwidth Management (EBM) Algorithm for NG-EPON

Next generation Ethernet passive optical network (NG-EPON) is a promising technology to cater the huge bandwidth and efficient distribution demands of the future Internet. An important task in NG-EPON is designing of dynamic bandwidth allocation (DBA) algorithms. A number of DBA algorithms proposed for TWDM EPON could be used for NG-EPON by making suitable parametric changes. However, new DBA algorithms for a class of NG-EPON called MSD EPON are required to proficiently meet the bandwidth demands of NG-EPON. In this paper, a new DBA algorithm for NG-EPON is reported. Proposed algorithm has been compared with the existing algorithms like earlier finished time (EFT) and earlier finished time with void filling (EFT-VF). Simulation results show that our DBA algorithm performs better in terms of important performance measures like average packet delay and average completion time.
A General Gray Code Quantized Method of Binary Feature Descriptors for Fast and Efficient Keypoint Matching

In this paper, a general Gray code quantized method of binary feature descriptors is proposed for fast and efficient keypoint matching on 3D point clouds. In our method, it includes 2 variable L and N. L is rule variable which can be used to set the encoding group length according to the feature of the real-valued descriptor, and N is bits variable which can be used to set the number of Gray code bits according to the actual system requirements. Be different from the exist method, such as B-SHOT, our proposed method has the advantages of reasonable and flexible. As an example, our method is applied on the feature descriptor SHOT and tested in a standard benchmark dataset, different variable combinations of L and N are tested in the Gray code quantized processes, the best combination of L and N is dubbed as GRAY-SHOT through performance comparison. At last, GRAY-SHOT is compared with the state-of-the-art binary 3D feature descriptor B-SHOT, experimental evaluation result shows that GRAY-SHOT offers better keypoint matching performances to B-SHOT on a standard benchmark dataset with a slight more memory footprint and time consumption.
Area-Efficient Transposable 6T SRAM for Fast Online Learning in Neuromorphic Processors

This paper presents a 6T SRAM-based transposable synapse memory aiming to improve online learning performance of neuromorphic processors at the minimum area cost. While a custom 8T SRAM was used in the previous transposable synapse memory, the proposed one uses 6T SRAM, which leads to substantial area savings. Based on the proposed hierarchical word line structure with row transition multiplexer, both row-wise and column-wise accesses are made possible in an integrated SRAM array. A 64K-synapse memory employing the proposed scheme is implemented in a 28nm CMOS technology, which has 17.7% area overhead compared to the non-transposable 6T synapse memory; 50.0% area savings compared to the transposable 8T synapse memory, and 35.5% area savings compared to the previous transposable 6T synapse memory. The estimated performance gain for online spike-timing-dependent plasticity learning using MNIST dataset is $6.7 \times$ compared to the non-transposable synapse memory.
Behavior of Magnetic Flux Density in Dynamic Wireless Charging of Electric Vehicles

Electric Vehicles (EVs) represent one of the key technologies aiming to minimize fossil fuel’s utilization in transportation sector and consequently mitigate air pollution. However, range anxiety and long charging time are still the biggest problems of the modern EVs. In addition, tripping hazards and possibility of being electrocuted associated with the conductors used in regular EV charging stations need to be addressed. One of the proposed solutions is to employ wireless power transfer techniques for charging. Generally, they can be divided into two groups, namely Dynamic Wireless Charging (DWC) and Static Wireless Charging (SWC). The latter is efficient, robust and relatively well studied, but is significantly limited in terms of charging time. On the other hand, DWC can potentially eliminate the charging time and reduce the size of the EV’s battery. However, when the charging in motion is performed the influence of the magnetic flux density on the driver’s and passengers’ safety needs to be studied. Thus, this study specifically focuses, discusses and analyzes the behavior of magnetic flux density in the DWC system for EVs. In addition, it investigates and elaborates on its relation to the mutual inductance between the transmitting and receiving coils. Finally, the issue of the passenger’s safety is studied via the simulation analysis of the magnetic flux leakage between the receiver coil and the EV’s cabin.
Optimizing Attention Mechanism for Neural Machine Transltion

Nowadays, Neural Machine Translation (NMT) has become the mainstream approach for machine translation, and has got considerable improvement after attention mechanism is introduced. However, usage of attention is insufficient, some defects such as rare word problem still remain in end-to-end NMT approach, which is limited by the training targets and priori knowledge. Traditionally, words out of vocabulary (OOV words) are simply represented with a signal ‘UNK’ or cut into sub-words. In this paper, we consider some optimizations on the alignment feature of attention mechanism, which is appropriate not only to guide every output to find the best match in input sentence, but also to help overall quality of the translation. We build English-Chinese and Chinese-English NMT systems based on our algorithm, and experiment on casia2015 corpus from WMT17. The result shows that the translation of our model gets a considerable improvement respectively.
Predicting Grade Promotion Using Decision Tree and Naïve Bayes Classification Algorithms

The rapid development of the digital era is now a major challenge for the education system in Indonesia, especially for high school students. Today's teenagers are in a digital age where the development and enhancement of knowledge in the field of digital technology has had a dramatic impact. Common problems faced by senior high school students include their social relationship, difficulty concentrating in learning, lack of discipline, disobedience to orderliness and disrespect toward teacher and friends. Of these problems have an impact that the student's value is less than the average so that students become class. For that reason, we need a solution to reduce the number of students who failing a grade. Data mining concerns the education world to improve the quality of education by minimizing the number of failing grade students. Classification techniques on data mining used to process data that is Decision Tree and Naïve Bayes. Many data can be managed in schools using the method of data mining, therefore it is necessary to limit the problem so that the assessment can be focused and not floating. Limitations in the problem include Cognitive values, Psychomotor values, Affective values, Number of attendance Number of remedial exams. The results of the analysis will be used to predict the class increase that will affect the quality improvement of students in the future. Measurement results using confusion matrix and ROC curve obtained decision tree accuracy values of 97.5% and naïve bayes of 97.5%.
Driver Fatigue Prediction Using Different Sensor Data with Deep Learning

Fatigue driving is one of the main causes of traffic accidents. If we can predict that a driver will be fatigued in the near future, and give warnings and parking suggestions to let the driver stop and rest, traffic accidents can be reduced effectively. In the past, most research work on fatigue driving used conventional Back Propagation Neural Network (BPNN) model or Support Vector Machine (SVM) to make predictions. BPNN and SVM could not co-relate sequential instances of data in that series, thus making predictions not so accurate. A system is proposed that uses Recurrent Neural Network (RNN) with Long Short-term Memory (LSTM) building blocks to predict driver fatigue. Two parameters, Heart Rate Variability (HRV) and the Percentage of Eyelid Closure (PERCLOS) are used as an input data to the LSTM model so as to predict whether the driver will be fatigued in some future time slot. Experiments show that the system PERCLOS best using LSTM-based driver fatigue in prediction module that can achieve a true positive rate of 75% and an accuracy of 88%. Whereas fatigue prediction of the HRV best uses BPNN-based prediction module that yields a true positive rate and accuracy of 80% and 88% respectively.