Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Alignment of DNA Sequence Based on Deep Reinforcement Learning

Goal: Over the decades, there have been improvements in the sequence alignment algorithm, with significant advances in various aspects such as complexity and accuracy. However, human-defined algorithms have an explicit limitation in view of developmental completeness. This paper introduces a novel local alignment method to obtain optimal sequence alignment based on reinforcement learning. Methods: There is a DQNalign algorithm that learns and performs sequence alignment through deep reinforcement learning. This paper proposes a DQN x-drop algorithm that performs local alignment without human intervention by combining the x-drop algorithm with this DQNalign algorithm. The proposed algorithm performs local alignment by repeatedly observing the subsequences and selecting the next alignment direction until the x-drop algorithm terminates the DQNalign algorithm. This proposed algorithm has an advantage in view of linear computational complexity compared to conventional local alignment algorithms. Results: This paper compares alignment performance (coverage and identity) and complexity for a fair comparison between the proposed DQN x-drop algorithm and the conventional greedy x-drop algorithm. Firstly, we prove the proposed algorithm's superiority by comparing the two algorithms’ computational complexity through numerical analysis. After that, we tested the alignment performance actual HEV and E.coli sequence datasets. The proposed method shows the comparable identity and coverage performance to the conventional alignment method while having linear complexity for the $X$ parameter. Conclusions: Through this study, it was possible to confirm the possibility of a new local alignment algorithm that minimizes computational complexity without human intervention.
A cross-blockchain based distributed crowdsouring system for Internet of Things

With the development of Internet of Things (IoT) technologies, the mobile crowdsourcing has become the most effective and reliable method for data collection and processing. However, as huge amounts of data are generated in mobile crowdsourcing services, data privacy and security has become a key challenge for IoT. To address this challenge, a Cross-Blockchain based Distributed mobile Crowdsourcing System, named CBDCS, is proposed in this paper. In CBDCS, to overcome the privacy leakage problem caused by a single-point failure, the blockchain is used as a crowdsourcing task server, based on which an improved multi-subchain structure is designed. Then, based on multi-subchain, a task hierarchical storage mechanism is proposed, which stores different levels of tasks in corresponding subchains to improve privacy protection. In addition, CBDCS meet the demands of workers for tasks through a cross-subchain mechanism based on root&sub CA and smart contracts, while guaranteeing data privacy. Extensive simulation results demonstrate that the CBDCS can obtain high throughput, low overhead, and data privacy under various IoT scenarios.
An 8-Bit 800 MS/s Loop-Unrolled SAR ADC With Common-Mode Adaptive Background Offset Calibration in 28 nm FDSOI

This paper presents a low-power single-channel 8-bit loop-unrolled (LU) successive approximation register (SAR) analog-to-digital-converter (ADC) with a novel common-mode adaptive background comparator offset calibration scheme. LU-SAR ADCs use multiple comparators to reduce the SAR loop delay. Offset mismatch between the comparators severely degrades the effective resolution. This paper addresses the common-mode voltage variation in the LU-SAR architecture due to comparator kickback and the problems related to the common-mode dependency of the comparator offset. The proposed offset calibration scheme ensures that the comparators are calibrated at the same input common-mode voltage at which they each operate during the SAR conversion to prevent the common-mode dependent offset mismatch between them. Moreover, the proposed ADC design exploits the common-mode variation immunity of the proposed calibration scheme to optimize the figure-of-merit (FoM). The prototype ADC manufactured in 28nm FDSOI CMOS achieves 42.57dB signal-to-noise-and-distortion ratio and 22.8fJ/conv. -step FoM at 800MS/s with near Nyquist frequency input, and occupies an area of 0.0037mm2.
Object Detection for Autonomous Driving using YOLO algorithm.

In this paper, we have presented the work that we have done pertaining to object detection for the purpose of aiding autonomous vehicles during navigation. The detections are carried out using the state-of-the-art YOLO model which is trained on a custom-made model on a custom-made dataset. The main objective of this paper is to highlight the building of the model on low-cost computing resources. After training, we have implemented the model on video data as well as on web camera.
A Low-Area and Low-Power Comma Detection and Word Alignment Circuits for JESD204B/C Controller

In an 8B/10B mode giga-bit-per-second serial data transactions, the de-serialized data is sent to a comma detection and word alignment (CDWA) module to identify the word boundaries, which is a prerequisite in the high-speed transceivers such as PCIe, USB and JESD204B/C. In order to ensure that the comma code (/K/-code) can be correctly detected. Ten 10-bit comma detector cells are adopted in a typical CDWA module, which require a complex circuitry and an enormous power consumption. To overcome these limitations, a low-area and low-power CDWA circuit for JESD204B/C transceiver chip in 8B/10B mode has been proposed in this paper. The bit width of the detector cells can be truncated from 10 to 6 under the condition, that CDWA module can detect a complete comma code correctly. On one hand, the proposed CDWA module is verified with a FPGA development platform with the reduction of the hardware resources and power consumption to 31.72% and 20.11% respectively as compared to the typical structure available. On the other hand, a 10-Gbps transceiver chip with the proposed CDWA module is fabricated with a 55-nm CMOS process and the word alignment function of the proposed module is proved by the measurement results. The area of this transceiver chip including $2\times $ transmitting links and $2\times $ receiving links is 2.89 mm2, and the power consumption is 467.8 mW, under a maximum data transmission rate of 10 Gbps.
A Wireless Artificial Mechanoreceptor in 180-nm CMOS

This article presents an implantable low-power wireless integrated system for tactile sensing applications. The reported application-specified integrated circuit (ASIC) uses a low-loss magnetic human body communication channel for both wireless power and data transfer. The chip is hybrid-integrated with an in-house fabricated MEMS capacitive force sensor to form an implantable artificial mechanoreceptor. An on-chip correlated double sampling capacitance to time converter (CTC) consumes 750 nW from a 1.2-V on-chip regulated supply, achieving a 2.0-fF resolution for an input capacitance of 14 pF and a FoM of 49 fJ/c-s while occupying an area of only 0.04 mm<sup>2</sup>. The CTC has a time-multiplexed mode to interface with four input capacitors. Wireless power management feedback is used to ensure robust operation in the presence of hand gesture changes and process–voltage–temperature variations. The on-chip data transmitter can operate in ON–OFF keying (OOK) or frequency-shift keying modulation formats, where it consumes only <inline-formula> <tex-math notation="LaTeX">$7.8~\mu \text{W}$ </tex-math></inline-formula> in the OOK mode. The 1.62-mm<sup>2</sup> chip is fabricated in a standard 180-nm CMOS process and consumes <inline-formula> <tex-math notation="LaTeX">$110.3~\mu \text{W}$ </tex-math></inline-formula>.
Investigation on Bitcoin Prognosis

Bitcoin, the ruler of cryptocurrency plays an important role in blockchain technology. Every transaction stored in blockchain costs certain amount of bitcoins. The price fluctuation of bitcoins are very unstable and doesn’t depend on any business or marketing strategies which builds both interest and fear in the minds of traders. By predicting the bitcoin price it is possible that users can analyze and invest in bitcoin which improves the utilization of digital money. So it is necessary to develop a prediction model with high prediction rate. This work focuses on implementing various machine learning models to predict the price of bitcoin. These machine learning models are evaluated using error percentage from which the best model for predicting bitcoin value is suggested. From the results it is evident that, among the state of the art ML algorithms, LSTM is found to be the best.
Utilization of 3D Printing Technology: A Case Study of UAE

The 3D technology has many benefits in several fields including mhealthcare and engineering, automobiles, and space. The purpose of this research is to identify factors that influence the future utilization of 3D printing technology in the manufacturing and industrial sectors of Abu Dhabi. The research approach focuses on different demographic, economic and technological factors that may affect the acceptability of this technology in Abu Dhabi. The methodology of this research is to review earlier results in this fild from several academic journals and conference proceeding. We have also used quantitative research method for this study. Survey questions were designed to collect first-hand information from different manufacturing organizations working in the industrial sector of Abu Dhabi. We have analysed the survey results and discussed findings from the analysis. As a result, the we conclude that 3D printing technology has the strong probability to replace traditional manufacturing methods because of different technological and economic factors. This technology has the ability to produce rapid prototypes, provide ease of operations, produce high quality products, and has negligible procurement needs, and lesser delivery and shipment costs. On the negative side, we have found that, while the 3D has tremendous advantages, the technology is associated with high cost. The findings of this study have implications for manufacturers, government, and local and foreign investors. The results of this study also provide future directions to identify other factors that might influence this acceptance of this technology in the manufacturing and industrial sectors of Abu Dhabi.
Adaptive Frequency-domain Granger Causal Inference from Neuronal Ensemble Data

Granger causality is an increasingly prevalent tool in extracting the functional networks that underlie neural processes. While its time domain formulation yields useful insights into these functional networks, the inferred Granger causal influences leave the spectral properties of said functional networks ambiguous: this is a question of particular interest when neural processes exhibit oscillatory behavior. The frequency-domain formulation of Granger causality proposed by Geweke has addressed the spectral properties of functional links between stationary processes. Based on Geweke’s method of conditional Granger causality, we introduce a framework to derive direct spectro-temporal causal interactions in a population of neurons from multivariate ensemble spiking observations, using point process modeling, state space estimation and multitaper spectral analysis. Further, we propose statistical tests that characterize the significance of these functional links. The utility of our methods is demonstrated through application to simulated and real data.
Analysis of the Suspension Elements for the Sensitive Element of a Micromechanical Accelerometer

Modern designs of sensitive elements of many MEMS, including micromechanical accelerometers, contain suspension elements, with the help of which the inertial mass is connected to a fixed frame. The simulation was carried out using the ANSYS program, the parameters and characteristics of the sensitive element of a micromechanical accelerometer with suspension elements of various designs made of silicon with different crystallographic orientations were investigated, and the values of their most important parameters were obtained. The natural frequencies of oscillations of the inertial mass, the residual mechanical stress in the structural elements of the sensitive element of the micromechanical accelerometer were calculated. Changes in the natural vibration frequency and residual mechanical stress in the suspension elements were determined when the temperature changed from +150 to –150 ° C in a short time interval (10 s). The change in the residual mechanical stress of the inertial mass of the sensitive element during impact was investigated. The results obtained made it possible to develop recommendations for choosing the design of a sensitive element with silicon suspension elements, which provide high sensitivity and stability of the parameters of a micromechanical accelerometer. The results obtained will be useful in the development of real designs of MEMS sensitive elements with optimal and stable parameters.
Network Traffic Analysis for Real-Time Detection of Cyber Attacks

Preventing the cyberattacks has been a concern for any organization. In this research, the authors propose a novel method to detect cyberattacks by monitoring and analyzing the network traffic. It was observed that the various log files that are created in the server does not contain all the relevant traces to detect a cyberattack. Hence, the HTTP traffic to the web server was analyzed to detect any potential cyberattacks. To validate the research, a web server was simulated using the Opensource Damn Vulnerable Web Application (DVWA) and the cyberattacks were simulated as per the OWASP standards. A python program was scripted that captured the network traffic to the DVWA server. This traffic was analyzed in real-time by reading the various HTTP parameters viz., URLs, Get / Post methods and the dependencies. The results were found to be encouraging as all the simulated attacks in real-time could be successfully detected. This work can be used as a template by various organizations to prevent any insider threat by monitoring the internal HTTP traffic.
Electrocardiogram (ECG) denoising method utilizing Empirical Mode Decomposition (EMD) with SWT and a Mean based filter

Electrocardiogram is an pivotal physiological signal that is exploited for the detection of cardiological ailments. An ECG signal necessarily gets polluted with different types of unwanted noise during its acquisition phase thereby deteriorating its quality. This imposes a constraint on its utility in disease diagnosis. It thus becomes necessary to remove these artifacts while at the same time preserving the main features of the signal. EMD based methods have been extensively used for the purpose. In this paper, we utilized a blended method that explores the denoising capability of EMD along with that of SWT and NLM filtering techniques to filter out 50 Hz sinusoidal AC noise and white noise. The efficiency of the presented method has been demonstrated in respect of the empirical parameters like SNR improvement and mean of square error values whilst using various records from the arrhythmia database of the MIT Beth Israel Hospital. The excellence of the method presented has been exhibited through comparison of the obtained results with an existing method
Automatic Diagnosis of Glaucoma using Ensemble based Deep Learning Model

In both developing and developed nations, glaucoma is the primary cause of vision loss. Identifying and classifying glaucoma in the early stage will provide the patients with sufficient care and an effective way to support the eye surgeon. This is the opinion of an initiative to identify and classify glaucoma early with the use of a comprehensive learning system using fundus images. Three pre-trained convolution neural network (ConvNet) architectures are being used for the classification of glaucoma in the proposed framework: the Residual Network (ResNet), the Visual geometry group network (VGGNet), and GoogLeNet. The tests are performed in private and standard benchmark data sets to verify the performance of the proposed system. In terms of accuracy, precision, specificity, sensitivity, and F1, the proposed algorithm is compared to three various ConvNets. The findings obtained are encouraging, and the dominance in performance measurements to detect and diagnose glaucoma using fundus images in the proposed ensemble of deep learning architectures will be verified.
Planar Dual-Mode Bandpass Filters Using Perturbed Substrate-Integrated Waveguide Rectangular Cavities

This article presents a novel class of dual-mode substrate-integrated waveguide (SIW) bandpass filters exploiting perturbed SIW rectangular cavities. By introducing metallized via-holes along the central line of an SIW rectangular cavity, the resonant frequency of TE101 mode can be shifted close to that of TE201 mode, thus creating a dual-mode resonator. This structure implements a doublet, which provides two poles and one or two transmission zeros (TZs). The pass bandwidth, spurious-free bandwidth, and positions of the TZs can be fully controlled by modifying the length of the perturbation, the side ratio of the rectangular cavity, and the offset of the input/output ports, respectively. Eight two-pole SIW filters are designed and investigated to demonstrate the high design flexibility. Moreover, a four-pole SIW filter with the asymmetric response and a five-pole SIW filter with quasi-elliptic response are designed, fabricated, and measured to describe the extension of the proposed approach to higher order filters.
A 24–28-GHz Four-Element Phased-Array Transceiver Front End With 21.1%/16.6% Transmitter Peak/OP1dB PAE and Subdegree Phase Resolution Supporting 2.4 Gb/s in 256-QAM for 5-G Communications

This article presents a power efficient 24–28-GHz four-element phased-array transceiver (TRX) front end (FE) for 5-G base stations and user equipment communications. A power efficient hybrid quadrature vector interpolation-based phase-shifting architecture is proposed to increase the TX and RX power efficiency and phase resolution. A transformer-based embedded T/R switch is proposed which minimizes TX and RX losses to ensure high TX output power and power efficiency while maintaining a low noise figure (NF) and extremely compact chip area. The 24–28-GHz four-element TRX FE is implemented in a 65-nm CMOS process (1P9M) for large-scale phased-array applications. The TX and RX paths achieve 0.8° phase resolution with <0.47°/0.17-dB RMS phase/gain error as well as 24-dB gain control range with 0.75-dB gain step and <0.1°/0.4-dB RMS gain/phase error over 24–30 GHz. The full TX path (including splitter and T/R switch) also achieves a measured 16.6-/18.9-dBm OP1dB/ $P_{\mathrm {sat}}$ with 21.1%/16.6% PAEpeak/OP1dB for each FE element while maintaining a minimum 4.4-dB NF in the full RX path (including combiner, T/R switch, and OFF-mode PA) at 28 GHz. Under the 64-/256-QAM modulation measurements, the full TX path achieves a state-of-the-art average output power of 13.5-/10.2-dBm per element (8.4%/3.5% full TX path PAE) with −26.4/−30.8 dB EVM and 27.8-/30.1-dB ACLR at 28 GHz, making the proposed TRX FE can be effectively deployed in both base stations and user equipment for 5-G communications. The chip size of the four-element TRX FE excluding I/O pads is only $3.4\times1.1$ mm2.
Machine Learning Approaches for Detection and Diagnosis of Parkinson’s Disease - A Review

Parkinson’s disease (PD) is disabling disease that affects the quality of life. It belimps due to the death of cells that produce dopamine’s in the substantia nigra part of the central nervous system (CNS) which affects the human body. People who have Parkinson’s disease feel difficulty in doing activities like speaking, writing, and walking. In the recent past, speech, gait and EEG signals have been investigated for the detection of PD. However, speech analysis is the most considered technique to be used. Researches have shown that 90% of the people who suffer from Parkinson’s disease have speech disorders. With the increase in the severity of the disease, the patient’s voice gets more and more deteriorated. The non-invasive treatments for voice analysis are available that helps in ameliorating the life quality of a patient. Thus, for building the telemonitoring and telediagnosis models for prediction, the speech analysis has been tremendously increased. The proper interpretation of speech signals is one of the important classification problems for Parkinson’s disease diagnosis. The main purpose of this paper is to contemplate the survey work of the machine learning techniques and deep learning procedures used for Parkinson’s disease classification. Deep learning and machine learning techniques have been used as a part of the discovery for the efficient classification of PD. The various classification models like support vector machines, naive Bayes, deep neural networks, decision tree and random forest are effectively employed for classification purposes. The analysis of results of different research works showed that both machine learning and deep learning algorithms have shown promising future and therefore paving a better way for the detection of Parkinson’s disease at its earlier stages. The classification accuracy achieved by the machine learning classifier. Among deep learning approaches, the deep neural network has achieved the best accuracy of 99.49%. The results obtained from different works suggest that artificial intelligence is becoming a powerful learning tool that has much to offer to data scientists as well as neurologists. In general the learning methods are adding value to decision-making problems especially in the field of medical diagnosis.
Probabilistic Optimization for Source Separation

We present a novel probabilistic Zeroth-Order optimization method, which can handle higher dimensions, and can also be used for fast online optimization, for instance for multichannel source separation. We compared it to the Gradientless Descent (GLD) algorithm on a multichannel source separation task, and found that our method results in faster and better separation (for the 2-channels case). For the multichannel case, only our method resulted in useful separation. We also applied it to separating sources from 3-dimensional microphone arrays, with comparable results.
Real-time detection of cloud tenant malicious behavior based on CNN

Cloud computing provides convenient on-demand services to cloud tenants while bringing many difficulties to cloud providers in detecting tenants' illegal activities. This paper proposes a convolutional neural network (CNN) based approach to detect cloud tenant malicious behaviors in real time. The proposed approach constructs a CNN model to automatically learn sequence patterns from system call sequences and detect tenant malicious behaviors effectively. It further utilizes Spark Streaming techniques, resulting in its capabilities of processing large amounts of tenant data in clouds in real time. The experimental results show that the proposed approach can not only outperform three existing methods but also achieve high detection rates in real time when deployed on the popular cloud platform OpenStack.
Design of a mm-Wave Double-Sided Substrate Blass Matrix Beamforming Network

This work presents a multilayer Blass matrix architecture for mm-Wave applications. Three main problems of the original Blass matrix are tackled with the proposed topology. First, the spurious excitation problem, which is caused by multiple signal paths and degrades the matrix performance, is reduced. Second, our design approach ensures the design of a Blass matrix with the minimum possible losses. Third, directional slot couplers are designed eliminating the crossovers. The proposed design shows a remarkably constant phase difference in the Ka frequency band.