Miracle-dz/UniFAR-FTUs huggingface dataset:
Artifact Removal Using GAN Network for Limited-Angle CT Reconstruction

Computed tomography (CT) plays an increasingly important role in clinical diagnosis. However, in practical applications of CT, physical limitations on acquisition lead to some blind regions where data cannot be sampled. CT image reconstruction from limited-angle would enable a rapid scanning with a reduced x-ray dose delivered to the patient. As it is known, Generative Adversarial Networks (GAN) can keep the original information and details of the sample very well. In this paper, we propose an end-to-end Generative Adversarial Networks model used for removing artifacts from limited-angle CT reconstruction images. The proposed GAN is based on the conditional GAN with the joint loss function, which . can remove the artifacts while retaining the complete details and sharp edges. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. Compared to several other classic methods, our GAN model shows better consequent, in terms of artifact reduction, feature preservation, and computational efficiency for limited-angle CT reconstruction.
Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study

The complexity of software has grown considerably in recent years, making it nearly impossible to detect all faults before pushing to production. Such faults can ultimately lead to failures at runtime. Recent works have shown that using Machine Learning (ML) algorithms it is possible to create models that can accurately predict such failures. At the same time, methods that combine several independent learners (i.e., ensembles) have proved to outperform individual models in various problems. While some well-known ensemble algorithms (e.g Bagging) use the same base learners (i.e., homogeneous), using different algorithms (i.e., heterogeneous) may exploit the different biases of each algorithm. However, this is not a trivial task, as it requires finding and choosing the most adequate base learners and methods to combine their outputs. This paper presents a case study on using several ML techniques to create heterogeneous ensembles for Online Failure Prediction (OFP). More precisely, it attempts to assess the viability of combining different learners to improve performance and to understand how different combination techniques influence the results. The paper also explores whether the interactions between learners can be studied and leveraged. The results suggest that the combination of certain learners and techniques, not necessarily individually the best, can improve the overall ability to predict failures. Additionally, studying the synergies in the best ensembles provides interesting insights into why some are able to perform better.
Hierarchical Multi-Beam Search Based ChannelEstimation for Millimeter-Wave Massive MIMO Systems

Accurate channel state information (CSI) acquisition is indispensable for mmWave massive MIMO systems to perform sophisticated signal processing. The existing channel estimation methods such as beam training and compressed sensing either have low accuracy or require considerably high training overhead. To achieve a fast yet accurate channel estimation, we propose a parallel hierarchical multi-beam search method in this paper. Unlike the existing beam search methods, which search multipath components (MPCs) one by one, the proposed method searches all the MPCs simultaneously by scanning all the beam pairs with MPCs in each layer of codebook in parallel. Therefore, the beam search of different MPCs does not interfere with each other. To improve the estimation accuracy and reduce the beam training overhead, the length of each training sequence under different codebook layers and different SNR is obtained by beam detection. Moreover, we particularly design an optimal beam pattern synthesis codebook for the hierarchical multi-beam search by using the design method of digital filter, which is applicable in other hierarchical search methods as well. Simulation results show that the proposed codebook is superior to other codebooks and the proposed multi-beam search method has higher channel estimation accuracy, but requires less training overhead, compared with the state-of-the-art alternatives.
A Novel Virtual Assembly Training System

In this paper, a novel virtual assembly training system which supports co-located collaboration is proposed. It aims to enhance the visualization level and operation efficiency of the assembly process on the basis of multi-view stereoscopic projection display and view-dependent rendering technologies. The assembly data are organized and displayed as a tree, and the scene of each view is rendered in real time from the viewpoint of each user in an adaptive manner. With strong immersion, the assembly target is clear, which improves the smoothness of the assembly operation. At the same time, the system supports multi-user co-located collaborative operation, which is more in line with the real assembly environment and is conducive to the completion of complex assembly tasks.
Performance Analysis of Different Decision Tree Based Methods for Identifying Drop Out Students

The main concern of an educational institution is to enhance the performance of students. But dropping out of students is a large obstacle that creates bad impact in this purpose. For this reason, most of the educational institutions are conscious about reducing the rate of drop out students. To reduce the drop out students, firstly it is needed to identify the reasons behind the drop out students. In this paper to address the issue, different Decision Tree (DT) based methods as well as the boosted approaches have been used. Firstly, the features of the dataset have been sorted according to their importance based on Information Gain (IG). After the features being sorted, the dataset is partitioned into three categories: the all features, twothird of the features and one-third of the features. Thereafter the standard DTs and the boosted DTs are being experimented over the partitioned dataset to identify drop out students.
Real Time Safety Alert System for Car

Driver's lethargy is one of the main causes of car accidents nowadays. We have discussed fatigue, object detection in hand, head pose estimation and siesta detection. This paper is focused on extracting and analyzing the various factors to alert the driver while driving the car. We have implemented real time face detection by using CNN. We have used light forbearance model as it will be easy to implement in real time situation. Eye tiredness by calculating percentage of eyelid closure over the pupil (PERCLOS), object detection in hand by tensor flow. Object is detected when the driver uses mobile phone and/or holds substance abuse material while driving. Mouth geometric movement detection is implemented by Deep Neural Network. This aims to reduce road accidents and the loss of life. In every 25 second a person dies in road accidents. We have proposed a method to reduce accidents by implementing certain factors. This will alert us whether or not the driver is in the proper condition for driving.
Periocular Region Based Biometric Identification Using SIFT and SURF Key Point Descriptors

In the proposed paper the discriminative capability of the periocular region of the face area is exploited in designing a robust biometric system. A periocular region-based biometric system is emerging as a better alternative for the unconstrained and uncontrolled environments. In the projected work the two key point descriptors Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) are employed for deriving the distinctive characteristics from the periocular and complete face portions. City Block Distance method is used to compare the two feature vectors obtained from the SURF and SIFT methods. FERET and FRGC databases are used to estimate the merits of both face and periocular biometric methods. By utilizing only 25% of entire face, periocular area-based biometric systems provided almost the similar performance equivalent to the face biometric systems.
A Novel 36-Pulse Star Rectifier with Passive Injection Circuits at DC Link

This paper presents a new 36-pulse star connected rectifier. It takes intimate sinusoidal currents from utility with the elimination of lower than 35th order harmonics. The proposed rectifier consists of a parallel four-star 12-pulse diode rectifier and two passive injection circuits at DC links. This passive injection circuit can triple the number of pulses of 12-pulse star rectifier system. The primary side of specially tapped interphase transformer (STIT) with two diodes constitutes the first injection circuit while the secondary winding of the STIT with a single phase full-wave rectifier (SPFR) constitutes the second passive injection circuit. This rectifier is compatible for large current rating application such as electroplating, plasma torches, dc arc furnaces etc. The simplicity of the proposed rectifier scheme makes it more economical compared to other existing designs of same performance. The proposed rectifier is examined and simulated in MATLAB environment. The simulation result confirms the requirement of IEEE-519 standards.
Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato

On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of pathological vibrato manifestations in sustain vowel phonation. The study’s experiments show that using the proposed acoustic analysis methods, the classifier based on linear discriminant analysis attains 90.7% accuracy with 86.7% sensitivity and 92.2% specificity.
Directive-Based Parallelization of For-Loops at LLVM IR Level

Currently, multicore processors widely spread and many computers can improve processing performance by multithreading. However, many programs are still processed sequentially, and they do not make full use of their parallelism. To solve this problem, it is quite promising to automatically parallelize the binary code of the program by using binary translation techniques. Based on this background, we have proposed a system that automatically optimizes and parallelizes a binary code using LLVM. In this paper, we design the IRlevel parallelization directives for the LLVM infrastructure and implement them in LLVM. This allows us to independently develop the compilation pass of code analysis and the pass of the code generation for parallelization. The separation of code analysis and code generation can realize the ease of the reuse of them. Evaluation results show that our LLVM compiler pass can generate parallelized IR code from sequential IR code with IRlevel parallelization directives. The parallelized IR code achieved speedup as highly as the parallelized source code using OpenMP.
Energy-Efficient Differential Spin Hall MRAM-Based 4-2 Magnetic Compressor

The compressors are widely used as bit compressing cells having key applications with multi-operand addition and multiplication hardware. As the CMOS technology scales down below 45 nm technology nodes, static power dissipation becomes a major concern. To overcome this constraint, spin transfer torque magnetic random access memory (STT-MRAM)-based hybrid CMOS/MTJ architectures are being used. Inception of perpendicular magnetic tunnel junction (PMTJ) has enhanced the growth of spintronics-based hybrid architectures, due to their low switching current, scalability, non-volatility, and CMOS compatibility. Recently, a large number of circuits based on STT-MRAM have been proposed. However, they have limitations related to reliability and high write energy. In this article, we propose a differential spin Hall MRAM (DSH-MRAM)-based hybrid CMOS/MTJ magnetic 4-2 compressor. A write voltage of 0.4 V and 300 ps current pulse are used to switch the magnetization state of the MTJs. When compared with previous STT-MRAM-based designs, the proposed compressor shows 97% improvement in power-delay product (PDP) characteristics. The write circuit in DSH-MRAM consumes merely 5 nW in comparison to $2~\mu \text{W}$ by conventional STT-MRAM designs. Moreover, the narrow write pulse promotes the proposed design for input frequencies up to 1 GHz.
Comparative Study of Deep Learning Based Features in SLAM

With the development of deep learning technology, plenty of learning-based feature detectors and descriptors methods are proposed. They achieve great performance on some specific datasets or environments. However, there are few experiments evaluating the performance of these learning-based features detectors and descriptors on a SLAM system. In this paper, we evaluate several learning-based feature detectors and descriptors on KITTI dataset with a SLAM system, using the accuracy of tracking as a metric of these methods. The results show that these learning-based feature detection methods perform poor on KITTI outdoor dataset.
On Processing of Correspondence Matrices in Transport Systems

The paper considers the analysis of data in so-called origin-destination matrices. Also in relation to them often use the term matrix of correspondence. As practical examples, we are using data from Moscow metro and Moscow suburb railways. The correspondence matrix shows the number of trips between two points (stations) for a selected time interval. Theoretically, such data being calculated for the given time intervals describe all the characteristics of the passenger load. But practically, it depends on the existing models for traffic. Currently, such data used, mostly, just to calculate simple statistics. But such a simplified approach completely ignores the temporal characteristics of the traffic. In this paper, we discuss data mining models and approaches for origin-destination matrices analysis. Also, we describe the results of applying such approaches to analyzing data from the Moscow region.
Effect of lithography on SOI, grating-based devices for sensor and telecommunications applications

We demonstrate how lithography smoothing and proximity effects affect the performance of silicon-on-insulator devices that include grating-based, contra-directional couplers (contra-DCs). Using lithography models developed for CMOS-compatible, deep ultraviolet lithography processes, we predict and verify the spectral responses of fabricated contra-DC test structures. These verified models are then used to simulate and analyze the effects of lithography on the performance of a microring resonator with an integrated contra-DC, as regards the device 3-dB bandwidth (BW), insertion loss (IL), and adjacent side mode suppression ratio (SMSR). We demonstrate how the corrugation profile of the contra-DC is affected by smoothing and the inner and outer corrugation depths are reduced due to proximity effects. We show that, if the effects of lithography are not taken in account during device design flow, large discrepancies result between the predicted "as-fabricated" and "as-designed" device performance. Specifically, we demonstrate how the BW is reduced from 47 GHz to 21 GHz, how the IL is increased from 0.5 dB to 5.8 dB, and how the adjacent SMSR is reduced to 26 dB. We also establish that it is possible to use the lithography models to compensate for lithographic effects during device design flow and layout and to design a contra-DC in which the as-fabricated device performance metrics matches the target/expected as-designed performance metrics.
Deep Mutual Encode Model for Network Embedding From Structural Identity

Network Embedding (NE) is one of the most popular learning methods in complex networks. It aims at learning the low-dimensional representations of nodes in networks and has been applied in a variety of network analytic tasks. Most existing methods of NE are designed by merely using the local, high-order or global proximity to preserve the network structure; hence they are incapable of fully capturing the structural identity of nodes, which is a concept of symmetry defined by the network structure and their relationship to other nodes. There are two limitations to existing NE models. First, the local and global node dependency information is not considered simultaneously. Second, there is no adequate framework that can reveal the role property of each node. In this paper, we propose an intuitive and unified deep learning framework named DMER, short for Deep Mutual Encode for Embedding, to learn node embeddings from structural identity. In our model, Graph Convolution Network (GCN) is adopted to model the dependency relations between nodes from a global perspective. An Auto-Encoder (AE) framework is proposed to reconstruct the features of nodes, and it can conclusively reveal the structural identity from network structure. By integrating the GCN and AE components with a shared and constrained mechanism, the proposed model implements mutual enhancement for node embedding from structural identity. Experimental results based on structural role classification and visualization demonstrate that our model achieves better performance compared with the state-of-the-art methods.
Fake Image Detection Using DCT and Local Binary Pattern

In the technological era, digital images occupy an important position in different life’s fields and image tampering has become affordable effortlessly, which results in a widespread of tampered and fake images through the internet and social media specifically. There are many techniques for image manipulation, some of the well-known methods are splicing and copymove. Splicing can be defined as cutting part from an image and pasting it into another picture, while copy-move is about copying part of an image and pasting it into the same picture. This paper challenges splicing and copy-move forgery detection methods on CASIA TIDE databases. The proposed method is based on Local Binary Pattern (LBP), and 2D Discrete Cosine Transform (DCT), which are used for feature extraction. Afterward, a Support Vector Machine (SVM) classifier distinguishes real and manipulated images. Initial performance was increased by applying Local Binary Pattern (LBP) to the whole image rather than in a blockbased fashion. The proposed model reaches the-state of- the-art in the CASIA TIDE v1.0 database with remarkable results in terms of accuracy.
An Improved Quadratic V/f-Based Control of Photovoltaic Battery-Less Induction Motor Driven Water Pumping System

This paper proposes a new quadratic V/f-based control of a PV induction motor driven water pumping system without storage batteries. The system consists of PV array, DC-DC boost converter, voltage source inverter (VSI), induction motor, and water pump. In order to extract the maximum power from the PV array, the incremental conductance (INC) method is simply utilized. The dc-link voltage is maintained constant using a proportional-integral (PI) controller by controlling the induction motor speed using simple open loop quadratic scalar V/f control. The proposed quadratic scalar V/f control is compared with the widely used linear V/f control using a simulation case study based on MATLAB/SIMULINK . The performance of the proposed method is examined under variable values of solar radiation. When compared with the available scalar controllers, the proposed controller generally improves the system dynamics as well as the overall system efficiency especially under low speed (low discharge) operation. The simulation results have proved that the system output power and the corresponding overall efficiency have been approximately doubled under low speed operation.
The Study of the Object Depth Estimation Based on Point Pair Features on Images

Using dual cameras to grab the object’s image and then estimating the distance of this object is a useful technique. However, the feature of the object is the key role, while identifying the conjugate pair pixels of this object between the left and right images. In this study, we projected laser light on the object to create obvious features, and then apply the oFAST algorithm to extract feature points. Furthermore, ORB algorithm was applied to identify the conjugate pair pixels from all feature points on the left and right images, respectively. Finally, an epipolar plane was applied to estimate the distance between the laser point and camera lens. According to the experimental results, the error of height of the object is 2.5%. Consequently, the advance study to extract more valuable feature pixels or conjugate pairs pixel is an interesting topic.
UAVs Reliable Transmission for Multicast Protocols in FANETs

“Communication in the sky” is a trend because the increment of Unmanned Aerial Vehicles (UAVs) use in wireless communications. UAVs have diverse applications in civil and military domains. Swarm of UAV system is able to combinedly finish the operations more reliably and financial more than single UAV. UAV system has rapidly changing topology due to high mobility devices. Currently, Mobile adhoc networks (MANETs) routing is used for communication in UAV networks, the standards for communication system in Flying adhoc Networks is yet to be developed. UAV system streaming information needs protocol with high bandwidth, high mobility, varying link stability and high energy consumption compare to adhoc networks. It leads to abruptly breaking communication in between UAV-to-UAV and UAV-to-ground. In this paper, we discuss the link stability issues in UAV communication systems. Link stability estimation is required for reliable communication in between end-to-end flying nodes. We have simulate the MANETs protocol for reliable communication in flying adhoc networks. We evaluate the performance of Flying adhoc networks (FANETs) communication model based on multiple performance metrics, and also suggested deployment scenarios on EXata/Cyber 2.0 to analyze the network performance.
High sensitivity biosensor for Staphylococcus Aureus detection based on tapered a singlemode-no core-singlemode fiber structure

A fiber biosensor based on tapered singlemode-no core-singlemode (SNS) fiber structure was investigated for the detection of staphylococcus aureus. The fibre sensor was functionalised by depositing the pig IgG antibodies onto tapered biosensor surface, which has specific binding with staphylococcus aureus. The experimental results showed that transmission spectrum of functionalized sensor undergone wavelength blue shift when it was immersed into Staphylococcus aureus solution, and 1.1 nm wavelength shift was observed with a concentration of 101colony-forming units (CFU)/mL.
Land-Use Detection Using Residual Convolutional Neural Network

Land-Use detection is an important application in the context of remote sensing. This objective is to classify, a chunk of a satellite or high-earth orbit image of the earth, as the type of land-use. In this work, we have used recent advancement in computer vision with deep learning to improve the performance to the classification task. We have used a very deep convolutional neural network with the residual connection as a building block and the concept of transfer learning to train this model. The experiment shows that the performance of the classifier is very high with transfer learning on a pre-trained model rather than training from the sketch with such small dataset that is available for land use classification task. Our proposed approach generated a better result than the previously existing method in benchmark dataset.