Miracle-dz/UniFAR-FTUs huggingface dataset:
Azathioprine-induced severe aplastic anaemia in thiopurine S-methyltransferase deficient patient with Crohn's disease

TPMT is an enzyme that catalyses S-methylation of thiopurine drugs, which activity is genetically determined. TPMT-deficient patients are at risk of toxicity after standard doses of thiopurine drugs. We describe a case of Crohn’s disease diagnosed patient inheriting genetic TPMT deficiency (TPMT*3A/*3A genotype), who developed severe aplastic anaemia, confirmed by biopsy, after 55 days of treatment with AZA at a standard daily dose of 2.5 mg/kg. This case demonstrates the importance of TPMT polymorphism testing in order to prevent myelotoxicity during treatment with thiopurine drugs.
A Fast Low-Rank Matrix Factorization Method for Dynamic Magnetic Resonance Imaging Restoration

Nowadays, over 90 percent of the medical data comes from the medical image. People can reduce the medical faults in diagnoses by using computer to analyze and process these medical image data. In this paper, we focus on the reconstruction of dynamic magnetic resonance imaging (MRI), which is crucial to medical diagnoses. Considering the successful application of the robust principal component analysis (RPCA) in MRI to increase the imaging speed and improve the imaging quality, we propose a model based on RPCA. However, the conventional models based on RPCA have two drawbacks: one is that the nuclear norm is often inexact rank approximation, especially when there exist some large singular values; the other is that dealing with the whole data matrix is always time consuming. Our strategies are to adopt a more exact nonconvex rank approximation and matrix factorization, respectively. The former has been proved to be better than the nuclear norm, while the latter can reduce computation complexity by turning the computing object into a core matrix, which is much smaller than the original data matrix. Then the alternating direction method (ADM) is used to solve the well-designed model. Experiments of cardiac cine and abdomen MRI data are conducted to verify the superior performance of our method in both image clarity and computation efficiency when compared with the conventional MRI reconstruction methods.
Some Actual Problems of Magnetic Filters With Magnetized Packed Beds and Comparative Analysis of Filter Performances

In this article, the filter performance characteristics of magnetic filters obtained by different physical models were compared. Performance models for magnetic filters were chosen based on the balance of forces and momentum that act on the captured particle, and on the collision efficiency of the particle with the filter matrix. The filter performance characteristics of the filtration systems are evaluated based on the magnitude of the external magnetic field, the rate of filtration, the variation of matrix element sizes, and captured particles. Three-dimensional variations of these characteristics were investigated. The experimental data from the literature for the different working conditions were used for comparing various models. The effect of filtration velocity and size variation of the captured particles on the magnetic filter performance over a wide range were evaluated using different models. As a result, a practical mathematical model with the exponential change of the system parameters was found to offer more possibilities to determine the magnetic filter performance. It was proposed that it is possible to determine whether the obtained filter performance models are valid either by making appropriate experiments or using the data in the literature as reference values.
Real-Time Hand Gesture Recognition Model Using Deep Learning Techniques and EMG Signals

Gesture recognition has multiple applications in medicine, engineering and robotics. It also allows us to develop new and more natural approaches to human-machine interaction. Real-time hand gesture recognition consists of identifying, with no perceivable delay, a given gesture performed by the hand at any moment. In this paper, we propose a model for real-time hand gesture recognition. The proposed model takes as input electromyographic (EMG) signals measured on the forearm, using the commercial sensor Myo Armband. We use an autoencoder for automatic feature extraction, and an artificial feed-forward neural network for classification. The proposed model can recognize the same 5 gestures as the proprietary recognition system of the Myo Armband, achieving an average recognition accuracy of 85.08% ± 15.21%, with an average response time of 3 ± 1 ms. The proposed model is general, which implies that it can recognize the gestures from any user, even when his$\backslash$her data are not included in the training dataset. Finally, for reproducing this work, we make publicly available the code that implements the proposed model.
Modulation-based Speech Emotion Recognition with Reconstruction Error Feature Expansion

Speech emotion recognition is an ongoing research area, with applicability to fields such as human-machine interfaces, virtual assistance software, and forensics and law enforcement operations. Targeting the latter, a deep neural network (DNN) system is proposed for human affective state classification. The system uses modulation-based spectral features, together with the reconstruction error (RE) of the feature vector input. The RE is obtained using an autoencoder (AE), structurally identical to the DNN classifier. With this approach, improvements have been obtained over existing results on the Berlin Database of Emotional Speech (Emo-DB), reaching an unweighted accuracy (UA) of 87.3% for a reduced (5-class) set relevant for the targeted applications.
A Cloud Gaming Architecture Leveraging Fog for Dynamic Load Balancing in Cluster-Based MMOs

Massively Multiplayer Online (MMO) games have been around for quite some time now, attracting millions of users worldwide, making them one of the most successful video game genres. Many load balancing approaches have been proposed over the years in order to optimize Quality of Experience (QoE) and achieve better scalability and user coverage. These usually split the game world into different clusters using a multi-server architecture. However, not many exist that take into consideration conditions that arise from the newly emerged Internet of Everything (IoE) and cloud/fog gaming paradigms. Hence, the aim here is to present a novel cloud gaming architecture that leverages the capabilities of fog computing for achieving a two level dynamic load balancing in cluster-based multi-server MMOs through the use of an appropriate algorithm. The proposed architecture and algorithm are also validated via preliminary simulation experiments, indicating a high user coverage ratio and a balanced distribution of the load in terms of client proportionality in the servers, even under forced strict gaming conditions.
Deep Learning Based 3D Object Detection for Automotive Radar and Camera

In this paper it is demonstrated how 3D object detection can be achieved using deep learning on radar pointclouds and camera images. A deep convolutional neural network is trained with manually labelled bounding boxes to detect cars. The results are compared to a deep neural network trained on lidar pointclouds and camera images. The average precision (AP) is used to evaluate the performance. For radar and camera the AP is 0.45, 0.48, and 0.61, whereas the AP for lidar and camera is 0.33, 0.35, and 0.46 for occluded, partially occluded and not occluded cars respectively. The performance of the network is significantly better with radar data compared to lidar data. Currently, the main limitation of the performance of the object detection with radar data and camera images is the dataset, which is until now rather small. However, the results show that deep learning is generally a suitable method for object detection on radar data.
A 112-142GHz Power Amplifier with Regenerative Reactive Feedback achieving 17dBm peak Psat at 13% PAE

A two-way power-combining amplifier operating from 112-142GHz is presented. Integrated in Infineon’s 0. 13μm SiGe-BiCMOS technology, it delivers 17dBm of peak saturated power to a 50Ω load at 13% PAE. Five fully-differential, transformer-coupled amplifier stages per path provide 34dB of forward transmission gain. Each 5-stage PA consists of capacitively gain-enhanced pre-drivers operated from 1.5V, followed by an inductively gain-enhanced cascoded driver powered by 3.3V. BJT models relevant at frequencies beyond 100GHz are evaluated to outline the trade-off between stability and gain exploited in this work. The design and layout of a folded, fully-differential, λ/4 power combiner is also presented, along with a full two-port characterization of the power-amplifier prototype.
HVS based perceptual pre-processing for video coding

This paper proposes a perceptually optimized preprocessing technique using state of the art Human Visual System (HVS) model suitable for video compression to reduce bitrate at the same quality. Visual masking models to accurately account HVS has been considered. Frequencies which are visually indistinguishable and need not be encoded are removed within visibility threshold. The scheme is optimized for multiple viewing distances to consider real-world scenarios. Extensive subjective and objective evaluation has been conducted to evaluate proposed pre-processing. Investigation shows significant bit-rate savings compared to a professional real time HEVC video encoder.
Control Heading on Three Wheel Electric Vehicles using Fuzzy Logic for E-Cak

The safety and comfort of a vehicle is very important when driving. One of the security and convenience that can be achieved is to maintain the stable condition of the vehicle when it turns. For this reason a prototype was designed with a control method to control the condition of turning a vehicle. In this paper electronic device system is designed to control turn conditions using the fuzzy logic method, where fuzzy logic processes data error yaw rates and turning angles obtained from the IMU sensor. The system was applied to a three-wheeled vehicle model with a two-wheeled steering system. Based on testing with variations in turning angles and a fixed speed of 20 km / h, the largest yaw rate occurs at a 30 degree turning angle which is 0.2355 rad / s and after being given control of the yaw rate value to 0.03901 rad / s a yaw rate value reduction of 1 , 5943 rad / s. Testing with variations in vehicle speed and fixed turn angle of 10 degrees produces a yaw rate of 0.1314 rad / s and after the control is given to 0.03892 rad / s, a reduction of 0.09248 rad / s occurs. From these tests fuzzy logic is capable of improve vehicle response by minimizing slippage that occurs when the vehicle makes a turn movement so that the three-wheeled vehicle is maintained stability and security.
Converter Nonlinear Fuzzy Control of PMSG-Based for Wind Energy System in Network Context

In this paper, a control of the grid-connected Permanent Magnet Synchronous Generator (PMSG) based on variable speed wind system, taking into account network parameters and the interaction of the wind system with the network is presented. The controlled variables are the speed of the machine and the DC voltage. The proposed Converter Nonlinear Fuzzy Controller (CNFC) is based on a Takagi-Sugeno (TS) fuzzy model and on a control model which allows the electrical system to which the PMSG is connected to be taken into account. Some sufficient conditions for robust stabilization of the TS fuzzy model were derived and have been formulated using a Linear Matrix Inequalities (LMI) format. Obtained performances have been compared with those obtained with Linear Quadratic method (LQ). The control model is a simplified model of the whole system performances which captures the dynamics that are important for the synthesis of the CNFC. The proposed controller methodology is finally demonstrated through the proposed model to illustrate the effectiveness of the proposed method.
Voltage Sag Source Identification Based on Few-Shot Learning

Efficient identification of the voltage sag sources is significant in the power quality studies. This paper presents a novel method for voltage sag source identification which performs automatic feature extraction and shows a superior performance regardless of the insufficient amount of training samples. In the proposed strategy, the input data are preprocessed and fetched into the feature extractor, which is designed based on the convolutional neural network. Then the weighted k-nearest neighbor classifier generates the identification results. In the training period, the few-shot learning technique is harnessed, and the siamese network is constructed such that the proposed model learns efficiently even with a small number of samples. The proposed scheme is implemented in Python and PyTorch framework. Case studies and comparisons with other methods are carried out on 700 samples of voltage sag events in Jiangsu Province, China. Experimental results show the superiority of the proposed method over other identification methods in the tested cases.
Spectrum Insensitive Sparse Recovery with Iterative Affine Projections

We propose a novel greedy algorithm for $\ell_{0}$-based sparse signal recovery, inspired by Iterative Hard Thresholding, which alternates a gradient descent step towards minimizing the sparsity error with a projection step on the affine solution space y = A x. We provide a theoretical guarantee based on Restricted Isometry Property for successful recovery of exact sparse signals, in the noiseless case, which does not depend on the singular values spectrum of the dictionary. This improves signal recovery by providing robustness in case of ill-conditioned dictionaries, as learned and coherent dictionaries tend to be. Simulation results on noiseless exact-sparse recovery indicate improvements compared to similar algorithms, especially in the case of ill-conditioned dictionaries.
Novel Sensing Mechanism for Full-Duplex Secondary Users in Cognitive Radio

In this paper, we present a new Transmitting-Receiving-Sensing (TRS) mechanism for full-duplex cognitive radio. Our proposed mechanism permits Secondary Users (SUs) to establish a bidirectional communication over the same frequency band while keeping aware of the Primary User (PU) activity status. The activity period of SU in our proposed mechanism is composed of two stages: In the first stage SU communicates in bidirectional way with his peer SU. At the second stage, one of the SUs becomes silent in order to do not disturb his peer, which performs a spectrum sensing and remains active at the same time using self-interference cancellation technique. The probability of collision related to our mechanism is derived as well as the probability of waste and the average throughput. Our simulation results show that the proposed mechanism can significantly decrease the probability of collision at low SNRp (Signal to Noise Ratio of PU at SU).
The Nearest Origin-Shield (NOS): A Jitter-Free Overlay Routing Framework for Content Delivery Networks

Although Content Delivery Networks (CDN) do their best to quickly deliver the contents, Internet Service Providers (ISP) and network conditions cause unexpected changes on the routing paths. Hence, it gets difficult for CDNs to adhere to the delay promises within the Service Level Agreements. This paper presents a jitter-free overlay routing framework for CDNs, which struggle to adapt timely content delivery to end users due to the dynamicity of the Internet. The Nearest Origin-Shield (NOS) framework which we propose in this paper overcomes this issue by creating an overlay network on top of the existing physical ISP networks and dynamically determining the fastest routes to the content sources (Origins) on this overlay network. NOS keeps track of changes in underlay network through periodically measuring the end-to-end routing paths delays via Helper Modules. Based on the underlay network delay measures and the load (i.e., CPU, disk I/O and network usage) of the overlay network entities, NOS Central Module determines the fastest routes to the Origins using our novel Delay-Aware and Jitter-Free Overlay Routing Algorithm. Hence making it possible for CDNs to provide jitter-free and timely content deliveries meanwhile ensuring the cache servers not to be overloaded. We created a new testbed for performance evaluations with real CDN servers, Origin accounts and measurements. Results show that, NOS provides shorter, more stable and jitter-free routing paths with jitter gains up to 98 % and improves the Hit ratio by 0.74%.
Computer Vision System Based for Personal Protective Equipment Detection, by Using Convolutional Neural Network

The number of work accidents happened in Indonesia does not decrease, in this moment. One of the cause of work accidents is human negligence in wearing personal protective equipment or what is called PPE. A system that can monitor the completeness of PPE worn by workers in an industrial environment automatically is needed. Computer vision technology one method known as convolutional neural network is used, this is a system to monitor directly and detect workers who do not wear PPE. The level system precision have seen by compare between the real condition and the system condition red which is shown the people without PPE completely. The resulted of the system resulted would compare with the examiner’s. The result shown that this system is able to know and distinguish between workers who wear PPE in full and not. With the number of dataset 14512 images, the accuracy of this system is 79.14% with the precision of the detection resulted 80%. This system can give a warning if there are workers who do not wear PPE completely and also could be implemented into several workplaces that have the requirements for the detection. This system give the contribution to decrease work accidents and to increase the safeness of the workers.
Firmware Synthesis for Ultra-Thin IoT Devices Based on Model Integration

Developing firmware for ultra-thin Internet of Things (IoT) devices is challenging due to exceedingly limited hardware resources, increasing functional requirements, and rigorous time-to-market constraints prevalent in industry. Model-driven approaches are often used to tackle these challenges. In the IoT and embedded systems domains, highly specialized metamodels are employed in systems engineering, including the development of firmware. However, these metamodels exist in isolation, limiting the capabilities of model-driven activities. In this paper, we show how firmware synthesis for ultra-thin IoT devices can be enhanced by model integration which is realized by a novel unifying modeling language that aims at integrating the large number of dedicated metamodels. We demonstrate our approach with an industrial use case where we synthesize parts of the firmware for one of the sensor peripherals of an IoT device along with contracts enabling static code verification.
A 45nm High-Throughput and Low Latency AES Encryption for Real-Time Applications

In this paper, we propose a high-throughput and low-latency AES architecture for wideband and real-time applications such as surveillance cameras, video conference, motion detection, IoT gateways, data store encryption… Our design uses an outer round pipeline technique to achieve high throughput. The design has been modelled in RTL VHDL and then synthesized with a 45nm CMOS technology using Synopsys Design Compiler. The implementation results show that the proposed architecture achieves a throughput of 111.3Gbps and a latency of 12.6ns at the maximum operating frequency of 870MHz. With the same 45nm CMOS technology, our design has area efficiency (2.4 times) and energy efficiency (4.7 times) higher than other related works.
Subjective Evaluation of Light Field Image Compression Methods based on View Synthesis

Light field (LF) images provide rich visual information enabling amazing applications, from post-capture image processing to immersive applications. However, this rich information requires significant storage and bandwidth capabilities thus urgently raises the question of their compression. Many studies have investigated the compression of LF images using both spatial and angular redundancies existing in the LF images. Recently, interesting LF compression approaches based on view synthesis technique have been proposed. In these approaches, only sparse samples of LF views are encoded and transmitted, while the other views are synthesized at decoder side. Different techniques have been proposed to synthesize the dropped views. In this paper, we describe subjective quality evaluation of two recent compression methods based on view synthesis and comparing them to two pseudo-video sequence based coding approaches. Results show that view synthesis based approaches provide higher visual quality than the naive LF coding approaches. In addition, the database as well as subjective scores are publicly available to help designing new objective metrics or can be used as a benchmark for future development of LF coding methods.1
Emotion Recognition in Video Clips Using Simple and Extended Center Symmetric LBP

The purpose of our visual emotion recognition system is to accurately discriminate six universal emotional states namely anger, disgust, fear, happiness, sadness, surprise. Emotion recognition in humans using visual information has proven to be quite challenging and an active area of research. To the best of our knowledge, simple and extended center symmetric local binary patterns (LBP) have not been used in literature on an emotional data-set and hence we have used it in our work to get a discriminating set of features. Various state of the art classifiers like Support Vector Machines (SVM), Random Forest (RF) and Logistic Regression (LR), are used in our paper to check the performance of our model. A well-known emotional video database known as eNTERFACE’05, has been employed in our research and the highest accuracy obtained from our approach is 97.9%. Our system has yielded higher accuracy on this dataset when compared with other well-known visual emotion recognition systems in literature.
Robust Spatio-Temporal Partial-Response Signaling over a Frequency-Selective Fading MIMO Channel with Imperfect CSI

Partial-response signaling is known to facilitate the equalizer design because a controlled amount of residual interference is permitted. The design of the target impulse response of the partial-response precoder often assumes perfect channel state information, which is unfortunately not available at the transmitter in most practical applications. Consequently, this contribution focuses instead on the robust and joint design of a spatio-temporal target impulse response and the equalization coefficients for a frequency-selective fading multiple-input multiple-output communication channel based on current and/or previous noisy channel estimates. More precisely, the error in the channel estimates is statistically modeled, and robustness is achieved by minimizing the mean-squared estimation error averaged over the joint distribution of the actual channel and the available channel estimates. Numerical results of the bit error rate confirm that the proposed robust partial-response signaling not only provides a significant performance gain compared to traditional full-response signaling, but also outperforms the naive approach, which ignores channel estimation errors.