Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment effect of neurotropin on radiation injury of spinal cord in rabbit′s

Objective:To observe the efficacy of neruotropin(NTP) in the treatment of the radiation injury of rabbit′s spinal cord,to identify this by MRI and pathological findings.Method:60 New Zealand white rabbits weighted 2.5~3kg,received irradiation of the thoracic segment(T4) spinal cord to setup the model of radiation injury.All 60 rabbits were equally divided into treatment group and control group,each group were divided into 5 groups according to the following single doses of irradiation:20,25,35,40,45Gy.Once having the symptoms of radiation injury of spinal cord,treatment group,were injected neruotaopin(3.6NU/d,4w,im),control group were injected saline(3ml/d,4w,im). The changes of MRI,pathological findings and symptoms between this 2 groups were analysed before and after the treatment.Result:The MR findings of rabbit′s spinal cord displayed abnormal signal 16 weeks after irradiation,it was earlier than the time the symptoms begin 18 weeks after irradiation.3 weeks after the end of treatment,we analyse all symptoms(such as paralysis,uroschesis,reaction) and found symptoms of rabbits treated with neurotropin were lightened markedly,the area of lesions became small on MR images and pathological finding were better than the control group(all P0.05). Conclusion:Neruotropin plays a role in the treatment of radiation injury of spinal cord.
Fine Tunable PT-Symmetric Optoelectronic Oscillator Based on Laser Wavelength Tuning

A fine tunable parity-time symmetric optoelectronic oscillator (PT-OEO) based on laser wavelength tuning is proposed and experimentally demonstrated. In the PT-OEO, an intensity modulator (IM) and a dispersion compensating fiber (DCF) are merged into the loop. Tuning the laser wavelength leads to a reconfigurable dispersion-induced power fading, serving as rough tunable oscillation mode selection. Meanwhile, to implement a fine-mode selection, parity-time symmetry breaking is introduced. Consequently, a fine tunable PT-OEO is achieved without using a high quality-factor (Q-factor) electrical filter. Experiment shows that the frequency of the generated signal can be tuned from 20.34 to 19.49 GHz with a step of 203 MHz, when the laser wavelength is manipulated from 1545 to 1565 nm. The phase noise is evaluated as −116.1 dBc/Hz @ 10-kHz offset frequency.
Performance Analysis of Concatenated Error Correction Code in Secret Key Generation System

Secret key generation from wireless channel is an emerging technique of physical layer security. At present, most of the secret key generation schemes use information reconciliation to obtain symmetric keys. This paper introduces a non-interactive information reconciliation scheme based on channel coding and stream encryption, and considering the error correction capability, we design a concatenated code of BCH and RS codes as channel coding. The performance of concatenated error correction code has been analyzed in this scheme. Then, we compare the concatenated code with first-level error correction code in different test environments. Extensive numerical simulations and experiments demonstrate that the decoding performance of this second-level concatenated code is better than the first-level error correction code, and it can also effectively eliminate third-party eavesdropping.
Failure-Aware Mobile Crowd Sensing: A Social Relationship-Based Transfer Approach

As an appealing sensing paradigm, Mobile Crowd Sensing (MCS) which provides a cost-efficient solution for large-scale urban sensing tasks has gained significant attention in recent years. However, in practice, many MCS applications usually suffer from the failure of sensing task execution, ranging from the randomness and autonomous in participant users’ behavior, to lacking of prior experience and monetary reward, etc. To mitigate the impact of these failures, in this paper, we propose and study a novel problem, namely failure-aware mobile crowd sensing. To solve our problem, we devise a two-stages framework, including offline task allocation and online task transfer. Towards enhancing task completion ratio, we propose an indeterminate fitness proportionate based task allocation approach FPSAll, and an utility evaluation-based task transfer approach FTASKTraf, respectively. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world data set.
The Application of a Hybrid Transfer Algorithm Based on a Convolutional Neural Network Model and an Improved Convolution Restricted Boltzmann Machine Model in Facial Expression Recognition

As an important part of emotion research, facial expression recognition is a necessary condition for intelligent interaction between human and machine, which has an important research significance and a potential commercial value. Convolutional neural network (CNN) is an effective method to recognize facial emotions, which can perform feature extraction and classification simultaneously, and can automatically discover multiple levels of representations in data. Due to the fact that there are millions of parameters involved in training the convolutional neural network model, and there is a large demand for marked samples, transfer learning is often used to fine-tune the pre-trained model for a small target sample set. However, there are often some content differences between data sets during deep transfer learning, which will affect the recognition ability of feature extraction. In order to improve the facial expression recognition ability in transfer learning, a hybrid transfer learning model based on an improved convolution restricted boltzmann machine (CRBM) model and a CNN model is proposed in this paper. This method is fused by two learning abilities of these two models. When the pre-trained CNN model is transferred to a small target set, the CRBM is used to replace the full connection layer in the CNN model, and the CRBM layer and the sofmax layer will be retrained on the target set. The added CRBM layer can not only fully connects all feature maps, but can also learn about the unique statistical characteristics about the target set, which eliminates the influence of content differences between data sets, and extracts higher-order statistical features of facial expression images from the target set. The proposed method is evaluated based on four publicly available facial expression databases: JAFFE, FER2013, SFEW and RAF-DB. The new method can achieve better performance than most state-of-the-art methods, and it can effectively prevent the negative influence of transfer learning features between different data sets.
NOMA-based Integrated Satellite-Terrestrial Multi-relay Networks with Hardware Impairments and Partial Relay Selection Scheme

This paper investigates the impacts of hardware impairments (HIs) and partial relay selection (PRS) scheme on non-orthogonal multiple access (NOMA)-based integrated satellite-terrestrial multi-relay networks (ISTMRNs). NOMA technology can bring us significant spectral efficiency, high frequency utilization ratio and reliability for it can transmit signal to multiple destinations simultaneously. Besides, we utilize PRS scheme to obtain better system performance. Specifically, we derive the closed-form expression of the outage probability (OP) for the considered system in the presence of HIs and PRS scheme. To gain further insights at high signal-to-noise ratios (SNRs), the asymptotic expression for the OP is also derived. Numerical Monte Carlo (MC) results are provided to verify the correctness
Atrial Fibrillation Detection with a Domain Adaptation Neural Network Approach

Atrial fibrillation (AF) is a life-threatening arrhythmia that is often paroxysmal and asymptomatic. Therefore, there is a great impetus towards an automatic AF early detection for providing crucial evidence-based treatment. To this end, the existing supervised classification methods typically rely on a large training set built from the majority of ECG records. By contrast, in this paper, we focus on interpatient scenarios affected by the data shift problem between the training and test distributions referred to as source and target domains. The intrinsic discrepancy between the source target domains due to the generation of ECG data in varied acquisition conditions and from different devices renders the classification problem very challenging. To overcome this constraint and to enhance the AF detection system performance, we propose a classification system based on neural networks called an asymmetric domain adaptation neural network (ADANN). First, a denoising auto-encoder (DAE) is used to perform feature learning, and then extra fully connected hidden layers are employed to eliminate the data shift problem. The performances of the proposed AF detection system are evaluated and compared to those of state-of-the-art systems using a publicly available landmark database.
Energy Efficiency Maximization in Bistatic Backscatter Communications with QoS Constraint

Bistatic backscatter radio is expected to be applied in Internet of Things since it can achieve information transfer while consuming extreme low energy. However, no effort have been made to design an energy-efficient resource allocation algorithm with Quality of Service (QoS) requirement, and this observation motivates our work. In this paper, we consider a classical bistatic backscatter system, where the backscatter node first harvests energy from the dedicated power source in the sleep phase, then backscatters its information to the receiver and harvests energy simultaneously through a reflection coefficient in the active phase. We formulate an optimization problem to maximize the achievable energy efficiency, subject to the energy causality and QoS requirement, by optimizing the transmit power at the dedicated power source, the reflection coefficient, the time for sleep phase and active phase. As the formulated problem is a non-convex fractional programming problem and hard to solve, by means of the fractional programming, we firstly transform it in the fractional form as an equivalent problem in the subtractive form. By introducing several auxiliary variables and employing the monotonicity of the objective function, we show that the transformed problem can be reduced to one of the following two convex problems corresponding to two scenarios, i.e., the transmitter always operates in the active state and the dedicated power source adopts the maximum allowed power. Simulation results are provided to verify our findings and show the advantage of the proposed scheme in terms of energy efficiency.
A Fast Session Key Generation Scheme for LoRaWAN

LoRaWAN is one of the emerging Low Power Wide Area Networks being developed for IoT applications which can be applied to Industrial Control System (ICS). The latest LoRaWAN release v.1. 1 uses Advanced Encryption Standard Electronic (AES) with 128 bits key for encryption and authentication. The release has a security framework which covers the end devices, LoRaWAN gateways, network servers and application servers. In its key management part, the key generation is recommended, but not clearly specified. However, the key generation is a very important part of the security key management scheme, as it will affect the soundness of the whole security framework. In this paper, a new key generation scheme for LoRaWAN is proposed. The scheme has been shown that it not only has high computing efficiency but also meets the randomness requirement of the security key. The proposed scheme can enhance the LoRaWAN security framework if it is adopted in its future release.
Designing the Four-Tooth-Shaped Microstrip Antenna for Wi-Fi Applications

A monopole microstrip antenna with the radiator having two pairs of rectangular teeth of different sizes is considered. The influence of the size of the teeth and the position of the cutouts between them on the electrodynamic characteristics of the antenna at the first and second resonance frequencies is studied. Based on the results of the analysis, the approach is proposed to optimize the electrodynamic characteristics of this antenna type for given parameters. The practical application of this approach in the designing the four-tooth-shaped dual-band antenna for Wi-Fi applications is shown.
A New Compressional Wave Speed Inversion Method Based on Granularity Parameters

How to improve the prediction accuracy of compressional wave speed has always been one of the basic research subjects in geoacoustics study field. Due to the stability of granularity, whether in the laboratory or in the seabed environment, the regression relationship between compressional wave speed and granularity is an important sound speed inversion method. Machine Learning (ML) provides a new solution for more efficient sound speed prediction systems. In this study, two ML algorithm, Random forest (RF) and Support Vector Regression (SVR), combined with nine granularity parameters (mean grain size, median grain size, skewness, kurtosis, sorting coefficient, gravel, sand, silt, and clay content respectively.) to analysis the effect of granularity property on sound speed. As a result, the sound speed-granularity predictive models were established, and the sound speed accuracy obtained based on the predictive models are higher than that of the regression equations, and the RF model has a higher accuracy than the SVR model. Based on the RF predictive model, the feature selection was conducted and the results show that the most influential parameter of granularity is mean grain size. Furthermore, the RF model can also predict the sound speed with high precision in the absence of partial parameters, which can be a useful tool for ocean engineering and seismic inversion.
Guidance control for parallel parking tasks

Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies ( called parking trajectory planning ) designs a detailed reference trajectory that links the start and ending points of a special parking task and let the vehicle track this reference trajectory so as to park into the berth. The second kind of strategies ( called guidance control ) just characterizes several regimes of driving actions as well as the important switching points in certain rule style and let the vehicle follows the pre-selected series of actions so as to park into the berth. Parking guidance control is simpler than parking trajectory planning. However, no studies thoroughly validated parking guidance control before. In this paper, a new automatic parking method is presented, which could characterize the desired control actions directly. Then the feasibility is examined carefully. Tests show that a simple parking guidance control strategy can work in most parallel parking tasks, if the available parking berth is not too small. This finding helps to build more concise automatic parking systems that can efficiently guide human drivers.
4H-SiC CMOS Transimpedance Amplifier of Gamma-Irradiation Resistance Over 1 MGy

A transimpedance amplifier (TIA)—with gamma-irradiation resistance of over 1 MGy—based on a novel 4H-SiC complementary MOS (CMOS) technology was fabricated. This TIA is robust enough to be applied in measuring instruments installed in nuclear power plants or other harshly irradiated environments. The SiC CMOS transistors comprising the TIA feature a thin (8-nm-thick) gate oxide to reduce the threshold-voltage shift ( ${V}_{{\text {th}}}$ ) due to irradiation by more than 90% compared with that of the conventional transistors. Oxynitride protection formed at the SiC–SiO2 interface in the thin gate-oxide region suppresses the deterioration of mobility by interface traps generated by the gamma radiation. The TIA consisting of these SiC-CMOS transistors operated properly up to at least 1.2 MGy without an increase in the offset voltage, although its open-loop gain was degraded due to deteriorated mobility of the p-channel metal–oxide–semiconductor field-effect transistor (MOSFET). On the other hand, increasing the drain leakage current in the nonactive region impeded further improvement of the SiC TIA under a high integral dose. To decrease the drain leakage current, a structure with a high doping concentration layer between the source and the drain in the nonactive region was fabricated. The structure stops the parasitic transistor turning on and the trap-assisted current increasing. The leakage current of the improved structure is about 42% lower than that of a conventional structure.
Abstraction of Heterogeneous IoT Devices for Management of Smart Homes

During the last few years, smart homes became reality due to the popularization of Internet of Things (IoT). This kind of environment is composed of several types of IoT heterogeneous wireless devices, where each one has singular characteristics and a particular set of protocols. This fact makes the smart home very complex network to deploy new applications and services. Therefore, a smart home needs an approach to abstract the communication with these IoT devices, improving the flexibility and the manageability of the applications running on the top of the smart home, as well as the network infrastructure as a whole. Within this context, this paper proposes a middleware for communication abstraction of IoT devices in smart homes. The proposed middleware allows the communication with devices through a high-level interface, enabling the management of heterogeneous IoT devices as a generic interface. The experiments performed in a real testbed scenario suggest that the proposed middleware is scalable and improves the management of IoT devices.
ECG Arrhythmia Classification By Using Convolutional Neural Network And Spectrogram

In this study, the electrocardiography (ECG) arrhythmias have been classified by the proposed framework depend on deep neural networks in order to features information. The proposed approaches operates with a large volume of raw ECG time-series data and ECG signal spectrograms as inputs to a deep convolutional neural networks (CNN). Heartbeats are classified as normal (N), premature ventricular contractions (PVC), right bundle branch block (RBBB) rhythm by using ECG signals obtained from MIT-BIH arrhythmia database. The first approach is to directly use ECG time-series signals as input to CNN, and in the second approach ECG signals are converted into time-frequency domain matrices and sent to CNN. The most appropriate parameters such as number of the layers, size and number of the filters are optimized heuristically for fast and efficient operation of the CNN algorithm. The proposed system demonstrated high classification rate for the time-series data and spectrograms by using deep learning algorithms without standard feature extraction methods. Performance evaluation is based on the average sensitivity, specificity and accuracy values. It is also worth to note that spectrogram increases the performance of classification since it extracts the useful time-frequency information of the signal.
Representing Character Sequences as Sets : A simple and intuitive string encoding algorithm for NLP data cleaning

Data preparation is vital in the field of machine learning, especially in natural language processing. This paper briefly describes a method of encoding character sequences, which is inspired by biological memory systems. The algorithm can be thought of as a type of artificial neural network, which is only used to store text data in a specific way, not generate predictions.To test the viability of the approach, the task of searching for words in a dictionary was used, utilizing two simple heuristic rules. The goal is to produce tools capable of cleaning texts written in any language, which uses a phonemic writing system. Texts written in two specific languages are being processed during development and experimentation, they are English and Bulgarian.User-friendliness and minimization of data obfuscation are the driving requirements. Another requirement is compatibility with other machine learning methods, since the algorithm is not intended for solving an entire NLP case from start to finish. It is to be used only during the data preparation phase.
Software for Modeling the Temperature Distribution of Strip and Work Rolls during Hot Rolling

The software for calculation the thermal conditions of the hot rolling mill is developed. The software includes several mathematical models of the temperature distribution along the thickness of the strip and working rolls. The base of all models is the heat equation with different boundary conditions. The type of model is defined by the current zone (rolling gap, interstand gap, cooling zone on the surface of the roll). The heat flows in the boundary conditions for a strip at the interstand gaps are taking into account the parameters of interstand cooling systems. The information about the construction and settings of the nozzles and roll anti-peeling systems is used for calculating the heat flow on the surface of the working roll.
An Approach for Indirectly Adopting a Performance Portability Layer in Large Legacy Codes

Diversity among supported architectures in current and emerging high performance computing systems, including those for exascale, makes portable codebases desirable. Portabil- ity of a codebase can be improved using a performance portability layer to provide access to multiple underlying programming mod- els through a single interface. Direct adoption of a performance portability layer, however, poses challenges for large pre-existing software frameworks that may need to preserve legacy code and/or adopt other programming models in the future. This paper describes an approach for indirect adoption that introduces a framework-specific portability layer between the application developer and the adopted performance portability layer to help improve legacy code support and long-term portability for future architectures and programming models. This intermediate layer uses loop-level, application-level, and build-level components to ease adoption of a performance portability layer in large legacy codebases. Results are shown for two challenging case studies using this approach to make portable use of OpenMP and CUDA via Kokkos in an asynchronous many-task runtime system, Uintah. These results show performance improvements up to 2.7x when refactoring for portability and 2.6x when more efficiently using a node. Good strong-scaling to 442,368 threads across 1,728 Knights Landing processors are also shown using MPI+Kokkos at scale.
An Analytical Dynamics Approach for Nonlinear Trajectory-Tracking Control of Quadrotors: Numerical and Experimental Results

This paper presents an alternative approach for nonlinear trajectory-tracking control of quadrotor unmanned aerial vehicles (UAVs) based on analytical dynamics and quaternion theories. The equations of motion of constrained mechanical systems are reinterpreted as a solution for obtaining an explicit nonlinear control law. In addition, practical control aspects and the impact of nonidealities are investigated by providing qualitative and quantitative comparisons between simulated and experimental results. Numerical simulations demonstrate the efficacy of the proposed controller to track desired trajectories with very small tracking error for simple as well as aggressive or sharp trajectories. Using common open-source solutions, real-world implementation feasibility, performance, and limitations of the control method are discussed.