Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical therapies for motor symptoms in Parkinson’s Disease

Parkinson’s disease is the second most common neurodegenerative disorder in the aging population. It is associated with progressive loss of nigrostriatal dopaminergic neurons. Parkinsonism is a clinical syndrome that consists of motor and non-motor symptoms. It has been well described that many if not all of these deficits are associated with abnormalities related to the neurotransmitter dopamine. This review is intended for primary care physicians who often establish the diagnosis and initiate Parkinson’s treatments. One challenge faced by the primary care physician is to decide the appropriate medication and starting dose. Often medications are used at very low doses or incorrect dosing schedules. This review aims to summarize these medications, their potential side effects and the rationale for medication choices.
Neuromuscular and biomechanical adaptations associated with chronic low back pain

i Declaration . iv Acknowledgement . v Publications and conference papers . vii List of Tables . xiii List of Figures . xv Abbreviations . xvi Chapter 1 . 1 1.1. Thesis overview . 1 1.1.1. Research background . 1 1.1.2. Thesis structure . 1 1.1. 3. Nomenclature within the thesis . 4 1.2. Introduction . 5 Chapter 2 . 9 2.
A Comparative Study to Assess the Effectivenesss of Nebulised 3% Hypertonic Saline, 0.9% Normal Saline and Salbutalmol in Management of Acute Bronchiolitis

Aims And Objectives: Bronchiolitis is the most common lower respiratory tract infection among infants in both developed and developing countries. Study aim is to compare the effects of nebulized 3% hypertonic saline, 0.9% saline and Salbutamol in acute bronchiolitis. Material and Methods: 60 Children of age 1 month to 24 months with clinical diagnosis of acute bronchiolitis were included in the study. Cases were divided into 3 groups randomly – 3% hypertonic saline (HS) as Group A, 0.9% normal saline as Group B and Salbutamol as Group C. Four doses of nebulization at an interval of six hours were given daily to patient of each group till discharge. Results: Baseline Clinical Severity (CS) scores in 3%HS, 0.9% Normal Saline and Salbutamol groups were 6 ±1, 5.8±1.5 and 5.5±2.5 respectively (p=0.14), which after treatment dropped to 1.0±0.2, 3.19±0.8 and 1.65±0.7 respectively on the 4th day of treatment (p<0.01). Length of hospital stay in 3% HS, 0.9% Normal Saline and Salbutamol groups was 3.0±1.5, 5.0±1.2 and 3.5±1.7 days respectively, which was found to be statistically significant (p= 0.001). Conclusion: 3% Hypertonic Saline nebulization (without additional bronchodilator) significantly reduces the CS scores and length of hospital stay as compared to 0.9% Normal Saline and Salbutamol nebulization.
Time-Delay And Doppler Range Model For Synthetic Aperture Radar

Synthetic aperture radar (SAR) owns a lot of advantages such as high-resolution imaging, day-and-night working and so on, which has been widely applied in military and civilian areas. In this paper, we propose a time-delay and Doppler range model (TDRM) for SAR, which achieves very high accuracy, and is easy to use because it is two-dimensional linear with respect to time-delay and Doppler frequency. First, by establishing a polar coordinate system, the range history of SAR in the new coordinate system is obtained. Then, by combining round-trip time-delay and Doppler frequency with TDRM, we derive the range model in time-delay and Doppler domain. Finally, the analytical expression of TDRM is obtained by first-order Taylor approximation. We simulate TDRM with a specific SAR system. The simulation results verify the validity of the model. Specifically, for all targets in $2km\times 2km$ scene, the relative errors and the quadratic phase errors of TDRM are less than $7\times 10^{-9}$ and $\pi/4$, respectively.
3-D Reflector Mapping Using Sensor Network

Many indoor localization and navigation systems require the information of the wall layout. The paper proposes a 3-D wall mapping algorithm using arbitrarily placed nodes. The proposed algorithm estimates the normal vector of the reflector surface and the scaling factor of the translation vector. The constrained CRLB of the reflector parameters is derived. The performance of the wall mapping algorithm is examined by comparing the MSE with the constrained Cramer-Rao lower bound (CRLB).
Near Sensor Decision Making via Compressed Measurements for Highly Constrained Hardware

This work presents and compare three realistic scenarios to perform near sensor decision making based on Dimensionality Reduction (DR) techniques of high dimensional signals in the context of highly constrained hardware. The studied DR techniques are learned according to two alternative strategies: one whose parameters are learned in a compressed signal representation, as being achieved by random projections in a compressive sensing device, the others being performed in the original signal domain. For both strategies, the inference is yet indifferently performed in the compressed domain with dedicated algorithm depending on the selected learning technique. Our results, based on two common datasets, show that performing the inference in the compressed domain represents a competitive approach compared to the classical classification strategy (inference in the original signal domain) regarding memory and computational requirements. We also exhibit the fact that it is especially well suited for embedded applications in the context of hardware implementations with limited resources even with specific hardware design and limitations.
Structural Relationships Among In-Service Teacher's Behavioral Intention, Perceived Usefulness, Perceived Ease of Use and Online Professional Development System Quality

Mongolia is a country with low population density in the world. Provision of sustainable and up to date professional development for in-service teachers is one of the biggest issues in the education system of the country. Institute of Teachers Professional Development (ITPD) is a central organization of the Ministry of Education responsible for teacher's developments. ITPD started its online training system for teachers from 2014. In this paper we will study structural relationships among in-service teacher's behavioral intention, perceived usefulness and perceived ease of use, and the quality of this online system using a Technology acceptance model. Important conclusions of this study are as follows. The high quality of the online training system makes it more easy to use for teachers. In-service teachers' perceived usefulness and system quality are most important determinants of intention to use the system in the future.
Robust Source Localization Exploiting Collaborative UAV Network

In this paper, we propose a robust strategy to localize multiple ground sources exploiting a distributed unmanned aerial vehicle (UAV) network in the presence of impulse noise. We achieve robust source localization by using ℓ1-principal component analysis (ℓ1-PCA) based signal subspace estimation at each individual UAV. This approach significantly reduces the signal subspace perturbation compared to the conventional ℓ2-PCA based counterpart. The obtained robust signal subspace estimate is exploited to provide an improved estimate of the noise subspace, which is in turn utilized by the MUSIC algorithm to render coarse source localization at each individual UAV. The source localization information obtained at multiple UAVs is then fused by exploiting group sparsity using the re-weighted ℓ1 minimization. Simulation results demonstrate the effectiveness of the proposed approach.
DBFT: A Byzantine Fault Tolerant Protocol with Graceful Performance Degradation

The surging interest in blockchain has revitalized the search for efficient Byzantine fault-tolerant (BFT) protocols, which are used for blockchains to achieve consensus among replicated data blocks. Most existing BFT protocols perform well in fault-free cases, but they usually suffer from serious performance degradation when faults occur. In this paper, we present DBFT, a BFT protocol that realizes graceful performance degradation in normal cases. The major novelty of DBFT lies in the double-response mechanism, which lets replica nodes deterministically respond to clients twice: one is after the speculative execution phase and the other is after the commitment phase. The double-response mechanism can handle inconsistency in speculative execution, so as to alleviate performance degradation caused by faults. Moreover, DBFT does not involve clients in critical consensus operations so as to reduce the load of clients. The correctness properties, i.e., safety and liveness, of DBFT is rigorously proved. The performance of DBFT is evaluated via experiments and the results show that, DBFT outperforms similar BFT protocols obviously in normal cases.
Closed-Loop DPD for Digital MIMO Transmitters Under Antenna Crosstalk

Due to the closely-spaced antenna elements in largearray or massive MIMO transmitters, antenna crosstalk is inevitable. This imposes additional challenges when seeking to linearize the power amplifiers at the transmitter through digital predistortion (DPD). In the commonly applied indirect learning architecture (ILA), the antenna crosstalk is known to result in a large amount of additional basis functions (BFs) in order to account for all the coupling signal terms and achieve good linearization. In this article, we propose a novel closed-loop DPD architecture and associated parameter learning algorithms that can provide efficient linearization of digital MIMO transmitters under antenna crosstalk. The proposed solution does not need extra basis functions, and is thus shown to provide large benefits in terms of computational complexity compared to existing state-of-the-art. Comprehensive numerical results are also provided, showing excellent linearization performance outperforming the existing reference methods.
Complex-Value Convolutional Neural Network for Classification of Human Activities

Human target is one of the main targets of radar detection. Many methods have been proposed to improve the performance of human activities classification, including the neural networks methods. In the current research, the neural network methods only utilize the amplitude information of the target echoes and ignores the phase information. To solve this problem, this paper proposes a human activities classification method based on Complex-Value Conventional Neural Network (CV-CNN). In this method, the motion capture data are utilized to obtain the motion trajectories of various parts of the human, the time-frequency (TF) analysis is utilized to obtain the TF representations, and a CV-CNN structure is built to train the complex-value TF data of different human activities. The experiment results verify the effectiveness of the proposed method. Meantime, the results under different SNRs demonstrate the robustness of the proposed method.
A Moving Target Imaging Algorithm Based on Compressive Sensing for Multi-channel in Azimuth HRWS SAR System

For high-resolution wide-swath (HRWS) multichannel synthetic aperture radar (MC-SAR) system, this paper proposes a novel imaging mode for the ocean moving target. Based on the sparsity of the ocean target images, the new mode uses compressive sensing (CS) to reduce the amount of data by sampling below the Nyquist sampling rate and increases the swath width. Due to the existence of channel error when using the space-time equivalent sampling technique for azimuth signal reconstruction, the traditional single-channel or dual-channel CS-based method is no longer applicable for MC-SAR. In this paper, the moving target imaging is transformed into the sparse signal reconstruction problem under a certain dictionary which constructed based on phase errors called FD-PM (frequency-dependence phase mismatch, FD-PM). The proposed method eliminates the azimuth defocus and blur caused by channel errors and we can obtain images with higher azimuth resolution by a lower sampling rate. Meanwhile, the amount of data is greatly reduced and the imaging efficiency is improved. Simulation data demonstrates the effectiveness of the proposed method.
A V-band Bidirectional Amplifier-Module for Hybrid Phased-Array Systems in BiCMOS Technology

This paper presents a V-band T/R amplifer-module in a SiGe:C 130nm BiCMOS technology, with ft/fmax=250/340GHz. The T/R amplifer-module is a bidirectional architecture consists of IN/OUT RF switches, PA in Tx path and LNA in Rx path with a digital control circuit for hybrid phased array systems. On wafer measurements show that the designed module has a peak gain of 15.5dB and 19dB at 60GHz in Tx mode and Rx mode respectively, while maintaining wideband performance over the desired band from 57-66GHz. In Rx mode, the simulated NF is 8dB over the required band. Furthermore, an OP1dB of 10dBm in Tx mode and an IP1dB of -26dBm in Rx mode have been achieved. The T/R amplifer-module occupies 1.1mm2 area and consumes 160mW/50mW at Tx and Rx modes.
An Outline Design of Standalone Renewable Energy Generation for a Remote Mobile Phone Base Station in Shalbandi, Pakistan

A standalone solar-wind hybrid system has been proposed in this paper to supply power to a cellular phone base station of Shalbandi which is a rural/remote area of Pakistan. The comparison of proposed system has been done with a conventional power generator system. The analysis of three cases with different % shares of renewable energy based system has been carried out and the most viable combination of renewables is suggested depending upon the site conditions. It has been concluded from the results that the off-grid hybrid renewable energy based systems have myriad benefits if deployed with base station in rural or remote areas.
A Research of Arcuate Antenna Array Design Based on HFSS_MATLAB_API

This paper presents a new arcuate antenna array which is introducing to operate at W band with HFSS_MATLAB_API script design. The complex arcuate antenna array has been designed with MATLAB and HFSS according to the script programming framework which has already finished the modeling, numerical solution and simulate analysis. The azimuthal and pitch-oriented array antennas radiation pattern and the 3D polar plot have been obtained from the signal processing. Then the simulated results show that the late-model arcuate antenna provides favorable performance in terms of low side lobes radiation pattern and directionality. Therefore, the feasibility and practicability of arc antenna array can be realized through optimization design.
Robust EEG-based Emotion Recognition using Multi-feature Joint Sparse Representation

The key problem of emotion recognition lie in effective electroencephalography (EEG) signal processing and feature extraction. Traditional methods mostly extract single feature or simply combine different features together, but emotion recognition based merely on a single feature may be unreliable and simple feature combination will be affected by complex feature interdependencies. To improve recognition accuracy, we propose a multi-feature emotion recognition method based on Joint Sparse Representation (JSR) to transform the simple feature fusion into an optimization problem. Specifically, sparse matrices for each individual feature are combined to obtain the JSR of these features, and three different EEG features including wavelet energy, Hurst index, and fractal dimension are employed to produce multi-feature fusion results. Due to high computational complexity and restrictions on kernel function selection of Support Vector Machine (SVM), we adopt Relevance Vector Machine (RVM) to classify emotions. Simulation results show our proposed multi-feature fusion algorithm has an average recognition accuracy of over 85%, which is 8% higher than the traditional method.
SAR Image Scattering Center Estimation via Atomic Norm Minimization

Scatter center matching is one of the choke points of radar automatic target recognition (RATR) based on Synthetic Aperture Radar (SAR). Accuracy of scattering center extraction is an important factor affecting target recognition performance. This paper proposes a SAR images scattering center estimation method based on atomic norm minimization (ANM) model. This method models the scattering center estimation problem as a two-dimensional line spectrum estimation problem, then the estimation is performed in a continuous domain via ANM. The experimental results indicate the superiorities of the proposed method in terms of accuracy and robustness.
Mitigation of Jamming Attack in Massive MIMO With One-Bit FBB Sigma-Delta ADCs

We study the uplink performance of a massive multiple-input multiple-output (MIMO) system with one-bit analog to digital converters (ADCs) in the presence of a disruptive jammer. We propose spatial Sigma-Delta (Σ∆) quantization with an interference cancellation feedback beamformer (FBB Σ∆) to mitigate the adverse impact of the jammer on the system performance. Then we analyze the performance of this architecture by adopting an appropriate linear model and present a recursive algorithm to calculate the power of the quantization noise. Simulation results show that the spatial FBB Σ∆ architecture can achieve the same symbol error rate as in systems with high-resolution ADCs.
Estimation of Mobile Point Distance based on the Signal Strength of Wireless Network Routers

The necessity to obtain the localization on the terrestrial globe surface allowed the expansion and emergence of primordial technologies to contemporary engineering, especially in the context of Intelligent Transportation Systems. Several systems have been based on the information obtained by GNSS (Global Navigation Satellite System) for localization. However, these kinds of systems demonstrate structural limitations, which prevent users from obtaining more accurate positions, especially in closed environments such as underground parking lots, environments where satellite signals are not possible or where interference occurs. In this sense, positioning systems based on RSSI (received signal strength indication) function is support for traditional localization. The strategy used in this work consists of using signals provided by wireless network routers, available in the infrastructure of domestic, corporate or public networks, to estimate the position based on signal power received by wireless receivers of mobile devices. Moreover, different models were studied and evaluated that equate the distance relationship as a function of the power of the RSSI signal received. To evaluate the models from desired external scenarios, an optimization was applied using the Particle Swarm Optimization (PSO) algorithm to obtain the optimum parameters of each model, conducting an accurate position estimation. Experiments in a real environment were performed to evaluate these different models concerning a GPS-RTK system.
Examining the Case for Multicore Fibers in Submarine Cable Systems Based on Fiber Count Limits

The potential role of multicore fibers (MCFs) in submarine cable systems is investigated in the context of electrical power limitations and physical fiber count limitations. We compare the maximum cable capacity theoretically possible for cables using MCFs with 2, 3, and 4 cores to that of cables built with conventional single-core fibers. The comparisons are made based on ideal Shannon-limited capacity, systems with constant output power amplifiers, and individual amplification of MCF cores using fan-in/fan-out devices. MCFs are assumed to have nominally uncoupled cores for which crosstalk between cores is a system impairment. We examine the relative cost/bit of systems built with MCFs and single-core fibers when designed for maximum capacity, and when designed for minimum cost/bit operation. The relative capacity of a cable with MCFs depends strongly on the number of fibers able to be physically accommodated and is greatest when the fiber count number is relatively small. We find only small differences in the relative capacities of MCFs with 2-4 cores and that any advantage over single-core fiber systems can be eroded with minimum signal-to-noise ratio (SNR) requirements above the theoretical level near 0 dB that maximizes cable capacity. The cost/bit of MCF cable systems is determined to be higher than that of single-core fiber systems when maximum cable capacity is the design goal, according to the cost model employed and the specific system and fiber parameters assumed here. Different results may be obtained with cost model refinements and different parameters.
5G Implementation: Major Issues and Challenges

Fifth generation networks have been introduced in response to the need for exponential growth of mobile data traffic and the provision of new generation of services. The ability to provide such services with high flexibility requires the use of new technologies and the extensive changes to existing mobile networks. This paper examines the most important challenges in the implementation of 5G from the technology aspects including mmWave communications, backhaul technology, Technology maturity, energy consumption, EMF and business aspects including business models, ecosystem maturity, Coordination of industry verticals and regulation aspects including spectrum management and fragmentation. Moreover, for some of them solutions suggested by vendor reports and academic works are discussed and analyzed in this article.
Assessing Security and Dependability of a Network System Susceptible to Lateral Movement Attacks

Lateral movement attack performs malicious activities by infecting part of a network system first and then moving laterally to the left system in order to compromise more computers. It is widely used in various sophisticated attacks and plays a critical role. This paper aims to quantitatively analyze the transient security and dependability of a critical network system under lateral movement attacks, whose intruding capability increases with the increasing number of attacked computers. We propose a survivability model for capturing the system and adversary behaviors from the time instant of the first intrusion launched from any attacked computer to the other vulnerable computers until defense solution is developed and deployed. Stochastic Reward Nets (SRN) is applied to automatically build and solve the model. The formulas are also derived for calculating the metrics of interest. Simulation is carried out to validate the approximate accuracy of our model and formulas. The quantitative analysis can help network administrators make a trade-off between damage loss and defense cost.
Ultra-Fast Saliency Detection Using Qr Factorization

In this work, we propose an ultra-fast unsupervised saliency detection algorithm based on QR matrix factorization. The algorithm works by dividing an image into blocks and computing the QR factors in each local neighborhood where salient patches correspond to columns in R with largest L0 norm. Experimental results on the MSRA10K salient object database show that the proposed algorithm is competitive with state-of-the-art real-time saliency detection algorithms in terms of AUC and execution time. Additionally, we study the various parameters that control the performance of the proposed algorithm such as threshold values and processing-block sizes and their effect on the overall performance.