Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive sliding mode control for a lower-limb exoskeleton rehabilitation robot

This paper presents an adaptive sliding mode control (ASMC) method for wearable lower extremity exoskeleton. Since the dynamic control system of lower limb exoskeleton robot is non-linear, it is difficult to track the desired trajectory accurately. In this paper, an adaptive single-input single-output (SISO) system, whose adaptive law is designed based on the Lyapunov method, is applied to calculate the element of the control gain vector in a sliding mode controller. A simulating model was built, different control methods, like PID control, classical sliding mode control, and adaptive sliding mode control, have been tested on the prototype. The results show that ASMC has better performance in eliminating jitter and tracking trajectories than PID and classical sliding mode control.
Compressed Sensing Based Synthetic Transmit Aperture for Phased Array Using Hadamard Encoded Diverging Wave Transmissions

Previously, we proposed compressed sensing based synthetic transmit aperture (CS-STA) to improve the contrast and frame rate of STA while maintaining its spatial resolution in linear array by choosing uniform random matrix as the measurement matrix and transmitting the plane waves (PWs). In this paper, to extend CS-STA for phased array imaging and further improve its performance, we design four types of CS-STA implementations with different combinations of measurement matrices (i.e., uniform random and Hadamard matrices) and transmitted waves [i.e., PW and diverging wave (DW)]. Through simulations and phantom experiments with a 3 MHz, 64-element phased array, we find that type-IV CS-STA with the combination of a Hadamard matrix and DW outperforms the other three implementations including the previously proposed type-I CS-STA in terms of image quality and reconstruction time. Specifically, PW transmission produces visible discontinuity and the reconstruction time with uniform random matrix is about 100-fold longer than that with the Hadamard matrix. Compared with STA, with eightfold higher frame rate, type-IV CS-STA achieves 8.2 and 12.3 dB higher contrast-to-noise ratio and signal-to-noise ratio in the simulations, respectively. These improvements are slightly lower in the phantom experiments, which are 6.2 and 6.6 dB, respectively. In addition, CS-STA does not deteriorate the spatial resolution of STA, with the maximum deterioration being smaller than 1/8 wavelength. These results demonstrate that type-IV CS-STA can achieve phased array imaging with high image quality at high frame rate and may be beneficial to cardiac imaging.
Vehicle detection, tracking and counting using linear quadratic estimation technique

In modern digital world one of the most challenging issue is the detection of moving object in a video frame and further tracking it. Same thing becomes more and more difficult when we replace these moving objects with a moving vehicle on a road. With the increasing number of vehicles the traffic monitoring system has become a very vast field for research. Although there already have been many works done in the field however our paper presents a very simple approach for detecting vehicles in urban traffic scenes by means of background subtraction and Linear Quadratic Estimation (LQE). In our proposed system, a video is taken and then the video is subtracted into masks to detect the vehicles. Binary Morphological operation is used to remove the noise from the video. Simple Linear Quadratic Estimation (LQE) is then used to predict the vehicle path and count all detected vehicles. Our proposed method is less complex and acquires almost 99% result successfully.
Aided inertial navigation of small unmanned aerial vehicles using an ultra-wideband real time localization system

This paper presents an ultra-wideband (UWB) radio aided inertial navigation system (INS), estimating position, velocity and attitude (PVA), based on a low-cost microelectro-mechanical system (MEMS) Inertial Measurement Units (IMUs). This ensures that a drift free INS is available for local unmanned aerial vehicle (UAV) navigation independent of global navigation satellite systems (GNSS). The experimental results show that the presented integration of UWB and INS is promising for navigating independent of satellite-based positioning systems, and illustrates the possible enhancements that are possible when adding an additional vertical position measurement.
Canine Visceral Leishmaniasis; A Seroepidemiological Survey in Jiroft District, Southern Kerman Province, Southeastern Iran in 2015

Background: Domestic dogs have been implicated as the main reservoir host of Mediterranean type of visceral leishmaniasis (VL) that is endemic in some parts of Iran. This study was performed about role of dogs in canine VL (CVL) epidemiology in Jiroft District, south of Kerman Province, southeastern Iran. Methods: Totally, 165 dogs including 100 stray and 65 sheepdogs were randomly selected. After complete clinical examination blood sample was taken from each dog. All the collected samples were examined following the serum separation by direct agglutination test (DAT) for detection of anti-Leishmania infantum antibodies. The titers of ≥1:320 were defined as positive. Results: Overall, of 165 serum samples, 13 samples (7.9%) were positive by DAT at titers of ≥1:320. The seroprevalence was 11% among the stray dogs and 3% among the sheepdogs. There was no significant difference between stray and sheepdogs in CVL infection. The highest seroprevalence rate (14.3%) was found in seven-year old dogs. Conclusion: The present finding indicates the role of stray and sheepdogs in CVL epidemiology in this area. Further investigations are needed to evaluate the status of VL infection in human subjects in this area.
Application of multilevel demand subscription pricing for mobilizing residential demand response in Belgium

The large-scale integration of renewable energy is progressing at an unprecedented pace. The highly variable, non-controllable and unpredictable variation of renewable energy supply requires fast-response generators for balancing, which results in increased operating costs and emissions. This ultimately undermines the economic and environmental benefits of renewable energy integration. These adverse effects can be mitigated by mobilizing the flexibility on the demand side. In this paper, we revisit a multilevel demand subscription pricing policy for electric power, in which a menu of service contracts for assigning different reliability combined with a non-linear energy price schedule is offered. The motivation of this paradigm is to provide differentiated products that can mobilize massive residential demand response resources while respecting the requirement of consumers for simplicity, privacy and control. In this paper, we apply multilevel demand subscription pricing on Belgium to design such a contract for residential consumers.
Optimal secrecy throughput and efficient energy harvesting for SWIPT system

In recent years, simultaneous wireless information and power transfer (SWIPT) has attracted people's research interest, but many researchers only consider one of the secrecy throughput and harvested energy. In this paper, we optimize the secrecy throughput and improve the utilization of the harvested energy in a certain way with a based beamforming artificial noise aided SWIPT system over a Rayleigh fading channel. We investigate the relationship between the secrecy outage probability and the target secrecy rate and the power allocation ratio through formula derivation and simulation results. Then we propose an algorithm of joint power allocation and splitting to achieve the maximum secrecy throughput. Furthermore, we investigate the maximum secrecy throughput in the beamforming mode and that in broadcast mode. The simulation results show that the use of beamforming technique can greatly improve the performance of the secrecy throughput and the harvested energy.
A framework for reliable and ubiquitous inference in wireless sensor network

Development of Internet of things (IoT) applications brings a new movement to the functionality of wireless sensor networks (WSNs) from only environment sensing and data gathering to collaborative inferring and ubiquitous intelligence. In intelligent WSNs, nodes collaborate to exchange the information needed to achieve the required inference or smartness. Efficiency or correctness of many smart applications rely on efficient, timely, reliable, and ubiquitous inference of information which may continually change in interaction with other nodes. In this paper, we introduce a framework which provides a generic solution for such common ubiquitous inferences. This framework brings the reliability and ubiquity for inferences using the redundancy characteristic of the gossiping protocols. With this framework the implementation of such inferences and the control of their speed and cost is abstracted by providing the developers with a proposed middleware and some dissemination control services. We develop a prototype implementation of the proposed framework and an inference example on TinyOS to evaluate the effectiveness of the framework and the efficiency of the related inferences. To this aim, we utilize TOSSIM simulator in various densities and different number of nodes. Results of the evaluations demonstrate that in all nodes, the inferring time after a change is about a few seconds (less than a minute) and the cost of maintenance in steady state is about a few sends (less than 10) per hour.
Channel estimation in a MIMO relay system: Challenges & approaches channel estimation in MIMO relay system: A review

The overall performance of the communication system is highly influenced by the accuracy with which the channel is estimated at the receiver node. It is imperative to design methods that can efficiently provide the channel estimates even in worst case channel scenarios. It becomes extremely challenging to extract reliable channel estimates in the mobile node scenario, wherein the Doppler influences are also participating. Hence, the designed channel estimation methods need to be robust and spectrally efficient, provided that these are not computationally demanding, as well. In this paper, an extensive review of the channel estimation methods for MIMO relay systems that are adopted under varied channel conditions is conducted. We discuss the concept of relaying, the advantage of including relays in MIMO systems and their classification. This is followed by a comparison of these approaches that is made in terms of their spectral efficiency, computational complexity as well as their limitations. Further, we discuss the channel estimation methods specifically designed for the time-varying frequency-selective channels. We emphasize that more work needs to be done for improving the channel estimation methods, especially when the channel is influenced by the Doppler Effect.
A study on neural networks approach to time-series analysis

Prediction and classification of time-series data play a vital role in multiple fields. This paper aims to compare modern machine learning techniques to analyze time series data. A discussion of existing algorithms is presented. Artificial Neural Networks have seen major applications in various computational fields in diverse ways. Its application extends to time-series data in an essential way. A number of recently developed Artificial Neural Network approaches to time-series prediction are provided. We discuss merits and demerits of each algorithm are. We then provide a comparative study of the approaches and address their applications.
A  ${D}$ -Band Digital Transmitter with 64-QAM and OFDM Free-Space Constellation Formation

A <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>-band I/Q RF-DAC featuring two 6-bit DAC elements driven in quadrature, each with its own on-die antenna, and a total effective isotropic radiated power (EIRP) of 13.2 dBm, is demonstrated in a 45-nm SOI-CMOS technology. The carrier signal is first amplified by the 30-dB gain LO path and is directly modulated by the 12 baseband bit streams, without linear upconversion or power amplification. QPSK, 8-PSK, 16-QAM, 32-QAM, and 64-QAM single-carrier (SC) and OFDM constellations are formed in free space and measured above the die at data rates up to 12 Gb/s and at distances over 15 cm. The large-signal bandwidth—from the carrier input pad to the output of the transmitter above the antennas—is 130–142 GHz and was obtained by sweeping the frequency of the <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>-band external carrier, modulating it on die at 0.4 Gb/s using 16-QAM format, and measuring the error vector magnitude (EVM) with an instrumentation receiver. The highest data rate of 12 Gb/s was measured for QPSK SC modulation with a corresponding EVM of −12.2 dB. Lower maximum data rates of 7 Gb/s with −14.3-dB EVM and 3.6 Gb/s with −19-dB EVM were observed for 16-QAM and 64-QAM formats, respectively. Spectral shaping and OFDM transmission were also demonstrated at up to 2.5 Gb/s. The prototype consumes a total of 1.25 W, with an energy efficiency of 104 pJ/b.
Simultaneous Ablation of Neuronal Neurofascin and Ankyrin G in Young and Adult Mice Reveals Age-Dependent Increase in Nodal Stability in Myelinated Axons and Differential Effects on the Lifespan

Visual Abstract Nodes of Ranvier are unique regions where voltage-gated sodium channels are highly enriched to drive saltatory conduction. Genetic ablations in adult mice with loss of specific nodal proteins causes slow but progressive nodal deterioration associated with decreased nerve conduction and axonopathy. What has remained unaddressed is whether loss of nodal proteins at different time points in postnatal life follows similar timelines of nodal disorganization. Here we utilized simultaneous ablation of Neurofascin (NF186) and Ankyrin G (AnkG) in mice of both sexes at three specific time points. We report that concurrent ablation of these core nodal components at postnatal day 13 (P13) leads to accelerated nodal destabilization in comparison with P23, and this disorganization is even slower when ablated at P93. Ablation of NF186 with AnkG at P13 reduced the half-life of NF186 to 15 days compared to 1 month at P23, which increased to 2 months at P93, indicating increasing nodal stability. The half-life of AnkG at the nodes also increased with age but showed enhanced disappearance from the node in the absence of NF186, with a half-life of 3 days at P13 ablation. The nodal disorganization occurred in a sequential manner, with AnkG disappearing first from the nodal areas irrespective of the timing of ablation, and led to decreased nerve conduction and affected axonal health. Together, our studies reveal that nodes of Ranvier in myelinated axons continue to become more stable with age and suggest that nodal disorganization in adult human demyelinating disorders occurs slowly until neurological symptoms become evident.
High performance neural network based acoustic scene classification

Acoustic Scene Classification (ASC) has become an integral component in applications such as smart hearing aids, user alert applications for physically challenged persons and robot based navigation. The Deep Learning (DL) techniques such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) etc. improve the accuracy and efficiency of ASC but increase computational complexity. This paper has explored the possibility of using neural network as classifiers for ASC. The observations show that an appropriately trained simple neural network can achieve similar performance as DL techniques. The developed model utilizes Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and has been verified on TUT Acoustic Scenes 2016 dataset. The developed model has attained 14.3% better accuracy than existing DL models for frame based analysis.
Optimized Asymmetrical Arcsine Phase Mask for Extending the Depth of Field

With the development of wavefront coding technique, various x-y separable phase masks have been proposed for the purpose of extending the depth of field. In this letter, a new asymmetrical arcsine phase mask combing the power and arcsine function was proposed to enrich the separable odd symmetrical family of wavefront coding phase masks. Using the simulated annealing algorithm based on Fisher information for optimization, we evaluated the performance of arcsine phase mask with four typical phase masks: the tangent, sinusoidal, improved-2 logarithmic, and cubic phase masks. The comparison results of Fisher information, defocused modulation transfer function, Hilbert space angle, and digital restoration demonstrated that the arcsine phase mask could restore clear and homogeneous images with both high fidelity and high signal to noise ratio along with a wide range of defocus, proving its superiority in extending the depth of field.
High-Performance Architecture Using Fast Dynamic Reconfigurable Accelerators

System accelerators (ACCs) improve performance and break power and utilization walls. They can be implemented by fixed-function hard macros or reconfigurable logic such as field-programmable gate arrays (FPGAs). For systems running various applications, dynamic reconfigurable ACCs offer a very attractive feature; however, the reconfiguration time is an unavoidable overhead. This paper proposes high-performance architecture with fast dynamic reconfigurable FPGA ACCs (F-RACCs) based on a novel bitstream reprogramming method, which is feasible by using emerging technologies. The architecture includes CPU cores, caches, memories, ACCs, and network-on-chips. A portion of the computing tasks can be offloaded from CPUs to ACCs to improve performance. The ACCs can be reprogrammed rapidly to accommodate various functions required by wide spectrum of applications. The performance is evaluated by platform for ACC-rich architectural design and exploration, a gem5-based cycle-accurate full-system simulation platform. The 11 benchmark applications from different domains are evaluated. Comparing with systems using conventional FPGA ACCs partially configured using fastest configuration speed; this architecture improves system performance on all applications and achieves maximum <inline-formula> <tex-math notation="LaTeX">$1.31\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2.82\times $ </tex-math></inline-formula> speedup using 1 and 12 ACC instances, respectively. It achieves maximum speedup of <inline-formula> <tex-math notation="LaTeX">$94.93\times $ </tex-math></inline-formula> with one and <inline-formula> <tex-math notation="LaTeX">$565.12\times $ </tex-math></inline-formula> with 12 F-RACCs over CPU software path with no ACCs.
Simultaneous Vascular Strain and Blood Vector Velocity Imaging Using High-Frequency Versus Conventional-Frequency Plane Wave Ultrasound: A Phantom Study

Plaque strain and blood vector velocity imaging of stenosed arteries are expected to aid in diagnosis and prevention of cerebrovascular disease. Ultrafast plane wave imaging enables simultaneous strain and velocity estimation. Multiple ultrasound vendors are introducing high-frequency ultrasound probes and systems. This paper investigates whether the use of high-frequency ultrafast ultrasound is beneficial for assessing blood velocities and strain in arteries. The performance of strain and blood flow velocity estimation was compared between a high-frequency transducer (MS250, fc = 21 MHz) and a clinically utilized transducer (L12-5, fc = 9 MHz). Quantitative analysis based on straight tube phantom experiments revealed that the MS250 outperformed the L12-5 in the superficial region: low velocities near the wall were more accurately estimated and wall strains were better resolved. At greater than 2-cm echo depth, the L12-5 performed better due to the high attenuation of the MS250 probe. Qualitative comparison using a perfused patient-specific carotid bifurcation phantom confirmed these findings. Thus, in conclusion, for strain and blood velocity estimation for depths up to ~2 cm, a high-frequency probe is recommended.
Identification and Characterization of Alternaria iridiaustralis Causing Leaf Spot on Iris ensata in China

Abstract In 2016, a severe leaf spot disease was found on Iris ensata Thumb. in Nanjing, China. The symptom was elliptical, fusiform, or irregularly necrotic lesion surrounded by a yellow halo, from which a small-spored Alternaria species was isolated. The fungus was identified as Alternaria iridiaustralis based on morphological characteristics. The pathogenicity tests revealed that the fungus was the causal pathogen of the disease. Phylogenic analyses using sequences of ITS, gpd, endoPG, and RPB2 genes confirmed the morphological identification. This study is the first report of A. iridiaustralis causing leaf spots on I. ensata in China.
SHARE: Scalable hybrid adaptive routing for dynamic multi-hop environments

No longer are wireless networks isolated islands: now devices in one network may communicate with devices in other networks. But as the density of devices increases and the range of wireless transmissions decreases, there is a critical need for scalable multi-hop wireless networking. In this paper, we propose SHARE, a novel approach to scalable multi-hop routing for wireless networks. SHARE is a hybrid of gradient routing, locally scoped link-state routing, multiple-path braid forwarding, and flooding. SHARE adapts the amount of control traffic and the amount of data traffic according to data traffic patterns and network dynamics to maximize network scalability and robustness while minimizing control overhead. We evaluate a real-world implementation of SHARE in ns-3 using Direct Code Execution (DCE), and compare SHARE against a DCE version of the optimized link state routing protocol (OLSR) for a static grid and for the Gauss-Markov mobility model. We show that SHARE is able to deliver up to 31 % more data packets than OLSR across a range of network sizes and speeds, while reducing overhead by a factor of 7x.
An Optimized Third Harmonic Compensation Strategy for Single-Phase Cascaded H-Bridge Photovoltaic Inverter

Due to the unequal solar radiations or dust accumulation of photovoltaic modules in a single-phase cascaded H-bridge photovoltaic inverter, the unbalanced output power among photovoltaic modules will make the H-bridges with higher power overmodulation, resulting in deteriorated grid current. Concerning this issue, this paper proposes an optimized third harmonic compensation strategy that injects moderate amount of third harmonic into the overmodulation H-bridges to keep the amplitudes of their modulation waveforms just being unity, then compensates the same amount of opposite harmonic sequence and properly distributes it to rest of the H-bridges. The proposed method can ensure that all the power units will be free from overmodulation under some heavy power imbalance conditions and will not increase the third harmonic component of the grid current. The validity and effectiveness of the proposed method is verified by simulation and experimental results.
Spatio-temporal anomaly detection in crowd movement using SIFT

Crowd behaviour detection is very promising area of research these days in field of computer vision and artificial intelligence. A novel framework based algorithm has been proposed in this research paper which focusses three key elements of moving crowd [i] Dynamics based appearance of the crowd [ii] Temporal — Spatial deviation based anomaly [iii] Intrinsic interaction based deviation flow change. This framework deals with micro as well as macro level anomaly detection using SIFT techniques in structured & Semi-Structured crowd. This proposed framework out performs several existing state of art techniques being used for anomaly detection.