Miracle-dz/UniFAR-FTUs huggingface dataset:
Switched Inductor-Capacitor Network Based Non-Isolated DC-DC Converter: A Double2 Gain Converter with Single Switch

This paper proposes a novel non-isolated high gain dc-dc (NHGDC) converter using a switched inductor-capacitor network. The converter has twelve components including the four capacitors, two inductors, five diodes and one semiconductor switch. Though the NHGDC converter has twelve components, it uses only one semiconductor switch and achieves a maximum theoretical gain of twenty at a duty ratio of 0.8. The steady-state output voltage equation of the converter is derived with the help of the analytical waveforms. The theoretical voltage gain of the NHGDC converter is 4/(1-d). Thus, the NHGDC converter is named as a double2 gain converter. The voltage gain of the high gain dc-dc converters is significantly affected due to the parasitic elements of various components present in the converter. So, a detailed analysis has been conducted to assess the effect of parasitic elements on the output voltage of the double2 gain converter. The proposed structure has only passive elements in the discharging path when the energy is discharged into the load side. The performance comparison has been conducted to measure the potential merits of the double2 gain converter compared to the recently reported high gain dc-dc converters. A hardware prototype of the double2 gain converter has been fabricated, and an experimental setup is assembled in the laboratory for testing. The output waveforms from various parts of the converter are observed during the testing, and the waveforms are presented in this paper. The double2 gain converter will be useful for the integration of the low voltage dc sources such as ultracapacitor, fuel cell, battery, etc.
High Torque/Ampere Direct Torque Control of Switched Reluctance Motor Drives

Direct flux and torque (DFTC) scheme is introduced for switched reluctance motors (SRMs) to minimize the inherent output torque ripples. In DTFC voltage vectors are chosen in such a way that the stator flux is kept constant, making it very sensitive to working condition. However, in SRM phases are independently excited, resulting in a nonlinear flux trajectory. In this paper, a new direct torque control (DTC) scheme is proposed, where sector boundaries are redesigned, and alternative optimized voltage vectors are selected. The proposed control technique also makes stator flux less sensitive to the working condition. This allows the phase to de-energize faster than the conventional DFTC, along with achieving high torque/ampere ratio with reduced ripples in torque. To show the effectiveness of the proposed DTC scheme over the conventional DTFC scheme simulation studies for the same operating conditions is carried out using Matlab/Simulink environment.
Mobile-Edge-Computing-Based Hierarchical Machine Learning Tasks Distribution for IIoT

In this article, we propose a novel framework of mobile edge computing (MEC)-based hierarchical machine learning (ML) tasks distribution for the Industrial Internet of Things. It is assumed that a batch of ML tasks, such as anomaly detection, need to be executed timely in an MEC setting, where the devices have limited computing capability while the MEC server (MES) has rich computing resources. Thus, a small ML model for the device and a deep ML model for the MES are pretrained offline using historical data, and then they are deployed accordingly. However, offloading tasks to the MES introduces communications delay. Thus, each device must decide the portion of the tasks to offload to minimize the processing delay. Since the delay and the error of data processing are incurred by communications and ML computing, a joint optimization problem is formulated to minimize the total delay subject to the ML model complexity and inference error rate, data quality, computing capability at the device and MES, and communications bandwidth. A closed-form solution is derived analytically and an optimal offloading strategy selection algorithm is proposed. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions, and the effects of key parameters in the proposed algorithm are investigated. The numerical results demonstrate the effectiveness of the proposed algorithm.
QoS Driven Dynamic Partial Reconfiguration: Tracking Case Study

In hybrid systems on chip (SoC) the algorithms must share limited resources classified as hardware (FPGA logic, memory and DSP blocks) and software (CPU cycle budget). Embedded applications are implemented with concurrent processes, which have to coexist. Therefore, the algorithm performance or precision is often downgraded to fit on a FPGA or to meet timing constraints. One of the solutions to relax these constraints is to reconfigure the SoC according to application requirements observed at runtime. Hardware (HW) reconfiguration can be performed by means of dynamic partial reconfiguration (DPR). Beyond the intrinsic DPR complexity, the most critical aspect is the implementation of the reconfiguration decision. Academic adaptive architectures are usually based on power / performance rules. Nevertheless such solutions are usually application agnostic and so cannot fully exploit the possible adaptation to the environment. So real-life systems require solutions to capture the knowledge of algorithm experts that can be leveraged at runtime to drive the reconfiguration decision according to application scenarios. In this paper we propose a methodology to design a reconfiguration controller based on quality of service (QoS) indicators to be specified by experts. This controller monitors the reconfigurable partitions (RP) and can make DPR decisions to optimize the global QoS. We illustrate our methodology in the Radar domain with a tracking system based on Kalman filters implemented on a Zynq Ultrascale+. This study highlights expected gains and obstacles, it presents the different strategies to cope with the issues and draws perspectives.
Mechanistic Properties of Five-bar Parallel Mechanism for Leg Structure Based on Spring Loaded Inverted Pendulum

To achieve robotic walking, a successful approach is to approximate a robots dynamics as a simplified model. However, the difference between the mechanistic properties of a robot and the simplified model causes a problem of unstable and inefficient walking. To solve this problem mechanically, this paper proposes a design principle for the leg structures of bipedal robots that match the mechanistic properties of a simplified model, specifically the spring-loaded inverted pendulum (SLIP) model. The SLIP model is widely applied to robots because it has passive stability and dynamic properties similar to those of animal gaits. We have analyzed the effects of parameters of five-bar linkages with springs as a part of the leg structure of a bipedal robot. Our analysis showed that the spring parameters can impart the same mechanistic properties as the SLIP model in any configuration of a five-bar parallel mechanism. Moreover, a simplified case of a parallel linkage structure using two springs with the same properties also produced the mechanical properties of the SLIP model. These theoretical analyses were also validated with an experimental model.
Joint User Scheduling and Power Allocation for Weighted Sum-Rate Maximization in NOMA Networks

This paper investigates optimal user selection and power distribution among users for Weighted Sum-Rate Maximization (WSRM) in downlink Non-Orthogonal Multiple Access (NOMA) networks. For a two-user WSRM problem, closed form solution which guarantees successful Successive Interference Cancellation (SIC) decoding is derived. Based on this result, a novel algorithm for joint user selection and power allocation for a multi-user scenario is presented. The proposed algorithm has the following advantages (i) solution exists under all user weigh-tratio scenarios irrespective of the convexity of the optimization problem (ii) successful SIC decoding is guaranteed (ii) has low complexity (iv) provides optimal solution. Extensive simulation results reveal that the optimal result is obtained under different user weight-ratio scenarios.
2D Push Recovery and Balancing of the EVER3 - a Humanoid Robot with Wheel-Base, using Model Predictive Control and Gain Scheduling

In this paper an efficient 2D push recovery and balancing controller for the EVER3 humanoid robotic platform with differentially steered wheel-base is presented. Real world utility of humanoid robots requires a balance algorithm that minimizes motion due to small disturbances while simultaneously being quick enough to recover from large pushes. The proposed work uses a Model Predictive Control scheme along with a whole body controller to achieve desired performance. Various experimental runs were performed on the robot with the presented approach to do the performance analysis. The experimental results presented here prove the efficacy of the proposed control scheme.
Ankle-hip-stepping stabilizer on tendon-driven humanoid Kengoro by integration of muscle-joint-work space controllers for knee-stretched humanoid balance

In this paper, we proposed and implemented ankle-hip-stepping stabilizer for tendon-driven humanoid as a stabilizer that can be used in humanlike knee-stretched posture. The stabilizer is inspired from ankle-hip-stepping strategy of human balance control. The stabilizer is implemented as the integration of two stabilizer, ankle-hip stabilizer as a joint space controller and stepping stabilizer as a work space controller. We conducted evaluation experiments, and confirmed effectiveness of the stabilizer.
Lifestyle Factors Associated With Body Mass Index, Waist To Hip Ratio And Blood Pressure Among Undergraduates

Obesity and hypertension are major risk factors of morbidity and mortality in modern world. Numerous studies have been performed to assess the lifestyle factors associated with obesity and hypertension among western population. There is a lack of studies conducted on this area among south Asian population. A cross sectional descriptive study was conducted to examine the lifestyle factors associated with Body Mass Index(BMI), Waist Hip Ratio(WHR) and Blood Pressure(BP) among undergraduates of university of Peradeniya, Sri Lanka in 2017. Lifestyle factors such as nutrition, smoking, alcoholism, physical activity and sleep were assessed using a validated self-administered questionnaire. Body weight, height, waist circumference, Hip circumference and systolic and diastolic blood pressure were measured and the statistical associations were assessed. Nutritional factors, smoking, physical activity sleep and stress were significantly associated with anthropometric measures and/or blood pressure values. These associations were gender dependent.
High resolution spatiotemporal distribution of rainfall seasonality and extreme 1 events based on a 12-year TRMM time series 2

5 Seasonal rainfall in the tropics modulates hydrology and vegetation and is ultimately responsible for many 6 mass-transport processes at different spatial and temporal scales. In order to link hydrologic, geomorphic, 7 and ecologic processes with rainfall, a mean-monthly climatology based on high spatial resolution (~5x5 8 km) TRMM 2B31 data from 1998 to 2009 has been calibrated and validated against GPCC rain gauges. 9 Data calibration reveals that averaging over >6 years provide robust monthly rainfall amounts (r = 0.82). 10 The 12-year time series for rainfall-extreme events (>90 percentile) shows that more than twice as many 11 events occur in some mountainous settings compared to immediately adjacent lower-elevation areas. 12 Peak extreme events spatially correlate with sites of highest mean annual rainfall, but several extreme 13 events can occur in semi-arid to arid regions leeward of orographic rainfall peaks. An example application 14 for the central Andes demonstrates how contrasting climatic settings impact vegetation, hydrology, and 15 surface-mass transport. 16 Introduction 17 The seasonal water cycle is the driver of many processes in hydrology, ecology, and geomorphology. 18 Additionally, some of the most densely populated areas in the tropics are affected by high rainfall 19 seasonality associated for example with the Asian and South American Monsoon [e.g., Vera et al., 2006; 20 Webster, 1987]. During the rainy season, the moisture-soaked, windward sides of mountain ranges, such 21 as the southern Himalaya and eastern Andean flanks, are associated with some of the highest terrestrial 22 rainfall amounts [e.g., Bookhagen and Burbank, 2006; Bookhagen and Strecker, 2008; Nesbitt and 23 Anders, 2009]. In these areas, the interaction of atmospheric processes with steep terrain and high 24 topographic relief leads to high surface-rainfall amounts that exhibit steep orographic gradients. 25 Ultimately, the combination of mountainous landscapes and seasonal, high runoff results in high sediment 26 erosion and transport rates. Thus, seasonal rainfall (e.g., during the monsoon) controls river discharge, 27 vegetation growth, and sediment transport. However, several field studies document that the important, 28
OBJECTIVIZATION OF A PATIENT’S STATE WITH MYOFASCIAL PAIN SYNDROME IN PHYSIOTHERAPY PRACTICE

Myofascial pain syndrome (MPS) with myofascial trigger points (TrPs) diagnosed in patients is a very common musculoskeletal disorder. Beside pain symptoms MPS is concerned with many other symptoms like muscle weakness, decreased range of motion or muscle stiffness. Although many researches indicate objective changes in the area of trigger points, in clinical practice subjective methods of patients state assessment are still used. This article presents an objective diagnostic methods enhancing reliability of trigger point examination. Apart from palpation, examination of pressure pain threshold using algometry and bioelectrical activity of muscles using electromyography will be presented. Trigger points are characterised by a lower pressure pain threshold, different depending on their irritability. Needle electromyography examination provides an opportunity to obtain characteristic spontaneous electrical activity recordings, while surface electromyography examination gives an opportunity to obtain increased muscle tension recordings at the resting state and decreased muscle motor units efficiency during maximal voluntary contraction.
Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search: Preliminary Results

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Path Finding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependencies between agents. Empirically, CBS with either new heuristic significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50.
Groundwater Contamination Issues in Chiniot Area, Punjab, Pakistan

This study was planned to assess the groundwater quality of the District Chiniot, Pakistan. The majority of the people in the area use the groundwater from private wells. Water quality is very poor in the area especially close to the Pre Cambrian exposed Hills. TDS (total dissolved solids), hardness, Chloride, Sulfate and Calcium were very high in groundwater. Trace element chemistry shows that As, Mn and S are above the recommended limit. As and Mn contamination is due to water-rock interaction. Whereas S is encountered in shallow wells, which may be due to improper solid waste management and septic tanks. Microbiological analysis of municipal supply water shows that 25% of water samples have faecal contamination. High TDS groundwater is the cause of economic loss due to damage of sanitary fittings.
ICIMP 2014

—Trafﬁc monitoring, trafﬁc engineering, quality of service applications, network intrusion detection systems, as well as network management systems require the basic knowledge of which trafﬁc is transmitted over a network. The increasing number of applications which are using encryption techniques such as TLS lower the ability to determine the applications that are running within a network. In this paper, we propose a method to detect applications in TLS encrypted connections. Our method uses a hybrid approach which combines protocol decoding to identify TLS trafﬁc and to gather reliable information about the application data. Furthermore, a machine learning algorithm is used to determine the application which is protected by TLS. We describe our approach and compare it with other related methods in theory and prove its advantages on network measurements. The results show a signiﬁcant improvement on classiﬁcation Recall and Precision.
Effects of Nozzle Secondary Vortices on Unsteady Hub-Endwall Flow of a Turbine Rotor

The unsteady flow field of an axial-flow turbine rotor was investigated experimentally using an LDV system. Detailed measurements of the time-averaged and time-dependent distributions of the velocity, the flow angle, and the turbulence intensity were carried out. The obtained data was analyzed from the viewpoints of both absolute (stationary) frame of reference and relative (rotating) frame of reference. This paper focuses on the effect of the turbine nozzle secondary vortices (a passage vortex and a trailing edge vortex) near the hub endwall on the flow field inside the rotor passage. The nozzle passage vortex are accumulated on the rotor suction surface because of both the negative jet effect of the nozzle passage vortex itself and the formation of the rotor passage vortex. The nozzle passage vortex induces large fluctuations of velocity and turbulence intensity inside the rotor.
Sample-Efficient Robot Motion Learning using Gaussian Process Latent Variable Models

Robotic manipulators are reaching a state where we could see them in household environments in the following decade. Nevertheless, such robots need to be easy to instruct by lay people. This is why kinesthetic teaching has become very popular in recent years, in which the robot is taught a motion that is encoded as a parametric function - usually a Movement Primitive (MP)-. This approach produces trajectories that are usually suboptimal, and the robot needs to be able to improve them through trial-and-error. Such optimization is often done with Policy Search (PS) reinforcement learning, using a given reward function. PS algorithms can be classified as model-free, where neither the environment nor the reward function are modelled, or model-based, which can use a surrogate model of the reward function and/or a model for the dynamics of the task.However, MPs can become very high-dimensional in terms of parameters, which constitute the search space, so their optimization often requires too many samples. In this paper, we assume we have a robot motion task characterized with an MP of which we cannot model the dynamics. We build a surrogate model for the reward function, that maps an MP parameter latent space (obtained through a Mutual-information-weighted Gaussian Process Latent Variable Model) into a reward. While we do not model the task dynamics, using mutual information to shrink the task space makes it more consistent with the reward and so the policy improvement is faster in terms of sample efficiency.
Bangladeshi Plant Recognition using Deep Learning based Leaf Classification

At present deep learning-based object recognition approaches have placed a tremendous effect for classifying different objects. Leaves recognition using supervised learning has shown satisfying performance which may help in various research purposes also. In our work, we have used a deep convolutional neural network as a classifier. We have used a transfer learning approach. We have prepared our work dataset based on Bangladeshi plants which contains eight different classes of leaves. We have experimented with VGG16, VGG19, Resnet50, InceptionV3, Inception-Resnetv2 and Xception deep convolutional neural network models where we have found the highest value in VGG 16 which shows almost 96% classification accuracy. Recognition of useful plants using leaf image will be greatly helpful in the research of ayurvedic and endangered plants.
Analysis of Modulation techniques for Matrix Converters Applied to PMSG Based Wind Energy Conversion Systems

This paper presents a review of the recent advances in matrix converter topologies used with the permanent magnet synchronous generator (PMSG) in wind power generation systems. Two-stage matrix converters known as sparse matrix converters are compared to the conventional matrix converter in terms of implementation complexity, cost, power rating and voltage ratio. Control strategies are combinations of two elementary blocs: the MPPT algorithms and the regulation approach. MPPT algorithms are crucial for the improvement of wind harvest and regulation approaches are generally based on adaptive, non-linear or linear automatic methods that are responsible for current control or field oriented control or reactive power control. On the other hand, several applied to the matrix converter-PMSG scheme are briefly presented. IndexTerms—PMSG-based wind conversion system, matrix converter, sparse matrix converter, modulation techniques, control strategies _________________________________________________________________________________________________________________
Higenamine Decreased Oxidative Kidney Damage Induced By Ischemia Reperfusion in Rats

The aim of this research is to determine protective effects of higenamine on kidney tissue injury caused by ischemia reperfusion. In this study, 24 Sprague Dawley female rats were divided into 3 groups. The groups were designed as follows; control, ischemia reperfusion, and ischemia reperfusion + higenamine. Some oxidant, antioxidant and inflammatory parameters were evaluated in kidney tissues at the end of the experimental procedure. It was confirmed that the oxidant and inflammatory parameters of kidney tissue increased and antioxidant parameters decreased in ischemia reperfusion group compared to control group. Antioxidant parameters increased while oxidant and inflammatory parameters decreased in the ischemia reperfusion + higenamine group compared to ischemia reperfusion group. These results have demonstrated that higenamine administration as single dose is effective against oxidative kidney damage originating from ischemia reperfusion.
0 . 1 Dimension Reduction with ` q-distortion

We note that it can be proved that the result is tight for all q ≥ √ k. The embedding in the theorem is the JL transform (implemented with Gaussian entries). The analysis we did worked for q < k. Applying some more technical considerations, we actually can prove that the JL transform, normalized by an appropriate factor, has bounded `q-distortion for any q ≥ k: Theorem 4.2. Let X ⊂ `2 be any n-point set. The following two statements hold: For any k ≥ 1, there is an embedding f : X → `2 with `k-dist(f) = O ( ( √ lnn) 1 k ) .