Miracle-dz/UniFAR-FTUs huggingface dataset:
The Real Time Gait Phase Detection Based on Long Short-Term Memory

Multiple sensors system has been adopted in most existing gait phase research. In this paper, we present a novel method to detect and discriminate gait phase based on single Inertial Measurement Unit (IMU). At first, we built a data acquisition system consisting of single IMU measuring the kinematic data of shank and foot switches labeling the data to collect the gait samples. And then we developed a gait phase recognition algorithm based on long short-term memory (LSTM) and trained it with phase-labeled data. The experimental results show that the algorithm has 91.4% accuracy on the testing set. Compared with existing approaches, the proposed method ensures the accuracy of gait phase detection and avoids the complex sensor system.
Constant Current Charging and Maximum Efficiency Tracking Control Scheme for Supercapacitor Wireless Charging

Charging current and efficiency of a wireless power transfer system depend on the equivalent load resistance of supercapacitor load, which varies during charging. To achieve constant current (CC) charging and maximized efficiency under variable loads, control method that employs primary-side phase shift H-bridge inverter and secondary-side semiactive rectifier are proposed. The proposed secondary-side control method not only achieves CC charging, but also ensures no reactive impedance seen into the semiactive rectifier. Furthermore, the maximum efficiency tracking is based on searching the minimum system dc input current for any given output power. It is realized by phase shift H-bridge inverter that is controlled by the perturbation and observation algorithm. Simulation and experimental results validate the feasibility of the proposed control method. During CC charging of 3 A, the maximum efficiency is 81% and largest efficiency improvement is 15.2% at light load. The proposed control method improves the system performance and is suitable for the applications that require compact receiver and no wireless communication link between the transmitter and the receiver.
Intention-Aware Multi-channel Keyword Extension for Content Security

In content security, the administrators should get the related words, appositive words and synonyms of keywords according to execute the semantic search. To solve the dynamic query requirements, we proposed a new model on keyword extension that is based on intention-aware multi-channel word extraction in semantic search. By integrating the existing keyword extension methods and analyzing user's intention, the model can output weight-based words from related words, appositive words and synonyms. We mainly use knowledge graph, word2vec and dictionary to construct the three channels. Finally, we can set the proportion of each channel's weight, so we can optionally filter and output the final extended word set. Our experiments show the effectiveness and feasibility of the scheme.
Prediction of Disorder of Brain using EEG Signal Processing in MATLAB GUI Platform

Electroencephalography (EEG) records electrical activity of the brain. In this paper, a user-friendly platform using MATLAB with GUI (Graphical User Interface) is designed for analyzing EEG data and spotting patterns in it for fast diagnosis purposes. At first the noise is omitted by using a Butterworth low pass and FIR bandpass filter respectively and then explored to determine power spectral density. Several parameters (mean, variance, skewness and kurtosis) are extracted from the diagrams of power spectral density and contour of the peaks. It is seen that mean is high and skewness and kurtosis are low for affected brain. The relationship can be more useful to find out the presence of any disorder in brain.
Optimized flocking of autonomous drones in confined environments

A flocking model that uses an evolutionary optimization framework is validated with a self-organized swarm of 30 drones. We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.
Investigation of Self-Injection Locked Visible Laser Diodes for High Bit-Rate Visible Light Communication

We report on self-injection locking in InGaN/GaN (blue/green) and InGaP/AlGaInP (red) visible-light laser diodes. The free-space optical feedback path was accomplished via an external mirror. The effect of injection current, optical power injection ratio, and external cavity length on the spectral linewidth and modulation bandwidth of the lasers is investigated. Our results show that the laser performance was substantially improved. In particular, we achieved a significant increase of ∼57% (1.53–2.41 GHz) and ∼31% (1.72–2.26 GHz) in the modulation bandwidth, and ∼9 (1.0–0.11 nm) and ∼9 (0.63–0.07 nm) times reduction in spectral linewidth of the green and blue lasers, respectively. Consequently, side-mode-suppression-ratio was considerably increased in all the cases, reaching as high as ∼20 dB in self-injection locked blue laser diode, thus enabling a close to single-mode operation. This paper paves the way for attaining high-speed optical wireless communications by overcoming the challenges of limited modulation bandwidth and multimode operation of visible laser diodes with this simple scheme.
Problem List Clustering for Improved Patient-Based Disease Perception

The amount of electronic health record (EHR) content accumulates in cases of patients with chronic diseases and multiple hospital stays. Problem lists grow beyond an acceptable limit and hamper a fast visual perception of key elements of the EHR. We propose a semantic grouping of problem list items in order to facilitate navigation through EHR topic spaces. The clustering approach, which makes use of ICD-10 codes and an NLP pipeline is evaluated in an intra- and inter-patient scenario. We achieve an average compression rate of 80% of the initial list view items forming consistent semantic topic spaces with an F-measure greater than 0.80 in both cases.
Inhibiting the integrated stress response pathway prevents aberrant chondrocyte differentiation thereby alleviating chondrodysplasia

The integrated stress response (ISR) is activated by diverse forms of cellular stress, including endoplasmic reticulum (ER) stress, and is associated with diseases. However, the molecular mechanism(s) whereby the ISR impacts on differentiation is incompletely understood. Here, we exploited a mouse model of Metaphyseal Chondrodysplasia type Schmid (MCDS) to provide insight into the impact of the ISR on cell fate. We show the protein kinase RNA-like ER kinase (PERK) pathway that mediates preferential synthesis of ATF4 and CHOP, dominates in causing dysplasia by reverting chondrocyte differentiation via ATF4-directed transactivation of Sox9. Chondrocyte survival is enabled, cell autonomously, by CHOP and dual CHOP-ATF4 transactivation of Fgf21. Treatment of mutant mice with a chemical inhibitor of PERK signaling prevents the differentiation defects and ameliorates chondrodysplasia. By preventing aberrant differentiation, titrated inhibition of the ISR emerges as a rationale therapeutic strategy for stress-induced skeletal disorders.
Early Detection of Rumors in Heterogeneous Mobile Social Network

Online rumors may stimulate the diffusion of misinformation in the social network or even panic among people. The rapid development of mobile social network (MSN) provides a favourable environment for the rumors to be widely spread. Previous research mainly focuses on detecting the rumor in the interest-based social network and designing mechanisms to minimize the diffusion of rumors. However, many proposals for interest-based social network will not work well in MSN, due to the fact that few lexicon features can be extracted through MSN. In this paper, we analyze the structure of the information diffusion graph of MSN, and propose a diffusion model to explain the spread of information in MSN. As the MSN can be treated as a heterogeneous network, we design an algorithm to learn the latent factors of each piece of information. The learned factors can be leveraged to detect the rumors. Experiments on a real-world MSN dataset show that our proposed algorithm outperforms other approaches, and can reach 61% on the precision metric.
A Multi-layer Network Topology Visualization Layout Based on Louvain Community Detection

Community is one of the most important structure features of complex networks. It has important research significance for topological structure analysis, behavior prediction, cascading failure, percolation and spreading of multi-layer networks. Aiming at the problem that the existing multi-layer network visualization can't clearly show the community structure, a multi-layer network topology visualization layout based on Louvain community detection is proposed. Firstly, a complex network community discovery algorithm (Louvain) is used to perform the multi-layer network nodes at each layer and the community affiliation of the nodes is obtained at each layer; then, based on the traditional force-directed layout, the community force is added to the internal nodes of the community, so that all the nodes that belong to the same community are gathered to the community center, so as to achieve the effect of displaying the network community structure. The simulation results show that the algorithm has the characteristics of faster convergence and clearer community structure than the traditional visual layout algorithm.
Predict Student's Academic Performance and Evaluate the Impact of Different Attributes on the Performance Using Data Mining Techniques

In Educational Data Mining, many data mining techniques are applied to extract hidden knowledge from student data. This knowledge helps educational institutes to improve their teaching quality. Different attributes have an impact on student's academic performance. Behavioral and student absent in class has an effect on their academic performance. In this paper, the effect of above two categories of features will be measured using some data mining techniques. For classification Naïve Bayes, Artificial Neural Network, Decision Tree, and K-Nearest Neighbor algorithms are used in this paper. We also use ensemble methods like Bagging, Adaboosting, and Random Forest to get more accuracy. An ensemble filtering technique has been applied to detect misclassified instance from training dataset which improves the accuracy of predicting. In this paper, ensemble filtering technique gives the best accuracy of 84.3 percent where adaboosting on Artificial Neural Network gives 78.6 percent accuracy.
SAQP++: Bridging the Gap between Sampling-Based Approximate Query Processing and Aggregate Precomputation

In booming Big Data era, interactive data analytics is becoming a regular demand for data scientists. With the continuous growing of the data, timely response for aggregation queries is becoming increasingly challenging. To address this challenge, scientists have proposed two separate methods: sampling-based approximate query processing (SAQP) and aggregate precomputation (Materialization) such as data cubes. In this paper, we propose a novel framework: SAQP++, which combines sampling and precomputed aggregate together to reach the goal of both relative accurate results and acceptable preparation time. Using SUM aggregate function as the example, we propose an optimal solution of materializing under uniform distribution, and a hill climbing based algorithm of materializing under non-uniform distribution, respectively. Our experiments show that SAQP++ achieves a more flexible and better trade-off among preprocessing cost, query response time, and answer quality than SAQP or Materialization alternatives.
Designing of a Plasmonic High Pass Wavelength Filter With Apodized Grating Waveguide

In this paper, a nanoscale plasmonic high pass filter is proposed using metal-insulator-metal (MIM) waveguide consisting of inward and outward grating profiles which are apodized by super Gaussian function. The frequency dependent model of the metal is developed using Lorentz-Drude model which is solved numerically using finite-difference time-domain (FDTD) simulation in order to calculate the transmission efficiency of the filter. Both inward and outward gratings are simulated individually and compared. It has been found that the cut-off wavelength can be controlled precisely with the variation of the maximum grating width. Moreover, the proposed design is easier to fabricate since the simple geometrical shape is proposed.
Comparison of survival outcomes of locally advanced breast cancer patients receiving post-mastectomy radiotherapy with and without immediate breast reconstruction: a population-based analysis

Objective To compare the survival outcomes in locally advanced breast cancer (LABC) patients receiving post-mastectomy radiotherapy (PMRT) with and without immediate breast reconstruction. Methods We used the Surveillance, Epidemiology, and End Results program to include LABC patients who were treated/not treated with immediate breast reconstruction followed by PMRT between 2003 and 2010. Statistical analysis was performed using the chi-squared test, Kaplan–Meier survival analysis, and Cox regression analysis. A 1:1 propensity score matching method was performed to decrease the selection bias. Results We identified 1,732 patient-pairs that were completely matched. In the unmatched population, 8,198 and 1,802 patients received mastectomy only and immediate breast reconstruction, respectively. Patients who received immediate breast reconstruction had better breast cancer-specific survival (BCSS) (hazard ratio [HR] 0.880, 95% CI 0.783–0.989, P = 0.032) and overall survival (OS) (HR 0.846, 95% CI 0.758–0.943, P = 0.003) than patients who underwent mastectomy alone. However, in the matched population, there was comparable BCSS and OS between patients who received immediate breast reconstruction and mastectomy alone. Subset analysis in the matched population found that immediate breast reconstruction was associated with better BCSS (HR 0.750, 95% CI 0.614–0.917, P = 0.005) and OS (HR 0.779, 95% CI 0.644–0.942, P = 0.010) compared to patients aged <50 years who received mastectomy alone. Conclusion There are comparable survival outcomes in LABC patients who received immediate breast reconstruction or mastectomy alone followed by PMRT. However, patients aged <50 years had a survival advantage after immediate breast reconstruction.
Development of sarcosine quantification in urine based on enzyme-coupled colorimetric method for prostate cancer diagnosis

An enzyme-coupled colorimetric assay for quantification of urinary sarcosine was developed. The proposed method is a specific reaction based on hydrogen peroxide (H2O2) formation via sarcosine oxidase (SOX). The liberated H2O2 reacts with Amplex Red in the presence of horseradish peroxidase (HRP) to produce the red-fluorescent oxidation product, resorufin, which can be measured spectrophotometrically (OD570). The method was performed in the 96-well microtiter plate. Reaction conditions, such as pH and reaction time were optimized. At the optimum conditions, the limit of detection (LOD) and quantification (LOQ) were found to be 0.7 and 1 µM, respectively. A good linearity was revealed with a coefficient of 0.990. The assay showed no significant interference from ascorbic acid, glucose and bilirubin. In addition, it is extremely specific for sarcosine rather than other amino acids. The determination of sarcosine in human urine displayed high accuracy and good reproducibility. This method is promising to differentiate prostate cancer patients from healthy subjects according to urinary sarcosine level. Altogether, this study provides a rapid, simple and specific tool to determine urinary sarcosine which could be useful for prostate cancer diagnosis.
MODELADO, SIMULACIÓN Y OPTIMIZACIÓN DE PROCESOS QUÍMICOS

El proposito de esta obra es brindar una introduccion al modelado, simulacion y optimizacion de procesos quimicos. El contenido y configuracion del libro se han diagramado poniendo enfasis en la simulacion de procesos quimicos tanto estacionarios como dinamicos, al mismo tiempo que se introduce al lector en areas fundamentales para afrontar la tarea del diseno y modelado integral de procesos quimicos.
A Software Measurement Plan Management Guided by an Automated Metrics Suggestion Framework

Due to the complexity of the current software, the measurement processes become crucial activities. However, due to the quantity of aspects to be measured and thus the big amount of data to manipulate, the software measurement plans are heavy to manage. It leads to very complex measurement plans engendering eventual losses of time and performance. The main objective of our paper is the improvement of the measurement plans by making the metrics use more flexible. This is an important requirements for the project managers. This allows to tackle specific useful metrics in avoiding measures that are not always relevant during an identified measured period of time. We propose to analyze and classify the measurements at runtime using a learning approach (Support Vector Machine, SVM) in order to define the relevant metrics that should be used at a specific time t. We designed a suggestion process that selects metrics from a current measurement plan or reorient (suggest) that measurement plan by proposing to execute other metrics. We implemented our framework on an efficient platform and successfully ran several experiments that we discuss and comment.
Molecularly selective nanoporous membrane-based wearable organic electrochemical device for noninvasive cortisol sensing

A new wearable nanoporous organic electrochemical device used as a medical diagnostics tool for noninvasive hormone sensing. Wearable biosensors have emerged as an alternative evolutionary development in the field of healthcare technology due to their potential to change conventional medical diagnostics and health monitoring. However, a number of critical technological challenges including selectivity, stability of (bio)recognition, efficient sample handling, invasiveness, and mechanical compliance to increase user comfort must still be overcome to successfully bring devices closer to commercial applications. We introduce the integration of an electrochemical transistor and a tailor-made synthetic and biomimetic polymeric membrane, which acts as a molecular memory layer facilitating the stable and selective molecular recognition of the human stress hormone cortisol. The sensor and a laser-patterned microcapillary channel array are integrated in a wearable sweat diagnostics platform, providing accurate sweat acquisition and precise sample delivery to the sensor interface. The integrated devices were successfully used with both ex situ methods using skin-like microfluidics and on human subjects with on-body real-sample analysis using a wearable sensor assembly.
Evaluating the Rationality of Judicial Decision with LSTM-Based Case Modeling

In the judicial domain, enormous growth of various legal data has greatly increased the workload of law professionals, which may reduce the efficiency of case handling or even threaten the judicial justice. In this paper, we present an approach to the evaluation of judgment rationality with thorough analysis of Chinese judicial decision texts. A novel metric, judgment deviation, is proposed to measure the likelihood of a certain case's misjudgment according to the judgment results of its similar cases. We employ long short-term memory (LSTM) model to implement case modeling by extracting the feature vector based on the key factors that affect the judicial decision. Experiments on a real dataset including about 25,000 judgments of theft cases and artificial validation demonstrate that the results of the proposed evaluation approach conform to reality.
Recognizing Hand-based Actions based on Hip-Joint centered Features using KINECT

Microsoft Kinect provides skeletal joints to extract different features which can be applied to identify different actions performed by subjects. As human moves, skeletal joints contribute to the movements and human actions are nothing but different types of movements in specific orders. Hand wave, hand shaking, push, pull, clapping, throw, catch these are some hand based actions which are difficult to recognize properly in an automated system. Hip-joint plays a vital role to determine joint-based features from human skeleton, as it is approximately the middle joint of the whole skeleton. The joint relative distances (JRD) and joint relative angles (JRA) are used as principle features in recent action recognition methodologies. In this paper, we have proposed a new methodology based on hip-joint centered features which are based on basic physiological movements that contributes to the decent accuracy in identifying hand based actions.
An Improved MPPT Technique – Alternative to Fractional Open Circuit Voltage Method

This paper discusses an improved constant voltage based maximum power point tracking method for extracting maximum power from photovoltaic (PV) system. The improved method has better performance than the conventional fractional open circuit voltage (FOCV) method. By calculating accurate maximum power point voltage for any changing environmental conditions, the improved method can determine maximum power more accurately. MATLAB/Simulink has been used to implement a PV module (considering a single diode model). Simulation results are analyzed for variable solar irradiance to compare the performance of these two methods.
PWM Frequency Voltage Noise Cancelation in Three-Phase VSI Using the Novel SVPWM Strategy

This paper has focused on the novel space vector pulse-width modulation (SVPWM) strategy for the two-level three-phase inverter, which could eliminate the harmonics nearby pulse width modulation (PWM) frequency. Ear-piercing high-frequency electromagnetic noise from motor is common due to PWM technique. The proposed SVPWM is able to remove the high-frequency harmonics located nearby the PWM frequency and other odd multiples by changing the original switching states. The unpleasant acoustic noise could be removed with the novel strategy whose PWM frequency is selected near 10 kHz. By comparison, it is near 20 kHz for conventional SVPWM to achieve the similar performance. Therefore, the switching losses are significantly reduced using the novel method. Furthermore, the proposed technique neither changes the characteristics of the fundamental wave nor employs the additional circuits in the drive. At last, the effectiveness of proposed strategy is verified by simulation and experiment.