Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability analysis for active control with a sky-hook and ground-hook inerter-damper configuration

Abstract—In this paper, a sky-hook and ground-hook inerter-damper configuration is proposed, and its active realization using active control actuators is studied as it cannot be directly implemented in practice. Especially, the stability problem caused by the active realization is analyzed. A general two-degree-of-freedom model which can be used to model various mechanical systems including quarter-car models and dynamic vibration absorbers is employed to analyze the influence of the passive inerter and the skyhook/ground-hook inerter on the stability of the overall active control system. It is demonstrated that for the sky-hook damper control, the passive inerter has negative effect on the stability; while for the ground-hook damper control, the passive inerter has positive effect on the stability. In contrast, the sky-hook inerter has positive effect on the stability and the ground-hook inerter has negative effect on the stability. The influence of the passive inerter and the skyhook/ground-hook inerter on the sky-hook and ground-hook damping coefficient is also investigated, where it is shown that the passive inerter decreases the admissible range for the sky-hook damping coefficient, and increases the admissible range for the ground-hook damping coefficient. Meanwhile, the sky-hook inerter increases the admissible range for the sky-hook/ground-hook damping coefficient, while the ground-hook inerter decreases the admissible range for the skyhook/ground-hook damping coefficient.
Emotion Recognition from EEG Signal using XGBoost Algorithm

Of late, emotion detection from brain signal has become a topic of research. Various machine learning algorithms have been applied to classify the emotion as a psychological measurement. However, good accuracy in measurements is yet to be achieved. This work proposes an approach to classify the different type of estimation in human being with improved accuracy. This work extracts several features from EEG brain signals and then optimize the set of features using the correlation matrix, information gain calculation, and recursive feature elimination method. The optimized feature set is then used in classification. For the classification technique, the Extreme Gradient Boosting (XGBoost) algorithm has been followed. The XGBoost algorithm works on the Gradient Boosting framework and follows linear model solver and tree learning concept. The results show that feature optimization followed by the XGBoost algorithm gives better classification accuracy. The proposed approach has been tested with DEAP data set. The proposed approach also has been compared with different classification algorithms, namely Naive Bayes, KNN, C4.5, Decision Tree, Random Forest algorithms. The results substantiate the efficacy in favor of the proposed approach.
Adaptive Mitigation Policy to Avoid Resource Draining Attacks in Wireless Sensor Networks

Wireless Sensor Networks [WSN] is formation of sensor nodes deployed in remote areas. It enables sensing of various parameters through wireless network. Low computation and energy consumption with less storage and strong sensing capability has found. Open nature communication makes it vulnerable for different security threat. Low power availability and proper balancing between power consumption, communication and computation motivates huge effort in research activities, standardization of security policy and prevent from security attacks. Limited availability & maximum utilization make it most targetable component in sensor node. Exhausted draining of sensor node energy not only reduces life of sensor nodes but also disrupt performance of whole network. This research work considers rate of power draining as primary information to diagnose power draining and attempts to mitigate source of attack. Proposed work has been implemented for IEEE 802.15. 4 standards using Qualnet 5.2 Simulator. Moreover, performance of proposed solution has been evaluated on the basis of Packet Delivery Ratio (PDR), and Power Draining.
Teaching Programming to School Children Using Elementary Patterns

Teaching programming to school children is a challenging task. This report summarizes the research problem, and outlines the research questions around the issues with block-based languages and suitability of elementary patterns as a possible solution. Results to date show a little use of important programming elements even after many years of use, and there is no clear sign of progression in skills with or without remixing.
Application of the V-HUB Standard using LoRa Beacons, Mobile Cloud, UAVs, and DTN for Disaster-Resilient Communications

The Philippines is a country constantly visited by natural disasters. In post-disaster situations, conventional communication systems and infrastructures are likely to be disrupted, complicating rescue and relief efforts. During disaster-response scenarios, situational awareness becomes crucial for all parties involved, hence the need for disaster-resilient communication and decision-support systems. In this study, the research group developed a communication system to help in providing contextual situational awareness of survivors and a delay-tolerant network for opportunistic communication. This system employs unmanned aerial vehicles to boost the coverage of transmission and reception of this system. Development of this system used LoRa (Long Range) technology, a new communication protocol for low-powered, long range data transmission, and medium agnostic delay tolerant network (DTN) software stack. Integrated with the DTN is a decentralized near-cloud infrastructure that allow pre-positioning and in-situ allocation of digital assets and services. Outside a disaster scenario, the locals, especially those in remote areas, can use the capabilities of this system for their everyday information and communication purposes. Demonstrations were conducted both inside and outside campus to properly evaluate the performance of the system. Indoor and outdoor use-cases were simulated for various operational scenarios such as long-range detection and messaging, and detection under rubble.
Time and Energy-Optimal Lane Change Maneuvers for Cooperating Connected and Automated Vehicles⋆

We derive optimal control policies for a Connected and Automated Vehicle (CAV) cooperating with neighboring CAVs to implement a highway lane change maneuver. We optimize the maneuver time and subsequently minimize the associated energy consumption of all cooperating vehicles in this maneuver. We prove structural properties of the optimal policies which simplify the solution derivations and lead to analytical optimal control expressions. The solutions, when they exist, are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver. Simulation results show the effectiveness of the proposed solution and significant performance improvements compared to maneuvers performed by human-driven vehicles.
PhasePack: A Phase Retrieval Library

Phase retrieval deals with the estimation of complex-valued signals solely from the magnitudes of linear measurements. While there has been a recent explosion in the development of phase retrieval algorithms, the lack of a common interface has made it difficult to compare new methods against the state-of-the-art. The purpose of PhasePack is to create a common software interface for a wide range of phase retrieval algorithms and to provide a common testbed using both synthetic data and empirical imaging datasets. PhasePack is able to benchmark a large number of recent phase retrieval methods against one another to generate comparisons using a range of different performance metrics. The software package handles single method testing as well as multiple method comparisons.The algorithm implementations in PhasePack differ slightly from their original descriptions in the literature in order to achieve faster speed and improved robustness. In particular, PhasePack uses adaptive stepsizes, line-search methods, and fast eigensolvers to speed up and automate convergence.
A Comprehensive Overview of Person Re-Identification Approaches

Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities. With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged. This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework. By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion. This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed. Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference.
High-Density Parking for Automated Vehicles: A Complete Evaluation of Coordination Mechanisms

Three main changes are currently taking place in the context of the automotive industry: electrification of vehicle propulsion, automated driving and a shift towards mobility as a service. While the first two represent opportunities for the industry growth, the later questions the private ownership of a car. Keeping the concept of a privately owned car will involve reducing the economical and environmental cost of such ownership. In this paper, we address the reduction of the parking footprint of cars, leveraging on electrification and low-level driving automation to more than double the density of cars parked in a given area, compared to conventional parking lots. We perform a complete evaluation of different strategies of vehicle coordination based on large-scale datasets of parking sessions in distinct scenarios and under varying demand patterns. Our results on the key metrics, namely area per vehicle, travel distance while parked, and removal time - clearly highlight the relevance and efficiency of this novel approach to parking. We also empirically derive guidelines for designing high-density parking systems (e.g. parking lot layout or capacity) and show the involved trade-offs.
A New Method for Arbitrary Amplitude Distribution Generation in ${4}\times{8}$ Butler Matrix

We propose a new technique in this letter to design a <inline-formula> <tex-math notation="LaTeX">$4 \times 8$ </tex-math></inline-formula> Butler matrix (BM) using a four-port network and variable phase shifters. This method helps the proposed BM to produce arbitrary output amplitudes for beam steering applications. It also contributes to reducing the size of its configuration. In addition to the two mentioned advantages, we can develop a <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> BM, which generates unequal amplitudes. For validation, a single-layer <inline-formula> <tex-math notation="LaTeX">$4\times 8$ </tex-math></inline-formula> BM with −21-dB Chebyshev distribution was designed and manufactured. We also used the high-frequency simulation structure (HFSS) software to simulate the proposed feed network and observed a good agreement between the simulation and measurement results.
Human Activity Classification Based On Breathing Patterns Using IR-UWB Radar

In this paper, human activities were detected and classified in a non-contact method based on the respiration pattern of the human using impulse radio ultra-wideband (IR-UWB) radar. To detect the respiration pattern of the human subject corresponding to different activities, a commercial IR-UWB radar was employed. Firstly, a dataset of respiration signal using IR-UWB radar was created while the subject performed four different kinds of activities i.e. sitting idle (activity 1), kapalbhati pranayama (activity 2), speaking loudly (activity 3) and deep breathing meditation (activity 4). The respiration patterns of 20 subjects were collected while the subject performs different kinds of mentioned activities. After the respiration patterns are collected, the short-time Fourier transform (STFT) is applied to the received signal. Support vector machine (SVM) is used for the classification of human activities using the STFT of the respiration pattern as the feature. The method was able to achieve the best classification accuracy of 99% for the activities 1 & 2, 92% for the activities 1, 2 & 3 and 85.25% for the activities 1, 2, 3 & 4.
Nonlinearity measures for data-driven system analysis and control

The paper deals with nonlinearity measures that quantify the nonlinearity of dynamical systems by the deviation to their ‘best’ linear approximation. We link these measures to conic relations and dynamic conic sectors. Moreover, a characterization of stability for feedback interconnections using nonlinearity measures is derived. Furthermore, we calculate guaranteed lower and upper bounds for nonlinearity measures from a finite number of input-output samples.
Data-driven control for SISO feedback linearizable systems with unknown control gain

In this paper we consider the problem of controlling an unknown system without making use of prior data or training. By relying on a feedback linearizability assumption we show how, based on prior ideas by Fliess and co-workers on model-free control, it is possible to accomplish such objective. The key idea is to learn a model that is only valid at the current state and re-learn this model as time progresses. Since this requires learning two real numbers rather than functions, it results in an approach quite different from: 1) deep learning since it requires no prior data neither large amounts of data; 2) reinforcement learning since it converges much faster and does not suffer from the curse of dimensionality.
Assessment of Meniscal Healing Status by Magnetic Resonance Imaging T2 Mapping After Meniscal Repair

Background: Although the sensitivity and specificity of magnetic resonance imaging (MRI) for the diagnosis of primary meniscal tears are high, these values are lower for the assessment of healing status of repaired menisci. Purpose: To compare the accuracy of MRI T2 mapping and conventional MRI in assessing meniscal healing after repair. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Patients who underwent meniscal repair with concurrent anterior cruciate ligament reconstruction between 2012 and 2016 and had a follow-up second-look arthroscopy were enrolled. The patients were divided into healed and incompletely/not healed groups based on the second-look arthroscopy findings. For the repaired menisci, the following were compared between the groups, (1) Stoller and Crues classification on conventional MRI with a proton density-weighted fat-saturated sequence and (2) the remaining colored meniscal tear line on T2 mapping coincident with the high signal line showing the primary tear on conventional MRI were compared. The change of T2 relaxation time (ΔT2) of the colored meniscal tear line pre- to postoperatively was compared between the groups. The mean T2 relaxation time of the whole area of the postoperative meniscus at each slice was also compared with that of control menisci to assess the whole quality of the repaired meniscus. Results: A total of 26 menisci from 24 knees were assessed (16 healed menisci, 10 incompletely/not healed menisci). According to the Crues classification on conventional MRI, 8 of 16 healed menisci and 3 of 10 incompletely/not healed menisci improved from grade 3 to 2, with there being no significant difference between the groups (P = .43). However, the colored meniscal tear line remained in only 3 of the 16 healed menisci as compared with 9 of the 10 incompletely/not healed menisci, and the presence of this colored line allowed differentiation between healed menisci and incompletely/not healed menisci (sensitivity, 81.3%; specificity, 90.0%; odds ratio, 39.0; P = .001). The mean (SD) ΔT2 was −31.1 ± 3.2 and −19.9 ± 4.4 ms in the healed and incompletely/not healed groups, respectively (P < .001). Receiver operating characteristic curve analysis showed a cutoff ΔT2 value of −22.3 ms for separation of meniscal healing (P < .001). The T2 relaxation times of the whole area of the repaired menisci were 31.7 ± 3.4 and 32.8 ± 3.8 ms in the healed and incompletely/unhealed groups, respectively (P = .69), with these values being significantly longer than the 26.9 ± 2.2 ms in the controls (P < .001). Conclusion: MRI T2 mapping allowed the differentiation of healing status after meniscal repair, with high sensitivity and specificity as compared with conventional MRI.
UNIVERSITE DE PICARDIE JULES VERNE FACULTE DE MEDECINE D'AMIENS

Introduction : Falls are common among the elderly. They regard about 30% of the over-65 each year, with serious consequences in terms of morbi-mortality and major socio-economic impacts. Fall management must be precocious, coordinated and multidisciplinary. The aim of this work was to investigate possible difficulties for the general practitioner in his practice. Material and methods : An observational descriptive epidemiological study was conducted through a self-administered questionnaire sent to the general practitioners in Amiens Metropolis. We decided to evaluate their potential difficulties in care of the falling elderly, to describe their practice and the main solutions considered to improve their exercise. Results : The participation rate was 29,9% (53/177). 37,7% (n = 20) of physicians reported difficulties. These difficulties were related to the patient (environment, isolation, trivialization of falls, iatrogeny), to the access to balance and rehabilitation structures, or regarding coordination between stakeholders. In most of the cases, general practitioners did not agree with the recommendations, particularly regarding preventive measures and reevaluation. This was due to the complexity and importance of the prevention, the time-consuming nature of the practice and the trivialization of the falls by the doctor and his patient. The solutions reported to improve the practice were : environmental adaptation, iatrogenic control, time-consuming care and the need for a standardized strategy with clear recommendations. Conclusion : The general practitioner cannot take care alone of elderly patients fallers. A dedicated hospital consultation for falls can be beneficial for patients but requires an effort of information, coordination and communication.
UTS_CETC_D2DCRC Submission at the TRECVID 2018 Video to Text Description Task

In this paper, we report our methods on the video to text description task of TRECVID 2018[1]. The task consists of two subtasks, i.e., Description generation and Matching & Ranking. In the description generation subtask, because no standard training data provided, we principally focused on saturating the generalization ability of our model. Instead of exploring complex models, we investigated the widely used LSTM based sequence to sequence model[10] and some of its variants, which are simple yet robust enough. Besides, we also reviewed some training strategies to expand the generalization ability of our model. In the matching and ranking subtask, we designed a two-branch deep model[6] to embed visual content and semantic content respectively. The model helps to project the information from different modalities into the common embedding space. Further, we examined some metric learning losses, like triplet loss and its variants, in our experiments.
Effects of Postnatal Hyperoxia Exposure on Cardiac Muscle Examined with 3D Serial Block Face SEM using Rapid Automated Sample Preparation

Premature birth is associated with increased risk for cardiac dysfunction and failure throughout life. Postnatal hyperoxia exposure in rodents is a well-established model of chronic lung disease of prematurity which recapitulates the pulmonary vascular and cardiovascular phenotype of premature birth, including decreased skeletal muscle fatigability, mitochondrial oxidative capacity, and altered morphology [1]. In the present study, we sought to investigate the effect of postnatal hyperoxia on cardiac (right ventricle) muscle including mitochondrial morphology.
Oral Health Benefits of Cranberry: A Review

Cranberry has a unique combination of phytochemicals which are used for treatment of various systemic diseases including oral diseases like caries,periodontitis and oral cancer. Many in vitro studies have outlined the potential health benefits of cranberry but in vivo studies are still inconclusive. Cranberry inhibit acid production, attachment and biofilm formation by Streptococcus mutans thereby being an effective anticaries agent. It also inhibits host inflammatory response and adherance of periodontal pathogens on tooth surfaces. Proanthocyanidins in cranberries demonstrate significant cancer prevention. The review aims to well into the potential benefits of cranberry in improving oral health as well as a peep into the still unexplored facets of natural medicaments in oral disease prevention.
Wide Frequency Characterization of Intra-Body Communication for Leadless Pacemakers

Leadless Cardiac Pacemakers (LCP) have the potential to revolutionize Cardiac Rhythm Management (CRM). Current LCPs can only pace a single location of the heart limiting their use to patients requiring single-chamber stimulation. A Multi-node system of synchronized LCPs could be used in a significantly larger patient population. Synchronization using standard communication techniques involves high power consumption decreasing the longevity of the device. In this work, we investigate Galvanic Intra Body Communication (IBC) as a method to synchronize multi-node LCP systems. First, an accurate computational torso model was used for quasi-static simulations to estimate channel pathloss in the frequency range [40 kHz–20 MHz]. The model was then verified with in-vivo measurements using a novel experimental setup, where two LCP devices were placed in the right atrium, right ventricle and left ventricle. All channels involved in a potential multi-node LCP system were characterized. The orientation of the transducers relative to each other had a great impact on the results, with the attenuation level ranging between 55 dB and 70 dB between the best and worst orientations. The best results were achieved in the MHz range. Coupled with the fact that it does not require additional electrodes, this study suggests Galvanic IBC be superior to conventional communication methods for LCP devices. This analysis defines a methodology for galvanic IBC channel characterization for LCP systems, which is an important step for the design of efficient transceivers for IBC applications. More experiments with larger datasets are needed to bring this method to practice.
CELASTRUS PANICULATUS, AN ENDANGERED INDIAN MEDICINAL PLANT WITH MIRACULOUS COGNITIVE AND OTHER THERAPEUTIC PROPERTIES: AN OVERVIEW

Celastrus paniculatus is an Indian medicinal plant which has been used for thousands of years in the traditional Ayurvedic system of medicine. It is fast gaining importance in the primary healthcare systems as well as in herbal drug formulations. Oil obtained from the seeds of the plant is reported to be highly beneficial in stimulating intellect and sharpening