Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-Time UWB Multi-Channel Indoor Positioning System for Industrial Scenarios

Tracking products, machines and important objects in factories is a key task for future efficient manufacturing processes. Providing workers and robots with accurate and reliable location information is the foundation for this innovation. In industrial facilities, the environment is more complex compared to consumer indoor positioning scenarios. Heavy machinery often blocks the line of sight path. Metal walls and surfaces increase reflections and multi-path effects. An ultra-wideband based time-difference-of-arrival positioning system is investigated. All base stations use four transceivers instead of one to increase the system robustness. Multi-path and non-line of sight situations are evaluated with respect to the channel effects and the influence on the position accuracy.
Crowdfunding für Stiftungen und Nonprofit Organisationen - Chancen und Herausforderungen am Beispiel der Crowdfunding-Kampagne www.neustartdario.ch

Die voranschreitende „Digitale Transformation“ unserer Gesellschaft be‐ trifft alle politischen, sozialen und wirtschaftlichen Verhältnisse. Stiftun‐ gen und Nonprofit-Organisationen (NPO) sind ebenso davon betroffen wie auch alle anderen Akteure. Dieser Beitrag basiert auf den Erfahrungen aus der Crowdfunding-Kampagne neustartdario.ch, die durch den Kompetenz‐ schwerpunkt „Digitale Strategien“ des Schweizerischen Instituts für Entre‐ preneurship inhaltlich begleitet wurde. Anhand dieser Kampagne wird aufgezeigt, wie Mittel der digitalen Kommunikation neue Möglichkeiten des Online-Fundraisings für Einzelpersonen bieten. Diese Entwicklungen resultieren in unterschiedlichen Chancen und Herausforderungen für Stif‐ tungen (folgend als Spendenstiftungen bezeichnet) und NPO, die ein eige‐ nes Fundraising betreiben. So müssen sich diese zukünftig verstärkt mit der Frage auseinandersetzen, wie sie mit den aufgezeigten Veränderungen umgehen wollen.
MIMO Equalization to Mitigate Turbulence in a 2-Channel 40-Gbit/s QPSK Free-Space Optical 100-m Round-Trip Orbital-Angular-Momentum-Multiplexed Link Between a Ground Station and a Retro-Reflecting UAV

We experimentally demonstrate turbulence mitigation using $2\times 2$ MIMO equalization in a 100-m round-trip, 40-Gbit/s OAM-multiplexed FSO link between a ground transmitter and receiver via a retro-reflecting hovering UA V. Two OAM modes are transmitted, each achieving a BER of $< 3.8\times 10^{-3}$.
Visual Evoked Potential in Color: The Instrumentation and Method

The Visual Evoked Potential (VEP) in color is a field that leads us to understand the vision and the way we see the colors. This research focuses on the interaction between the pupil and the response of VEP. The technology has been developed and adapted in order to introduce the color and retinal illumination in these studies. Also the methodology of the traditional Visual Evoked Potential has been changed in order to analyze the pupil. The goal of this project is isolate the effects of color making the luminance constant with the purpose of study the effect of color in the retinal illumination and VEP.The study was made with an electroencephalogram in a Faraday cage in order to reduce the environmental noise. The stimuli were isoluminicent to obtain only the influence of color in the PEV.The preliminary results shows that the instrumentation implemented will allow to study the effect of color in PEV and retinal illumination in an adequate way because it was recovered without noise. This study presents the instrumentation and methodology to research the relationship between retinal illumination and the influence on color observing the PEV. The methodology exposed in this paper is adequate to study the PEV in color using stimuli isoluminicent and the relationship with retinal illumination
Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype

The memory requirement of deep learning algorithms is considered incompatible with the memory restriction of energy-efficient hardware. A low memory footprint can be achieved by pruning obsolete connections or reducing the precision of connection strengths after the network has been trained. Yet, these techniques are not applicable to the case when neural networks have to be trained directly on hardware due to the hard memory constraints. Deep Rewiring (DEEP R) is a training algorithm which continuously rewires the network while preserving very sparse connectivity all along the training procedure. We apply DEEP R to a deep neural network implementation on a prototype chip of the 2nd generation SpiNNaker system. The local memory of a single core on this chip is limited to 64 KB and a deep network architecture is trained entirely within this constraint without the use of external memory. Throughout training, the proportion of active connections is limited to 1.3%. On the handwritten digits dataset MNIST, this extremely sparse network achieves 96.6% classification accuracy at convergence. Utilizing the multi-processor feature of the SpiNNaker system, we found very good scaling in terms of computation time, per-core memory consumption, and energy constraints. When compared to a X86 CPU implementation, neural network training on the SpiNNaker 2 prototype improves power and energy consumption by two orders of magnitude.
Time Delay Neural Network to Estimate the Elbow Joint Angle Based on Electromyography

Elbow joint angle estimation is essential in the field of biomechanical engineering especially for an apparatus based on myoelectric control. The purpose of this study is to develop a model of electromyography (EMG) signal to elbow joint angle estimation using time delay neural network (TDANN). The EMG signals were recorded only from biceps muscle from ten healthy male subjects. In order to obtain the features, the EMG signal is extracted for every 100 samples using sign slope change (SSC) features. The EMG features are used as the training data, in order the TDANN able to recognize the elbow joint angle. The results of this study reveal that the performance of the estimation is better if it is compared to the other studies. The RMSE values for the continuous and random motion are 14.97°±5.17° and 18.69°± 2.76°, respectively. The Pearson correlation coefficients are 0.87± 0.0087 and 0.78±0.11 for continuous and random motion, respectively. The results have confirmed the usefulness of the proposed method to estimate the elbow joint angle.
Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.
An Enhanced ESIM Model for Sentence Pair Matching with Self-Attention

School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China Corresponding Author: renfeiliang@cse.neu. edu.cn Abstract. Sentence pair matching is one of the most basic tasks in natural language processing and is receiving extensive research attention. Most of existing models can be categorized into sentence interaction-based models and sentence encoding-based models. Here we propose a new enhanced ESIM model that could take full advantage of both these two kinds of methods. Specifically, our method can obtain the common information of a sentence pair as the sentence interaction-based models do, and can also extract the key information of a sentence pair itself as the sentence encoding-based models do. Besides, our method also uses Siamese networks to learn a unique structure to naturally rank the similarity between two sentences, which reduces both the model complexity and the size of parameters. With the proposed method, we participated in the task-3 of CCKS2018, which is called as WeBank Intelligent Customer Service Question Match. Finally, our method ranks the first among the hugely competitive models.
An Ab Initio Study of Connections between Tensorial Elastic Properties and Chemical Bonds in Σ5(210) Grain Boundaries in Ni3Si

Using quantum-mechanical methods we calculate and analyze (tensorial) anisotropic elastic properties of the ground-state configurations of interface states associated with Σ5(210) grain boundaries (GBs) in cubic L12-structure Ni3Si. We assess the mechanical stability of interface states with two different chemical compositions at the studied GB by checking rigorous elasticity-based Born stability criteria. In particular, we show that a GB variant containing both Ni and Si atoms at the interface is unstable with respect to shear deformation (one of the elastic constants, C55, is negative). This instability is found for a rectangular-parallelepiped supercell obtained when applying standard coincidence-lattice construction. Our elastic-constant analysis allowed us to identify a shear-deformation mode reducing the energy and, eventually, to obtain mechanically stable ground-state characterized by a shear-deformed parallelepiped supercell. Alternatively, we tested a stabilization of this GB interface state by Al substituents replacing Si atoms at the GB. We further discuss an atomistic origin of this instability in terms of the crystal orbital Hamilton population (COHP) and phonon dispersion calculations. We find that the unstable GB variant shows a very strong interaction between the Si atoms in the GB plane and Ni atoms in the 3rd plane off the GB interface. However, such bond reinforcement results in weakening of interaction between the Ni atoms in the 3rd plane and the Si atoms in the 5th plane making this GB variant mechanically unstable.
Vision based Methodology for Diagnosis of Convulsion Patients

–Convulsion patients suffer from unforeseen loss of responsiveness likewise as uncontrolled jerking movements. It’s additionally addressed as epileptic seizures. Most of the existing methods use electroencephalogram (EEG) for monitoring of epilepsy. The recording of encephalogram is made with the electrodes connected to scalp. This makes it effortful for the patient to stay with the attachment of the electrodes is effortful that makes long home monitoring that is byzantine. So as to manage these related problems, vision based methodology has been introduced in which video input is segmented first and segmented images are processed to detect & classify the type of convulsion. Optical flow calculation method is used for feature extraction and extracted features are distributed with the assistance of support vector machine. The introduced system is enabled with IoT that helps medical practitioner to look at the patient’s status distantly. The major benefit of the introduced system is, it will hardly disrupt the regular sleep of the patient. This gives a price effective solution where patients can be monitored for long duration; thus it will contribute to enhance the diagnosis and so the quality of patient’s life.
Real-Time Awareness Scheduling for Multimedia Big Data Oriented In-Memory Computing

As one of the most striking research hotspots in both academia and industry, Internet of Things (IoT) has been constantly changing our daily life by joining together nearly all we can imagine. From home furnishings and vehicles to urban facilities, all these smart things need powerful managing and processing capabilities to deal with mass multimedia data in different content forms such as images, audios, and videos. Nowadays, since Moore’s Law is no longer applicable, conventional thinking may not be adequate in facing the explosive growing amount of data. Hence, in this paper, we adopt the idea of in-memory processing to solve the problem of real-time multimedia big data computing in IoT. We apply closed-loop feedback in the scheduling method design to integrate in-memory storages of all devices within a 3-tier network structure. In addition, we consider the respective conditions of different real-time required levels and content forms. The analysis results show that our scheduling method can achieve better workload allocation with less latency in comparison of existing methods.
First-in-human randomized controlled trial of an oral, replicating adenovirus 26 vector vaccine for HIV-1

Background Live, attenuated viral vectors that express HIV-1 antigens are being investigated as an approach to generating durable immune responses against HIV-1 in humans. We recently developed a replication-competent, highly attenuated Ad26 vector that expresses mosaic HIV-1 Env (rcAd26.MOS1.HIV-Env, “rcAd26”). Here we present the results of a first-in-human, placebo-controlled clinical trial to test the safety, immunogenicity and mucosal shedding of rcAd26 given orally. Methods Healthy adults were randomly assigned to receive a single oral dose of vaccine or placebo at 5:1 ratio in a dosage escalation of 10^8 to 10^11 rcAd26 VP (nominal doses) at University of Rochester Medical Center, Rochester, NY, USA. Participants were isolated and monitored for reactogenicity for 10 days post-vaccination, and adverse events were recorded up to day 112. Rectal and oropharyngeal secretions were evaluated for shedding of the vaccine. Humoral and cellular immune responses were measured. Household contacts were monitored for secondary vaccine transmission. Results We enrolled 22 participants and 11 household contacts between February 7 and June 24, 2015. 18 participants received one dose of HIV-1 vaccine and 4 participants received placebo. The vaccine caused only mild to moderate adverse events. No vaccine-related SAEs were observed. No infectious rcAd26 viral particles were detected in rectal or oropharyngeal secretions from any participant. Env-specific ELISA and ELISPOT responses were undetectable. No household contacts developed vaccine-induced HIV-1 seropositivity or vaccine-associated illness. Conclusions The highly attenuated rcAd26.MOS1. HIV-Env vaccine was well tolerated up to 10^11 VP in healthy, HIV-1-uninfected adults, though the single dose was poorly immunogenic suggesting the replicative capacity of the vector was too attenuated. There was no evidence of shedding of infectious virus or secondary vaccine transmission following the isolation period. These data suggest the use of less attenuated viral vectors in future studies of live, oral HIV-1 vaccines. Trial registration ClinicalTrials.gov NCT02366013.
Wideband Circularly Polarized Printed Slot Antenna With A Pair of Slant Line Slots

A circularly polarized (CP) printed equilateral triangular-ring slot (ETRS) antenna performs 14.7% of CP bandwidth. To improve this bandwidth, a pair of slant line slots are introduced to the printed slot antenna. Each line slot represents lowest and highest frequency of the CP bandwidth. Both line slots have successfully enhanced the fractional bandwidth to 37.5%, more than double of the design without them. The measured results confirm that the antenna has good agreement with the simulated one.
Operational Experience With the GEM Detector Assembly Lines for the CMS Forward Muon Upgrade

The CMS Collaboration has been developing large-area triple-gas electron multiplier (GEM) detectors to be installed in the muon Endcap regions of the CMS experiment in 2019 to maintain forward muon trigger and tracking performance at the High-Luminosity upgrade of the Large Hadron Collider (LHC); 10 preproduction detectors were built at CERN to commission the first assembly line and the quality controls (QCs). These were installed in the CMS detector in early 2017 and participated in the 2017 LHC run. The collaboration has prepared several additional assembly and QC lines for distributed mass production of 160 GEM detectors at various sites worldwide. In 2017, these additional production sites have optimized construction techniques and QC procedures and validated them against common specifications by constructing additional preproduction detectors. Using the specific experience from one production site as an example, we discuss how the QCs make use of independent hardware and trained personnel to ensure fast and reliable production. Preliminary results on the construction status of CMS GEM detectors are presented with details of the assembly sites involvement.
On Training Targets and Objective Functions for Deep-learning-based Audio-visual Speech Enhancement

Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker. Recently, deep learning techniques have been adopted to solve the AV-SE task in a supervised manner. In this context, the choice of the target, i.e. the quantity to be estimated, and the objective function, which quantifies the quality of this estimate, to be used for training is critical for the performance. This work is the first that presents an experimental study of a range of different targets and objective functions used to train a deep-learning-based AV-SE system. The results show that the approaches that directly estimate a mask perform the best overall in terms of estimated speech quality and intelligibility, although the model that directly estimates the log magnitude spectrum performs as good in terms of estimated speech quality.
Single Lane 168 Gb/s PAM-8 Short Reach Transmission Using an EAM with Receiver Skew Compensation

Future data centre networks require single lane high data rate optical links. This paper experimentally demonstrates transmission of 56GBd PAM-8, in the $C$-band, using a low drive voltage electro-absorption modulator. 0.5dB performance improvement is achieved through digital receiver eye-skew compensation.
Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models

Falls are a leading health risk for the elderly. Various wearable-based fall detection systems based on machine learning models have been developed to provide emergency alarms and services to improve safety and health-related quality of life. To support long-term healthcare services, the appropriate sampling rate, which plays a vital role in a fall detection system, must be investigated to guarantee the performance accuracy and energy efficiency. Intuitively, decreasing the sampling rate of sensor nodes can provide computing and energy savings. However, there is a lack of research exploring the performance accuracy of fall detection systems in terms of sampling rate, especially for machine learning models. In this paper, the effects of decreasing sampling rates (ranging from 200/128 to 3 Hz) on wearable-based fall detection systems are investigated based on four machine learning models: support vector machine (SVM), $k$ -nearest neighbor, Naïve Bayes, and decision tree. Two emulated fall data sets, the SisFall public data set and the proposed data set of this paper, are used to allow an objective investigation of sampling rates. The findings show that fall detection systems based on SVM modeling and a radial basis function could achieve at least 98% and 97% accuracy, with sampling rates of 11.6 and 5.8 Hz, respectively. Overall, the experimental results demonstrate that a sampling rate of 22 Hz is sufficient for most machine learning models to support wearable-based fall detection systems (accuracy ≥97%).
Discovery and analysis of a novel type of the serine biosynthetic enzyme phosphoserine phosphatase in Thermus thermophilus

Studying the diversity of extant metabolisms and enzymes, especially those involved in the biosynthesis of primary metabolites including amino acids, is important to shed light on the evolution of life. Many organisms synthesize serine from phosphoserine via a reaction catalyzed by phosphoserine phosphatase (PSP). Two types of PSP, belonging to distinct protein superfamilies, have been reported. Genomic analyses have revealed that the thermophilic bacterium Thermus thermophilus lacks both homologs while still having the ability to synthesize serine. Here, we purified a protein from T. thermophilus which we biochemically identified as a PSP. A knockout mutant of the responsible gene (TT_C1695) was constructed, which showed serine auxotrophy. These results indicated the involvement of this gene in serine biosynthesis in T. thermophilus. TT_C1695 was originally annotated as a protein with unknown function belonging to the haloacid dehalogenase‐like hydrolase (HAD) superfamily. The HAD superfamily, which comprises phosphatases against a variety of substrates, includes also the classical PSP as a member. However, the amino acid sequence of the TT_C1695 was more similar to phosphatases acting on non‐phosphoserine substrates than classical PSP; therefore, a BLASTP search and phylogenetic analysis failed to predict TT_C1695 as a PSP. Our results strongly suggest that the T. thermophilus PSP and classical PSP evolved specificity for phosphoserine independently.
A Wearable EEG Real-time Measure and Analysis Platform for Home Applications

Scalp electroencephalography (EEG) is widely used to study human electrophysiological activity noninvasively. With the gradual improvement of the quality of the multi-channel bioelectric signal acquisition device, the volume and power consumption are gradually reduced, which makes it suitable for portable and wearable application. To ensure sufficient signal gain and acquisition accuracy, it is a huge challenge to effectively suppress external interference and obtain a better bioelectric signal. This paper presents a new kind of wireless EEG signal real-time analysis and acquisition system. The system can be wearable at home environment and wirelessly send the real-time EEG signal to the host. The results show that the system can collect EEG signal robustly. The noise of the test system is reduced by 60% through the design of the isolated circuit. The software algorithm also enables the ability to perform basic analysis of biological signals by using Python digital signal processing module. The system provides a new safety platform for human-machine interfacing, rehabilitation and mental disease monitoring at home.
Saturation Behavior of Fiber Optical Parametric Amplifier in Presence of Dispersion Fluctuations

The impact of random dispersion fluctuations on the saturation behavior of fiber optical parametric amplifier was investigated. The coupled amplitude equations which represents the parametric amplification process were solved numerically. The saturation behavior was analyzed with variation of dispersion fluctuations amplitudes. It is found that the parametric gain with low fluctuations amplitude saturates quicker than that with high amplitude. This analysis is beneficial in understanding the evolution of the pump and signal power in the small and saturated regimes of parametric amplifiers.