Miracle-dz/UniFAR-FTUs huggingface dataset:
NOMA-based 802.11n for Broadcasting Multimedia Content in Factory Automation Environments

Industry 4.0 requirements in reliability and latency are still a challenge for wireless communications systems due to the factory critical propagation scenarios. Additionally, further improvements are required in order to enhance the performance of wireless technologies in terms of reliability and latency before presenting them as a valid alternative to the wired technologies. These challenges are added to the increasing needs of bitrate to deliver multimedia content in Factory Automation (FA) environments. This paper proposes several use cases, which include video transmission and a specific technical solution based on a combination of NOMA and the IEEE 802.11n standard. The PHY/MAC proposal is evaluated on a reference scenario based on the deployment of the typical industrial applications. Results show that NOMA-based systems offer a very promising reliability gain in comparison to classical TDMA-based systems. In fact, for the most unbalanced service combinations a PER (Packet Error Rate) improvement of more than one order of magnitude can be obtained.
Fabrication of Coaxis-Gated ZnO Nanowire Field-Emitter Arrays With In-Plane Focusing Gate Electrode Structure

Addressable nanowire field-emitter arrays (FEAs) show potential for application in vacuum microelectronic devices, and a focusing structure is essential for modulation of the electron beam in FEAs. However, because of the complicated device structure induced by the focusing structure, nanowire integration in such a device is very difficult. To address this issue, a coaxis-gated ZnO nanowire FEA structure with an in-plane focusing gate electrode is proposed. The focusing gate electrode is located in the same plane as the cathode and control gate, and the addressing function is realized using a via structure. The coaxis-gated ZnO nanowire FEAs were fabricated using a four-mask microfabrication process, thus requiring one mask less than traditional focused FEAs. The characteristics of the fabricated ZnO nanowire FEAs were studied, and their addressing and focusing abilities were verified.
3D Location Accuracy Estimation of Radio Emission Sources for Beamforming in Ultra-Dense Radio Networks

Accurate location estimation of Radio Emission Sources (RES) is very important for location-based applications in 5G radio networks. Multiple Input Multiple Output (MIMO), millimeter Wave (mmWave) and Ultra Dense Networks (UDN) are among emerging technologies to increase not only communication performance, but also positioning accuracy and vice versa. In addition to leverage coverage and capacity new technologies give rise to beamforming techniques with successful implementation of simultaneous transmission and reception by means of preliminary location of neighboring stations. Contribution of the current research is 3D location accuracy limits estimation for positioning of radio emission sources using Time Difference of Arrival (TDOA), Angle of Arrival (AOA) and their combination TDOA/AOA. Resulting accuracy limits, such as Cramer-Rao Lower Bound (CRLB) ellipsoid and Spherical Error Probable (SEP) contour were analyzed and visualized to confirm spatial splitting possibility of neighboring RES for short-range communications thus enabling location aware beamforming.
Event-related potential from EEG for a two-step Identity Authentication System

Current problems related to high-level security access are increasing, leaving organizations and persons unsafe. A recent good candidate to create a robust identity authentication system is based on brain signals recorded with electroencephalograms (EEG). In this paper, EEG-based brain signals of 56 channels, from event-related potentials (ERPs), are used for Subject identification. The ERPs are from positive or negative feedback-related responses of a P300-speller system. The feature extraction part was done with empirical mode decomposition (EMD) extracting 2 intrinsic mode functions (IMFs) per channel, that were selected based on the Minkowski distance. After that, 4 features are computed per IMF; 2 energy features (instantaneous and teager energy) and 2 fractal features (Higuchi and Petrosian fractal dimension). Support vector machine (SVM) was used for the classification stage with an accuracy index computed using 10-folds cross-validation for evaluating the classifier’s performance. Since high-density EEG information was available, the well-known backward-elimination and forward-addition greedy algorithms were used to reduce or increase the number of channels, step by step. Using the proposed method for subject identification from a positive or negative feedback-related response and then identify the subject will add a layer to improve the security system. The results obtained show that subject identification is feasible even using a low number of channels: E.g., 0.89 of accuracy using 5 channels with a mixed population and 0.93 with a male-only population.
Efficient Bayesian Optimization for Uncertainty Reduction Over Perceived Optima Locations

Bayesian optimization (BO) is concerned with efficient optimization using probabilistic methods. Predictive entropy search (PES) is a popular and successful BO strategy to find a point that maximizes the information gained about the optima location of an unknown function. Since the PES analytical form is intractable, it requires approximations and is computationally expensive. These approximations may degrade PES performance in terms of accuracy and efficiency. In this paper, we propose an alternative scheme - predictive variance reduction search (PVRS) – to find a point that maximally reduces the uncertainty at the perceived optima locations. The optimization converges to the true optimum when the uncertainty at all perceived optima locations is vanished. Our novel modification is beneficial in two ways. First, PVRS can be computed in closed-form, unlike the approximations made in PES. Second, PVRS is simple and easy to implement. As a result, the proposed PVRS gains huge speed up for scalable BO whilst showing favorable optimization efficiency. Furthermore, we extend our PVRS framework for batch setting where we select multiple experiments for parallel evaluations at each iteration. Empirically, we demonstrate the effectiveness of the PVRS on both benchmark functions and real-world applications in standard and batch BO settings.
Radiation-Induced Variable Retention Time in Dynamic Random Access Memories

The effect of gamma-ray and neutron radiations on the variable retention time (VRT) phenomenon occurring in dynamic random access memory (DRAM) is studied. It is shown that both ionizing radiation and nonionizing radiation induce VRT behaviors in DRAM cells. It demonstrates that both Si/SiO2 interface states and silicon bulk defects can be a source of VRT. It is also highlighted that radiation-induced VRT in DRAMs is very similar to the radiation-induced dark current random telegraph signal in image sensors. Both phenomena probably share the same origin, but high-magnitude electric fields seem to play an important role in VRT only. Defect structural fluctuations (without change of charge state) seem to be the root cause of the observed VRT whereas processes involving trapping and emission of charge carriers are unlikely to be a source of VRT. VRT also appears to be the most probable cause of intermittent stuck bits in irradiated DRAMs.
A New Unity-Gain 5-Level Active Neutral-Point-Clamped (UG-5L-ANPC) Inverter

The active neutral-point-clamped (ANPC) inverter is a popular multilevel inverter for various industry applications. In a recent attempt, an improved topology that integrates a flying capacitor to enhance the voltage gain from half to unity has been presented [11]. Retaining the benefit of unity-gain, this paper proposes a new ANPC topology with two improvements. Firstly, the voltage stress of the flying capacitor is reduced by half. Secondly, the charging of the flying capacitor at 0 level is made possible to achieve uniform charging over the power cycle. Comprehensive analysis is presented and experimental results of a prototype are presented for validation. Finally, the extension of the topology with increased number of output voltage levels generation is briefly discussed.
Unconstrained Ear Recognition Using Deep Scattering Wavelet Network

There has been significant progress in the field of automatic ear recognition, wherein ear images are captured in a constrained environment. But unconstrained ear recognition have acquired less attention due to the unavailability of such a database having variations in illumination, pose, size, resolution and occlusions. It is a challenging pattern recognition problem due to large intra-class variability. In this paper, we propose a novel local descriptor for unconstrained ear recognition based on scattering wavelet network (ScatNet) to extract translation and small deformation invariant local features. The experiments conducted on a recently released unconstrained ear benchmark databases, such as Annotated Web Ears (AWE) and USTB-Helloear databases, and also on our newly created IIT-Bombay smartphone-captured ear database show the effectiveness and robustness of the proposed local feature descriptor in terms of Equal Error Rate (EER) and Rank-1 (R1) accuracy.
Storage-Reliability-Repair Trade-Offs in Distributed Storage System

For reliability purpose, data redundancy is anessential in distributed storage system. In the evolving of data redundancy schemes, storage-reliability-repair trade-offs always exist. From replication to erasure codes, higher reliability achieves with lower storage cost, but the higher repair penalty can not be ignored. Optimized data redundancy schemes like regenerating codes or basic pyramid codes are proposed to relieve the high repair penalty. In order to study the storage-reliability-repair trade-offs, we propose a new model based on the standard Markov Chain, it takes the irregular fault tolerance, latent sector error and correlated failures into account, and measures the storage and repair cost from system perspective. Through inter-scheme and intra-scheme discussions, we find some principles about choosing data redundancy schemes and corresponding parameters to achieve better storage-reliability-repair trade-offs. Our goal is to provide system designers and administrators with concrete information to help them achieve proper storage-reliability-repair trade-offs.
Nonsteroidal antiinflammatory drug modulation of prosthesis pseudomembrane induced bone resorption.

OBJECTIVE To study the in vitro effect of therapeutic levels of select nonsteroidal antiinflammatory drugs (NSAID) on prosthesis associated pseudomembrane induced bone resorption and cytokine and prostanoid synthesis using tissue obtained from osteoarthritic patients undergoing revision of cemented implants. METHODS Pseudomembranes were cultured in the presence and absence of therapeutic levels of select NSAID: Processed conditioned media were evaluated for bone resorbing capacity by assessing murine neonatal limb rudiment release of 45Ca. Interleukin 1 (IL-1) and tumor necrosis factor (TNF) were analyzed by bioassay and prostaglandin E2 (PGE2) by radioimmunoassay. RESULTS Piroxicam, in contrast to indomethacin and sodium salicylate, significantly decreased membrane associated bone resorption. The NSAID effect correlated more consistently with piroxicam downregulation of TNF synthesis than that of IL-1. Consistent drug associated suppression of PGE2 synthesis did not enable differentiation of NSAID effectiveness in suppressing pseudomembrane induced bone resorption. CONCLUSION Results provide a rational basis for design of animal models and/or further human studies to test the hypothesis that the prophylactic administration of an NSAID can potentially retard the pseudomembrane effected bone resorptive process associated with aseptic cemented prosthesis failure.
Study on hierarchical transmission toward the advanced ISDB-T development : – Comparison of FDM with LDM –

In Japan, the next generation of the Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) system is being developed to transmit large-volume content services such as ultra-high definition television (UHDTV). Since 2016, we have been researching and developing transmission systems for terrestrial broadcasting as part of the research program “Research and Development for advanced Digital Terrestrial Television Broadcasting System”, under the auspices of the Ministry of Internal Affairs and Communications, Japan. The transmission system studied in this program is called “advanced ISDB-T”. The transmission performance of the advanced ISDB-T is better than that of ISDB-T. The advanced ISDB-T has been considered to adopt frequency division multiplexing (FDM) for the baseline of hierarchical transmission to transmit mobile services and fixed services in one channel. However, layered division multiplexing (LDM), which can improve spectrum efficiency, has been also studied. In this paper, we compare FDM with LDM, adopt a new signal structure, modulation, and forward error collection (FEC) studied in the development of the advanced ISDB-T. Finally, we clarify how multiplexing technology could affect both mobile- and fixed-layer transmission.
DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models

Container-based microservice architectures are emerging as a new approach for building distributed applications as a collection of independent services that works together. As a result, with microservices, we are able to scale and update their applications based on the load attributed to each service. Monitoring and managing the load in a distributed system is a complex task as the degradation of performance within a single service will cascade reducing the performance of other dependent services. Such performance degradations may result in anomalous behaviour observed for instance for the response time of a service. This paper presents a Detection and Localization system for Anomalies (DLA) that monitors and analyzes performance-related anomalies in container-based microservice architectures. To evaluate the DLA, an experiment is done using R, Docker and Kubernetes, and different performance metrics are considered. The results show that DLA is able to accurately detect and localize anomalous behaviour.
An Efficient Algorithm for Dispatch of Modular FACTS devices in Transmission Planning

With growing uncertainty in the underlying assumptions that are used to plan for a secure operation of the electric power grid, planning engineers are looking for more flexible transmission upgrade in which to invest. One such technology is modular flexible AC transmission systems (M-FACTS), notably variable impedance power flow control devices (VI-PFC). VI-PFC technology can be used to route power through parallel paths in the transmission system, thereby eliminating congestion. This technology can also be used in the reliability context of resolving intact and post-contingency overloading. The true value of VI-PFC technology is its ability dynamically increase or decrease reactance of a line, making it more flexible than fixed series reactors or series capacitors. The first step in unlocking this value is to enable transmission planners to incorporate these optimal dispatches in their planning studies. This paper develops an efficient method to optimally dispatch multiple VI-PFC deployments to resolve post-contingency overloads from a planning perspective. The dispatch method is based on a PTDF DC power flow formulation. However, the method is illustrated on the IEEE 39-bus test system solving with full AC power flow assumptions.
Cascading Failure Propogation With Dynamic Load Variations: A Simulation and Mitigation Framework

This paper presents a cascading failure simulation algorithm with a novel stage division method, while considering dynamic load variation during the cascading propagation process. By accurately estimating the duration of each cascading failure stage, a receding-horizon optimal control model is further presented to mitigate the effects of severe line-overload disturbances and prevent the propagation of outages in electrical power systems. Specifically, the mitigation strategy adaptively adjusts branch flows by re-dispatching generation and load shedding, while taking into account ramping limits and available time duration to determine optimal control actions of each stage. Numerical simulations on the IEEE 118-bus system illustrate effects of the proposed algorithms with load variation and mitigation control strategy.
Bilinear Robust Control Design for Virotherapy Model

In this paper we concerned with the application bilinear robust control for virotherapy model. In designing the controller, it requires a solution to the state dependent algebraic Riccati equation (SDARE). However, it is difficult to solve the SDARE. Successive method is one of the methods that can be used to solve this issue. The idea of this method is converting the bilinear systems into time-varying linear system. This method has the following step: firstly, we need to obtain the robust control for the linear system by ignoring the multiplicative term of bilinear system. Second, convert the bilinear systems into the time-varying linear systems using the previous result, and then solve the SDARE by the new performance index and the associated Hamilton-Jacobi-Isaacs equation. Last, iterate the steps until the convergence of state satisfied. The virotherapy model has been widely developed and can be considered as a model in bilinear system. There are four groups in this model: quiescent cells (Q), cancer cells (S), virus (V), and infected cells (I). Virus are injected into the human body as the control input to control the amount of the cancer cells. In this case, virus can only infect the cancer cells, and the infected cells will die when the lysis process occurs. Virus, as a control, is given with the aim of minimizing the energy used in the system. In this model we consider the body's immune response as an additive disturbance to the model. From the simulation results, it is shown that virotherapy can reduce the number of cancer cells in the body on day 50th, so the number of cancer cells in the body is only 16.6%. Based on the simulation, the next virototherapy can be done after the day 50th.
Magnetic Field Tunable Ferromagnetic Shape Memory Alloy-Based Piezo-Resonator

In this letter, we have performed a comprehensive analysis of the influence of the magnetic field on BAW resonator consisting of a highly magnetostrictive layer and AlN thin film. The fundamental resonant frequency of the fabricated BAW resonator is about ~4.22 GHz. In the presence of a magnetic field, we studied the effect on the resonator parameters such as resonant frequency, acoustic velocity, and coupling coefficient. For the magnetic field of strength 1200 Oe, the resonant frequency significantly shifts by ~360 MHz. Resonant frequency increases and electromechanical coupling coefficient (<inline-formula> <tex-math notation="LaTeX">$\text{k}_{t}$ </tex-math></inline-formula>) decreases with the increase in the DC magnetic field. The maximum acoustic velocity of ~7350 m/sec was observed at the magnetic field of 1500 Oe when applied parallel to the surface. Agilent Advanced Design Software (ADS) was used to extract the equivalent Modified Butterworth-Van Dyke circuit parameters (R<italic><sub>m</sub></italic>, C<italic><sub>m</sub></italic>, and L<italic><sub>m</sub></italic>) of the resonator. Further, in the presence of the magnetic field, we obtained the variation in values of R<italic><sub>m</sub></italic>, C<italic><sub>m</sub></italic>, and L<italic><sub>m</sub></italic> of the resonator structure. Such tunable resonators can be useful and vital in dealing with varying frequency bands for sustainable growth in wireless communication and magnetic field sensor applications.
An Energy Prediction Approach for a Nonintrusive Load Monitoring in Home Appliances

Home energy monitoring by appliance-level information can provide consumers awareness on energy saving. The system can be implemented through a smart meter which requires an efficient data analysis algorithm for providing an accurate energy consumption profile, the purpose for proper home energy management. This article proposes a set of data analysis procedures for extracting appliances power state from its power consumption data. The approach is based on multitarget classification, a new data learning framework for nonintrusive load monitoring. The procedures include: 1) partitioning the appliance power data into an effective number of power states using K-means clustering, and 2) determining the optimal number of power states using the Area Under the ROC Curve index. The design objective is to obtain the optimal predictive performance for identification of the appliance power state which could result in a proper power and energy prediction. Applying the multitarget classification algorithm of RAndom k-labELsets by disjoint subsets with the decision tree, the identification of appliance power state achieved F-score and accuracy values greater than 89% for high-power loads such as A/C and water heater. The normalized error values of power prediction outperformed the use of Factorial Hidden Markov Model and binary state modeling system.
On-Site Dry-type Power Transformer Condition Assessment based on Partial Discharge Activity

As part of the energy transition and the associated development of renewable energies, the number of installed dry-type transformers is increasing on the medium-voltage power grid. Therefore, the paper at hand focuses on onsite condition assessment of dry-type power transformers which are often installed in wind turbine nacelles, industrial sites as well as in marine and civil applications. The assessment of the insulation condition is based on an induced voltage test in combination with a partial discharge measurement. In contrast to laboratory tests under controlled conditions, an increased level of interference from rotating machinery, power electronics and other consumers is to be expected on site. The approach of energizing transformers of power ratings up to 15 – 20 MVA with a portable, scalable single-phase voltage source is presented. Due to the single-phase injection the power intake is reduced, and an individual evaluation of each coil becomes possible. Practical examples show how partial discharge signals can be separated from these interferences using a synchronous multi-frequency PD measurement.
Patent Analytic Citation-Based VSM: Challenges and Applications

Patent citations are significant components of patents, which play a vital role in the implementation of patent analysis. However, most of the existed models only focus on the text of patents and do not realize that citations can remedy missing information in the text. A method for citation modeling in patent analysis is proposed to generate patent citation trees in this paper. Correspondingly, a specific neural network is designed for extracting abstract features in patent citation trees. Then, on the basis of extracted features, a new citation-based vector space model (CVSM) combining citations with text of the patent database is constructed for the subsequent applications. An experiment is conducted based on real patents of USPTO. The experimental results show that the proposed CVSM has good performances in several applications, which demonstrate the effectiveness of the proposed CVSM.