Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing high decibel honking effect on driving behavior using VR and bio-sensors

Honking in traffic is an auditory warning to indicate: driver's actions, alert pedestrians and to convey an emergency situation requiring right of way. Though studies have shown the importance of honking in maintaining traffic flow, there have been cases where irrational use leads to an increase in stress/irritation with effects on driver decision making. Most drivers are unaware of the adverse health effects of high decibel honking. In this report, we looked at the effects of honking on driving behavior in a lab-developed VR driving simulator and anxiety/stress as indicated by changes in skin conductance/pulse rate measured using sensors.
A robot made of robots: Emergent transport and control of a smarticle ensemble

Directed locomotion of a collective robot composed of stochastically interacting immotile robots was achieved. Robot locomotion is typically generated by coordinated integration of single-purpose components, like actuators, sensors, body segments, and limbs. We posit that certain future robots could self-propel using systems in which a delineation of components and their interactions is not so clear, becoming robust and flexible entities composed of functional components that are redundant and generic and can interact stochastically. Control of such a collective becomes a challenge because synthesis techniques typically assume known input-output relationships. To discover principles by which such future robots can be built and controlled, we study a model robophysical system: planar ensembles of periodically deforming smart, active particles—smarticles. When enclosed, these individually immotile robots could collectively diffuse via stochastic mechanical interactions. We show experimentally and theoretically that directed drift of such a supersmarticle could be achieved via inactivation of individual smarticles and used this phenomenon to generate endogenous phototaxis. By numerically modeling the relationship between smarticle activity and transport, we elucidated the role of smarticle deactivation on supersmarticle dynamics from little data—a single experimental trial. From this mapping, we demonstrate that the supersmarticle could be exogenously steered anywhere in the plane, expanding supersmarticle capabilities while simultaneously enabling decentralized closed-loop control. We suggest that the smarticle model system may aid discovery of principles by which a class of future “stochastic” robots can rely on collective internal mechanical interactions to perform tasks.
Dynamic Reconfigurable Packet Distribution Unit for Embedded Systems

The requirement of mode change (change of task’s set) and faults mitigation is very actual for the most modern embedded systems. Typically, an embedded system is exploited during several years. New tasks (applications), new versions of transport protocols or new transport protocols could be developed during this time. Possibility of these new features support without change of equipment is very important today. Packet flow distribution unit is used in many embedded Systems-on-chip (SoC) and Network-on-chip (NoC) for distribution of packet flows between units of network layer and units of transport layer (for example, different transport protocol controllers or Direct Memory Access (DMA) channels). Support of new rules for packet distribution is required, when new applications and/or new transport protocols are added to system. Therefore, the packet distribution unit should be dynamically reconfigurable to meet these requirements. Today the development of dynamically reconfigurable components for embedded systems typically based on FPGA technology. The density of blocks on FPGA is essentially less, than in ASIC with the same design rules. As result FPGA implementations typically have essentially larger power consumption and essentially worse timing parameters, than ASIC implementations, the area of component on FPGA is essentially large than the area of same component on ASIC. These factors essentially constrain the application area of FPGA based dynamically reconfigurable systems. Therefore we consider the existing methods of dynamic reconfigurable systems development for ASIC, evaluate its suitability for packet distribution unit realization in this paper. We propose an approach to development of dynamically reconfigurable packet distribution unit that corresponds to requirements specific for embedded systems. In the paper we present several examples of the proposed approach using. We evaluate reachable parameters and overheads for these examples. These results are suitable for developed systems.
Optical Vector Analysis With Improved Accuracy and Enhanced Dynamic Range

An optical vector analyzer (OVA) based on phase-shift (PS) technique is proposed and experimentally demonstrated, which enables improved accuracy and enhanced dynamic range. A carrier-suppressed optical double-sideband (ODSB) signal achieved by external modulation is used as the probe signal, which is then transmitted through a device under test (DUT). By photodetecting, a photocurrent carrying the frequency responses is generated. By scanning the wavelength of the optical carrier while extracting the magnitude and phase information of the photocurrent, the responses of the optical DUT are thus acquired. Since the carrier-suppressed ODSB signal has lower sensitivity to the nonlinearity and generates larger photocurrent than the intensity modulated ODSB signal, the proposed OVA has improved accuracy and enhanced dynamic range compared to the conventional PS-based OVA, which is comprehensively investigated in analytical analysis and simulation. In an experiment, the accuracy improvement is confirmed, and the enhancement of the dynamic range is evidently observed, which agrees well with the simulation results.
Integration of Real-Time Air Pollution Analysis and Mapping Based on Multisource Data

This paper presents a novel method of real-time air pollution analysis. First, we acquire real-time air quality data in several methods via Internet. Second, by combining them with population, elevation, and industry information, we analyze the air quality using the classification model of random forest. Last, we generate on-the-fly thematic maps automatically to visualize the classification results.
Fuzzy Multiple Model Adaptive Control

The task of organizing a multiple model adaptive control system using fuzzy logic is considered. It is proposed to use the partition of the parametric space of the control plant into a variety of areas, which correspond to local controllers. To match the signals of the controllers, fuzzy evaluations of suitability are used, which describe the correspondence of the current output of the plant and the output of the model with the local controller. The given examples of modeling show that the offered option of the organization of the adaptive controller can be successfully used in control of plant with variable delay.
Radiology: An Illustrated History

Eisenberg presents a well-illustrated, one-of-a-kind look at radiology's 100-year history. The book ranges from Roentgen's discovery of X-rays (1895) and the Curie's discovery of radioactivity (1896) to the development and growth of today's various clinical subspecialities, emphasizing human ingenuity and rapidly advancing technology.
A utilização de Toxina Onabotulínica A para bruxismo: Revisão de Literatura

Revisao de literatura para avaliar a eficacia da Toxina Onabotulinica A (BOTOX®) no bruxismo atraves de pesquisa nas bases de dados eletronicas PubMed Central Journals e Allergan Product Literature – botulinum toxin (APL), nos ultimos 10 anos. Foram selecionados sete trabalhos clinicos relevantes e a partir destes, outras referencias foram localizadas. Os estudos clinicos mostram variacoes entre dosagem, pontos de aplicacao e musculatura. Concluem que aplicacoes de toxina botulinica podem diminuir os niveis de dor, frequencia dos eventos de bruxismo e satisfazer os pacientes no que diz respeito a eficacia da toxina botulinica nesta patologia. Tambem nao provoca efeitos adversos importantes. Assim, o tratamento com Toxina Onabotulinica A (BOTOX®) parece ser um tratamento seguro e eficaz para pacientes com bruxismo.
Edge Computing in 5G: A Review

5G is the next generation cellular network that aspires to achieve substantial improvement on quality of service, such as higher throughput and lower latency. Edge computing is an emerging technology that enables the evolution to 5G by bringing cloud capabilities near to the end users (or user equipment, UEs) in order to overcome the intrinsic problems of the traditional cloud, such as high latency and the lack of security. In this paper, we establish a taxonomy of edge computing in 5G, which gives an overview of existing state-of-the-art solutions of edge computing in 5G on the basis of objectives, computational platforms, attributes, 5G functions, performance measures, and roles. We also present other important aspects, including the key requirements for its successful deployment in 5G and the applications of edge computing in 5G. Then, we explore, highlight, and categorize recent advancements in edge computing for 5G. By doing so, we reveal the salient features of different edge computing paradigms for 5G. Finally, open research issues are outlined.
Cross-Subject EEG Signal Recognition Using Deep Domain Adaptation Network

Collecting sufficient labeled electroencephalography (EEG) data to build an individual classifier for each subject is extremely time-consuming and labor-intensive, especially for the disabled patients. A feasible way is to use labeled EEG data from other subjects (source domains) to train a model for classifying EEG data from the new subjects (target domains). However, the model trained using other subjects EEG data may degrade the classification performance of the target subject, when there exists the substantial inter-subject variability of EEG data. In this paper, to account for the domain shift between different subjects, we propose a novel deep domain adaptation network (DDAN) for cross-subject EEG signal recognition. Specifically, a special end-to-end convolutional neural network (CNN) is firstly adopted to automatically extract deep features from the raw EEG data. Then, maximum mean discrepancy (MMD) is used to minimize the distribution discrepancy of deep features between source and target subjects. Finally, a center-based discriminative feature learning (CDFL) method is used to force the deep features closer to their corresponding class centers and make the inter-class centers more separable, so that it is possible to further improve the recognition performance of target domain EEG data. Experiments on public EEG datasets prove the effectiveness of the proposed method. This study may promote the practical use of EEG signal processing technology and expand its application range.
Low-Profile Conformal UWB Antenna for UAV Applications

This article presents a new design of an ultra-wideband conformal antenna for Unmanned Aerial Vehicle (UAV) applications. The antenna is a low-profile structure which is very suitable to diminish aerial vehicle drag problem. The design utilizes Rogers Duroid 5880LZ material with dimensions of 29 mm $\times39$ mm. The measurement results show an ultrawideband performance of the S11 < −10dB from 2.9 GHz to 15.9 GHz. A Y-rounded shape achieves this frequency range with a parasitic circular element at the center of the structure. The acceptable radiation pattern performance is until 10.1 GHz, and the system fidelity factor is SFF > 0.9 in the maximum radiation. The proposed antenna was simulated and measured to test its performance when it is mounted on an UAV model.
Context-Based Knowledge Discovery and Querying for Social Media Data

Modern Early Warning Systems (EWS) rely on scientific methods to analyse a variety of Earth Observation (EO) and ancillary data provided by multiple and heterogeneous data sources for the prediction and monitoring of hazard events. Furthermore, through social media, the general public can also contribute to the monitoring by reporting warning signs related to hazardous events. However, the warning signs reported by people require additional processing to verify the possibility of the occurrence of hazards. Such processing requires potential data sources to be discovered and accessed. However, the complexity and high variety of these data sources makes this particularly challenging. Moreover, sophisticated domain knowledge of natural hazards and risk management are also required to enable dynamic and timely decision making about serious hazards. In this paper we propose a data integration and analytics system which allows social media users to contribute to hazard monitoring and supports decision making for its prediction. We prototype the system using landslides as an example hazard. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. The system also consists of an interactive agent that allows social media users to report their observations. Using the knowledge modelled within the system, the agent can raise an alert about a potential occurrence of landslides and perform new processes using the data sources suggested by the knowledge base to verify the event.
Spatially-Coupled Protograph for NOMA Optimized by the Genetic Algorithm

In this paper we investigate the codebook design of the non-orthogonal multiple access through the spatially-coupled protograph. The optimization is realized by changing the edge connections in the protograph to maximize the sum rate and then permutating the identity matrices in the spatial coupling to improve the girth distribution. The edge connection in the protograph determines the interference at each resource element and the diversity for each user equipment, and the optimization finds the best tradeoff in between. The spatial coupling enlarges or removes the short girths in the protograph in favor of message-passing decoding, which makes the low-complexity detection applicable. The genetic algorithm is employed in the optimizations of both the protograph and the spatial coupling. Numerical results reveal the characteristics of the optimal superposition structures for different SNR regions and the improvement of girth distributions for different spatial coupling mechanisms. The optimized structures demonstrate better error performance than those proposed in the literature.
Plane-by-Plane Written, Low-Loss Polymer Optical Fiber Bragg Grating Arrays for Multiparameter Sensing in a Smart Walker

In this paper, we report the development of fiber Bragg gratings (FBGs) arrays in polymer optical fibers (POFs), specifically cyclic transparent optical polymer (CYTOP) fibers, using a femtosecond (fs) laser. The CYTOP is almost transparent to near-ultraviolet and visible light, thus femtosecond lasers appear to be a very promising technology for the development of gratings in such fibers. Bearing this in mind, we have applied the direct write, plane-by-plane inscription method to obtain ‘single-peak’ FBGs in long arrays. Following this, we examine the performance of a 5-FBG array applied in a smart walker (SW) and characterized for four different functionalities of the proposed healthcare device. The polymer sensors were used to analyze 1) structural health monitoring (SHM) of the SW’s mechanical structure; 2) detection of users’ movement intention; 3) gait cadence estimation; and 4) detection of floor-induced vibrations for localization and mapping applications. The good results obtained in comparison to commercially available devices show the suitability of the proposed FBG-array on the instrumentation of healthcare devices, where multiple parameters can be measured using the same fiber, which leads to a compact and energy efficient system.
A New Efficient Algorithm for Quorum Planted Motif Search on Large DNA Datasets

Quorum planted (<inline-formula> <tex-math notation="LaTeX">$l, d$ </tex-math></inline-formula>) motif search (qPMS) is a challenging computational problem in bioinformatics, mainly for the identification of regulatory elements such as transcription factor binding sites in DNA sequences. Large DNA datasets play an important role in identifying high-quality (<inline-formula> <tex-math notation="LaTeX">$l, d$ </tex-math></inline-formula>) motifs, while most existing qPMS algorithms are too time-consuming to complete the calculation of qPMS in a reasonable time. We propose an approximate qPMS algorithm called APMS to deal with large DNA datasets mainly by accelerating neighboring substring search and filtering redundant substrings. Experimental results on them show that APMS can not only identify the implanted (<inline-formula> <tex-math notation="LaTeX">$l, d$ </tex-math></inline-formula>) motifs, but also run orders of magnitude faster than the state-of-the-art qPMS algorithms. The source code of APMS and the python wrapper for the code are freely available at <uri>https://github.com/qyu071/apms</uri>.
Design Considerations of GaN GIT Devices for High Speed Power Switching Applications

This paper designed and optimized Gallium nitride (GaN) GIT (gate-injection-transistor) devices for power switching applications. We proposed three kinds of structures namely gate insulator (SiNx), field plate and AlGaN buffer to improve GaN GIT devices’ switching speed. The results of TCAD simulation indicated that all the three structures can effectively alter GaN GIT devices’ turn-on time (ton) or turn-off time (toff). The smallestton and toff were identified as 0.58 ns and 0.59 ns respectively, when we introduced the specific structures into the devices. The GaN GIT device with gate insulator and AlGaN buffer (Al component of 0.25) was identified as the optimal structure design, which had small ton (0.76 ns) and small toff (0.59 ns) at the same time. For the device with the optimal structure, ton and toff were only 63% and 52% of the recorded results for GaN power switching devices, respectively.
Effects of Some Operation Parameters on the Performance of a Reservoir Computing System Based on a Delay Feedback Semiconductor Laser With Information Injection by Current Modulation

Via a Santa Fe time series prediction task, the performance of a reservoir computing (RC) system based on a delay feedback semiconductor laser (SL) under current modulation is experimentally investigated and evaluated in detail. Experimental results show that some key operation parameters including the modulation index, the feedback ratio and the SL bias current, seriously affect the prediction performance of this RC system. By optimizing parameter settings, the degradation of the RC performance with the increase of the SL bias current can be efficiently alleviated, and the RC system can still realize a good performance with a NMSE smaller than 0.1 even for the SL biased at a high level of three times threshold current <inline-formula> <tex-math notation="LaTeX">${I} _{th}$ </tex-math></inline-formula>. This research is an improvement to a previously experimental report in which such RC system can achieve a good performance only under a SL bias level near <inline-formula> <tex-math notation="LaTeX">${I} _{th}$ </tex-math></inline-formula>.
A Novel Grammatical Evolution Algorithm for Automatic Design of Scheduling Heuristics*

There are two reasons making the existing intercell scheduling algorithms hardly be put into use in practice. One is the negligence of transportation among cells that affects the accuracy of the scheduling solutions, and the other one is the large problem size that decrease the computation efficiency. A novel grammatical evolution (NGE) algorithm is proposed to address the intercell scheduling problem with limited transportation capacity. This problem is divided into two subproblems: sequencing for parts and routing for vehicles. A two-stage evolutionary is developed to evolve better heuristic rules. In the first stage, string-form individuals are evolved, and in the second one, tree-form individuals are evolved. Through the comparative experiments, the effectiveness and efficiency of NGE are verified.
Cloud EPR Adoption factors in Large Companies

The reason for this examination is to decide the components that impact the acknowledgment and utilization of Cloud ERP by users in big companies. The quantitative method was used in this study with UTAUT 2 model approach. Analysis was carried out on the results of the distributed questionnaire through Google Form to respondents in various big companies using SEM-PLS analysis technique. The outcomes demonstrated that the Habit factor bigly affected the acknowledgment and utilization of Cloud ERP, while Behavioral Intention had a big effect on long-term use by users in big companies in Indonesia. Apart from these factors, it was also found that many users knew and wanted to use Cloud ERP, but there were still a few big companies in Indonesia that adopted the system. Based on all of these, Cloud ERP vendors have a great opportunity to market their products to big companies in Indonesia due to the high level of user interest in the system. In addition, Cloud ERP vendors can pay attention to the Habit and Behavioral Intention factors as priorities in marketing their product.
Scalable Analysis of Open Data Graphs

We have studied Open Data as a connected graph. Each data package is considered a vertex, and we studied the similarity graph induced by several different similarity measures. We analyzed the resulting similarity graph using different metrics to estimate its quality and informativeness. In order to cope with the size of the open data graph (over 6 billion edges), the graph constructions and analysis are done using a distributed computation framework, Apache Spark. The algorithms were implemented using the Spark resilient distributed data algebra, and executed on the Google Cloud Platform (GCP).
Characterization of 100 Micron Long Fiber Bragg Gratings and Applications in Ultrafast Shock Wave Experiments

Ultrashort Fiber Bragg Gratings (FBGs) may be very useful whenever highly localized pressure measurements are desired. Obviously, very short FBGs will exhibit lower, as well as spectrally broader, reflections; however, these deficiencies may be outweighed by the advantage of the potential for highly localized measurement. In addition, with such short gratings, their accurate localization along the fiber is of great importance. In this paper, commercial 100 μm FBGs are characterized, and their location along the fiber is accurately determined by two methods: Spectral and white-light interferometry. We also demonstrate the advantage of ultrashort gratings in a shock-wave experiment; when a solid target is hit by a projectile, the resultant pressure waves may change rapidly in both location and time. In this paper, the suitability of a 100 μm grating to this scenario is demonstrated, revealing features in good agreement with theory—in particular, an approximately 0.7 mm long constant pressure region. Although these pressure waves may be monitored by ordinary (∼6–8 mm long) FBGs, due to the high-pressure gradients involved, the FBG response will be very complicated and data retrieval may be ambiguous. Thus the advantage of ultrashort FBGs is demonstrated.
Adaptive Linear Address Map for Bank Interleaving in DRAMs

The conventional linear address map can degrade memory utilization and system performance when an access pattern is not linear. To improve memory system performance, the adaptive bank-interleaved linear address map for a DRAM technology is proposed. In our approach, the addresses are efficiently rearranged using the bank-flipping technique for a given application and a memory configuration. The system can configure the address map based on the bank interleaving metric in the systematic way when an application is invoked. Considering image processing applications, the algorithm, the analysis, the design, and the evaluation of the proposed address map are presented. The experimental results show that the presented method can effectively improve the performance with a moderate hardware cost.