Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Moduli Sets for Residue Number System With Special Diagonal Functions

The paper presents algorithms for the generation of Residue Number System (RNS) triples with <inline-formula> <tex-math notation="LaTeX">$SQ=2^{k}-1$ </tex-math></inline-formula> and quadruples with <inline-formula> <tex-math notation="LaTeX">$SQ=2^{k}$ </tex-math></inline-formula> for some k. Triples and quadruples allow us to design efficient hardware implementations of non-modular operations in RNS such as division, sign detection, comparison of numbers, reverse conversion with using of a diagonal function from requiring division with the remainder by the diagonal module SQ. Division with a remainder in the general case is the most complex arithmetic operation in computer technology. However, the consideration of special cases can significantly simplify this operation and increase the efficiency of hardware implementation. We show that there are 5573 good RNS triples (2301 even and 2372 odd) with elements less than 10 000, as the values of SQ vary from <inline-formula> <tex-math notation="LaTeX">$2^{5}-1$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$2^{27}-1$ </tex-math></inline-formula>. In contrast, RNS quadruples with <inline-formula> <tex-math notation="LaTeX">$SQ=2^{k}$ </tex-math></inline-formula> seem to be quite rare. Restricting our search to sums of the elements in a quadruple less than 4000 we find that exactly 31 such quadruples exist. Their values of SQ vary between 2<sup>20</sup> and 2<sup>30</sup> with always even exponent. We suggest the measure of RNS balance and find perfectly balanced RNS among triples according to this measure. We demonstrate the advantages of more balanced quadruples by means of hardware implementation.
SingleNet: An end-to-end single network for non-intrusive load monitoring of multiple appliances

An end-to-end convolution neural network (CNN) model SingleNet is proposed for non-intrusive load monitoring (NILM) in this paper. A SingleNet can identify several typical types of household appliances, and give the probability, start time, stop time and the average power of the appliances. The network can receive data input with variable-length by using multi-scale pooling technology. A new loss function is proposed by combining classification loss, positioning loss, and power loss with the help of cross entropy, smooth L1, and Euclidean distance respectively. The new loss function can better converge the training process and significantly shorten the training time. Finally, The SingleNet achieves an end-to-end recognition effect by inputting variable-length total-power sequence data, and outputting the category, start time, stop time and average power of household appliances.
Audio Pre-Processing and Neural Network Models for Identification of Orthopedic Reamers in Use

In order for a successful outcome of Total Hip Arthroplasty (THA) to occur, implant stability is a key concern. A means to achieve this is ensuring that the implant has an optimum seating within the femur cavity. This is achieved during surgery by progressive reaming of the cavity interior. Both under and over reaming have undesirable effects towards implant longevity and post-operative prognosis, and so through education/experience, orthopedic surgeons have learned to anticipate when optimal reaming occurs. The work presented here is part of a larger research project which seeks to use bone resonance as an indicator of good implant seating. Here we present results of initial work using several neural network models on classification of orthopedic reamer type on the basis of sound signature. These results are discussed in the context of an interesting feature found when comparing differing audio- preprocessing methods. Despite identical audio raw data being used for both representations, the models that used the Mel Spectrograms categorically outperformed those which used the STFT Spectrogram.
Towards Human-Aware D2D Communication

Mobility, social interactions, and other human characteristics shall support Future Mobile Networks in routine prediction and resource management. This work investigates human-aware metrics supporting services or protocols leveraging opportunistic communication. These metrics represent different types of knowledge extracted from people routine present in their movements. Because of the strong routine component of human mobility, such metrics capture different but recurrent behaviors on wireless encounters between mobile users. We report the experience through a case study with a real-world dataset along with results from trace and metrics analysis. The results show heterogeneity in metric coefficients and contact occurrence and duration in different periods of the day, highlighting the need for characterising traces before their use.
Real-Time Visual-Inertial Localization Using Semantic Segmentation Towards Dynamic Environments

Simultaneous localization and mapping(SLAM), focusing on addressing the joint estimation problem of self-localization and scene mapping, has been widely used in many applications such as mobile robot, drone, and augmented reality(AR). However, traditional state-of-the-art SLAM approaches are typically designed under the static-world assumption and prone to be degraded by moving objects when running in dynamic scenes. This article presents a novel semantic visual-inertial SLAM system for dynamic environments that, building on VINS-Mono, performs real-time trajectory estimation by utilizing the pixel-wise results of semantic segmentation. We integrate the feature tracking and extraction framework into the front-end of the SLAM system, which could make full use of the time waiting for the completion of the semantic segmentation module, to effectively track the feature points on subsequent images from the camera. In this way, the system can track feature points stably even in high-speed movement. We also construct the dynamic feature detection module that combines the pixel-wise semantic segmentation results and the multi-view geometric constraints to exclude dynamic feature points. We evaluate our system in public datasets, including dynamic indoor scenes and outdoor scenes. Several experiments demonstrate that our system could achieve higher localization accuracy and robustness than state-of-the-art SLAM systems in challenging environments.
Distributed Adaptive Video Streaming using Inter-Server Data Distribution and Agent-based Adaptive Load Balancing

As the number and hours of videos available within an organisation increases, as well as it’s demand, the need for fast video streaming applications arises. Cloud based services are not cost effective and are not an ideal choice for storing the ever-increasing video data that is usually stored and used only within a particular organisation, like a University. Hence, this paper proposes a web based system design to store and stream videos at a small-scale within an organisation. To improve the video viewing experience for the user, the system is flexible to handle sudden changes, like increase in number of requests. The system requires the use of a cluster of servers to deliver the content as a single server cannot handle the load as number of requests increases. This requires effective load distribution among the servers. This paper proposes a way to design this system for adaptive video streaming. This system is highly scalable and can handle high loads, i.e. a higher number of users connecting to the application simultaneously. This paper proposes an algorithm called inter-server load balancing algorithm with Adaptive Agent-based load balancing to solve this problem. The algorithms also incorporates dynamic video resolution delivery techniques to ensure smooth viewing experience in the whole user experience irrespective of the network speed and bandwidth.
Rectangular DRA Array for 24 GHz ISM-Band Applications

A planar array antenna consisting of 6 units of rectangular dielectric resonators fed by means of novel microstrip lines through aperture coupling is presented. The structure is designed in such a way that two different modes are excited in the array elements that yield a high bandwidth and gain. Based on the experimental results, the antenna is seen to have an impedance bandwidth of 6.98% centered at 24.09 GHz. Furthermore, the antenna gain is seen to lie between 7 and 9.5 dBi within its entire bandwidth, whereas the cross-polarization discrimination is better than 45 dB in the boresight. These features, along with its low-profile structure, make this antenna a suitable candidate for usage in short-range radars, wireless sensor nodes, and other sensors for the 24 GHz Industrial, Scientific, and Medical (ISM) band.
Intrinsic and Extrinsic Evaluation of the Quality of Biomedical Embeddings in Different Languages

Lately, language models have been applied to several tasks in biomedical natural language processing. Some public language models are available online, each built with different corpora. In this paper, we evaluate different public word embedding models trained with both general and biomedical corpora for English and Portuguese. We present intrinsic evaluations based on semantic analogies that use word pairs extracted from the MeSH biomedical thesaurus and also from benchmarks that are available for general-domain evaluation. For extrinsic evaluations we rely on a classification task over Eletronic Health Records. Our experiments show that biomedical embeddings can better capture semantics for biomedical analogies in both languages. On the other hand for extrinsic evaluation, based on classification tasks using the language models, larger general textual corpora appeared equally or more effective.
Po-Miner: Random Poem Generator and Security Model

The growing importance of data science and analytics has led to various innovative applications over time. While the data and science remain a challenge, applications solving existing problems have also introduced new obstacles. In this paper, we propose a data science application model called Po-Miner. Po-Miner is a random online poem generator tool based on scraped online web data. Along with syllable counts, rhymes, and other poem notions, Po-Miner induces mathematics and learning into the poem generation. It makes the prediction of the poem generation complex and undefined through the Po-Select algorithm. Though a random poem makes petite sense, the very aspect of randomness brings in the creativity and depth. A security system enforcing a safe transaction is always a need of the hour issue for any system. Though there have been solutions provided from an assortment of domains and methodologies, there is always a noted drawback endowing with scope for improvement. This paper discusses Po-Miner as an alternative and enhancement to the one-time password system as its application. The paper presents the Po-Miner model, implementation, poems, and analysis, and then its applicability in the security domain. The paper presents the Po-Select implementation along with sample created poems. Po-Miner appears to be a promising application that can be extended to several other areas.
Material Measurements Using VNA-Based Material Characterization Kits Subject to Thru-Reflect-Line Calibration

This article presents a study of different calibration techniques for vector network analyzer -based material measurements using commercially available material characterization kits (MCKs). Such MCKs are designed for fast broadband material characterization at millimeter-wave and terahertz frequencies and are based on gated-reflect-line calibration for removal of errors associated with the fixture. In this article, we have applied thru-reflect-line (TRL) calibration to the MCKs, yielding robust S-parameters that are used to calculate the relative permittivity and loss tangent of materials. Two different types of line standards, i.e., custom machined corrugated lines and air lines (a quarter-wave air gap between the ports of the MCKs), were employed to produce the desired phase shift. Both types of standard produced similar results. These investigations were carried out on three types of low-loss dielectric material, using MCKs operating at two selected waveguides bands within the range 140–750 GHz. The same samples were measured using time-domain spectroscopy (TDS) for comparison. The extracted relative permittivities and loss tangents using MCKs with TRL calibration and TDS are compared against literature values. Good agreement is achieved.
Human Emotions Recognition from Thermal Images using Yolo Algorithm

Acknowledgment of facial expression is a prospective application in emotion analysis. This research is mostly concentrated to examine emotions concealed in the face which includes stress, anxiety or exhilaration that can set off a warm spread across the cheeks. In a similar way, face temperature changes in an individual when he experience different situations. The estimation of these thermal images was performed through its thermal value of pixel, not with its pixel’s intensity value. Regions of interest were obtained through image processing methods, which include segmenting out the subject from its background. Despite countless readily available strategies for acknowledgment of human emotions, efficiency, robustness, and accuracy can be enhanced by integrating one modality with various others and they are based on visual facial expressions, thermal face imaging, and anatomical parameters. This paper checks out the strong point and also shortages of the currently offered emotion recognition systems based on faces, face thermal imaging and also physiological criteria.
Combining m=2 Multipliers and Adder Compressors for Power Efficient Radix-4 Butterfly

In this work, we propose a power-efficient hardware architecture for 16-bit m Radix-4 DIT (Decimation in Time) butterfly using radix-2m multipliers, with m=2, and 4-2 adder compressors. The multiplier uses both Wallace and Dadda schemes in the addition tree, with Carry-Select, Kogge Stone, and ’+’ operator from the tool internally. We used a method for a realistic power extraction with the standard delay format. The results show that our best-proposed Radix-4 butterfly saves up to 25% of power dissipation when compared with the original Radix-4 butterfly using the synthesis tool operators for both adders and multipliers. The optimized structure combines the m=2 Wallace-based multiplier, employing Kogge-Stone adder into its addition tree, and 4-2 adder compressors.
A survey on Memristor and CMOS based Spiking Neural Networks

In this era, there is utmost requirement of fast computation, parallel processing and energy efficient hardware for neuromorphic computing. This leads to the usage of Graphics Processing Units (GPUs) and machine learning structures which provides massive parallel processing but the drawback is high power consumption compared to biological brain. There can be a solution for serving all requirements of computing and processing using Spiking Neural Network (SNNs) based CMOS nanoscale memristive neuronal implementations and thus using these small basic building blocks at large scale to build huge and complicated spiking based neuronal networks consisting of millions of neurons. This paper presents a survey on the SNN learning techniques and outlines the existing hardware designs of neuron and neuronal network based on CMOS and memristors. Further, current challenges and shortcomings in hardware design of neuronal networks and future advancements has been presented.
Minimum On-the-node Data Security for the Next-generation Miniaturized Wireless Biomedical Devices

As continuous health monitoring and treatment outside of the traditional clinical environment has become of interest to healthcare providers and governments, the manufacturers of miniaturized wireless biomedical devices have sought to facilitate this idea. Much research has been devoted to smart-and-connected health technologies of various form factors including injectables, implantables, ingestibles, and wearables. Such devices are constrained in physical size, power-consumption budget, storage capacity, and computing power. Yet, they handle sensitive, private information and require trust as they directly affect the health of the patient by means of stimulation and/or drug delivery. In this work, we discuss the role of security as a fundamental component of these devices. We propose a generic layered model to support lightweight and cost-effective implementation of data security and protection mechanisms against possible attacks.
Single Electron Control by a Uniform Magnetic Field in a Focusing Double-Well Potential Structure

Double-well potential structures provide rich capabilities for single electron control via different physical characteristics of the potentials and the electron state. Here, we investigate the effect of a uniform magnetic field on the electron state interference pattern manifesting in a focusing double-well potential structure by conducting Wigner quantum transport experiments. We analyze the electron density and the negativity of the Wigner function and show how the magnetic field controls the electron state but also destroys the coherence of the evolution dynamics. Our work contributes to the fundamental understanding of magnetic field based single electron control mechanisms and sheds light on the critical decoherence processes.
Flexibility-Oriented Collaborative Planning Model for Distribution Network and EV Parking Lots Considering Uncertain Behaviour of EVs

Increasing grid integration of intermittent renewable energy sources (RESs) and plug-in electric vehicles (PEVs) with uncertain behaviours have necessitated enhancing the flexibility requirements of distribution networks. Thus, in the state-of-the-art distribution network expansion planning (DNEP) models, both flexibility requirements and high penetration of RESs and PEVs should be taken into consideration. In this respect, a novel collaborative planning model for power distribution network (PDN) and plug-in Electric Vehicle Parking Lots (EVPLs) is proposed in this paper, which leverages sizing, siting, and operation of EVPLs to enhance the distribution network flexibility. Also, to model the uncertain traffic flow of PEVs, a new model is proposed and is utilized to obtain a preliminary dispatch of PEV charging and, in turn, an estimated EVPL demand. Afterwards, this estimated demand is fed into the collaborative planning model to obtain the optimal expansion planning solution for PDN, and the size and location of EVPLs. Nonetheless, to provide the network operator with more flexibility sources, it is assumed that the operator can reschedule the charging pattern of some PEVs by compensating the EVPL owners for the difference in retail electricity prices of various hours. Finally, to illustrate the effectiveness of the proposed model, it is implemented on a test network, and the obtained results are discussed.
An Enhanced Visual-Inertial Navigation System Based on Multi-State Constraint Kalman Filter

Over the last few years, the Visual-Inertial navigation systems have attracted considerable attention mainly due to the higher accuracy that is promised by the so-called tightly-coupled scheme where visual and inertial data are integrated at a low level in a common estimation problem. However, the calibration parameters of the camera (e.g. intrinsic and extrinsic parameters) and of the inertial sensor (e.g. sensor’s mounting mis-orientation) are often left to be calibrated offline that makes the developed navigation system far from an off-the-shelf product. In this work, an enhanced tightly-coupled Visual-Inertial navigation system, based on the Multi-State Constraint Kalman Filter scheme is proposed that includes the sensors’ calibration parameters in the state list to be estimated along with the navigation states. Experimental results on the KITTI odometry dataset shows a considerable improvement in the odometry accuracy compared to the case where those values are obtained from the calibration file of the KITTI dataset.
A multi-agent deep reinforcement learning framework for automated driving on highways

We apply deep reinforcement learning to automated driving on highways. We propose a novel, simple framework with improved performance with respect to the state of the art. When implementing our algorithm on multilane highway scenarios, after the training phase, we observe via numerical simulations that the vehicles are able to avoid collisions and to reach their respective destination lanes with very high probability.
Voltage Stability Improvement by Optimal Location of Wind Sources

In this article, we have proposed to maximize the voltage stability of an electrical grid by integrating several wind turbines. For this purpose, we have applied the genetic algorithm method to obtain optimal locations of these wind turbines in the grid. This optimization aims to maximize the load margin while taking into account all constraints. The proposed optimization method was applied on the modified IEEE 30 bus network.
MCRB: A Multiclassifier Tool for Risk of Bias Assessment in a Systematic Review to Produce Health Evidence to Decision Making

Healthcare is receiving many improvements from real world evidence. The advances in data management and machine learning models are driving better resources to support decision making. One of the most common techniques to develop recommendations and guidelines to physicians is through systematic reviews. To evaluate the quality of the evidence, among other methods, the researchers uses the Risk of Bias Assessment. Besides, that method is widely used and provides good results it has been done manually by researchers. This work provides the MCRB a multiclassifier tool for the risk of bias assessment using machine learning techniques. The software was tested with four machine learning models: Logistic Regression, SVM, Naive Bayes, and XGBoost. The results show that is quite possible to classify well the seven dimensions of the problem with a macro average AUC Score of 75%
Understanding User Satisfaction and Continuous Use of the Face Recognition Technology Applying to Sportsman Check-In

The 2019 National Intercollegiate Athletic Games event was held in the National Chung Cheng University in April and May for around 1 month. An innovative face recognition technology was applied to sportsman check-in before participating in the games. This study developed a research model to understand user satisfaction and continuous use of the face recognition technology applying to sportsman check-in based on the Expectation-Confirmation Theory. Eight hypotheses were developed in the proposed model. A qualitative evaluation was conducted to assess the model. The results indicated that the participants were quite satisfied with the face recognition technology and also showed high interests in continuous use of the information technology in the future.
Cloud-Edge Collaborative Industrial Robotic Intelligent Service Platform

With the development of industrial automation and intelligence deepening, industrial robots' installations increase rapidly. The traditional robot operation and management methods are no longer suitable for current requirements. Cloud computing and big data technology together are deemed to form a promising and possible solution to this challenge. From this point of view, this paper proposes a novel industrial robotics cloud platform architecture, and application orchestration and containerized software appliance technology-enabled cloud-edge collaboration mechanism is designed therein. Two exemplary and real-world case studies, supervisory control and data acquisition, and industrial robot predictive maintenance, are further conducted to demonstrate our architecture's generality and versatility.