Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Analysis of Radio Communication Capabilities of Multiple Autonomous Surface Vehicles

Autonomous exploration and rescue vehicles have been gaining wide interest over the past few years. Nowadays, demonstrations showed that those vehicles can fly, dive, surf, or drive while carrying out missions autonomously in some specific scenarios. Monitoring vehicles during missions is a crucial and challenging task to avoid the unnecessary cost of losing vehicles or potential accidents. In this paper, we present a cheap yet effective way for monitoring and communicating with autonomous vehicles over long distances by using off-the-shelf 900 MHz modems namely RFD 900+ and high gain antennas. Although the 900 MHz band has been around for over two decades, no complete analysis exists providing guidelines to use off the shelf modems for point-to-point and multi-point communications. Our main contribution is to provide experimental analysis of the communication capabilities in point-to-point and multi-point scenarios in both line of sight (LOS) and non line of sight (NLOS) using an affordable setup ($70 per modem). Experiments were carried out using autonomous surface vehicles (ASVs) as remote nodes and computers as Ground Control Stations (GCSs).
Exploiting Third Harmonic of Differential Charge Pump for Wireless Power Transfer Antenna Alignment

Wireless power transfer (WPT) always suffers from the antenna radiation direction and polarization misalignments between the base station and the WPT terminal. In this letter, the third harmonic of a differential charge pump is exploited for antenna alignment with only one pair of antennas. Due to the intrinsic nonlinearity of diodes, the third harmonic can be generated and reflected by the differential charge pump. A ring coupler is employed to distribute the received fundamental frequency power for rectification by the differential charge pump and couple the third-harmonic generation back to its input port. Such third-harmonic power is feedback by a dual-band antenna operating at the third-harmonic frequency. In consequence, the WPT antennas can be aligned by obtaining the maximum third-harmonic feedback at the base station. The whole system is theoretically analyzed and experimentally validated.
Distributed cooperative control for frequency and voltage stability in isolated microgrid under event-triggered mechanism

Considering the limitation of communication network, the distributed event-triggered cooperative control for frequency and voltage stability in microgrid is studied in this paper. The event-triggered mechanism with distributed architecture is proposed to reduce the communication burdens among power source inverters. The constructed distributed event-triggered restoration mechanism is used to restore the frequency and voltage magnitude of microgrid and realize the fair utilization of all power sources. Simulation shows the effectiveness of the proposed method.
Comparison of FaaS Orchestration Systems

Since the appearance of Amazon Lambda in 2014, all major cloud providers have embraced the "Function as a Service" (FaaS) model, because of its enormous potential for a wide variety of applications. As expected (and also desired), the competition is fierce in the serverless world, and includes aspects such as the run-time support for the orchestration of serverless functions. In this regard, the three major production services are currently Amazon Step Functions (December 2016), Azure Durable Functions (June 2017), and IBM Composer (October 2017), still young and experimental projects with a long way ahead. In this article, we will compare and analyze these three serverless orchestration systems under a common evaluation framework. We will study their architectures, programming and billing models, and their effective support for parallel execution, among others. Through a series of experiments, we will also evaluate the run-time overhead of the different infrastructures for different types of workflows.
An Empirical Performance Evaluation of Semantic-Based Similarity Measures in Microblogging Social Media

Measuring textual semantic similarity has been a subject of intense discussion in NLP and AI for many years. A new area of research has emerged that applies semantic similarity measures within Twitter. However, the development of these measures for the semantic analysis of tweets imposes fundamental challenges. The sparsity, ambiguity, and informality present in social media are hampering the performance of traditional textual similarity measures as "tweets", have special syntactic and semantic characteristics. This paper reviews and evaluates the performance of topological, statistical, and hybrid similarity measures, in the context of Twitter analysis. Furthermore, the performance of each measure is compared against a naïve keyword-based similarity computation method to assess the significance of semantic computation in capturing the meaning in tweets. An experiment is designed and conducted to evaluate the different measures through examining various metrics, including correlation, error rates, and statistical tests on a benchmark dataset. The potential weaknesses of semantic similarity measures in relation to Twitter applications of textual similarity assessment and the research contributions are discussed. This research highlights challenges and potential improvement areas for the semantic similarity of tweets, a resource for researchers and practitioners.
Mapping the 3D Distribution of Shorea Tree Species Based Upon Information Extracted from Worldview-2 and LiDAR Data

Mapping and monitoring trees in tropical forest is deemed necessary especially for forest personnel. Unfortunately, acquisition of tree parameters for mapping purposes are tedious due to labour intensive, timely and cost-consuming. Thus, the advancement of remote sensing technology which provides tree parameters economically in term of cost and time saving over large forest area is in demand. The intent of this study is to map the distribution of Shorea tree species in the Ampang Forest Reserve using information extracted from Worldview-2 and LiDAR datasets. The pan-sharpening Worldview-2 imagery was used to classify the tropical trees using the support vector machine (SVM) image classification method. The overall classification accuracy for SVM method was 90.28% and the individual accuracy for Shorea and mixed tree species ranges from 68.25% to 82.86%. Finally, the classified result was overlaid with tree height information extracted from LiDAR data and forming the 3D distribution of Shorea tree species in the dense tropical forest area.
Big Data Quality Assessment Model for Unstructured Data

Big Data has gained an enormous momentum the past few years because of the tremendous volume of generated and processed Data from diverse application domains. Nowadays, it is estimated that 80% of all the generated data is unstructured. Evaluating the quality of Big data has been identified to be essential to guarantee data quality dimensions including for example completeness, and accuracy. Current initiatives for unstructured data quality evaluation are still under investigations. In this paper, we propose a quality evaluation model to handle quality of Unstructured Big Data (UBD). The later captures and discover first key properties of unstructured big data and its characteristics, provides some comprehensive mechanisms to sample, profile the UBD dataset and extract features and characteristics from heterogeneous data types in different formats. A Data Quality repository manage relationships between Data quality dimensions, quality Metrics, features extraction methods, mining methodologies, data types and data domains. An analysis of the samples provides a data profile of UBD. This profile is extended to a quality profile that contains the quality mapping with selected features for quality assessment. We developed an UBD quality assessment model that handles all the processes from the UBD profiling exploration to the Quality report. The model provides an initial blueprint for quality estimation of unstructured Big data. It also, states a set of quality characteristics and indicators that can be used to outline an initial data quality schema of UBD.
Adaptive General Event Popularity Analysis on Streaming Data

In today's big data era, breaking events are disseminating on various platforms like social media and search engine at a high velocity, generating large volumes of event-related data. Numerous studies focus on the event dissemination trend analysis on a particular media platform, while very few works are conducted to adaptively identify hot words and analyze an event's popularity in a platform independent manner from streaming data. In this work, we propose GAPES(General Analysis and Prediction of Event popularity on Streaming data), a novel and unified approach to analyze and predict the event popularity on different media platforms. GAPES first processes event streaming data and adaptively selects event hot words by considering word's importance, correlation and completeness aspects, then it uses an unified event popularity analysis model and an probability distribution based event popularity prediction model to accurately analyze and forecast event trend with a flexible timescale. Experimental results based on 16 real-world event datasets produce several interesting findings and validate the capability of GAPES in adaptively selecting event's representative words, effectively analyzing and accurately predicting event popularity on both social network and search engine platforms.
Loss Minimization Using Variable DC-Link Voltage Technique for Permanent Magnet Synchronous Motor Traction System in Battery Electric Vehicle

Optimization of traction losses is achieved using the novel variable dc-link voltage technique in battery electric vehicle (EV). In the proposed technique, the dc-link voltage is controlled by the cascaded multilevel inverter (CMI) depending on different torque/speed requirements. The optimal pulse pattern (OPP) modulation method is employed to reduce the switching frequency of the power inverter. Loss comparison confirms that the proposed technique shows substantial traction loss reduction due to the decreased dc-link voltage and switching frequency. In addition, it has effectively reduced the torque distortion and mechanical noise at the high-speed condition.
Collaboratively Tracking Interests for User Clustering in Streams of Short Texts

In this paper, we aim at tackling the problem of user clustering in the context of their published short text streams. Clustering users by short text streams is more challenging than in the case of long documents associated with them as it is difficult to track users’ dynamic interests in streaming sparse data. To obtain better user clustering performance, we propose two user collaborative interest tracking models that aim at tracking changes of each user's dynamic topic distributions in collaboration with their followees’ dynamic topic distributions, based both on the content of current short texts and the previously estimated distributions. Our models can be either short-term or long-term dependency topic models. Short-term dependency model collaboratively tracks users’ interests based on users’ topic distributions at the previous time period only, whereas long-term dependency model collaboratively tracks users’ interests based on users’ topic distributions at multiple time periods in the past. We also propose two collapsed Gibbs sampling algorithms for collaboratively inferring users’ dynamic interests for their clustering in our short-term and long-term dependency topic models, respectively. We evaluate our proposed models via a benchmark dataset consisting of Twitter users and their tweets. Experimental results validate the effectiveness of our proposed models that integrate both users’ and their collaborative interests for user clustering by short text streams.
Implementing Quality of Service and Confidentiality for Batch Processing Applications

With a massive volume of information generated from many kinds of sources and for many different purposes comes the need for processing systems that help digest these data. One approach for efficient processing of data is to batch process larger sets of data. For example, periodically, recent data can be analyzed and filtered, having as a result actions, updated models, or cleaned versions of the data uploaded to databases. Increasingly often, such applications have strong requirements regarding data security, quality of service, and scalability. While scalability is typically addressed by leveraging the abundant resources and fair pricing of cloud computing techniques, using a shared environment triggers resistance from developers regarding data security and quality of service. In this work, we present a system that combines a novel technology for data protection, Intel SGX, with a popular, open source tool for managing interactive and data processing applications, Kubernetes, to provide a batch processing system that is easy to use while still providing quality of service and strong confidentiality guarantees. Our evaluation shows that independent tasks that consume data from scalable storages can be implemented in a controllable, secure fashion, without added complexity or considerable performance or resource overheads.
Developing an e-Health System Based on IoT, Fog and Cloud Computing

The world population's life expectancy has gradually increased. According to the World Health Organization (WHO), the life expectation will reach 90 years by 2030, and this quality of life is one of most important aging aspects. The academic and business communities are devoting many efforts to develop new applications that promote quality of life for this portion of the population; services, such as vital signs monitoring, fall detection systems, heart attacks, among others, are increasingly in evidence. Most of these e-health systems are focused on intelligent devices - Internet of Things (IoT). However, IoT by itself is not able to process, store and guarantee the quality of service of these services due to hardware capacity limitations. So, to mitigate this issue, IoT has two major allies in order to be able to provide e-health services with high availability and quality, fog and cloud computing. This paper presents in progress e-health architecture using IoT for data acquisition, fog for data pre-processing and short-term storage, and cloud for data processing, analyze and long-term storage. We also describe main challenges to provide an e-health application with high availability, high performance and accessibility, at low deployment and maintenance cost.
A Polyphase Decimation Filter for Time-Interleaved ADCs in Direct-RF Sampling Receivers

Decimation filters in direct-RF sampling receivers are implemented with the polyphase decompositions of cascaded integrator-comb (CIC) filters to decrease gigasamples per second (GS/s) rates to low ones. First delays and decimators of the first decimation filter in the receiver can be eliminated in a time-interleaved ADC (TI-ADC) with the same number of channels as a decimation factor of the filter. However, the remaining adders need to operate at the same rate as each ADC samples (>1 GS/s), limiting the maximum operating speed of the receiver. We present a polyphase filter with a decimation factor of twice the channel number. This enables decimators to be inserted before the adders, reducing their operating frequencies to a half of the sampling frequency of each ADC. We synthesize a 7-bit polyphase filter based on a 2nd-order CIC filter with a decimation factor of 8 for a 4-channel TI-ADC by using a 65-nm CMOS process. Simulations show that the filter combined with I/Q mixers operates at twice the operating frequency (1.67 GS/s) of the conventional one with 2.0 mW, while consuming twice or more the chip area.
Revisiting the MIMO Capacity With Per-Antenna Power Constraint: Fixed-Point Iteration and Alternating Optimization

In this paper, we revisit the fundamental problem of computing MIMO capacity under per-antenna power constraint (PAPC). Unlike the sum power constraint counterpart which likely admits water-filling-like solutions, MIMO capacity with PAPC has been largely studied under the framework of generic convex optimization. The two main shortcomings of these approaches are 1) their complexity scales quickly with the problem size, which is not appealing for large-scale antenna systems and/or 2) their convergence properties are sensitive to the problem data. As a starting point, we first consider a single user MIMO scenario and propose two provably-convergent iterative algorithms to find its capacity, the first method based on fixed-point iteration and the other based on alternating optimization and minimax duality. In particular, the two proposed methods can leverage the water-filling algorithm in each iteration and converge faster, compared with current methods. We then extend the proposed solutions to multiuser MIMO systems with dirty paper coding-based transmission strategies. In this regard, capacity regions of Gaussian broadcast channels with PAPC are also computed using closed-form expressions. Numerical results are provided to demonstrate the outperformance of the proposed solutions over existing approaches.
Proton Exchange Membrane Fuel Cell Model for Prognosis

The deployment at a large-scale of fuel cells will only occur if it is sufficiently competitive with other conventional energy generation solutions. Indeed, it is essential that these new technologies can provide acceptable performance and guarantee continuity of service for users. This involves the deployment of a robust, fault tolerant and ageing resilient systems. Fuel cell systems, by their technology, have behaviors that are difficult to apprehend. The nonlinear nature of the phenomena, the reversible or non-reversible character of the degradations, and the interactions between components and parameters make the modeling of aging a difficult task [2]. It is however necessary to develop a complete ageing model of a Fuel Cell system depending on the operating conditions in order to extend the lifetime of the system by setting appropriate control. Fault and degradation tolerant control, or by setting predictive maintenance. In this work, we present a mathematical model that will be used for fuel cell system prognosis, part of a global fault resilient approach. The model developed has been validated on experimental tests.
EMG-based Control Scheme with SVM Classifier for Assistive Robot Arm

Establishing an appropriate control technique with high accuracy has become the central issue for robotic applications to achieve anthropomorphic functions. The electromyography (EMG) method, which approaches various human motions by adjusting signals in the manipulator control scheme, is especially gaining popularity among assistive robotics. In this study, an EMG-based classification method is incorporated to control an assistive robot arm and perform interaction in human arm movements. The system is implemented based on a support-vector machine (SVM); it classifies motions of upper human limbs according to EMG signals recorded from the brachioradialis, the biceps and the anterior deltoid as the input features. With the proposed method, six categories of human arm movements can be identified, and the classification results can thus be remotely transmitted to control an assistive robot to mimic motions of the human arm. The performance of the proposed classification method was evaluated using 72 segments in 5 second EMG signals from two subject. The overall accuracy rate can reach 94% based on the selected EMG features for each measured muscle. The results suggest the importance of feature selection according to the morphology of EMG waveforms recorded from different muscle bundles. The performance of the proposed method has been quantitatively evaluated, and an assistive robot arm can be remotely controlled using EMG signals with a high accuracy rate.
Detecting Social Bots on Twitter: A Literature Review

Due to the exponential growth in the popularity of online social networks (OSNs), such as Twitter and Facebook, the number of machine accounts that are designed to mimic human users has increased. Social bots accounts (Sybils) have become more sophisticated and deceptive in their efforts to replicate the behaviors of normal accounts. As such, there is a distinct need for the research community to develop technologies that can detect social bots. This paper presents a review of the recent techniques that have emerged that are designed to differentiate between social bot account and human accounts. We limit the analysis to the detection of social bots on the Twitter social media platform. We review the various detection schemes that are currently in use and examine common aspects such as the classifier, datasets, and selected features employed. We also compare the evaluation techniques that are employed to validate the classifiers. Finally, we highlight the challenges that remain in the domain of social bot detection and consider future directions for research efforts that are designed to address this problem.
The Impact of Dislocation on Bulk -Si FinFET Technologies: Physical Modeling of Strain Relaxation and Enhancement by Dislocation

The optimal position of dislocation stress memorization technique (DSMT) to maximize n-FinFET performance as well as the stacking fault (SF) number, [Ge] concentration limit and p-FinFET DC tradeoff in eSiGe are newly investigated by using the scanning moiré fringe (SMF) and scanning transmission electron microscopy-geometrical phase analysis (STEM-GPA) validated in-house 3D TCAD model in various bulk finFET structures.
Projective Synchronization of Three Memristor Chaotic Systems with Unknown Parameters via Adaptive Control

This paper studies the synchronization of three memristor chaotic systems (MCSs) with unknown parameters. The presented projective synchronization extends traditional synchronization between one master system and one slave system to that of three MCSs. Based on parameter estimation method, an adaptive controller is designed to realize the synchronization of MCSs, where unknown parameters can be tracked automatically. Finally, the numerical simulations are provided to verify the effectiveness of the theoretical results.
Improving Tradeoff Among Downlink Rates of Service Providers in a VWN by Using NOMA

In this letter, we improve the tradeoff among the downlink (DL) rates of service providers (SPs), provided by a mobile virtual network operator (MVNO) using non-orthogonal multiple access (NOMA) technique. In this respect, NOMA provides a suitable opportunity for the MVNO to share resources among SPs more efficiently. Regarding interaction among the DL rates provided for SPs we formulate our resource allocation problem as a multiobjective optimization problem (MOOP) while taking into account user fairness. By using the weighted Tchebycheff technique, we are able to solve the formulated MOOP by considering different tradeoffs among DL rates of different SPs. Our numerical results show that by using NOMA, each SP gains a higher rate compared with orthogonal frequency-division multiple access.
Low Cost LSTM Implementation based on Stochastic Computing for Channel State Information Prediction

This paper proposes a low-cost hardware implementation of Long Short-Term Memory (LSTM) Neural Network for channel state information (CSI) prediction. We first employ LSTM algorithm to the predication of channel state information. To reduce the hardware cost, the stochastic computing is employed to design the LSTM accelerator. The complex arithmetic operations are converted to simple logic gates. For instance, the multiplication is performed by an AND gate and tanh function is implemented by a finite state machine. According to the implementation report, the proposed stochastic LSTM reduce the hardware cost about 70%, which provides a promising technology for the future communication system design.