Miracle-dz/UniFAR-FTUs huggingface dataset:
Flight Optimization for an Agricultural Unmanned Air Vehicle

A method is proposed to generate paths for an agricultural fixed-wing Unmanned Air Vehicle (UAV) covering an arbitrary polygonal field. The method minimizes time, accounting for the effect of wind on the UAV's ground track. Under the assumption that the path must be a set of parallel lines, covering the field with strips, the method determines the best angle for those strips and the best sequence and directions in which they should be visited. Numerical examples show the dependence of the resulting flight paths on both wind and field geometry.
QoS and MPLS design issues in NoCs

Nowadays real application traffic, and especially streaming applications running in multi core environments require more and more bandwidth. Networks on Chips (NoC) should be able not only to provide adequate bandwidth resources but also variable length traffic with multi rate packets, each representing a different class of service (FEC classes) and hence, it should provide QoS support. It is common knowledge that in original networks, QoS provision along with packet forwarding and routing at high rates is supported by the MPLS concept. This paper is a brief and compact survey, that gives a smart look on critical design issues of MPLS technology applied in NoCs, based on recent reference work. All types of MPLS based NoC architectures are presented and their pros and cons are discussed. Moreover, other important issues are pointed out, such as non interference, security and fault management, giving researchers motivation and directions for extended research.
Facial Attribute Editing by Latent Space Adversarial Variational Autoencoders

This work focuses on the problem of editing facial images by manipulating specified attributes of interest. To learn latent representations disentangled with respect to specified face attribute, a novel attribute-disentangled generative model is proposed by combining variational autoencoders (VAEs) and generative adversarial networks (GANs). In the proposed model, only two deep mappings are included: an encoder and a decoder, similarly as the counterparts in the context of VAEs. Latent space mapped by the encoder is split into two parts: style space and attribute space. The former represents attribute-irrelevant factors, such as identity, position, illumination and background, etc. The latter represents the attributes, such as hair color, gender, with or without glasses, etc, of which each dimension represents one single attribute. By regarding constraints on the output of the encoder as discriminative objectives, the encoder can act not only as a discriminator that is expected to discriminate a sample is a real or a generated one, but also as an attribute classifier that can discriminate whether a sample has the specified attributes or not. Combining reconstruction and Kullback-Leibler (KL) divergence regularization losses like in VAEs, the adversarial training loss defined for the style and the attribute in the latent space is introduced, which drives the proposed model to generate images whose distribution are close to the real data distribution in the latent space. Finally, the model was evaluated on the CelebA dataset and experimental results showed its effectiveness in disentangling face attributes and generating high-quality face images.
FPGA Based Implementation of Linear SVM for Facial Expression Classification

This work presents a Field Programmable Gate Array (FPGA) based hardware efficient implementation of One-Versus-All (OVA) multi-class linear Support Vector Machine (SVM) classifier for classifying the facial expressions of an individual. The aim is to achieve a real-time classification of the facial expressions into three different states viz., neutral, happy, and pain. The target is to make the designed architecture feasible for an embedded platform based FER system so that it could assist in monitoring patients in hospitals. Thus, in the context of architectural design, the challenge here is to achieve classification accuracy equivalent to the software-based implementation with a multi-fold improvement in the execution speed. The acceleration in the execution speed of the designed classifier is achieved utilizing the parallelism and the pipelining concepts of the VLSI architecture design. Moreover, to reduce the computational cost and to further boost the execution speed of the architecture, we have adopted the fixed-point data format (Q24.16) in our design. We have trained the classifier offline and then used the designed architecture with learned parameters to perform testing on the hardware. The designed architecture after synthesis operates at a maximum clock frequency of 241.55 MHz on Xilinx ML510 FPGA platform. Classification accuracy of 98.50% equivalent to its software counterpart has been achieved on simulating the designed architecture with different test images. Thus, the designed classifier architecture achieved good performance in terms of speed, area, and accuracy, and is thus, a favorable candidate for real-time classification of the facial expressions on an embedded platform based on FPGA.
Enhanced Network Embedding with Text Information

Network embedding aims at learning the low-dimensional and continuous vector representation for each node in networks, which is useful in many real applications. While most existing network embedding methods only focus on the network structure, the rich text information associated with nodes, which is often closely related to network structure, is widely neglected. Thus, how to effectively incorporate text information into network embedding is a problem worth studying. To solve the problem, we propose a Text Enhanced Network Embedding (TENE) method under the framework of non-negative matrix factorization to integrate network structure and text information together. We explore the consistent relationship between node representations and text cluster structure to make the network embedding more informative and discriminative. TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information. We evaluate the quality of network embedding on the task of multi-class classification of nodes. Experimental results on all three real-world datasets show the superior performance of TENE compared with baselines.
IoT Based Road Travel Time Detection

Traffic congestion is not uniform across the metropolitan cities like Bengaluru, in India. Road travel time becomes unpredictable many a times. In this aspect, several prediction models have been built. All these models require data on a daily basis for training. IoT based model to collect the input data using sensors can be the best choice. The model can be placed on all vehicles easily with minimum or no modification. This paper presents four varieties of approaches to measure the time taken for the journey. The timestamp is calculated based on engine run time. The collected data is stored in a file as well as synchronized to cloud storage. In the case of the problem with internet connectivity, the data written down in the file would serve as a backup. Travel time modelling is constructed using various sensors. This also helps advanced traveller information systems. Inevitably there is need to combine and make the best out of the available. GPS can be used to identify the source, destination, and few cardinal values even in the absence of internet connectivity.
Design Optimization of Traction Motor for Electric Race Car Considering Driving Duty-Cycle

This paper presents the design optimization process of traction motor for electric race car in order to balance the power-density and efficiency under a given typical race duty-cycle while maintaining the required torque-speed range. The investigated electric race car energy distribution over the race duty-cycle is modelled and representative points are identified. Design optimization is carried out based on these representative points by parametrized finite element motor model. The optimization results are evaluated by performance indexes such as active total mass, motor losses, torque ripple and discussed by multi-physics analysis of the optimal design choices. Finally, a 10-pole 12-slot spoke type permanent magnet motor is considered, both electromagnetic and thermal performance are evaluated to improve the motor efficiency and power density over motor torque-speed plane.
Theory Game as Priority Area of Researches for Development of Blockchain Technology

It is shown that game theory with imperfect information “cover” the most of the deep challenges facing blockchain technology. Various challenges of this technology are analyzed and allocated main of them. For each of critical challenges, it considered in what measure the answer to him connected with the need of creation of new scientific results for some sections of game theory. The conclusion is drawn that an essential part of the deep challenges of a blockchain it is possible to reduce to a game-theoretic design of a situation of multiagency interaction. In this case, the consensus of users a blockchain system concerning the reliability of any transaction established.
Predicting transmission success with Support Vector Machine in VANETs

In this article we study the use of the Support Vector Machine technique to estimate the probability of the reception of a given transmission in a Vehicular Ad hoc NETwork (VANET). The transmission takes place between a vehicle and a RoadSide Unit (RSU) at a given distance and with a given transmission rate. The RSU computes the statistics of the receptions and is able to compute the percentage of successful transmissions versus the distance between the vehicle and the RSU and the transmission rate. Starting from this statistic, a Support Vector Machine (SVM) scheme can produce a model. Then, given a transmission rate and a distance between the vehicle and the RSU, the SVM technique can estimate the probability of a succcessful reception. This probability can be used to build an adaptive technique which optimizes the expected throughput between the vehicle and the RSU. Instead of using transmission values of a real experiment, we use the results of an analytical model of CSMA that is customized for 1D VANETs. The model we adopt to perform this task uses a Matern selection process to mimic the transmission in a CSMA IEEE 802.11p VANET. With this model we obtain a closed formula for the probability of successful transmissions. Thus with these results we can train an SVM model and predict other values for other couples : distance, transmission rate. The numerical results we obtain show that SVM seems very suitable to predict the reception probability in a VANET.
Open -source technologies and IoT as a tool for meeting the challenge of mass-scale Solar Home Systems monitoring in developing countries

Solar Home Systems have been widely introduced in developing regions, usually in poor and isolated locations with lack of access to electricity, clean water and/or sanitation. Massscale monitoring of these photovoltaic systems would provide valuable information such as their detailed real performance, facilitating the detection of operation failures associated with technical and/or maintenance issues, improving the long-term usage by the final users. Current challenges that new monitoring systems will require are presented, along with an assessment of current available methods and technologies, including the recent advances on open-hardware and software platforms and the rapid upcoming of the Internet of Things.
TDOA-based ASF Map Generation to Increase Loran Positioning Accuracy in Korea

The long-range navigation (Loran) system can be a complement to global navigation satellite systems that are vulnerable to radio frequency interference. To increase the positioning accuracy of Loran in Korea, this study generates a time-difference-of-arrival-based additional secondary factor (ASF) map, which is different from the conventional time-of-arrival-based ASF map that is currently not suitable in Korea because Korean Loran transmitters are not synchronized to the coordinated universal time.
A 5G MIMO Antenna for Broadcast and Traffic Communication Topologies Based on Pseudo Inverse Synthesis

This paper proposes a new design of a dual-polarized base station antenna for sub-6-GHz 5G communication covering the band from 3.3 to 3.8 GHz. The proposed antenna offers a good impedance matching across the desired frequency band with $\text {VSWR} \le 1.5$ , a stable radiation pattern with a half power beam width of 65° ± 5°, and a high isolation between the ports with a small size and a low profile. The antenna is converted to a planar MIMO antenna array with low envelop correlation coefficients between the array elements to perform in either a broadcast or traffic communication topology whenever required. In the broadcast communication topology, the proposed MIMO antenna array covers a sector of 65° in the horizontal plane with three different down-tilted beam directions at 0°, 10°, and 20° in the vertical plane as conventional base station antennas used for 2G, 3G, and 4G. In traffic topology, the proposed MIMO antenna array can direct multiple beams to concurrent multiple users in a point-to-multipoint communication based on a novel pseudoinverse synthesis (PIS). The novel PIS is proved to be better than the other approaches which use spatial or frequency diversity as it possesses the advantage of the spatial diversity (by using a single frequency for multi-user coverage) and the advantages of the frequency diversity (by offering good steering capability and high directivity for each user). It is capable of radiating dynamic multiple beams simultaneously with an excellent flexibility and steering capability in real-time processing and outperforming other multi-beam antenna array methods. Thus, this new design is an ideal candidate for the new 5G mobile base stations.
Impact of an Automotive Chirp-Sequence Interferer on a Wideband OFDM Radar

OFDM-based radar schemes are promising candidates to solve inherent limitations of frequency-modulated schemes. However, for a practical implementation in the automotive environment with an increasing amount of sensors and disturbances, not only the performance of the modulation scheme is important but also how it can perform in coexistence with other modulation schemes. In this paper, measurements are shown to investigate the impact of chirp-sequence-based sensors on a wideband OFDM radar. Furthermore, a straightforward method to mitigate and eliminate the interference on the receive signal is presented that allows reliable target detection even under severe chirp-sequence interference.
How Co-creation Processes Unfold and Predict Submission Quality in Crowd-based Open Innovation

Crowd-based open innovation communities have received increasing attention, based on the premise that leveraging the power and diversity of the crowd can lead to innovative outcomes. However, we still know little about how work is coordinated over time in this context, especially as the innovation process moves from idea generation to elaboration. Based on literature and theories of coordination and collaboration in traditional creative contexts and on emergent evidence from research on crowd work, we develop hypotheses about the unique interaction patterns that characterize co-creation and how these patterns impact, over time, submission quality. To test our hypotheses, we conducted a study of a crowd-based open innovation platform. We found that, in general, the diversity of contributors increased over time, but for high quality submissions, the number of contributors decreased and a small group of involved people became more dominant in providing feedback. Further, we observe that the creators of more successful submissions, while not dominating the discussion, were particularly engaged in the discussions in later stages. Our work contributes to understanding the temporal dynamics in open innovation communities by providing evidence that successful interaction patterns vary depending on the phase of the project.
A Branching Strategy Based on the Length of Learnt Clause

Decision strategy is as a great part in the CDCL algorithm, so the efficient branching strategy is one of the key features of the SAT problem solver. Originally some basic knowledge and concepts are brought in, then there are some concrete steps of the branching variable heuristic algorithm-VSIDS algorithm, finally based on the relationship between the learnt clause length and the decision level, a new branching strategy presents,which is also based on VSIDS strategy, the new branching strategy makes the difference between the decision levels of variables involved in conflict analysis and the the current conflict level as the conflict adding score conditions, and considering the learnt clause length as the adding score, in order to enhance the efficiency of variable selection, some examples of the VSIDS strategy and the new strategy are analyzed lastly.
Study and Design of an X-Band 4-Channel Multifunctional T/R Module

An X-band four-channel multifunctional T/R module is presented. Two working modes in one channel are highly integrated in this module. The output of low power continuous wave or high power pulse wave can be switched fast. The modules are designed through MMIC technology. Cavity effect is weak which is contributed to reasonable module architecture. It brings high gain by connecting driving amplifier and power amplifier. The self-excited problem of channel is discussed and the solutions are presented. Partion wall which is set between front and end amplifier can reduce the channel isolation. It makes the module stable. The measured results show that the output power of pulse wave is more than 10 W and the output power of continuous wave is more than 200 mW. The T/R module has less than 3 dB of noise figure and about (25 ± 0. 5)dB of gain, less than 3° of RMS phase error and less than ± 10° of Phase non-linear error from 8 GHz to 12 GHz.
Deep Signal Recovery with One-bit Quantization

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the problem of signal reconstruction from its one-bit noisy measurements. Namely, we propose a model-based machine learning method and unfold the iterations of an inference optimization algorithm into the layers of a deep neural network for one-bit signal recovery. The resulting network, which we refer to as DeepRec, can efficiently handle the recovery of high-dimensional signals from acquired one-bit noisy measurements. The proposed method results in an improvement in accuracy and computational efficiency with respect to the original framework as shown through numerical analysis.
A Supervised Machine Learning Approach for Events Extraction out of Arabic Tweets

Tweets provide a continuous update on daily events, however they are noisy text, personalized and challenging to be understood by machines. This shows a need for event extraction and representation approaches. This research describes a state-of-the-art supervised machine learning approach for extracting events out of Arabic tweets. The proposed approach focuses on three research tasks: Task 1: Event Trigger Extraction, Task 2: Event Time Expression Extraction, Task 3: Event Type Identification. The proposed approach was evaluated on a dataset of 2k Arabic tweets and the evaluation results were promising. The approach performance was compared to an unsupervised rule-based approach from previous work using the same dataset. Results show that the proposed approach outperforms the unsupervised rule-based approach in tasks T1:event trigger extraction (F-1= 92.6 vs. F-1= 78.7) and T2:event time expression extraction (F-1= 92.8 vs. F-1= 88.35), whereas is acting relatively worse in T3: event type identification (Accuracy= 80.1 vs. Accuracv= 95.9).
A Novel Checkerboard Metasurface Based on Optimized Multielement Phase Cancellation for Superwideband RCS Reduction

In this paper, a checkerboard metasurface based on a novel physical mechanism, optimized multielement phase cancellation, is proposed for greatly expanding the bandwidth of radar cross section (RCS) reduction. More basic metaparticles and, in particular, the variable phase difference between them, greatly increase the ability to control electromagnetic waves. Interactions between multiple local waves produced by the basic metaparticles at multiple frequencies sampled in a superwide frequency band are manipulated and optimized simultaneously to achieve phase cancellation. The proposed metasurface can achieve a 10 dB RCS reduction in a superwide frequency band from 5.5 to 32.3 GHz with a ratio bandwidth ( ${f}_{H}/{f}_{L}$ ) of 5.87:1 under normal incidence for both polarizations. Furthermore, the RCS reduction is larger than 8 dB from 5.4 to 40 GHz with a ratio bandwidth of 7.4:1. The metasurface also has a good performance under wide-angle oblique incidences. The optimal metaparticle distribution is found to obtain the superwideband bistatic RCS reduction. The theoretical analysis, simulation, and experimental results are in good agreement and verify the ability and capability of the proposed mechanism.
Characterization of Mental States through Node Connectivity between Brain Signals

Discriminating mental states from brain signals is crucial for many applications in cognitive and clinical neuroscience. Most of the studies relied on the feature extraction from the activity of single brain areas, thus neglecting the potential contribution of their functional coupling, or connectivity. Here, we consider spectral coherence and imaginary coherence to infer brain connectivity networks from electroencephalographic (EEG) signals recorded during motor imagery and resting states in a group of healthy subjects. By using a graph theoretic approach, we then extract the weighted node degree from each network and evaluate its ability to discriminate the two mental states as a function of the number of available observations. The obtained results show that the features extracted from spectral coherence networks outperform those obtained from imaginary coherence in terms of significant difference, neurophysiological interpretation and reliability with fewer observations. Taken together, these findings suggest that graph algebraic descriptors of brain connectivity networks can be further explored to classify mental states.
Reducing Feature Space and Analyzing Effects of Using Non Linear Kernels in SVM for Bangla News Categorization

Text categorization is a trending topic nowadays. In this paper, we analyzed some existing approaches for Bangla document categorization and proposed some modifications of them. Using our modified approach we achieved an accuracy of 92.79% which is the best accuracy so far in the dataset that we used which consists of more than 30000 documents. In short we used tf-idf mixed with term frequency threshold as our feature selection technique and SVM as our classifier to classify the documents. We also greatly reduced the feature space and computation time using our approach.