Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Vehicular Localization Accuracy With Non-Line-of-Sight Identification

Recently, vehicular localization has drawn a lot of attention due to increasing location-based applications in vehicular ad-hoc networks (VANETs). However, similar to the conventional localization problem, vehicular localization suffers from the biases caused by non-line-of-sight (NLOS) propagation. To reduce its impact, NLOS identification and mitigation are commonly used, where the accuracy of NLOS identification is crucial. This paper aims to investigate the relationship between localization performance and misidentification of NLOS. The impacts to vehicle localization accuracy of two types of NLOS misidentification, namely false and miss respectively, are analyzed. It is demonstrated with Monte Carlo simulations that those two factors have different impacts on localization accuracy. Furthermore, the dominating factor of localization for different scenarios with two types of localization strategies is concluded, which could be further used to chose the strategy leading to higher localization accuracy in different scenarios.
MISO Measurements in Indoor Environments at 25 GHz

This paper presents the results of parameter measurements in two indoor environments for both line of sight (LoS) and non-line of sight (NLoS) scenarios at 25 GHz using multiple directional antennae at the transmitter and a single omnidirectional antenna at the receiver (MISO) with the custom-designed channel sounder developed at Durham University. The data are analysed to estimate the K-factor, path loss parameters and root mean square (RMS) delay spread for each scenario. The 50% and 90% values of the RMS delay spread values for a 20 dB threshold are presented for both scenarios.
A Multi-Objective Approach for Optimizing Content Delivery Network System Configuration

Optimizing the Content Delivery Network system configuration has been addressed as an interesting problem for the system owners. They want to minimize the investment cost while guaranteeing their system's quality. Several works have resolved this problem as a single-objective optimization (SOO) problem with heuristic methods. These approaches usually aggregate the objectives into a scalar function and resolve the problem with SOO algorithms. A typical drawback of these approaches is that they cannot capture the trade-off between the objectives, which usually leads to a sub-optimal solution. To overcome this drawback, this paper considers the problem as a discrete multiobjective problem and resolves it with meta-heuristic techniques, namely Bayesian optimization (BO) and evolutionary methods. More importantly, we also propose an empirical method to improve the convergence speed of the standard BO methods in discrete space. Our experiments show that our proposed method can dramatically improve the rate of convergence. Moreover, we apply our method to a real CDN system and compare our solution with the system's current solution. Our experimental results show that our proposed solution can save about 39% of the current cost with the same internal traffic.
A New Design and Implementation of 4x4 Butler Matrix for Ka Band Applications

This study presents the optimum design of a $4\times 4$ planar Butler matrix array as a central feature of a switched beam smart antenna system operating at 28 GHz for Ka-band applications. The design is developed on a multilayer structure and the antenna arrays are feeding by using slots. In this work the slot optimized and defined on the ground plane to achieve an optimum feeding, with RO3003 substrate of dielectric constant equal to 3 and loss tangent equal to 0.001. In the 4x4 Butler matrix, four input ports are used to feed four networks composed of four microstrip patch antennas creating the beamforming array. Such network can produce four uniform orthogonal beams, at 12°, −12°, 28° and −28°. Design details and simulation results for the components (hybrid coupler, crossover, phase shifter) used to implement the matrix are provided by using two electromagnetic solvers one based on Moements method and the second one on Finite Integration Technique (FIT).
A Novel Corrugated-Shank Anchor-Shaped Antenna for Wireless Power Transfer

In this paper we propose a new class of anchor-shaped antenna referred to as corrugated-shank anchor-shaped suitable for angular misalignment resilience. This new topology to operate at 360 MHz differs from previously published anchor-shaped antenna [1] by introducing corrugations in the shank of the anchor for stronger magnetic fields. The results suggest a 10% of power transfer efficiency (PTE) improvement with respect to that of the generic anchor. This improvement is achieved when considering misalignments in the elevational direction. This design can be used as a guideline for future misalignment-free wireless charging modalities integrated into clothing.
Assessment of Human Exposure to Electromagnetic Fields: Review and Future Directions

—This paper reviews recent standardization activities and scientific studies related to the assessment of human exposure to electromagnetic fields (EMF). The differences of human exposure standards and assessment of consumer products and medical applications are summarized. First, we reviewed human body modeling and tissue dielectric properties. Then, we explain the rationale of current exposure standards from the viewpoint of EMF and the standardization process for product compliance based on these exposure standards. The assessment of wireless power transfer, as an example of emerging wireless devices, and environmental EMFs in our daily lives are reviewed. Safety in magnetic resonance systems, where the EMF exposure is much larger than from typical consumer devices, is also reviewed. Finally, we summarize future research directions and research needs for EMF safety.
SPENT+: A Category- and Region-aware Successive POI Recommendation Model

To facilitate successive Point-of-Interests (POI) recommendation, the categories of POIs and the regions where POIs are located are seldom considered in existing models. In view of this, we extend a state-of-the-art model SPENT, named SPENT+, by taking the category and the region into considerations. In SPENT+, we formulate category- and region-aware check-in sequences, design the similarity trees to aggregate similar features, and finally establish the category latent vectors and region latent vectors, respectively. The above two latent vectors are aggregated as the category-region-aware latent vectors. Therefore, the category-region-latent vectors are sent to an LSTM together with conventional check-in sequences to improve successive POI recommendation. We conduct two real datasets, Gowalla and Foursquare, and compare with state-of-the-art methods in experiments. Results show that SPENT+ outperforms the baselines in terms of precision and recall.
Phased-Array Transmission for Secure Multiuser mmWave Communication via Kronecker Decomposition

This paper proposes a multiuser physical layer secure transmission scheme for millimeter-wave communication. We use Kronecker decomposition to design the common transmit vector to synthesize the desired symbols for multiple legitimate receivers in the proposed scheme. Specifically, the algorithm converts the design of common transmit weight for multiuser into the design of sub-weight vectors having the Vandermonde structure. By using Kronecker decomposition, the sub-weight vectors are decomposed into Kronecker factors and designed. To eliminate the message sending towards the undesired directions while guaranteeing the quality of transmission in the target directions, we separate the Kronecker factors into two parts by using the property of the Kronecker product. The first part (the interference cancellation factor) cancels the interference between legitimate users; the second part (the signal enhancement factor) achieves signal enhancement. For a given information symbol, multiple solutions of transmit weight are obtained. By utilizing this time-varying transmit weight, we realize the secure transmission for multiuser. In addition, the proposed algorithm has no restrictions on modulation, and it also has analytical solutions with low computational complexities. Simulations are presented to verify the effectiveness of the algorithm under various situations.
A Two-Layer Risky Driver Recognition Model With Context Awareness

For autonomous vehicles and intelligent connected vehicles, the real-time recognition of risky drivers can play an important role in traffic accident prevention. However, the external environment substantially impacts driving behavior and driving risk and is usually costly to acquire. Existing risky driver recognition models often ignore external environment information or assume this information is given. We propose two hierarchical two-layer context-aware machine learning structures. The first layer can speculate external context, for example, traffic states. The second layer recognizes risky drivers based on the contextual information speculated from the first layer. The German Highway Drone Dataset is used to establish risky driver recognition and traffic state recognition models. Rear-end collision risk and side collision risk are evaluated for each vehicle. Drivers with high collision risk are labeled as risky drivers. By analyzing vehicle trajectory data from three traffic states: free-flow, saturated, and congested, we find that traffic states have a significant influence on vehicle’s longitudinal speed, lateral speed, longitudinal acceleration/deceleration, and collision risk. Six classifiers, including SVM, KNN, RF, Adaboost, Extra trees, and XGBoost, are applied to train recognition models. Results show that the proposed structures can significantly improve model’s ability to recognize risky drivers.
Modeling of Return-Stroke Current Evolution Over a Realistic Ground

The return-stroke phase of the cloud-to-ground lightning flash is of primary concern for lightning-protection engineering. This also holds for buried cables and other sensitive devices in the soil. For evaluating the field in the soil, two approaches are employed: first, the quasi-static approach, in which a current source injects the stroke current into the soil; and second, a hybrid approach employing the “engineering model” for the channel-current evolution. In both the approches, the current injected into the soil is independent of the soil resistivity, which needs further scrutiny. The return-stroke model developed by Balaram Raysaha et al. (2011) self-consistently emulates the stroke-current evolution without any such assumptions and is, therefore, very suitable for a comprehensive assessment of the situation. However, the field-computation method employed by Balaram Raysaha et al. had to be changed to the domain-based finite-difference time-domain method for a realistic soil model. After this augmentation, the return-stroke model is employed to investigate the possible role of soil's electrical parameters on the return-stroke current evolution. It is found that for fast-rising currents of low amplitudes, the soil resistivity noticeably influences the current amplitude. Also, it is found that the soil dispersion leads to a significantly lower electric field in the soil.
Calcium Oscillations In Differentiating Mesenchymal Stem Cells: Analysis And Control Using Pulsed Electric Fields

Mesenchymal Stem Cells or Multipotent Stromal Cells (MSCs) are adult stem cells able to differentiate into many cell types such as osteoblasts, adipocytes or chondrocytes. These cells have become of particular interest for cell-based therapy these last decades. In another respect, calcium is a ubiquitous secondary cell messenger, known to encode important information for the cells in the form of oscillations. It has been shown that MSCs naturally exhibit calcium oscillations, whose frequency is varying over the course of differentiation processes. The main question addressed in this work is to assess whether, by manipulating the frequency of calcium oscillations, we could influence proliferation or differentiation events in MSCs. The technology used for this purpose involves the use of short high voltage pulsed electric fields capable of permeabilizing the cell membrane to calcium ions presents in the surrounding medium. Long-term exposure of the cells to the pulsed electric fields was performed using a programmable generator along with an original custom device to expose the cells under regular attached cell culture conditions. We report the experimental constraints that were found and how they were solved.
Optimization of Communication Schemes for DMA-Controlled Accelerators

The hardware accelerator controlled by direct memory access (DMA) is greatly influenced by the communication bandwidth from/to DRAM through on-chip buses. This paper proposes a novel performance estimation algorithm to optimize the communication schemes (CSs), which are defined by the number of direct memory access controllers (DMACs) and the bank allocation of DRAM. In order to facilitate the optimization of CSs, a communication primitive (CP) is defined by the bank allocation and the set of activated DMACs. By using the communication bandwidths of CPs obtained from prior full-system simulations, the proposed performance estimation algorithm can predict the communication performance of CSs more accurately, compared with the conventional performance estimation algorithms. When it is applied to convolutional neural networks (CNNs) and wireless communications (LDPC-coded MIMO-OFDM), the estimation error is measured to be no more than 6.4% and 5%, respectively. In addition, compared with the conventional simulation-based approaches, the proposed estimation algorithm provides a speedup of two orders of magnitudes. The proposed performance estimation algorithm is used to optimize the CS of the CNNs and explore a design space characterized by bank interleaving, outstanding transactions, layer shape, tile size, and hardware frequency. It is shown that the optimized CS improves communication performance by up to 68% for the third convolutional layers of AlexNet and 60% for the MIMO of LDPC-coded MIMO-OFDM. In addition, the DRAM latency is minimized by setting the bank interleaving to the number of outstanding transactions. Moreover, the simulation results show that the optimum CS depends on the application. It is also shown that the use of an extra DMAC does not necessarily improve the communication performance.
Generalizable Sample-efficient Siamese Autoencoder for Hidden Hearing Loss Diagnosis in Listeners with Subjective Tinnitus

Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts’ cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEGbased tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a Siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects’ EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%-94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method’s stability in performance.
N-Doped CNT: Distribution Electron on Different Ratio N/C for Oxygen Reduction Reaction

Carbon nanotubes (CNTs) have the advantage to improve electrochemical fuel cell reactions by increasing the dispersion of nanoparticles as well as having high strength in mechanical and electrical properties. Therefore, this study presents the comparison of nitrogen doped CNT and other doping heteroatom on CNT as a catalyst support for application in the Direct Liquid Fuel Cell (DLFC). The doping ratio in CNT was conducted to determine the effect of the oxygen reduction reaction. In addition, the effect of CNT diameter in oxygen adsorption also has been tested. The models and calculations were done using density functional theory (DFT). The result showed that the ratio of N/C is decreases as the diameter of CNT decreases. The value is even dropped as the unit cell is increased to 1x1x2. The band gap value of CNT before doping is higher compared to after nitrogen doping. While increasing the diameter of doped CNT has increased the mulliken charge distribution and thus make the model more stable. In the adsorption energy of molecule oxygen, the 1x1x2 unit cell of CNT and large diameter show a remarkable and stable compared to in 1x1x1 unit cell and small diameter of CNT. Thus, the adsorption energy of the oxygen molecule shows a more stable value in the low doping ratio and high CNT diameter compared to a high doping ratio and a small diameter of the CNT. This result shows the potential of the CNT in helping to improve electrochemical reaction in DLFC.
STUDY OF THE CURRENT RESISTANCE PATTERN TO QUINOLONES AND OTHER ANTIMICROBIALS BY MULTIDRUG RESISTANT ESCHERICHIA COLI RECOVERED FROM RETAIL CHICKEN MEAT

Microbial food safety has become a worldwide public health concern. The indiscriminate use of antimicrobials in poultry had rendered bacteria resistant to multiple drugs. The dissemination of resistant genes through the food chain has probably lead to multidrug resistant strains of Escherichia coli in chicken meat. With this background in mind, the current study was conducted to ascertain the current antimicrobial susceptibility and multiple drug resistance patterns in E. coli recovered from retailed chicken meat. About 100 chicken meat samples were collected from different retail meat shops located across Lahore city. About 85 samples (85%) were found positive for E. coli. Further confirmation was done through PCR by amplifying their universal stress protein (uspA) gene. As per Kirby Bauer disk diffusion method, E. coli displayed the susceptibility towards gentamicin (86%) and ciprofloxacin (67%). The 100% resistance was observed towards chloramphenicol, colistin, tylosin each, 93% against doxycycline and 92% against ampicillin, and oxytetracycline, each. Significant resistance was observed against the quinolone group (enrofloxacine, 81.1%). All the isolates were resistant to more than 8 drugs belonging to 3 or more than 3 different classes of drugs. About 23.5% of the isolates were resistant against nine different antimicrobials and 15.3% showed resistance against eleven different drugs. The results of the in vitro susceptibility testing and multidrug resistance pattern observed in the study may provide valuable guidance to adopt good hygienic practices and curb unnecessary antimicrobial usage.
Content Analysis of Language-Sensitive Recruitment Influenced by Corporate Language Policy Using Topic Modeling

Language-sensitive recruitment is a language management tool frequently used by corporate organizations. However, its relationship with corporate policy is lacking; hence, this study aims to consider language-sensitive job advertisements from a computational text analysis perspective and explore the match (or mismatch) between language-sensitive recruitment (English, Japanese, or bilingual) and corporate language policy. This study uses corpus methods combined with topic modeling and text analysis to investigate the influence of corporate language policy on the textual practice of language-sensitive recruitment in a Japanese multinational corporation (MNC). This study finds a considerable discrepancy between recruitment needs and corporate language policy. It also finds that bilinguals still play a key role in crossing language boundaries 10 years post-mandate of the English language policy in this Japanese MNC. The study contributes to business language by exploring an additional scenario for linking language competency with actual recruitment needs. Thus, this study sheds light on the implementation of language-sensitive recruitment in a multilingual corporate context, affecting the communication patterns and recruitment tactics.
Influence of COVID-19 Epidemic on Dark Web Contents

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.
Methods for the Detection and Quantification of Micro and Nanoplastics- A Review

Over the past 35 years, synthetic or semi-synthetic polymers called “plastics” have been widely used across multiple fields due to their low cost, versatility, durability. Plastics have proved to be a boon to mankind. However, overuse of non- biodegradable plastics comes with its own downsides. Despite constant efforts to reuse and recycle plastics, these polymers substantially contribute towards the accumulation of debris hazardous to the environment. Plastic materials are slowly broken into fragments of micro- and nano plastics due to aging and weathering. Micro- and nano plastics were found capable of entering the food chain and hence are viewed as threats. This review paper revolves around methods used for the detection and quantification of micro- and nano plastics. Detection of micro- and nano plastics using methods like Raman spectroscopy, Infrared Spectroscopy, SERS, MALDI-TOF, and machine learning approaches are discussed here. The research efforts carried out in this article aims to further facilitate the R&D initiatives of Jozbiz Technologies.
Minimal Redundancy Linear Array and Uniform Linear Arrays Beamforming Applications in 5G Smart Devices

Minimum Redundancy Linear Arrays (MRLAs) and Uniform Linear Arrays (ULAs) investigation conducted with the possibility of using them in future 5G smart devices. MRLAs are designed to minimize the number of sensor pairs with the same spatially correlated delay. It eliminates selected antennas from the entire composite antenna array and preserves all possible antenna spacing. MRLAs have attractive features for linear sparse arrays, even if the built-in surface is deformed, it works without problems. To our knowledge, MRLAs have not been applied to smart devices so far. In this work, a 7-element ULAs and 4-element MRLAs (same aperture) were used for the simulation. The Half Power Beamwidth (HPBW) is 0.666 and the Null-to-Null Beamwidth ( ) is 1.385 in ψ-space. In comparison, the standard 4-element arrays are 1.429 and 3.1416, while the standard 7-element linear arrays are 0.801 and 1.795 respectively. Experimental results show that 4-element MLRAs have a narrower mean beam, much higher sidelobes and shallow nulls. Therefore, in terms of main lobe features, 4- elements MRLAs have an improvement over the standard 7-element ULAs. Doi: 10.28991/esj-2021-SP1-05 Full Text: PDF
Removal Efficiency Adsorption Kinetics And Isotherms Of

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