Miracle-dz/UniFAR-FTUs huggingface dataset:
AVB/TSN Protocols in Automotive Networking

In the last decade, Ethernet networks that require real time constraints are massively increased. Switched Ethernet is reshaping in-vehicle communications. To meet real-time requirements for diverse data types in automotive communications, Quality-of-Service protocols that go beyond the mere use of priorities are required. In Vehicle networks requirements are evolving and need better Quality-of-Service (QoS) options. This applies also for industrial networks implementation. Time Sensitive Networking (TSN) IEEE 802.1 Task working group are providing wide variety of Standard. This standard widens the design space for car networks so lots of ideas exists now. We try in this paper to the best of our knowledge, give insights about the studies that were developed for mainly for car networks as well as open issues that are still needs further research.
Impact that Website Design Elements on the Users of e-Commerce Websites

This paper presents a systematic review of the literature surrounding the topic of e-commerce websites. It will look specifically at how the design of a website affects an individual's willingness to spend money on the site and see what other factors are impacted by the design of the website. This paper used keywords to search through 5 different academic databases. The search conducted returned 158 papers that met the inclusion criteria, from the returned papers the researcher was required to read through abstracts and titles, in doing so 54 papers were selected that met the criteria and would contribute towards the study. The following five factors were found to be the most commonly impacted: service quality, satisfaction, loyalty, willingness to buy and trust. These five factors were the result of the following six elements: aesthetic appeal, complexity, perceived risk, perceived value, website content and usefulness.
An Investigation of Ensemble Methods to Classify Electroencephalogram Signaling Modes

This research focuses on the feasibility of synthetic algorithms, including Boosted Trees, Bagged Trees, Subspace KNN, Subspace Discriminant, RUSBoosted Trees for identifying brain wave signal patterns. With two datasets used, it is the one that measures the four types of human emotions (valence, arousal, dominance, like). The receiver consists of 11 states composed of the groups of facial, normal, and thinking signals. The research focuses on researching the above algorithms, using the wavelet transform to determine the signal's characteristics, then classifying, comparing the results, improving, and reaching a conclusion.
Riverbed Modeler Reinforcement Learning M&S Framework Supported by Supervised Learning

Riverbed Modeler is a useful simulation tool that can simulate a variety of standard network models. However, it does not provide a related tool that does not suit the situation in which research on applying machine learning to the network domain is actively progressing. In this paper, we implemented a framework to apply reinforcement learning in a riverbed modeler environment. In order to efficiently perform reinforcement learning, we proposed a reinforcement learning structure that supports supervised learning to improve network performance using Riverbed Modeler and MATLAB. The proposed method was evaluated that the learning time was shortened compared to the existing reinforcement learning environment through experiments.
Simulation Framework for Platooning based on Gazebo and SUMO

The role of autonomous cooperative vehicles will undoubtedly be important in Intelligent Transportation Systems (ITS) to increase both the safety and the overall efficiency of a high traffic network system. An autonomous platooning provides one promising strategy for decreasing total fuel consumption of a fleet of vehicles and potential risk of accidents, especially during long-distance transportation. In this work, we provide a proof-of-concept for a simulation framework in which it is possible to simulate platoon and other multi-vehicle systems using realistic vehicle models within different traffic scenarios, which is based on ROS, Gazebo and SUMO. The framework enables an easy-to-use perception and control modules of the autonomous driving stack for a realistic vehicle models, while preserving a convenient setup of different high traffic platooning scenarios. Consequently, it provides a platooning design step for conducting reliable development analyses and a platform for comparisons of different platooning strategies. We illustrate the effectiveness of the proposed platooning framework through three typical scenarios using a distributed model predictive control scheme with a platoon consisted of Toyota Prius car models.
Recognize Vietnamese Sign Language Using Deep Neural Network

World Health Organization published an article called ‘Deafness and hearing loss' in March 2020, it said that more than 466 million people in the world lost their hearing ability, and 34 million of them were children. Sign Language has been born and developed for a long time, but its application to communicate has met with many inadequacies and difficulties. Many methods of Computer Vision-based approach gave good results on Sign Language Alphabet Recognition but all of them require the perfect result from background removing step. However, when it comes to real life, removing a complex background is too difficult for any simple background removing algorithms. In this work, our main purpose is to build a model based on deep learning that can recognize Vietnamese Sign Language Alphabet in a complex environment. Results obtained show a robust accuracy of this model in recognizing Vietnamese Sign Language Alphabet.
Methodological approach for creating an IoT manufacturing application

With the heterogeneity and diversity of electronic systems and their memory capacity, the Internet of Things technologies face several challenges, such as the difficulty in choosing in a growing catalog of IoT technologies and the lack of interoperability of these technologies. This leads to the creation of particular IoT architectures and to the selection of IoT technologies by trying and error approaches. We propose a methodological approach for creating an IoT application guided by the users’ needs and by the contextual environment. It enables the user to select the right hardware guided by the market supply and to check the performances of the IoT solutions. We implement and test our proposition in a picking system of a manufacturing factory. This practical implementation provides feedback regarding our methodological approach for creating an IoT application, which in turn enables us to improve and remodel the approach.
ELECTRICAL TRANSPORT IN PURE AND BORON-DOPED CARBON NANOTUBES

The resistivities of individual multiwalled pure and boron-doped carbon nanotubes have been measured in the temperature range from 25 to 300 °C. The connection patterns were formed by depositing two-terminal tungsten wires on a nanotube using focused-ion-beam lithography. A decrease of the resistivity with increasing temperature, i.e., a semiconductor-like behavior, was found for both B-doped and pure carbon nanotubes. B-doped nanotubes have a reduced room-temperature resistivity (7.4×10−7–7.7×10−6 Ωm) as compared to pure nanotubes (5.3×10−6–1.9×10−5 Ωm), making the resistivity of the doped tubes comparable to those along the basal plane of graphite. The activation energy derived from the resistivity versus temperature Arrhenius plots was found to be smaller for the B-doped (55–70 meV) than for the pure multiwalled nanotubes (190–290 meV).
Boosting car plate recognition systems performances with agile re-training

In this work, we report an experimental study on an Automatic Licence Plate Recognition system developed and commercialized by a partner company, with the main goals of critically analysing the original system and of devising effective but minimally invasive design changes. From a scientific point of view, ours is an attempt of reducing the gap between the different experimental approaches in academia and industry. The system is organized in layers, with an initial car plate proposal step followed by a OCR step. To cope with the drawbacks of the pre-existing system, we inserted an intermediate CNN binary classification step to discriminate between plates and non plates independently from the OCR module. Our solution incorporates new data available from working installations, in a closed refinement loop. We evaluate the modified system on 8 different installations. With respect to the original performances, we obtained significant improvements with an impact on both false positive (-9.8%) and false negatives (-5%).
Mental fatigue testing based on deep learning

In order to monitor driver fatigue status in real time and assist safe driving, a driver fatigue detection system based on visual information is designed. The system detects face position based on AdaBoost algorithm and MTCNN algorithm respectively, and then extracts key points of face based on landmark LBF model. The driver’s fatigue state was judged by eye aspect ratio, mouth aspect ratio, nodding frequency, combined with PRECLOS SO value and other fatigue characteristics. The experimental results show that compared with Adaboost algorithm, MTCNN algorithm based mental fatigue detection system has stronger environmental adaptability and higher detection accuracy.
Constructive Interference-Based Symbol-Level Precoding Design for Millimeter-Wave Massive Multiuser MIMO Systems With Hardware-Efficient Hybrid Precoding Architecture

This study considers the constructive interference-based symbol-level precoding design problem in millimeter-wave massive multiuser multiple-input multiple-output systems with hybrid analog-and-digital precoding architecture. In contrast to traditional hybrid precoding architecture in which the analog part is implemented by infinite-resolution phase shifters, analog precoders are achieved in this study using low-cost and energy-efficient switches and inverters. Our objective is to design a hybrid precoder that minimizes the Euclidean distance between the hybrid precoder and the optimal fully digital precoder. However, such a design problem is difficult due to the intricate coupling of digital and analog precoder variables. Thus, we apply the alternating minimization (AltMin) framework to decouple the considered problem into digital and analog precoder design problems and then solve for digital and analog precoders in an alternating manner. By adopting the AltMin framework, we obtain a closed-form solution for digital precoder. Nevertheless, solving the analog precoder design problem incurs combinatorial complexity, which we address by proposing a coordinate descent algorithm to sequentially update each element of the analog precoder with a closed-form expression. Simulation results reveal that the application of the proposed algorithm for the proposed hardware-efficient hybrid precoding architecture provides the desired balance among symbol error rate performance, power consumption, and hardware cost.
Malware Detection Using Gradient Boosting Decision Trees with Customized Log Loss Function

The increasing number of malicious software spread through the Internet has become a serious threat. Malware authors use obfuscation and deformation techniques to generate new types of malware to evade the detection of traditional detection methods so that it is widely expected for machine learning methods that classify malware and cleanware based on the characteristics of the samples. The current research trend is to use machine learning technology, especially decision tree technology, to identify new malicious software quickly and accurately. The purpose of this paper is to investigate malware classification accuracy based on the latest decision tree-based algorithms with a custom log loss function. Therefore, we use the FFRI Dataset 2019 to construct baseline malware detection models from surface analysis logs and PE header dumps. Then, we customize a classification log loss function, makes an 82% reduction of false positives with sacrificing twice false negatives. To keep malware detection covering and quick countermeasure to true positive results, we propose a hybrid usage of normal log loss function model and custom log loss function model to give additional priority to positive results.
EACNet: Enhanced Asymmetric Convolution for Real-Time Semantic Segmentation

Although deep neural networks have made significant progress in semantic segmentation, speed and computational cost still can’t meet the strict requirements of real-world applications. In this paper, we present an enhanced asymmetric convolution network (EACNet) to seek a balance between accuracy and speed. Specifically, we design a pair of enhancing asymmetric convolution modules constructed by depth-wise asymmetric convolution and dilated convolution to extract short-range and long-range features, which are efficient and powerful. Additionally, we apply a bilateral structure in which the detail branch preserves low-level spatial details while the semantic branch captures high-level context information. The two branches are merged at different stages of the network to strengthen information propagation between different levels. The experiments on the Cityscapes dataset show that our method achieves high accuracy and speed with relatively small parameters. Compared with other real-time semantic segmentation methods, our network attains a good trade-off among parameters, speed, and accuracy.
Design Space Exploration for Heterogenous SoC Integrated with Matrix Accelerator

Accelerators have been widely used in SoC systems as an effective method to improve computing power. There are many ways of which CPU is coupled with accelerator. In this paper, we design a matrix-matrix multiplication accelerator and couple the accelerator with RISC-V CPU in two different forms - independent accelerator and instruction accelerators. Then we evaluate the performance of independent accelerator and new instruction accelerator. The matrix independent accelerator achieves up to 19.6x speedup to the Rocket CPU. And the matrix instruction accelerator achieves up to 44.5x speedup to the Rocket CPU. The instruction accelerator is 2.26x faster than independent accelerator.
Controlled synthesis of TiO2 nanoparticles and nanospheres using a microwave assisted approach for their application in dye-sensitized solar cells

Rapid and facile synthesis of similar to 7 nm and similar to 100-400 nm nano-structures of anatase titania is achieved by exploiting the chemical nature of solvents through a microwave based approach. After using these nanostructures as a photoanode in dye-sensitized solar cells, a modest yet appreciable efficiency of 6.5% was achieved under the illumination of AM 1.5 G one sun (100 mW cm(-2)).
BlinkFruity: A Real-Time EEG Based Neurofeedback Game for Brain-Computer Interface

The Brain-Computer Interface (BCI) is a communication channel between the brain and the computer. It works by detecting the production of currents due to brain cell activity. Among many non-invasive brain signal recording methods, Electroencephalography (EEG) is one of the most popular methods. EEG controlled applications widely range from strictly medical to non-medical applications. Non-medical applications can not only be used for entertainment purposes, but also can help a subject to experience BCI application, achieve better control with rehabilitation systems and can be a strong motivation to practice BCI system. Brain signals are weak and contain lots of artifacts like muscle movements, cardiac, eye blink, power source, and amplitude artifacts. Although eye blink is considered as one of the strongest artifacts, this study uses eye blink as a tool to control signal to play BlinkFruity game, where users collect fruits into a basket using eye blink only. To attain this objective, at first, the brain activity was recorded using OpenBCI device and 500ms window data was taken to process in real-time. Then the notch filter was applied to remove powerline noise. The eye blink was detected by using signal thresholding method reading from EEG data. Blink detection accuracy of 86.23% was obtained using blink control applying on first three subjects.
Substrate Integrated Waveguide Leaky-Wave Antennas with Tailored Characteristics for Millimeter-Wave Applications

This paper reports and discusses various categories of our recently devised and demonstrated leaky-wave antennas (LWAs), which have been proposed for radiofrequency (RF) and millimeter-wave (mmW) applications. Special technical features and elegant radiation behaviors are obtained, discussed and shown theoretically and experimentally such as low cross-polarization, low side-lobe, rapid frequency-driven beam-scanning and stable radiation. In this work, three basic types of LWAs, namely uniform, quasi-uniform, and periodic LWAs, are all considered and presented through the use of SIW technology. Those LWAs can be tailored specifically for emerging mmW applications such as 5G, radar sensors, IoT, and internet of space.
Incorporating Multi-Agent Systems Technology in Power and Energy Systems of Bangladesh:A Feasibility Study

The power sector of Bangladesh is presently experiencing essential changes as demand for power services is increasing with rising population and economic development. With a gradual shift from a rigidly centralized structure to a more decentralized and fluid setup, fundamentally because of the enormous advancement of distributed renewable energy sources, the future power system of the nation requires new control strategies to work efficiently and sustainably in the face of evolving conditions and constraints. Multi-Agent Systems (MAS) technology has attributes that meet these prerequisites of modern power systems and has been shown to be effective in dealing with its distributed and complex nature. This is a literature-based feasibility study to explore whether MAS technology is suited to be applied in the context of Bangladesh. For this preliminary paper, we look at the topic from a holistic perspective and conduct a meta-review to curate common applications of Multi-Agent System-based concepts, tools and algorithms on the power and energy sector. We also identify the top challenges of this domain in Bangladesh and connect the potential MAS-based solutions to address each challenge. Our qualitative assessment is motivated to provide a starting point for local researchers eager to experiment with MAS technology for application in Bangladesh.
Trajectory prediction based on AIS and BP neural network

According to the characteristics of ship navigation and the demand of ship trajectory prediction, this paper proposes a prediction method combining AIS information and BP neural network. Based on the characteristics of AIS ship information, the BP network structure is constructed to predict the future trajectory with the input of historical trajectory and current information. The results show that the BP neural network is feasible for trajectory prediction.
Segmentation Algorithm for 3D LiDAR Point Cloud Based on Region Clustering

Environmental perception is a key technology in autonomous cars and mobile robotics system. The clustering and segmentation of the point cloud data obtained by sensors is an important step to realize environmental perception. In order to solve the shortcomings of over-segmentation in some current segmentation algorithms, a fast segmentation algorithm for 3D LIDAR cloud points is proposed. The Ground Plane Fitting algorithm is used to segment the ground surface, remove the ground cloud points interference, and combine the spatial Euclidean distance parameter with the angle parameter of the adjacent scan line of the LIDAR to cluster and segment the non-ground cloud points. The experimental result show that compared with some traditional algorithms, this method can effectively reduce the segmentation error rate, improve the accuracy of non-ground target segmentation, and significantly improve the segmentation efficiency.
Spatio-Temporal Degree of Freedom: Interference Management in 5G Edge SON Networks

According to Cisco’s recent white paper, the usage of mobile data is exponentially increasing by 2022, namely 330% enhancement compared to 2017. Therefore, improvement of network capacity can be placed on the top priority in the future 5G/6G network systems. To tackle this issue, in this paper, we develop a small cell interference management technology with controls of transmission power of base stations (BSs) and user scheduling under edge SON (Self-Organizing Networks) architecture. In order to resolve the severe interference problem as the cell size decreases, this paper proposes an idea to share power in time and space, and develop the joint transmission power and user scheduling algorithm in each time slot aiming to maximize sum utilities of all users leveraging the Lyapunov optimization framework. Finally, we verify and compare the performance of the proposed algorithm and comparing algorithms in the multicell and multi-user scenario.
Refined Signal Model of Meter Wave Radar for Height Measurement Under Complex Terrain

Meter wave radar height measurement is one of the most difficult problems in meter wave radar system. At present, the most popular height measurement methods of meter wave radar include beam splitting method, MUSIC algorithm and maximum likelihood estimation method, which can achieve good performance under flat ground. However, the real ground is not ideal condition, and the situation of undulating ground is also very much. Under this condition, the performance of the above algorithm decreases a lot. It is an effective way to solve the problem using enhance the robustness of ground mismatch. However, the authors think that the key to solve the problem is to establish a refined height measurement signal model. In this paper, the accurate model of reflection coefficient, reflection height and reflection point under the condition of undulating ground is established to provide accurate signal model for meter wave height measurement.
An Approach to Mitigate Composition Attacks on Privacy in Non-coordinated Environments

Nowadays individual’s private and confidential information is available at hospitals, banks, or any other organization. Organizations share that information in a manner to protect individuals’ privacy. Many well-known techniques have been proposed to do that such as k-anonymity, l-diversity, etc. But the majority fails to protect privacy when one patient goes to two hospitals for medical care; and his/her confidential information disclosed by tracking two disparate published data sets with a composition attack. Though this attack can be easily mitigated when organizations coordinate prior data publication. However, such coordination is not always possible. In this paper, we introduce a model that uses a public knowledge domain to reduce the risk of composition attacks without coordination. The result shows that our approach reduces the rate of composition attack compared with other well-known models.
Intelligent RF Circuits and Systems with Memory Elements

In this article, various RF circuits and systems with memory elements are discussed. The non-volatility of memory elements can be used to design novel reconfigurable RF circuits and systems with the abilities of self-learning and evolution. A memory RF switch with the copper/Nafion/gold structure, a tunable bandpass filter with memory RF switches, a smart antenna with memcapacitor-based phase shifters, and the concept of computational RF neural network are presented and the potential realization technologies are also provided. The memory RF circuits and systems are expect to open the door of many promising intelligent applications in future.