Miracle-dz/UniFAR-FTUs huggingface dataset:
The Design of Tilted Beam High Impedance Surface for Beam Steering Applications

The electromagnetic properties of a metal ground plane can be altered by including a periodic surface texture. Such surface textures can have high surface impedances, which consist of an array of metal patches with periodicity of patches less than the free space wavelength. The proposed high impedance surface eliminates the propagation of surface waves and thereby reducing the unwanted mutual coupling effect. In this paper the design of such high impedance surface is proposed for beam steering applications. The surface can steer in four corners with beam angle of 62’ and generates a tilted beam with maximum peak gain of 8dBi. This proposed electromagnetic surface offers a low cost alternative for beam steering antennas.
Smart Grid based decentralized Peer-to-Peer Energy Trading Using Whale Optimization Algorithm

Increased penetration of distributed energy resources (DERs) in smart grids (SG) has sparked a new movement to consumer-centric structure as marketplaces based on peer-to-peer (P2P) models. Participants can directly agree bilateral power transactions in P2P markets to balance producers and consumers. The trading mechanism should be well-designed to encourage participants to operate actively in the trading activity. This article proposes a trading strategy for P2P strategy in SG. The proposed framework is modeled using the Whale optimization algorithm (WOA). In order to evaluate the proposed optimization method, particle swarm optimization (PSO) and classical optimization methods are carried out. The compared results show that the convergency of proposed method is faster than PSO algorithm.
A Hybrid Deep Neural Network for Classifying Transportation Modes based on Human Activity Vibration

Sensor advanced technologies have facilitated the growth of various solutions for recognizing human movement through wearable devices. Characterization of the means of transportation has become beneficial applications in an intelligent transportation system since it enables context-aware support for the implementation of systems such as driver assistance and intelligent transportation management. Smartphone sensing technology has been employed to capture accurate real-time transportation information to improve urban transportation planning. Recently, several studies introduced machine learning and deep learning techniques to investigate transportation utilization from multimodal sensors, including accelerometer, gyroscope, and magnetometer sensors. However, prior work has been constrained by impractical mobile computing with a large number of model parameters. We tackle this issue in this study by providing a hybrid deep learning model for identifying vehicle usages utilizing data from smartphone sensors. We conducted experiments on a publicly available dataset of human activity vibrations called the HAV dataset. The proposed model is evaluated with a variety of conventional deep learning algorithms. The performance assessment demonstrates that the proposed hybrid deep learning model classifies people's transportation behaviors more accurately than previous studies.
POPDNet: Primitive Object Pose Detection Network Based on Voxel Data with Three Cartesian Channels

In this article, the vision problem in a robotic application is under focus to handle the grasping of objects based on a new method. Converting an object into primitive objects is assumed to be done in the first step of the vision scenario. The second step, which is the main contribution of this paper, is classifying a primitive object and determining its position, orientation, and dimensions. In this way, the voxel data with three Cartesian channels of a primitive object is considered the input of a convolutional neural network that extracts the required parameters. A virtual camera in the simulation tool (Gazebo) is used to prepare the necessary dataset for training the neural network. Although the use of voxel data with Cartesian channels increases the volume of input data and slows down the processing speed, it is shown in this study that it effectively improves the accuracy of the network in estimating the parameters of primitive objects. Based on the provided virtual dataset, the mean errors when using Cartesian channels are decreased 81%, −33%, and 53% for the position, orientation, and dimensions, respectively, compared to binary voxel data. In the same comparison, these errors are −7%, 80%, and 55% lower than RGB data.
Implementation of Dynamic HSV Adjusting Method for Lane Detection

We propose design and implementation of system that dynamically adjusts value range of HSV using a photo resistor. In autonomous driving, lane detection is operated by fixing the color range of the lane. However, the color range of the lane changes with environment (time variations, Illumination, etc.), so it is necessary to adjust the range. To do this, we create matrix that make correspond to measured illumination and HSV value. And we designed lane detection Including this matrix. Experiment was conducted in two ways: using headlight and not using it. Experimental results showed that lane was detected despite changing illumination levels.
CA-Market: A Partially Categorical AnnotatingApproach Based on Market1501 Dataset for Attribute Detection

In this paper, a new partial categorical attributes dataset (CA-Market) based on images of the Market1501 dataset has been introduced, for the sake of improving the attribute detection task. Most attributes detection datasets (human appearance features detection) are not partially categorical and do not properly take into account the inner classes diversity. Increasing the diversity of inner parts (gender, head, upper-body clothes, lower-body clothes, bags, shoes, and colors) before annotating can ease the decision-making process by dividing labels into individual categories. CA-Market contains 46 binary attributes in 10 parts from head to foot and their colors which are annotated in image-level. For example, the attributes of the leg part are skirts, shorts, and pants which are carefully chosen to be categorized for a classification task. In this research, the effect of the labeling approach is studied. Hence, a common classification method is used and only datasets or baselines are changed for comparisons. Baselines are based on Omni-Scale, Resnet50, and Hydra-Plus architectures to compare the CA-Market1501 dataset with the Market1501 attribute dataset in the same setting. CA-Market demonstrates a new representation of data as a part-based format which can gain better results. This approach, without adding any extra modules, achieved a significant enhancement. For instance, accuracy in the vectorized format is over 92%, in the categorized is over 90% which shows the effectiveness of part-based attribute annotating. Also, hair, backpack, upper color, and lower color as the common attributes between Market1501-attribute and CA-Market datasets are achieved 90.26, 88.04, 94.55, and 94.18 classification accuracy which can outperform existing state-of- the-art approaches.
Impact of datasets on machine learning based methods in Android malware detection: an empirical study

For Android malware detection, machine learning-based (ML-based) methods show promising performance. However, limited studies are performed to investigate the impact of factors related to datasets on ML-based methods, while the performance of ML-based methods dramatically relies on datasets. To partially bridge the gap, we conduct an empirical study to investigate the impact of factors related to datasets on ML-based Android malware detection methods. By investigating dataset differences between real-world scenarios and experimental settings, we summarize three dataset factors (i.e., class imbalance, quality, and timelines) and assess the impact of these factors on ML-based Android malware detection methods. We conduct experiments on more than 11K benign and 17K malicious applications. The results show that these three dataset factors yield significant biases in the existing ML-based Android malware detection methods. Based on these results, we learn some lessons about assessing ML-based Android malware detection methods when taking dataset factors into account.
Performance Analysis of Intelligent Reflecting Surface-Assisted Orbital Angular Momentum-Based Communication Systems

Uniformed circular array antenna (UCA) based orbital angular momentum (OAM) communication system has some disadvantages, such as not being suitable for long-distance multiplexing and requiring a high degree of synchronization between the transmitting and receiving antennas. To achieve non-line-of-sight (NLoS) communication using OAM radio waves, we propose an inter-mode interference (IMI) cancellation method for intelligent reflecting surface (IRS)-Assisted OAM multiplexing to eliminate the IMI from all OAM modes. We also propose a L2-ba11 projection method to optimize the transmit power allocation for OAM modes to increase the system capacity of IRS-assisted OAM communication systems. The IMI cancellation method and the L2-ball projection method are shown to achieve increasing the maximum system capacity as compared to the L1-ball projection method when the IRS is located closer to the middle between the transmitter UCA and receiver UCA.
State-of-Art Analysis of Multiplier designs for Image processing and Convolutional Neural Network Applications

Recently, due to the immense growth of computing power, image processing and Convolutional neural networks (CNN) have regained gigantic attention because of the exemplary performance in Image modification and classification applications. The multipliers are the indispensable circuit components in improving energy efficiency of hardware implementations of CNN and image processing techniques. In this paper, we have presented a comprehensive study on state-of-the-art multipliers for these applications. Hardware platforms for deploying image processing and CNN are briefly described and multiplier implementation on these are discussed. A detailed discussion on FPGA based embedded multipliers and ASIC based multipliers is presented. Emerging multiplier architectures for CNN applications are compared. The strategies used for designing approximate multipliers and error compensation are also summarized.
Design and Fabrication of a Flight Controller for Unmanned Aerial Vehicles

This paper sums up the approach of designing and fabrication of the flight controller for unmanned aerial vehicles. There are many existing flight controller designs which use a wide range of components. However, in order to understand the functioning of the flight controller in detail and to have a practical approach towards the same, we have designed a flight controller and have realized it into a 3D CAD model. This paper covers the selection of components, approach taken to initiate design process, theoretical analysis of performance of the components and visualization of 3D CAD model of the flight controller.
Design of Portable Intelligent 3-axis Magnetic Detective and Orientation Instrument

This paper presented a new idea of designing a 3-axis intelligently digital magnetic detective and orientation instrument for the weak magnetic field detecting.A HMC1043 magnetic-resistive sensor,which was manufactured by Honeywell,turned the magnetic intensity into difference electrical voltage.After amplification,the voltage signal was converted to digital signal by ADC.LCD screen was used to display the measurement result which was calibrated and compensated by the software of the microprocessor AT89S52.The testing results of the magnetometer prove its good performance.
TAIGA: a novel dataset for multitask learning of continuous and categorical forest variables from hyperspectral imagery

The spectral and spatial resolutions of modern optical Earth observation data are continuously increasing. To fully utilize the data, integrate them with other information sources and create applications relevant to real-world problems, extensive training data are required. We present TAIGA, an open dataset including continuous and categorical forestry data, accompanied by airborne hyperspectral imagery with a pixel size of 0.7 m. The dataset contains over 70 million labeled pixels belonging to more than 600 forest stands. To establish a baseline on TAIGA dataset for multitask learning, we train and validate a convolutional neural network to simultaneously retrieve 13 forest variables. Due to the size of the imagery, the training and testing sets were independent, with strictly no overlap for patches up to 45×45 pixels. Our retrieval results show that including both spectral and textural information improves the accuracy of mapping key boreal forest structural characteristics, compared with an earlier study including only spectral information from the same image. TAIGA responds to the increased availability of hyperspectral and very high resolution imagery, and includes the forestry variables relevant for forestry and environmental applications. We propose the dataset as a new benchmark for spatial-spectral methods that overcomes limitations of widely used small-scale hyperspectral datasets.
Automated Testing of Android Applications Integrating Residual Network and Deep Reinforcement Learning

With the improvements of Deep Reinforcement Learning (DRL), there have been tremendous interests in utilizing DRL for automated application testing. However, most automated testing methods based on reinforcement learning have the problem of interacting with invalid UI areas and invalid interactions with controls. To solve this problem, this paper extracts the page features, constructs the Interactive Control Feature Diagram(ICCD); improves the DDQN network structure, adds the residual network, makes the algorithm take the picture as the input, and splits the original single output action(n*w*h) into two successive outputs: the interaction(1,n) and the position(1,w*h); a new reward function which combines the interaction times and the image similarity of ICCD is proposed to explore different UIs and ensure that there will be more than one action will be executed under the same UI. Experiments are carried out on five open source applications. The experimental results show that the proposed method is superior to other methods in code coverage and branch coverage.
Beyond fear go viral: A machine learning study on infodemic detection during covid-19 pandemic

With the restrictions in our daily life activities under the current situation of the covid-19 pandemic worldwide, billions of people rely on social media platforms to share and obtaining covid-19 related news information. This made social media platforms easily be used as a source of myths and disinformation, which can cause severe public risks. It is thus of vital importance to constraint the spread of misinformation to the public. Although many works have shown promising results on the misinformation detection problem, only a few studies focus on the infodemic detection during the covid-19 pandemic, especially in the low resource language like Thai. Therefore, in this paper, we conduct extensive experiments on the real-world social network datasets to detect misinformation about covid-19 targeting both English and Thai languages. In particular, we perform an exploratory data analysis to get the statistic and characteristics of real and fake content. Also, we evaluate a series of three feature extraction, seven traditional machine learning, and eleven deep learning methods in detecting the fabricated content on social media platforms. The experimental results demonstrate that the transformer-based model significantly outperforms other deep learning and traditional machine learning methods in all metrics, including accuracy and F-measure.
Process Mining on Learning Activities in a Learning Management System

A learning management system is a system that supports the management of teaching and learning activities in an educational institution. Telkom University has its own learning management system called CeLOE which was developed based on the open-source Moodle application. This study analyses an event log, automatically generated by the CeLOE LMS, that records student and lecturer activities in learning. The event log is mined to obtain a process model representing learning behaviours of the lecturers and students during the learning process. The case study in this research is learning in the study program 365 during the first semester of 2020/2021. The results of the analysis obtained are expected to increase understanding of the learning process and produce recommendations for improving the learning management system. The conclusions obtained from the analysis of the process model include the identification of activities and learning content that are used and carried out by students and lecturers, the most frequent learning activities and resources done by students and lecturers during the first semester of 2020/2021 in the study program 365, and the gap of activities and learning resources during the first semester of 2020/2021 in the study program 365.
Real-Time Path Planning for Trajectory Control in Autonomous Driving

We propose an algorithm that enables real-time path planning and trajectory control of autonomous vehicles utilizing the detected lane, minimum turning radius, and driving radius. Path planning and trajectory control for autonomous driving are the key technologies in dynamic environments. For trajectory control, steering angle calculation is essential. This calculation mainly uses the next position information from GPS and predefined paths. For path planning and trajectory control, autonomous vehicles use sensors or communication systems to obtain information about their surroundings. However, it may be difficult to obtain the required path information for trajectory control because of environmental factors and surrounding conditions. To alleviate these uncertainties, we propose an algorithm that utilizes the next position generated in real time based on the detected lane information obtained from various sensors. The next position is generated by the intersection and midpoint based on the detected lane, minimum turning radius, and driving radius considering speed. Then, the steering angle is calculated based on the generated next position using a pure tracking algorithm. We simulated four different speeds at a roundabout. The obtained simulation results show that our algorithms work well in real-time driving at various speeds.
Design of Day-Lily Robot Recognition System Based on ZYNQ

With the development and wide application of robots in agricultural production, more and more agricultural fruit and vegetable robot schemes had been put forward, but the research on day-lily robot is less. Aiming at the problem of high labor cost in the current day-lily picking process, this paper designed a portable, low-cost embedded recognition system based on ZYNQ based on the characteristics of the ripe fruits of day-lily. A low-cost portable embedded recognition system was designed based on ZYNQ for day-lily robot. ZYNQ processing board was used to recognize the fruit image, and the combination of traditional image processing and machine learning recognition algorithm was used to realize the efficient real-time recognition of the target fruit. Traditional image processing combined with machine learning recognition algorithm could be transplanted to ZYNQ processing board which had the FPGA, which could better guarantee the real-time image recognition. Moreover, this recognition system has high recognition efficiency, suitable accuracy and low cost, which could meet the identification requirements of portable and low cost of agricultural robots at the same time.
Explainable APT Attribution for Malware Using NLP Techniques

APT attribution for malware refers to the process of identifying characteristics that are related to the APT group of an anonymous malware. This paper presents a novel approach for APT attribution. Our approach innovatively combines code features and string features for APT attribution, using paragraph vectors and bag-of-words vectors to represent function semantics and behavior reports, respectively. We apply the model interpretation to APT attribution for the first time, using Random Forest Classifier (RFC) and Local Interpretable Model-agnostic Explanations (LIME) to interpret the model results. We evaluate the method on a data set collected from threat intelligence reports. The results show that our method has advantages in feature selection, method application, and accuracy. Importantly, this article provides a detailed description and examples about the process of model interpretation. Model interpretation improves the trust of cyber security personnel in the model, and facilitates the analysis of network attacks and threat intelligence.
Spatially Variant Sidelobe Suppression for Linear Array MIMO SAR 3-D Imaging

Linear array (LA) multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) has the capacity of achieving 3-D images in a single pass. If processed by matched filtering-based linear 3-D imaging, sidelobe suppression is often required for image quality enhancement. However, in the case of imaging a large target in a short range, sidelobes of the target will become spatially variant and curved, and traditional sidelobe suppression methods will fail. This article proposes a new spatially variant curved sidelobe suppression method for LA MIMO SAR short-range 3-D imaging. The key technique is the employment of a new pseudopolar coordinate system where both the spatial variance and the curvature of 3-D sidelobes can be removed. Specifically, a new 3-D spatially variant apodization (SVA) kernel is applied for sidelobe suppression to maintain the resolution performance. The validity of the presented approach has been demonstrated via computer simulations, the tower crane experiment, and the unmanned ground vehicle experiment.
Financial Data Time Series Forecasting Using Neural Networks and a Comparative Study

Algorithms that are based on Machine learning & Deep learning are new methods to solve time series prediction challenges. These methods have been proved to yield better results than the traditional regression models. A study concluded that artificial Recurrent Neural Networks such as Long Short Term Memory and Autoregressive Integrated Moving Average models are top models for this application. This research reports analysis and comparison between neural networks such as Convolution Neural Network, Long Short Term Memory, Bidirectional LSTM and Autoregressive Integrated Moving Average models. Comparative analysis of the above models shows that the BiLSTM performs better than ARIMA, CNN and LSTM models.
Recent Advancements in FPGA-based LSTM Accelerator

In this paper, a review of execution of Long Short-Term Memory Accelerator on FPGA is presented. The paper starts with a brief overview on the significance of LSTM accelerators, historical background and general structure of LSTM accelerators. Then recent works in this area are highlighted. The research in this domain is focused on modifying the architecture of LSTM to overlap the computation of different stages in the network. This yields less power and higher performance as compared to the corresponding software implementation. The implementation on FPGA provides user with the ability to tradeoff resource utilization and speed of operation by adjusting the amount of parallelization for different operation such as vector multiplication and addition. This offers flexibility to adjust the computational load for different FPGA boards.