Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things (IoT) based product usage and Consumer’s Behavior: A Structural Model Approach

The term IoT or Internet of Things, refers to the shared network of connected devices and the technology that assists communication between devices and the cloud. Companies thus track these consumer’s data and analyze the consumer’s behavior to offer personalized products and services. However, consumers are still cynical in using IoT based products due to technology understanding level, privacy risk and information requirement. Thus to overcome these blockades, the study seeks to measure the relationship among the latent variables that affect consumer’s inclination and choices to use IoT enabled products. On the basis of motivational theories, two categories of motivational factors are fashioned: external factors such as entertainment and social interaction and internal factors such as information required, privacy risk and technology understanding level. The unidirectional relationships among these latent variables are established using the structural equation modeling (SEM). The research endeavors to offer descriptive study on the IoT based product and consumer’s behavior to augment the prevailing awareness and support firms in the process of appealing to more consumers and developing new personalized products. The major findings of the study corroborates that positive social interaction drives consumer’s behavior in using IoT based products for entertainment. The Information required is positively correlated with the desire to use IoT based products for Entertainment. Interestingly, the technology understanding level and low privacy risk do not drive consumers for IoT products either for entertainment or social interactions. The research concludes with comments on implications, limitations and recommendations for further studies.
Memristor based In-Memory Computing for Edge AI Applications

Cloud computation overload significantly reduces with the improvement in the computation capabilities of edge devices. The development of sensor devices and networks connected through the internet results in a continuous increase in the volume and variability of data. Processing-in-memory can significantly reduce the data processing challenges utilizing memristor-based neural network hardware for edge devices. In this paper, we have briefly reviewed various materials and logic styles for memristor-based computations. Various synapse structures using memristor and different neuron cells are presented, which shows superiority over CMOS-based neural networks. The comparative discussion evaluates the performance of various non-volatile memory technologies for edge applications.
Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications

In the last year, many modern attacks have targeted Internet of Things (IoT) devices. IoT botnet attacks are the most common. Despite the plethora of detection and prevention systems for cyberattacks, a security mechanism for IoT settings is still required since these devices have certain restrictions that make it difficult to properly implement the detection system. These memory resources are limited to processing, storing, and calculating. An effective defense against cyberattacks is an intrusion detection system (IDS). To develop the rules for IDS_MDL-IoT, we used the contemporary botnet attack dataset. Because IoT devices have limited resources, it is difficult to employ all attack patterns in the initial anomaly dataset in traditional anomaly-based IDS. Therefore, in this study, the tested ML model resulting in NB has proven to be the best classification algorithm to classify the risk of attack with 99.7% accuracy predicting from a time-speed of 3 seconds, and the tested DL model resulting in RNN has proven to be the best classification algorithm to classify the risk of attack with 99% accuracy predicting from a time-speed of 8 seconds.
Multivariable Disturbance Observer-Based Finite-Time Sliding Mode Attitude Control for Fixed-Wing UAVs Under Matched and Mismatched Disturbances

In this letter, we propose a multivariable disturbance observer-based finite-time sliding mode attitude control (MDOB-FT-SM-AC) for fixed-wing UAVs in the presence of both matched and mismatched disturbances. Compared with existing sliding mode attitude controllers, the significant improvements of the proposed MDOB-FT-SM-AC are the multivariable control structure, strong robustness, and high precision performance with continuous control input signal. In the proposed MDOB-FT-SM-AC, we first develop multivariable finite-time disturbance observers such that the precise estimation of both matched and mismatched disturbances is ensured. Next, a nonsingular terminal sliding manifold is designed such that the fixed-wing UAV is driven to track its desired attitude command in finite time. We finally present a multivariable super-twisting reaching law such that the finite-time convergence of the sliding variable and its derivative to zero is guaranteed. Attentive finite-time convergence analysis is derived based on the Lyapunov and homogeneity theories. Simulation results are given to illustrate the superiority of the proposed MDOB-FT-SM-AC.
Multimodal Transformer Network for Hyperspectral and LiDAR Classification

The land cover classification of single-modal remote sensing (RS) data has recently reached a bottleneck. The joint use of multimodal RS data to improve the classification performances has received much attention. Convolutional neural networks are powerful tools in feature extraction and contextual modeling. While they have attendant drawbacks to capture the sequence attributes of spectral signatures and struggle to acquire discriminative spectral–spatial features from a global perspective due to limitations inherent in their network backbones. The transformer backbone is a promising approach for addressing these challenges and generating novel insights in the multimodal RS image classification. In this article, we present a new model called multimodal transformer network (MTNet) that leverages transformer advantages to capture both the specific and shared characteristics of hyperspectral (HS) and light detection and ranging (LiDAR) data. HS images contain a wide range of bands with rich spectral information and LiDAR data provide accurate elevation information without affecting by environmental factors. The well-designed module HS spectral transformer can learn spectrally local sequence information from neighboring bands of HS images, yielding groupwise spectral embeddings comprising rich diagnostic information about land covers. Furthermore, the HS and LiDAR spatial transformers aim to mine the pixelwise feature embedding relationships in a global manner, capturing spatial and elevation information of HS and LiDAR, respectively. Finally, the feature embedding tokens of two modalities are integrated jointly and a new transformer encoder is redesigned to explore the shared spatial characteristics between the two modalities. We evaluate the classification performances of the proposed MTNet on three public HS-LiDAR datasets by conducting extensive experiments, exhibiting superiority over conventional classifiers and state-of-the-art networks.
Passive Control Modelization of a Fluid Flow Coupled with Convective Heat Transfer around a Heated Square Cylinder by using the LBM-MRT

This work presents a numerical study on the passive control of a fluid flow coupled with convective heat transfer by using the lattice Boltzmann method. The study focuses on simulating the fluid flow and temperature distribution around a heated square cylinder controlled by three partitions and placed in a two-dimensional channel (Figure 1) at a fixed Reynolds number (Re =160). The addition of the partitions allows to control the vortex shedding, reduces the fluidic forces applied to the cylinder and improves the heat transfer characteristics around the square cylinder. The effects of the location and length of the partitions on the streamlines, isotherms and drag force are studied. The results obtained show that the inclusion of partitions leads to a 51.23% reduction in the drag coefficient and allows significant heat exchange.
Detecting and Preventing for Performance Assessment of IoT Devices Under Dodag Information Solicitation (dis) Attacks

The Internet of Things (IoT) is an important area of research as the ever-increasing number of connected devices permeate every aspect of our daily lives. The main characteristic of IoT devices is that they tend to be low resource devices, that are incapable of sustaining high power consumption activity for a long time, and they also tend to operate on lossy networks. Therefore, the term (Low Power and Lossy Networks - LLNs) is used in reference to such networks. The routing protocol used on IoT networks is typically RPL (Routing Protocol for Low-Power and Lossy Networks). These characteristics make IoT devices vulnerable to a variety of attacks; the most common of which are (DIS-DODAG Information Solicitation) attacks. This is a routing protocol attack that targets IoT resources, especially the battery of devices. This paper proposes a simulation and analysis of the DIS attack impact on the network performance in terms of energy consumption, as well as study the relationship between the number of attacker nodes and the IoT lifetime. We analyze number of attack scenarios to figure out the relationship between the number of attacker nodes and the power consumption among the IOT device and the reflect on its lifetime. We found that the power consumption inversely proportional with the number of attacker nodes as the power consumption increased by 212.374% with four attack nodes and by 650% with one attacker node and so the IOT life time decreased 2.21 times and 6.5 times respectively. We also proposed two novel algorithms for detecting and mitigating the DIS-attacks, and we simulate and evaluate them finding that they are very effective in stopping both slow/fast attacks and minimizing the false positives, as we simulated six attack scenarios with different attack rates and by studding the Attack Discover Performance we found that The system is Robust Enough to discover/stop high wave of Security intrusions.
LUFlow: Attack Detection in the Internet of Things Using Machine Learning Approaches

The number of Internet of Things (IoT) equipment and the data those devices produce have both increased significantly over the past few years. Due to their resource limitations, IoT network participants can be problematic, hence it’s important to integrate security on these devices. Attackers now have more reasons to target IoT devices due to this. It is crucial to develop strategies to counter such attacks and shield IoT devices from malfunction. In order to accurately detect attacks and abnormalities in the IoT environment, the performances of multiple machine learning (ML) approaches have been compared in this paper. Here Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Random Forest (RF) have been applied as ML approaches utilizing the most recent LUFlow dataset. The performance measures such as accuracy, recall, precision and f1-score have been calculated for the comparison of the above-mentioned ML techniques. The system found that RF had the highest accuracy, at 97.7%, compared to SVM and KNN, which had accuracy rates of 92.80% and 94.7%, respectively.
A Synthesis Method for N-Section Unequal Ultra-Wideband Wilkinson Power Divider With Controllable Equal-Ripple Performance

In this paper, a novel synthesis method is presented to design of a class of multi-section ultra-wideband (UWB) Wilkinson power dividers (WPDs) with an arbitrary power ratio. The proposed UWB-WPD topology consists of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> cascaded asymmetric coupled line sections (ACLSs) and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> isolation resistors. Through even- and odd-mode analysis, the proposed synthesis method is demonstrated with a few unique novelties as summarized: (1) It is for the first time proved that the first kind Chebyshev equal-ripple performance can be exactly realized by the cascaded transmission-line transformer without Hansen’s approximation; (2) It is also for the first time revealed that all the <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-parameters (<inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$S_{31}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$S_{22}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$S_{33}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$S_{32}$ </tex-math></inline-formula>) of proposed WPD can achieve controllable in-band equal-ripple performance. To verify the effectiveness of proposed synthesis method, several design examples are presented and designed for demonstration of varied equal-ripple responses. Finally, two WPD circuits with <inline-formula> <tex-math notation="LaTeX">$N = 3$ </tex-math></inline-formula> are fabricated, and the measured results well validate the predicted ones from proposed method.
Quantifying Biomedical Amplifier Efficiency: The noise efficiency factor

There has been a long-standing interest in controlling and instrumenting the human body. Whether to restore lost function with neural prosthetics, monitor blood glucose levels, or augment human capabilities, there are countless opportunities for sensors inside (e.g., ingestible, injectable, and implantable) and outside (e.g., wearable) the body. However, many challenges exist when instrumenting the body. First, many use cases (e.g., implanted sensors) require long-term recording to capture anomalous behavior—sometimes with limited accessibility—necessitating ultralow power consumption. Second, the power reduction challenge is further exacerbated by size constraints, which limit battery capacity or harvestable energy levels. Third, the signals of interest are often low bandwidth (kHz) and small in amplitude (µV to mV); thus, low-noise front ends are needed. Addressing these challenges has led to a large body of work on the design of highly power-efficient, low-noise amplifiers for biomedical integrated circuits.
Human-Robot Collaboration Based on Gaussian-Mixture Model

A method based on Gaussian Mixture Model(GMM) is proposed for learning the relationship between image observation and robot joint angles. When robot and human are working together, observation variables are acquired through camera image and GMM is the used to compute the generative probability of that observation. The generative probabilities of different types are unified and the type which has biggest posterior probability is selected as the identification result. And the Gaussian Process of that type is used to generate a group of joint angles to control the movement of robot, which can adapt to the change of human movement. The whole process is proceeded in a Bayesian mechanism, which can reduce the need for training sample and improve system performance. And the experiments show that GMM can approximate the nonlinear relationships, which makes robot learn directly from demonstrations, and thus eliminates the need for camera calibration as well as inverse kinematics of robot. The proposed method can build an learning model in limited demonstration data set and is suitable for modeling multiple collaborative patterns in different space zones.
A Novel Multi-Agent Deep RL Approach for Traffic Signal Control

As travel demand increases and urban traffic condition becomes more complicated, applying multi-agent deep reinforcement learning (MARL) to traffic signal control becomes one of the hot topics. The rise of Reinforcement Learning (RL) has opened up opportunities for solving Adaptive Traffic Signal Control (ATSC) in complex urban traffic networks, and deep neural networks have further enhanced their ability to handle complex data. Traditional research in traffic signal control is based on the centralized Reinforcement Learning technique. However, in a large-scale road network, centralized RL is infeasible because of an exponential growth of joint state-action space. In this paper, we propose a Friend-Deep Q-network (Friend-DQN) approach for multiple traffic signal control in urban networks, which is based on an agent-cooperation scheme. In particular, the cooperation between multiple agents can reduce the state-action space and thus speed up the convergence. We use SUMO (Simulation of Urban Transport) platform to evaluate the performance of Friend-DQN model, and show its feasibility and superiority over other existing methods.
Evaluation of Web-Based Market Place Using Dynamic Programming Algorithm with Data Analytics for Agricultural Products

A web-based market place is developed to oblige sellers to increase their income and sales. Several approaches are utilized using the internet product markets, but none are legitimate or inaccurate. This study analyzes agricultural entities using a dynamic programming procedure to build a system application called “Online Agricultural Marketplace System (DEMETER),” which supports farmers and increases harvest deals and earnings. Incorporating information and communication technology into the agriculture has been studied. A DPA can be utilized to answer complicated questions that computer programmers face. Instead of explaining why a specific phenomenon occurred, researchers used descriptive research to describe the features of the area in which the primary interest in this study. interested. Moreover, the researchers used data visualization to provide a visual representation of the acquired data during the data analysis process. This enables researchers to construct a visual environment for gaining insights and identifying patterns and trends in the acquired data.
Variable-Order Fractional Calculus: from Old to New Approaches

Different approaches to introduce fractional derivatives and integrals of variable order have been proposed. The majority of these operators are obtained by replacing the constant order with some function in the time-domain formulation of the usual fractional-order operators. Here we present a completely different approach relying on the generalization of fractional integrals and derivatives in the Laplace transform domain, according to some pioneering ideas by Scarpi. By framing these ideas in the modern mathematical theory of General Fractional Derivatives and Integrals (based on the well-known Sonine condition) we are able to overcome the main issues of other variable-order operators. Numerical strategies for handling these operators are also discussed.
LMI-Based Design of an Affine PD Controller for the Robust Stabilization of the Knee Joint of a Lower-Limb Rehabilitation Exoskeleton

Lower limb exoskeletons have recently received a lot of attention in the world of robotics and have a wide range of uses. This work is concerned with the robust control of an actuated knee exoskeleton robotic system by means of an affine PD controller. Thus, a second-order nonlinear model with solid frictions, viscous frictions and parameters uncertainties is considered. Then, our central idea to design the controller lies in linearizing the nonlinear model around a desired state. Therefore, using the nonlinear model describing the difference between the nonlinear dynamics and its approximate linearized system, we adopt an affine PD control law to robustly stabilize the knee exoskeleton. Furthermore, using the S-procedure lemma, the Young inequality, and the Schur complement, we design LMI stability conditions for the feedback matrix gain of the controller. Finally, we present some results to demonstrate the validity of the developed condition and the robustness of the adopted controller.
Filter Characteristic in Micro-strip Structure Powered by Quasi-static Simulation and Measurements

The study’s findings on a modal filter (MF) based on a microstrip line with two side conductors grounded at both ends are presented in the paper. For the first time, the quasi-static simulation and measurements of the filter is conducted with two devices, and the results are compared. Frequency dependences of |S11| and |S21|are measured and calculated in the range from 0 to 10 GHz. At the MF output, the time response to an ultrashort pulse (USP) was analyzed. It was experimentally proved that the performed quasi-static simulation was correct. The simulation and measurement results showed remarkable consistency. The proposed MF provides the protection of electrical circuits from USP by decomposing it into 2 pulses with the amplitudes that are more than 3 times smaller.
Using known schemas and mappings to construct new semantic mappings

This dissertation studies the problem of constructing semantic mappings, i.e., expressions that relate different schemas. Semantic mappings play an important role in modern information systems. They let applications relate data in different sources and thus enable them to fruitfully leverage information residing in various forms across multiple data sources. With the proliferation of information systems that adopt distributed and often heterogeneous architectures, there is a need for automated support and tools to better facilitate the construction of semantic mappings. Our thesis is that, given a mapping construction task, knowledge that exists in other schemas and previously known mappings related to the mapping task, can be exploited to construct the required mapping. Our hypothesis is based on the two intuitions that mapping construction tasks are often repetitive in nature, and that different schemas within a domain are alternate representations for the same underlying entities. We demonstrate this in the context of two problems that are crucial to mapping construction. First, we address the problem of schema matching, i.e., the problem of identifying semantically similar elements in different schemas. We first describe techniques to match schemas elements based on their names, data instances, relationships with other elements, and known desirable properties of mappings. Then, given a new mapping task, we describe different ways in which known schemas and mappings within the same domain can be used to enhance the matching performance of a conventional schema matcher. Our approach is called corpus-based matching and our experimental results demonstrate its improved performance over direct matching techniques. Second, we address the problem of mapping composition, i.e., the problem of constructing a direct mapping between two schemas from existing mappings to a common schema. We show that, the composition problem can have very surprising consequences in the case of commonly used GLAV mappings. Specifically, the composition of mappings with finite formulas can in fact have infinite formulas. We further describe how and when compositions, even when infinite, can be encoded in a finite representation. Finally, we show how known query answering algorithms can be extended to handle compositions with infinite formulas.