Search

Broadcast control (BC) is a bio-inspired coordination technique for a swarm of agents in which a single coordinator broadcasts an identical scalar signal to all performing agents without discrimination, and the agents make appropriate moves towards the agents' collective optimal state without communicating with one another. The BC technique aims to accomplish a globally assigned task for which BC utilizes a stochastic optimization algorithm to coordinate a group of agents. However, the challenge intensifies as the system becomes larger: it requires a larger number of agents, which protracts the converging time for a single coordinator-based BC model. This paper proposes a revamped version of BC model, which assimilates distributed multiple coordinators to control a larger multi-agent system efficiently in a pragmatic manner. Precisely, in this hierarchical BC scheme, the distributed multiple sub-coordinators broadcast the identical feedback signal to the agents, which they receive from the global coordinator to accomplish the coverage control task of the ordinary agents. The dual role of sub-coordinators is manipulated by introducing weighted averaging of the gradient estimation under the stochastic optimization mechanism. The potency of the proposed model is analyzed with numerical simulation for a coverage control task, and various performance aspects are compared with the typical BC schemes to demonstrate its practicability and performance improvement. Particularly, the proposed scheme shows the same convergence with about 30% less traveling costs, and the near convergence is reached by only about one-third of iteration steps compared to the typical BC. [ABSTRACT FROM AUTHOR]

Uncontrolled abnormal cell growth, known as cancer, may lead to tumors, immune system deterioration, and other fatal disability. Early cancer identification makes cancer treatment easier and increases the recovery rate, resulting in less mortality. Gene expression data play a crucial role in cancer classification at an early stage. Accurate cancer classification is a complex and challenging task due to the high-dimensional nature of the gene expression data relative to the small sample size. This research proposes using a dimensionality-reduction technique to address this limitation. Specifically, the mutual information (MI) technique is first utilized to select influential biomarker genes. Next, an ensemble learning model is applied to the reduced dataset using only the most influential features (genes) to develop an effective cancer classification model. The bagging method, where the base classifiers are Multilayer Perceptrons (MLPs), is chosen as an ensemble technique. The proposed cancer classification model, the MI-Bagging method, is applied to several benchmark gene expression datasets containing distinctive cancer classes. The cancer classification accuracy of the proposed model is compared with the relevant existing methods. The experimental results indicate that the proposed model outperforms the existing methods, and it is effective and competent for cancer classification despite the limited size of gene expression data with high dimensionality. The highest accuracy achieved by the proposed method demonstrates that the proposed emerging gene-expression-based cancer classifier has the potential to help in cancer treatment and lead to a higher cancer survival rate in the future. [ABSTRACT FROM AUTHOR]

The kinematics of the human hand exhibit complex and diverse characteristics unique to each individual. Various techniques such as vision-based, ultrasonic-based, and data-glove-based approaches have been employed to analyze human hand movements. However, a critical challenge remains in efficiently analyzing and classifying hand grasp types based on time-series kinematic data. In this paper, we propose a novel sparse coding feature extraction technique based on dictionary learning to address this challenge. Our method enhances model accuracy, reduces training time, and minimizes overfitting risk. We benchmarked our approach against principal component analysis (PCA) and sparse coding based on a Gaussian random dictionary. Our results demonstrate a significant improvement in classification accuracy: achieving 81.78% with our method compared to 31.43% for PCA and 77.27% for the Gaussian random dictionary. Furthermore, our technique outperforms in terms of macro-average F1-score and average area under the curve (AUC) while also significantly reducing the number of features required. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) has emerged as the most favorable source for recognizing brain disorders like epileptic seizure (ES) using deep learning (DL) methods. This study investigated the well-performed EEG-based ES detection method by decomposing EEG signals. Specifically, empirical mode decomposition (EMD) decomposes EEG signals into six intrinsic mode functions (IMFs). Three distinct features, namely, fluctuation index, variance, and ellipse area of the second order difference plot (SODP), were extracted from each of the IMFs. The feature values from all EEG channels were arranged in two composite feature forms: a 1D (i.e., unidimensional) form and a 2D image-like form. For ES recognition, the convolutional neural network (CNN), the most prominent DL model for 2D input, was considered for the 2D feature form, and a 1D version of CNN was employed for the 1D feature form. The experiment was conducted on a benchmark CHB-MIT dataset as well as a dataset prepared from the EEG signals of ES patients from Prince Hospital Khulna (PHK), Bangladesh. The 2D feature-based CNN model outperformed the other 1D feature-based models, showing an accuracy of 99.78% for CHB-MIT and 95.26% for PHK. Furthermore, the cross-dataset evaluations also showed favorable outcomes. Therefore, the proposed method with 2D composite feature form can be a promising ES detection method. [ABSTRACT FROM AUTHOR]

Uncoordinated traffic flows at the traditional roundabouts, especially with a small circumference and fewer lanes, are often heavily affected by congestion, which escalates fuel consumption, CO[sub.2] emissions, idling, and travel delay. An intriguing way to mitigate such uncoordinated flows at junctions would be facilitated through optimal traffic signalization. For this purpose, this paper presents a novel holistic Three-Leg Signalized Roundabout (TLSR) model based on two signalized stop lines (2SL). The first stop line is placed at each entry curve of a roundabout with effectual lane markings as usual. Hereafter, the second stop line is set exclusively in the circulatory roadway to improve left-turning mobility with an additional “short-lane model” to deal with heavy traffic, following specific patterns for smooth vehicle merging. The capacity and optimal signal cycle relationships are derived to evaluate the performance of the proposed TLSR-2SL, considering the internal space constraints of the roundabout. Under the various scenarios, the parameters' sensitivity tests demonstrate that signal cycle and central radius play a significant role in enhancing the roundabout's operational performance. From the executed simulation, the proposed framework improves the traffic flow by 15% and controls the relative error within 10% compared to benchmark methods., Author(s): Khurrum Jalil [1]; Yuanqing Xia (corresponding author) [1]; Muhammad Noaman Zahid [2]; Md Abdus Samad Kamal [3] 1. Introduction Due to the tremendous vehicle influx, the existing road infrastructure [...]

Vehicular ad hoc networks (VANETs) incorporating vehicles as an active and fast topology are gaining popularity as wireless communication means in intelligent transportation systems (ITSs). The cybersecurity issue in VANETs has drawn attention due to the potential security threats these networks face. An effective cybersecurity measure is essential as security threats impact the overall system, from business disruptions to data corruption, theft, exposure, and unauthorized network access. Intrusion detection systems (IDSs) are popular cybersecurity measures that detect intrusive behavior in a network. Recently, the machine learning (ML)-based IDS has emerged as a new research direction in VANET security. ML-based IDS studies have focused on improving accuracy as a typical classification task without focusing on malicious data. This study proposes a novel IDS for VANETs that offers more attention to classifying attack cases correctly with minimal features required by applying principal component analysis. The proposed Cascaded ML framework recognizes the difference between the attack and normal cases in the first step and classifies the attack data in the second step. The framework emphasizes that an attack should not be classified into the normal class. Finally, the proposed framework is implemented with an artificial neural network, the most popular ML model, and evaluated with the Car Hacking dataset. In addition, the study also investigates the efficiency of typical classification tasks and compares them with results of the proposed framework. Experimental results on the Car Hacking dataset have revealed the proposed method to be an effective IDS and that it outperformed the existing state-of-the-art ML models. [ABSTRACT FROM AUTHOR]

Transportation is a key driver of development, enabling people to have access to jobs, education, health, and goods. Still, the unintended consequences of transportation constitute a big risk, threatening the [...]

Ecological (eco) driving features distinct driving behavior that aims at improving fuel economy of a car and consequently reducing carbon dioxide emissions. Various assistance systems have recently been developed to promote ecodriving among drivers. Through anticipation of the preceding traffic, an eco-driver smoothly regulates the velocity and headway of the car at some safe levels. Limited use of accelerator and brake often causes an eco-driven car slower than other vehicles and affects behavior of its following traffic greatly. This paper investigates the influence of eco-driving on the fuel consumption of vehicles in the following traffic. Performance of the eco-driven car and vehicles behind it are observed in various traffic flow conditions through simulation in AIMSUN microscopic traffic simulator.