In this study, we investigate a directional electromagnetic resonance antenna (EMRA) for application to wearable antennas. We designed an EMRA in free space and confirmed its unidirectional pattern through simulations...
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The challenge of distributing climacteric fruit is quality assurance due to the long-distance and perishability nature of the fruit. While monitoring transportation conditions is common, little research has developed ...
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With the increasing development of the Internet of Things (IoT), there is a growing need for efficient resource allocation to the many devices in the IoT system. This need is particularly acute in the case of time-sen...
With the increasing development of the Internet of Things (IoT), there is a growing need for efficient resource allocation to the many devices in the IoT system. This need is particularly acute in the case of time-sensitive applications, where fast resource allocation is essential. However, the resource allocation based on the Nash bargaining solution (NBS) necessitates exponentially increasing computational complexity for user dynamic systems. We propose an approach that makes groups based on the requested services and predicts a disagreement point whenever the group size changes, with the aim of reducing the computational complexity to find the NBS. Through simulation results, we demonstrate that our proposed approach outperforms the existing method in terms of execution time to find the NBS, maintaining the optimality in finding NBS.
In this work, we present an Integrated Guidance and Controller (IGC) scheme to drive quadcopters in path-following tasks with obstacle avoidance and constant uncertainty rejection. This scheme is based on the combinat...
In this work, we present an Integrated Guidance and Controller (IGC) scheme to drive quadcopters in path-following tasks with obstacle avoidance and constant uncertainty rejection. This scheme is based on the combination of a time-varying artificial vector field and Backstepping with integral action control. The vector field switches between two behaviors: (i) path-following; and (ii) obstacle circumnavigation to allow collision avoidance. This vector field is then integrated into a nonlinear controller designed via Backstepping with Integral Action to deal with the quadcopter vehicle dynamics and reject constant uncertainties. The considered vehicle model is based on quaternion algebra. The control inputs are considered to be the total thrust and torques. Stability is proved by using Lyapunov's Theory and Matrosov's Theorem.
Due to the need to update the current guidelines for highway design to focus on safety, this study sought to build an accident prediction model using a Geographic Information System (GIS) for single-lane rural highway...
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Due to the need to update the current guidelines for highway design to focus on safety, this study sought to build an accident prediction model using a Geographic Information System (GIS) for single-lane rural highways, with a minimum of statistically significant variables, adequate to the Brazilian reality, and improve accident prediction for places with similar characteristics. A database was created to associate the accident records with the geometric parameters of the highway and to fill in the gaps left by the absence of geometric highway plans through geometric reconstitution or semi-automatic extraction of highways using satellite images. The Generalized Estimating Equation (GEE) method was applied to estimate the coefficients of the model, assuming negative distribution of the binomial error for the count of observed accidents. The accident frequency and annual average daily traffic (AADT) were analyzed, along with the spatial and geometric characteristics of 215 km of federal single-lane rural highways between 2007 and 2016. The GEE procedure was applied to two models having three variations of distinct homogeneous segmentation, two based on segments and one based on the kernel density estimator. To assess the effect of constant traffic, two more variations of the models using AADT as an offset variable were considered. The predominant correlation structure in the models was the exchangeable. The principal contributing factors for the occurrence of collisions were the radius of the horizontal curve, the grade, segment length, and the AADT. The study produced clear indicators for the design parameters of roadways that influence the safety performance of rural highways.
With the rapidly spreading usage of internet of things (IoT) devices, a network intrusion detection system (NIDS) has an important role to detect and protect various types of attacks in the IoT network. To evaluate th...
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The increasing popularity of Internet of Things (IoT) devices has brought significant security challenges to IoT networks. However, most deep learning-based anomaly detection solutions often require high computation p...
The increasing popularity of Internet of Things (IoT) devices has brought significant security challenges to IoT networks. However, most deep learning-based anomaly detection solutions often require high computation performance so that it is difficult to be implanted on low-end IoT devices with limited power and memory capacity. In this paper, we propose a low-complexity network anomaly detection method based on feature selection using the Shapley value for the Isolation Forest algorithm. The proposed feature selection method using the Shapley value can reduce the dimension of input data, thereby improving the performance with reduced computational complexity. We provide simulation results to demonstrate the effectiveness of the proposed method. The results show that the proposed method based on Isolation Forest achieves comparable performance to the deep learning method based on neural networks while using fewer dimensions than the deep learning method.
AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science...
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AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a
In this work, the multiple-input, single-output (MISO) technique is implemented with OFDM modulation and the variation of the cyclic prefix (CP) is used to optimize the performance of an indoor VLC link, while maintai...
In this work, the multiple-input, single-output (MISO) technique is implemented with OFDM modulation and the variation of the cyclic prefix (CP) is used to optimize the performance of an indoor VLC link, while maintaining the lighting conditions within the recommended levels (300–500 lux). Results show that by increasing the number of luminaires and by selecting the appropriate value of the OFDM cyclic prefix, the system's bit error rate (BER ≤ 10– 3 ) is improved.
In tissue engineering, the mechanical properties of the extracellular matrix (ECM) or scaffolds have increasingly been considered to impact therapeutic efficacy by regulating cell behaviors, including differentiation,...
In tissue engineering, the mechanical properties of the extracellular matrix (ECM) or scaffolds have increasingly been considered to impact therapeutic efficacy by regulating cell behaviors, including differentiation, proliferation, migration, and adhesion. However, the understanding of how cells sense, integrate, and convert the mechanical cues from the ECM cues into biochemical signals to control certain cell behaviors is still elusive, especially in 3D, which more closely mimics the natural microenvironment than 2D systems. This review highlights the key differences between 2 and 3D in the contexts of mechanoregulative cell behaviors such as cell adhesion, spreading, migration, and force transmission. Furthermore, critical designing factors that needs to be considered for the fabrication of 3D tissue engineering scaffolds is discussed: stiffness, viscoelasticity, degradability, and the immobilization of biomolecules. Although mechanotransduction in 3D is actively being studied, understanding cellular mechanotransduction in 3D and designing of mechanoregulative 3D scaffolds still presents several challenges, including varying mechanical properties depending on different tissues, dynamic mechanical environments, and integration of multimodal cues. Interdisciplinary methodologies encompassing material engineering, cell biology, and mechanical engineering would serve to mitigate these challenges and augment our understanding of mechanoregulation governing cellular behaviors, thus fostering advancements in biomedical applications in the future.
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