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COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus’s variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework’s reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings. [ABSTRACT FROM AUTHOR]

Accurately estimated highway traffic flow info plays a decisive role in dynamic and real-time road management, planning, and preventing frequent/recurring traffic jams, traffic rule violations, and chain/fatal traffic accidents. Traffic flow information is extracted by processing raw camera images via vehicle detection and tracking algorithms. Object detectors including the Yolo, single-shot detector, and EfficientNet algorithms are used for vehicle detection; however, You only look once version 5 (Yolov5) has a clear advantage in terms of real-time performance. Due to this reason, the pre-trained Yolov5 models were utilized in the vehicle detection part, and in the vehicle tracking module, a novel tracker algorithm was developed using vehicle detection features. The performance of the proposed approach was measured by comparing it to the Kalman filter-based tracker. The evaluation results show that the proposed tracking approach outperformed the Kalman filter-based tracker with 5.82% (Buses), 2.24% (Cars), 36.50% (Trucks), and overall 2.58% better traffic counting accuracy for the 12 nighttime case study videos captured from the highways with different horizontal and vertical angle-of-views. [ABSTRACT FROM AUTHOR]

Silicon carbide (SiC) is a promising material for power devices because of its wide bandgap and high thermal conductivity. However, carbon remaining in the metal–SiC interface (carbon cluster) adversely affects the ohmic contacts. As a result, the ohmic contact characteristic worsens and the on-state power loss of the device increases. In addition, the carbon cluster in the metal–SiC interface degrades the device's high-temperature thermal stability. In this paper, Co/Si/Co/Si is used as an ohmic contact material to reduce the carbon cluster and improve the thermal stability. The specific contact resistivity was measured, and the effect of the Co:Si composition ratio and the annealing condition for the ohmic contact was analyzed. The thermal stability was investigated by measuring the specific contact resistivity through a thermal duration test. The measurement results were analyzed using the X-ray diffraction method. [ABSTRACT FROM AUTHOR]

Topology control is one of the important techniques in wireless multi-hop networks to preserve connectivity and extend the network lifetime. This is more significant in ZigBee, since the address assignment scheme is tightly coupled with topology construction. For example, there can be orphan nodes that cannot receive the network address and isolated from the network due to predefined network configurations. In this paper, we propose a distributed topology construction algorithm that controls the association time of each node in order to solve the orphan node problem in ZigBee as well as construct an efficient routing tree topology. The main idea of the distributed topology construction algorithm is to construct primary backbone nodes by propagating the invitation packets and controlling the association time based on the link quality. Since the dynamically selected primary nodes are spread throughout the network, they can provide backbone to accept the association requests from the remaining secondary nodes which are majority in a network. In the performance evaluation, we show that the proposed topology construction algorithm effectively solves the orphan node problem regardless of network density as well as provides efficient tree routing cost comparable to the approximation algorithm for degree constrained minimum routing cost tree (DC-MRCT) problem. [ABSTRACT FROM AUTHOR]

The Internet of Things will enable objects to be identified, sensed, and controlled remotely across the existing Internet infrastructure. Even though interacting with sensor nodes requires a priori knowledge about the application profile implemented on the desired nodes, it is infeasible for user applications to have any information in advance or to obtain such information from resource-constrained sensor nodes. In this paper, we propose a scalable and efficient metadata framework that allows user applications to learn all about sensor nodes at runtime without storing heavy descriptive data within the nodes themselves. In this framework, every sensor node has globally unique identifiers, and their metadata describing functions and capabilities are stored in distributed metadata servers. To enable metadata retrieval from the globally unique identifier of sensor nodes, we exploit a hierarchical resolution server architecture defined in global standards. We prove the feasibility as well as scalability and efficiency of the proposed framework by evaluating the metadata retrieval overhead from a ZigBee based testbed. [ABSTRACT FROM AUTHOR]

As software defined networking (SDN) extends its applications to carrier-grade networks, the need for high scalability and availability of a SDN controller is becoming increasingly important. Although existing works have shown the feasibility of a distributed controller, the switches in the data plane are required to know some of the internal specifics such as the IP addresses of the individual controller instances. This constraint increases the operational complexity as the number of controller instances increases. In this paper, we propose a distributed controller cluster architecture called IRIS-HiSA. The main goal is to support seamless load balancing and failover with horizontal scalability, as is done in existing works, but one of the distinguishing features of IRIS-HiSA is to provide transparency to the switches in the data plane. Thus, the switches do not need to know the internal details of the controller cluster, and they simply access it in the same way a single controller is accessed. In addition to proving seamless load balancing and a failover, a performance evaluation is conducted to analyze the high scalability in which the throughput of the flow setup is proportionally increased with the number of controller instances. [ABSTRACT FROM AUTHOR]

ZigBee is one of the key standards that enable low-cost and low-power wireless mesh networks. Working on top of IEEE 802.15.4 MAC/PHY standard, ZigBee defines higher layers that support reliable multi-hop wireless communications. In particular, ZigBee provides multiple routing protocols to support diverse application traffic types such as multipoint-to-point, point-to-multipoint, and point-to-point. However, there are no studies that thoroughly incorporate multiple ZigBee routing protocols. In this paper, we explore multiple unicast routing and many-to-one protocols incorporated into the ZigBee standard and compare them through extensive simulations using NS2. We discuss real-world implementation issues and introduce our implementation decisions. We also examine their suitability for different scenarios and discuss some of the improvements that can be made to the ZigBee standard routing protocols. [ABSTRACT FROM AUTHOR]

Owing to the technological advancements in Semantic Web and sensor networks, a large amount of data has been produced in association with the open data policy. However, data stream management systems that process stream data have focused on the processing of a large amount of data with little priority on data identification, integration, and external linkage. Furthermore, entity resolution is focused mainly on static database-based technologies. In this study, a real-time stream data processing architecture that can perform the integration and entity resolution of streaming-type heterogeneous input data and interlink with external data is designed. To achieve this goal, a light adapter to integrate heterogeneous data into standard scheme and blocking technique to reduce comparison candidates are applied. The implemented data adapters shows 4 times higher throughput than open source data parsers and the entity resolution results with streaming data shows similar performance with the static data sets. The proposed streaming data entity resolution architecture is expected to form the basis of data integration research that can integrate various information sources of data efficiently, enrich internal data. [ABSTRACT FROM AUTHOR]