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The LBP based face recognition. a The LBP operator thresholds every pixel against its neighborhood and infers the result in binary and b The LBP descriptors are created by splitting facial image into a grid and computing LBP histograms in each grid. The histograms are merged into a single feature vector that contains complete description of the face
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Accurate vehicle detection plays a vital role in intelligent transportation systems. Various day conditions, for instance, dawn, morning, noon, or non-uniform illuminations put restrictions on camera’s visibility. Such scenarios impact the performance of detection and recognition algorithms that are used in surveillance systems and autonomous drivi...
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Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local...
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... Recently, the rapid increase of road vehicles has created a concern about their issues and impacts on communication in the transportation system [1]. The Intelligent Transportation System (ITS) has evolved into an effective strategy for vehicle communication and security issues in satisfying the vehicle's safety in the road communication environment [2]. The primary goal of the ITS is to sense the surrounding information and distribute it to others. ...
... The higher energy node prediction function is explained in Eqn. (2). ...
Wireless Sensor Network (WSN) is a well-organizing network that provides efficient communication services. However, high energy usage and overloaded data sometimes become the key challenges that degrade the entire network's performance. Energy management and load balancing are critical processes for increasing the network lifespan. Therefore, in the present research, a novel Aquila-based Radial Basis Model (AbRBM) is created for vehicular network communication to reduce energy usage and balance the load with the CH selection. Initially, the required nodes are deployed in the network. Based on Aquila's optimal functions, the higher energy nodes are monitored and removed; optimal CH is selected for the data transfer. Finally, the load is balanced by sharing the data between the overhead and the rest of the nodes. The created framework was tested in the NS2 environment, and the network efficiency parameters were calculated. The model achieved a 98.3% Packet delivery rate, 1.10 mJ of average energy usage rate, 0.025-s delay, 3.8Mbps average throughput, and lifetime as 6210 rounds for the created 100 nodes in the WSN. Also, the control overhead rate is reduced to 0.42 KB. To validate the improvement, the results are related to the existing models. The higher Performance of the AbRBM satisfied the network's efficient energy management and load-balancing process. The model provided an efficient and reliable WSN for various applications.
