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Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks; this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive; they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model; the model can be trained and deployed on embedded devices such as smartphones efficiently.

Despite the incredible adoption of cryptocurrencies, blockchain-based cryptocurrencies have likewise raised some concerns. The scalability problem is the major one among them. An off-blockchain payment channel network (PCN) has been introduced to solve this issue. PCN can fundamentally reduce blockchain scalability by constructing a number of payment channels between the nodes and without committing every single transaction to the blockchain. But as a matter of fact, there has an unwanted assumption in PCN that channel participants must remain online and follow blockchain updates, for the synchronization with blockchain to protect the channel against deception. To mitigate this issue “Watchtower” concept has been proposed. Watchtower is a watching service and always stays online that a channel participant can hire it by offering incentives for monitoring the channel and checking blockchain updates consistently to prevent fraud on behalf of the hiring party. However, watchtower may be more beneficial by cooperating with the cheating counterparty and neglecting to perform the watching service properly. The efficiency drawback can occur for that. In this work, we have been motivated by this issue and tried to find out an effective and reliable watchtower for the channel watching service from multiple watchtower nodes or candidates in the PCN. In particular, we have been approached by using the distributed Peterson Leader-Election Algorithm to find the best watchtower among multiple of them where the more successfully performed work node or candidate will be selected for the channel monitoring job. We also have provided a detailed step-by-step process of the algorithm including experiments and illustrations for employing watchtower among multiple of them.

The volume of image data produced today is increasing, which makes storing and transferring them a difficult task. There are some fields where loss-less image compression can be valuable because it allows the compression of images without compromising their quality. In this paper, we propose a lossless image compression technique based on linear prediction, integer wavelet transformation (IWT), and Arithmetic coding to improve the compression ratio of lossless images. As compared to state-of-the-art algorithms, the proposed algorithm increases compression ratios by at least 2.553% and up to 32.546%.