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Listless set partitioning embedded block (LSK) and set partitioning embedded block (SPECK) are known for their low complexity and simple implementation. However, the drawback is that these block-based algorithms encode each insignificant subband by a zero. This generates many zeros at earlier passes because the number of significant coefficients at higher bitplanes is likely to be very few in a transformed image. An improved LSK (ILSK) algorithm that codes a single zero to several insignificant subbands is proposed. This reduces the length of the output bit string, encoding/decoding time and dynamic memory requirement at early passes. Furthermore, ILSK algorithm is coupled with discrete Tchebichef transform (DTT). This gives rise to a novel coder named as hierarchical listless DTT (HLDTT). The proposed HLDTT has desirable attributes like full embeddedness for progressive transmission, precise rate control for constant bit rate traffic and low complexity for low power applications. The performance of HLDTT is assessed using peak-signal-to-noise-ratio (PSNR) and structural-similarity-index-metric (SSIM). Extensive simulation conducted on various standard test images shows that HLDTT exhibits significant improvement in PSNR values from lower to medium bit rates. At the same time, HLDTT shows improvement in SSIM values on all bit rates.

Image mosaicing is widely used in present computer vision applications. A considerable measure of important information is represented by the feature points in an image. Accurate extraction of these features is an essential part of image mosaicing as it can reduce misalignment errors in the final mosaic. A number of feature detection algorithms have been developed in recent years which can be used for image mosaicing. However, the computational complexity and accuracy of feature matches limits the applicability of these algorithms. In this paper, four widely used feature detection algorithms, Harris, SURF (Speeded-Up Robust Features), FAST (Features from Accelerated Segment) and FREAK (Fast Retina Key point) feature detection algorithms are compared in terms of accuracy and time complexity for mosaicing of images correctly. First, these algorithms have been applied on a single image and then, different set of images are tested for the comparison. It is concluded that the FREAK algorithm is superior to the rest of the feature detection algorithm in terms of accuracy and run time.

Consumer lighting plays a significant role in the development of smart cities and smart villages. With the advancement of (IoT) technology, smart lighting solutions have become more prevalent in residential areas as well. These solutions provide consumers with increased energy efficiency, added convenience, and improved security. On the other hand, the growing number of IoT devices has become a global concern due to the carbon footprint and carbon emissions associated with these devices. The overuse of batteries increases maintenance and cost to IoT devices and simultaneously possesses adverse environmental effects, ultimately exacerbating the pace of climate change. Therefore, in tandom with the principles of Industry 4.0, it has become crucial for manufacturing and research industries to prioritize sustainable measures adhering to smart energy as a prevention to the negative impacts. Consequently, it has undoubtedly garnered global interest from scientists, researchers, and industrialists to integrate state-of-the-art technologies in order to solve the current issues in consumer light management systems making it a complete sustainable, and smart solution for consumer lighting application. This manuscript provides a thorough investigation of various methods as well as techniques to design a state-of-the-art IoT-enabled consumer light management system. It critically reviews the existing works done in consumer light management systems, emphasizing the significant limitations and the need for sustainability. The top-down approach of developing sustainable computing frameworks for IoT-enabled consumer light management has been reviewed based on the multidisciplinary technologies involved and state-of-the-art works in the respective domains. Lastly, this article concludes by highlighting possible avenues for future research.

In this paper, the performance of different tuning methods of PID controller is evaluated. Tuning of PID controller parameters is necessary due to the wide applications of controllers in industry. Different tuning methods such as Empirical Ziegler-Nichols, Cohen-Coon, Chien-Hrones-Reswick and optimum tuning methods are considered. The performance of the above mentioned tuning methods are evaluated on the feedback control problem which controls the outlet temperature of heat exchanger system. Set point regulation and load disturbance rejection property of the controller are evaluated. The simulation results exhibit low overshoot and low settling time for most of the tuning rules. A comparative evaluation of the performance is carried out.