Most common vector quantization (VQ) is Linde Buzo Gray (LBG), that designs a local optimal codebook for image compression. Recently firefly algorithm (FA), particle swarm optimization (PSO) and Honey bee mating optimization (HBMO) were designed which generate near global codebook, but search process follows Gaussian distribution function. FA experiences a problem when brighter fireflies are insignificant and PSO undergoes instability in convergence when particle velocity is very high. So, we proposed Cuckoo search (CS) metaheuristic optimization algorithm, that optimizes the LBG codebook by levy flight distribution function which follows the Mantegna’s algorithm instead of Gaussian distribution. Cuckoo search consumes 25% of convergence time for local and 75% of convergence time for global codebook, so it guarantees the global codebook with appropriate mutation probability and this behavior is the major merit of CS. Practically we observed that cuckoo search algorithm has high peak signal to noise ratio (PSNR) and better fitness value compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG at the cost of high convergence time

The intervention of noise into images during data acquisition and transmission is inevitable. Hence, the denoising of such affected images is essential in order to have effective image analysis where it needs image filtering. The Gabor filter is widely adapted in various image processing applications for feature extraction, texture analysis, pattern analysis, etc. The Gabor-based filtering technique adopted in work is aimed for image filtering in order to extract edges. The design of a low-power portable system deploys hardware accelerators to achieve high performance per watt in feature extraction and edge detection. In this paper, an image denoising hardware accelerator model is mapped from the Gabor filter function. Moreover, hardware models for realizing various parameters involved in the Gabor function are also presented. A MATLAB model for the proposed denoising hardware accelerator is simulated and performance is measured in terms of the peak-signal-to-noise ratio, mean square error, histograms and compared with algorithm level performance reported in the literature. It is observed that the proposed hardware architecture model showed better performance compared to the mathematical models reported in the literature. However, the key limitation is the degradation of hardware performance due to a truncation or rounding of the sample’s word length.

The rapid population growth, insecure lifestyle, wastage of natural resources, indiscipline behavior of human beings, urgency in the medical field, security of patient information, agricultural-related problems, and automation requirements in industries are the reasons for invention of technologies. Smart cities aim to address these challenges through the integration of technology, data, and innovative practices. Building a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living, improve resource efficiency, and create sustainable environments. This review presents five of the most critical technologies for smart and/or safe cities, addressing pertinent topics such as intelligent traffic management systems, information and communications technology, blockchain technology, re-identification, and the Internet of Things. The challenges, observations, and remarks of each technology in solving problems are discussed, and the dependency effects on the technologies’ performance are also explored. Especially deep learning models for various applications are analyzed. Different models performance, their dependency on dataset size, type, hyper-parameters, and the non-availability of labels or ground truth are discussed.

A novel Vector Quantization (VQ) technique for encoding the Bi-orthogonal wavelet decomposed image using hybrid Adaptive Differential Evolution (ADE) and a Pattern Search optimization algorithm (hADEPS) is proposed. ADE is a modified version of Differential Evolution (DE) in which mutation operation is made adaptive based on the ascending/descending objective function or fitness value and tested on twelve numerical benchmark functions and the results are compared and proved better than Genetic Algorithm (GA), ordinary DE and FA. ADE is a global optimizer which explore the global search space and PS is local optimizer which exploit a local search space, so ADE is hybridized with PS. In the proposed VQ, in a codebook of codewords, 62.5% of codewords are assigned and optimized for the approximation coefficients and the remaining 37.5% are equally assigned to horizontal, vertical and diagonal coefficients. The superiority of proposed hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) optimized vector quantization over DE is demonstrated. The proposed technique is compared with DE based VQ and ADE based quantization and with standard LBG algorithm. Results show higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similiraty Index Measure (SSIM) indicating better reconstruction.

Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.