A secret image transmission technique has been put forward in this paper using (3,3) visual cryptographic shares generated from secret image that are fabricated into frames in Graphics Interchange Format (GIFs) to prevent any intruder from knowing the secret contained in the GIFs. The (3, 3) Visual Cryptography technique creates shares from a binary secret image. Using GIFs as communication hosts, each share has been embedded into the Least Significant Bit (LSB) of the pixels of any single meaningful frame of GIFs. The shares obtained from the corresponding meaningful frames of the GIFs, during decoding, are combined to create the authenticated image. The combination of visual cryptography (VC) technique and steganographic principles ensures not only the secure distribution of shares but also adds an extra layer of protection through the integration of the GIF format.

Video synopsis is a promising technology that offers easy browsing and indexing of surveillance videos. This article presents an improved video synopsis framework, introducing the inclusion of anomalous tube detection module. The suggested framework performs better than the existing methodologies by offering a flexibility to the user to generate a synopsis video, which is based on user's choice of interest. Traditionally, to generate a synopsis video, the object tubes are temporally shifted for achieving compression. The applied temporal shift incurs a large number of collision artifacts along with temporal chronology violation. To address this issue for producing a visually comfortable synopsis video, the collision and temporal chronology violations are amended through Acceleration/Retardation of object's motion and spatial shift. Collision oriented Acceleration/Retardation and Spatial shift strategies are embedded sequentially in the proposed combined algorithm cSAScO. The unified representation of the proposed cSAScO algorithm combines the individual strength of Simulated Annealing (SA) and Scenario Optimization (ScO) and is employed to the formulated choice-based tube rearrangement problem. The efficacy of the proposed scheme is demonstrated through extensive experiments and its performance compared with that of the benchmark schemes.

Nowadays, surveillance cameras are ruling the world in terms of security measures. Worldwide application of surveillance cameras leads to the production of huge amounts of video data. Investigating and getting the necessary information from such a lengthy video is a challenging task. To ease the inspection, Video Synopsis (VS) technology was proposed. Video Synopsis is the technique used to shorten a long-duration video with the preservation of all activities that have occurred originally. The synopsis of a video can be produced through four modules, namely, Object Detection, Object Tracking, Optimization, and finally Stitching. The efficient implementation of the VS is mainly dependent on the Optimization module. Mostly, Simulated Annealing (SA) is applied to solve the optimization problem in Object-based Single-camera Offline Surveillance Video Synopsis. In this paper, an attempt is made to implement and study the performance of the Driving Training Based Optimization (DTBO) algorithm for solving the optimization problem. The experimental results of DTBO are compared with the performance of the SA, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Forest Optimization Algorithm (FOA), Elltist-Jaya, and SAMP-Jaya Algorithm. From the results and comparison, it is observed that the effectiveness of the DTBO algorithm in minimizing the energy function of Object-based Single-camera Offline Surveillance Video Synopsis is superior to the existing algorithms.

The present work describes a multi-area (two and three) renewable-energy-source-integrated thermal-hydro-wind power generation structure along with fleets of plug-in electrical vehicles (PEVs) in each control area. The generation–load balance is the prime objective, so automatic generation control (AGC) is adopted in the system. In the paper, a cascaded combination of proportional derivative with filter PDN and fractional-order proportional integral (FOPI) is proposed and tuned using the hybrid chemical reaction optimization with pattern search (hCRO-PS) algorithm. The hCRO-PS algorithm is designed successfully, and its effectiveness is checked through its application to various benchmark functions. Further, Eigen value analysis is carried out for the test system to verify the system stability. The impacts of diverse step load perturbation (i.e., case I, II, III, and IV) and time-varying load perturbation are also included in the study. Moreover, the impact of renewable sources, PEVs in different areas, and varied state of charge (SOC) levels on the system dynamics are reflected in the work. From the analysis, it can be inferred that the proposed controller provides comparable results with other fractional-order and conventional controllers under varying loading conditions.