Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

On the Development of Novel Encryption Methods for Conventional and Biometric Images

Information security refers to the technique of protecting information from unauthorized access, use, disclosure, disruption and modification. Governments, military, corporations, financial institutions, hospitals, and private businesses amass a great deal of confidential information about their employees, customers, products, research, and financial status. Most of this information is now collected, processed and stored on electronic media and transmitted across networks to other computers. Encryption clearly addresses the need for confidentiality of information, in process of storage and transmission. Popular application of multimedia technology and increasingly transmission ability of network gradually leads us to acquire information directly and clearly through images and hence the security of image data has become inevitable. Moreover in the recent years, biometrics is gaining popularity for security purposes in many applications. However, during communication and transmission over insecure network channels it has some risks of being hacked, modified and reused. Hence, there is a strong need to protect biometric images during communication and transmission. In this thesis, attempts have been made to encrypt image efficiently and to enhance the security of biometrics images during transmission. In the first contribution, three different key matrix generation methods invertible, involuntary, and permutation key matrix generation have been proposed. Invertible and involuntary key matrix generation methods solves the key matrix inversion problem in Hill cipher. Permutation key matrix generation method increases the Hill system’s security. The conventional Hill cipher technique fails to encrypt images properly if the image consists of large area covered with same colour or gray level. Thus, it does not hide all features of the image which reveals patterns in the plaintext. Moreover, it can be easily broken with a known plaintext attack revealing weak security. To address these issues two different techniques are proposed, those are advanced Hill cipher algorithm and H-S-X cryptosystem to encrypt the images properly. Security analysis of both the techniques reveals superiority of encryption and decryption of images. On the other hand, H-S-X cryptosystem has been used to instil more diffusion and confusion on the cryptanalysis. FPGA implementation of both the proposed techniques has been modeled to show the effectiveness of both the techniques. An extended Hill cipher algorithm based on XOR and zigzag operation is designed to reduce both encryption and decryption time. This technique not only reduces the encryption and decryption time but also ensures no loss of data during encryption and decryption process as compared to other techniques and possesses more resistance to intruder attack. The hybrid cryptosystem which is the combination of extended Hill cipher technique and RSA algorithm has been implemented to solve the key distribution problem and to enhance the security with reduced encryption and decryption time. Two distinct approaches for image encryption are proposed using chaos based DNA coding along with shifting and scrambling or poker shuffle to create grand disorder between the pixels of the images. In the first approach, results obtained from chaos based DNA coding scheme is shifted and scrambled to provide encryption. On the other hand in the second approach the results obtained from chaos based DNA coding encryption is followed by poker shuffle operation to generate the final result. Simulated results suggest performance superiority for encryption and decryption of image and the results obtained have been compared and discussed. Later on FPGA implementation of proposed cryptosystem has been performed. In another contribution, a modified Hill cipher is proposed which is the combination of three techniques. This proposed modified Hill cipher takes advantage of all the three techniques. To acquire the demands of authenticity, integrity, and non-repudiation along with confidentiality, a novel hybrid method has been implemented. This method has employed proposed modified Hill cipher to provide confidentiality. Produced message digest encrypted by private key of RSA algorithm to achieve other features such as authenticity, integrity, and non-repudiation To enhance the security of images, a biometric cryptosystem approach that combines cryptography and biometrics has been proposed. Under this approach, the image is encrypted with the help of fingerprint and password. A key generated with the combination of fingerprint and password and is used for image encryption. This mechanism is seen to enhance the security of biometrics images during transmission. Each proposed algorithm is studied separately, and simulation experiments are conducted to evaluate their performance. The security analyses are performed and performance compared with other competent schemes

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Emerging Converter Topologies and Control for Grid Connected Photovoltaic Systems

Continuous cost reduction of photovoltaic (PV) systems and the rise of power auctions resulted in the establishment of PV power not only as a green energy source but also as a cost-effective solution to the electricity generation market. Various commercial solutions for grid-connected PV systems are available at any power level, ranging from multi-megawatt utility-scale solar farms to sub-kilowatt residential PV installations. Compared to utility-scale systems, the feasibility of small-scale residential PV installations is still limited by existing technologies that have not yet properly address issues like operation in weak grids, opaque and partial shading, etc. New market drivers such as warranty improvement to match the PV module lifespan, operation voltage range extension for application flexibility, and embedded energy storage for load shifting have again put small-scale PV systems in the spotlight. This Special Issue collects the latest developments in the field of power electronic converter topologies, control, design, and optimization for better energy yield, power conversion efficiency, reliability, and longer lifetime of the small-scale PV systems. This Special Issue will serve as a reference and update for academics, researchers, and practicing engineers to inspire new research and developments that pave the way for next-generation PV systems for residential and small commercial applications

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field