Multiple-Description Lattice Vector Quantization For Image And Video Coding Based On Coincidings Similar Sublattices Of An

Nowadays applications of multimedia communication are found everywhere. Digital communication systems deal with representation of digital data for either storage or transmission. The size of the digital data is a crucial factor for storage and error resiliency of the data is a crucial factor for transmission systems. Thus, it is required to have more efficient encoding algorithms in terms of compression and error resiliency. Multiple-description (MD) coding has been a popular choice for robust data transmission over unreliable network channels

Improving OSPF Protocol based Latency : A new algorithm based on Dijkstra by using OSPF existing Metrics in SDN networks

SDN (software defined networking)-based networks may be defined as a new generation of networks using virtual layers and switches and central controller which try to handle a few controlling and managerial tasks of switches and rotors of networks in upper layers on a software basis. In fact, it reduced dependence to hardware and increases software capabilities. These networks face challenges such as quality of relationship between controller and devices existing in the network and delay in network that is subject of this thesis. According to the applied researches, one of offered solutions for reduction of delay is using path finding algorithms. Rotors’ task is transfer of information. Algorithms must be implemented on these rotors to choose the best path for data transfer in the network. Path finding table is used in rotor. According to the data available in path finding table, the best path is found. Each path finder must have complete information of network’s communication infrastructure and calculate and identify all other paths of communications between them and their costs. Later, data collection forms the data structure related to network infrastructure graph. In these conditions, to find the best path between path finders, the shortest path algorithms (SPT) are used such as Dijkstra. Since rotors receive the sent update massages due to network changes, path finding table amends itself and identifies the new probability path. Selection of best path is made by massages metric. Upon processor fastening and hardware cheapening, a standard protocol in the name of OSPF was presented that manufactured by CISCO, particularly in a network that its equipment are not necessarily made by CISCO is based on Dijkstra and uses cost and band broadness as metric, transfer the data related to connected network and rotors connected to network between adjacent rotors and records all of its information in the table. Later, Dijkstra's algorithm is implemented and the best paths led to different destinations are inserted in the path finding table. The objective of this study was presenting an algorithm based on Dijkstra there in addition to cost Metric, another metric to be used that highly reduces traffic in the network and improves delay time in the network

Secure Medical Image Communication Using Fragile Data Hiding Based on Discrete Wavelet Transform and A₅ Lattice Vector Quantization

Secure communication of medical images is essential to telemedicine. Message Authentication Codes (MAC) can be embedded inside medical images to protect their integrity. Fragile watermarking algorithms are suitable options since they can be used to detect any tampering attempt. In this paper, a novel fragile data-hiding algorithm based on Integer-to-Integer Discrete Wavelet Transforms (IIDWT) and $A_{5}$ Lattice Vector Quantization (LVQ) is proposed. In the proposed data-hiding algorithm, a combination of the medical image Metadata and a MAC is embedded into the medical image. The Metadata includes information about the patient such as name, family, birthday, the place where it is created such as the name of the hospital, and the physician’s name. To preserve the privacy of the patients and the physician/hospital, the Metadata is then replaced with fake information. The receiver can extract the Metadata and the MAC. If the extracted MAC is the same as the expected MAC, the integrity of the medical image is guaranteed. Otherwise, a tampering attempt is detected. The proposed algorithm can embed 50% more data than similar algorithms in medical images while keeping the Peak Signal to Noise Ratio (PSNR) in acceptable ranges. Furthermore, the proposed algorithm is applied to a dataset of medical images and high PSNR values above 53.88 dB are experienced

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymize the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations

Efficient Resume-Based Re-Education for Career Recommendation in Rapidly Evolving Job Markets

The impact of the COVID-19 pandemic and the introduction of artificial intelligence-based tools created significant job losses across various sectors in all countries around the world. A large portion of these job losses is permanent. Furthermore, the hidden unemployment numbers are higher than currently reported and the impact of Generative Pretrained Transformer (GPT) based tools will further increase the unemployed population in the coming years. Most businesses are likely to experience significant disruptions to their business-as-usual operations and will face business underperformance for long periods. To ensure business continuity and a smooth recovery process following severe disruptions, it is crucial to establish a recovery strategy. To provide enough workforce for the recovery strategy of various businesses, a large-scale rapid re-education of the workforce is required. Intelligent and virtual workplaces will replace traditional offices in various sectors in the upcoming years and many low-skilled jobs are in danger of being permanently lost. In this paper, an artificial intelligence-based framework for rapid work-skill re-education for evolving markets named Career-gAIde is presented. The proposed framework uses automatic analysis of the job resume of the workers for recommendations of a suitable new job with a higher salary and the best rapid re-education path toward that job. Custom build deep neural networks based on CNN-Random along with customized natural language processing tools are designed for large-scale automatic recommendation of a personalized education and career path to each job seeker. The proposed work is focused on software engineering job search and resume upgrades. There is also a book recommendation module for obtaining the knowledge of job seekers. Precision criteria were used to evaluate the job offer recommendations and the proposed framework achieves 67% in this measure. The Recall criteria were used to assess the required skills, with results of 84% and 79%, respectively. The experimental results show that the proposed framework can provide a solution for rapid work-skill re-adjustment for large-scale workforces