In the rapidly evolving digital media landscape, ensuring the security and privacy of multimedia data has become paramount. this research delves into the field of steganography, specifically focusing on developing a r...
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ISBN:
(数字)9798350350357
ISBN:
(纸本)9798350350364
In the rapidly evolving digital media landscape, ensuring the security and privacy of multimedia data has become paramount. this research delves into the field of steganography, specifically focusing on developing a robust method to hide information in images and videos. the proposed method combines the use of least significant bit (LSB) based steganography to hide images and RC4 algorithm to enhance security. the proposed method first works by encrypting secret data using RC4 algorithm. the encrypted data is then embedded into the cover art or video using a modified LSB embedding technique. this technique carefully selects pixels into which to embed secret data and uses a new, more powerful bit-embedding algorithm for compression and other image processing operations. the LSB technique involves embedding data into the bits withthe least significant pixel value, allowing for imperceptible changes in image content. this method provides a balance between concealment and maintaining the integrity of the host media. At the same time, the RC4 algorithm is used to encrypt hidden data, adding an additional layer of security to prevent unauthorized access. Integrating these techniques into a unified system provides a flexible solution for covert communication and secure data transmission. the study explores the complexity of steganography based on LSB and the RC4 algorithm, testing their effectiveness in hiding and protecting sensitive information in media files. Experimental validation includes comprehensive testing on images and videos to evaluate the system's stealth, robustness, and resilience against various attacks. the results aim to demonstrate the feasibility and effectiveness of the proposed method, providing insight into its potential applications in secure communications, digital forensics, and information protection. this research contributes to the growing field of steganography by presenting a practical and innovative approach to multimedia data hiding, addr
Grasping objects are common phenomenon in daily human activities. Force myography (FMG) signal, a noninvasive technique can record muscle movements while a human participant grasps different objects and be categorized...
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ISBN:
(数字)9781728184166
ISBN:
(纸本)9781728184173
Grasping objects are common phenomenon in daily human activities. Force myography (FMG) signal, a noninvasive technique can record muscle movements while a human participant grasps different objects and be categorized using machine learning (ML) algorithms. In this paper, a popular deep learning technique is presented for hand grasp recognition. A novel convolutional neural network (CNN) architecture was implemented in learning grasps via force myography. Twelve participants wearing an FMG band on dominant hand's forearm performed six hand grasps. Training dataset consisted of one-handed grasping small objects of different shapes and sizes either wrapping or pinching with fingers with a variety of arm poses. the proposed FMG-based CNN model obtained cross-trial classification accuracy of 96% (population mean) and was found comparable with other ML techniques. Pretranined Alexnet (with ImageNet dataset) through transfer learning was implemented to classify the hand grasps for comparison. the proposed model outperformed the pretrained Alexnet in terms of validation accuracy, loss, and training time. For future FMG-based practical applications, it would be advantageous to use the model for transfer learning where comparatively smaller datasets are desirable for training purpose.
Vector similarity search (VSS) for unstructured vectors generated via machine learning methods is a promising solution for many applications, such as face search. With increasing awareness and concern about data secur...
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ISBN:
(纸本)9781665462723
Vector similarity search (VSS) for unstructured vectors generated via machine learning methods is a promising solution for many applications, such as face search. With increasing awareness and concern about data security requirements, there is a compelling need to store data and process VSS applications locally on edge devices rather than send data to servers for computation. However, the explosive amount of data movement from NAND storage to DRAM across memory hierarchy and data processing of the entire dataset consume enormous energy and require long latency for VSS applications. Specifically, edge devices with insufficient DRAM capacity will trigger data swap and deteriorate the execution performance. To overcome this crucial hurdle, we propose an intelligent cognition engine (ICE) with cognitive 3D NAND, featuring non-volatile in-memory computing (nvIMC) to accelerate the processing, suppress the data movement, and reduce data swap between the processor and storage. this cognitive 3D NAND features digital nvIMC techniques (i.e., ADC/DAC-free approach), high-density 3D NAND, and compatibility with standard 3D NAND products with minor modifications. To facilitate parallel INT8/INT4 vector-vector multiplication (VVM) and mitigate the reliability issue of 3D NAND, we develop a bit-error-tolerance data encoding and a two's complement-based digital accumulator. VVM can support similarity computations (e.g., cosine similarity and Euclidean distance), which are required to search "the most similar data" right where they are stored. In addition, the proposed solution can be realized on edge storage products, e.g., embedded Multimedia Card (eMMC). the measured and simulated results on real 3D NAND chips show that ICE enhances the system execution time by 17× to 95× and energy efficiency by 11× to 140×, compared to traditional von Neumann approaches using state-of-the-art edge systems with MobileFaceNet on CASIA-WebFace dataset. To the best of our knowledge, this work demo
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