Deep learning based condition monitoring of road traffic for enhanced transportation routing

Multipliers are basic building blocks in various integrated circuits like microprocessors, micro controllers, and ALUs. The existing multipliers suffer from high power consumption and inefficient use of hardware resources. They often rely on traditional adder structures that are not tailored for specific operations, leading to suboptimal performance. Additionally, their fixed architectures limit adaptability and scalability in different applications. So, the proposed approach offers enhanced computational efficiency and reduced power consumption compared to conventional multiplier designs. By integrating multiplexer-dependent adders into the systolic array, the proposed method optimizes resource utilization and delivers improved performance for various arithmetic operations. This integration allows for dynamic selection of adder types based on the specific multiplication operation, significantly reducing power consumption and latency. By adapting the hardware resources to computational needs, the method achieves higher efficiency and flexibility, making it suitable for a wide range of applications in digital signal processing and data processing systems.

Section: Literature Surveymentioning

confidence: 99%

Implementation of Systolic Multiplier Using Hybrid Multiplexer Dependent Adder

BHARGAVI,

Mounika,

Sarika

et al. 2024

“…Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. Arvind, S., et.al [16] developed the Deep models had immensely contributed to this sector thus making it easier for quick diagnosis, early and effective treatment. X-Rays are the most used radiological medical imaging tool and radiologists.…”

Section: Literature Surveymentioning

confidence: 99%

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

Suresh,

Meghana,

Sujitha

et al. 2024

Medical image segmentation plays a crucial role in various clinical applications, facilitating accurate diagnosis and treatment planning. In communication systems, particularly telemedicine and remote healthcare monitoring, real-time processing and transmission of medical images are essential for timely diagnosis and intervention. Leveraging VLSI technology for implementing Kmeans offers a promising solution to address the computational demands of image segmentation while meeting the stringent requirements of communication systems. Existing systems often rely on software-based implementations such as threshold segmentation, leading to significant computational overhead and latency, particularly in resource-constrained environments. Moreover, these implementations struggle to achieve real-time performance, hindering their practical utility in communication systems for healthcare. So, this work proposed VLSI-based approach aims to overcome these challenges by offloading the computational burden from the software to hardware, enabling parallel processing and efficient utilization of resources. By exploiting the inherent parallelism of the K-means algorithm and optimizing hardware architecture, our design ensures high throughput and low latency, making it suitable for real-time medical image segmentation in communication systems.

“…However, its use was associated with the need to counter a variety of cyber threats. Semenov, et.al [14] implemented Secure data exchange in unmanned aerial vehicle management systems was an important aspect to prevent unauthorized access and ensure the security of aircraft. Hosny, et.al [15] developed, ensuring data confidentiality, authentication, integrity, and nonrepudiation was crucial.…”

Section: Literature Surveymentioning

confidence: 99%

VLSI Implementation of Speech Steganography with Advanced Wavelet Transform

Akhila,

Kareena,

Nikhitha

et al. 2024

In the realm of secure communication systems, the integration of advanced wavelet transforms into speech steganography systems has emerged as a promising path. Secure transmission of sensitive information in military communications, confidential exchange of data in corporate environments, and protection of personal information in telecommunication networks. However, the current system employs conventional methods of speech steganography, relying on basic encryption techniques and simplistic embedding algorithms. While functional, these approaches lack robustness and fail to adequately conceal secret data within speech signals, leaving them vulnerable to detection and interception. So, the proposed system leverages advanced wavelet transforms to enhance the security and efficiency of speech steganography. By exploiting the multi-resolution analysis capabilities of wavelets, the system achieves improved embedding capacity while maintaining perceptual transparency. Additionally, the use of dynamic embedding algorithms based on wavelet coefficients ensures adaptability to varying signal characteristics, enhancing robustness against attacks and noise interference. The VLSI implementation of this system optimizes resource utilization and computational efficiency, making it suitable for real-time applications in communication systems.