A HIGH SPEED VLSI ARCHITECTURE FOR DIGITAL SPEECH WATERMARKING WITH COMPRESSION

The need to provide a copy right protection on digital watermarking to multimedia data like speech, image or video is rapidly increasing with an intensification in the application in these areas. Digital watermarking has received a lot of attention in the past few years. A hardware system based solely on DSP processors are fast but may require more area, cost or power if the target application requires a large amount of parallel processing. An FPGA co-processor can provide as many as 550 parallel multiply and accumulate operations on a single device, but FPGAs excel at processing large amounts of data in parallel, as they are not optimized as processors for tasks such as periodic coefficient updates, decision- making control tasks. Combination of both the FPGA and DSP processor delivers an attractive solution for a wide range of applications. A hardware implementation of digital speech watermarking combined with speech compression, encryption on heterogeneous platform is made in this paper. It is observed that the proposed architecture is able to attain high speed while utilizing optimal resources in terms of area

A Mind Operated Computer Mouse Using Discrete Wavelet Transforms for Elderly People with Multiple Disabilities

AbstractAccording to a statistical survey made by World Health Organization (WHO) India suffers from highest number of road accidents and out of which more than ten percent of them are prone to head injuries. This scenario leads to patient's death or make the victim to become comatose. Also, many different disorders can disrupt the neuromuscular channels through which the brain communicates with and controls its external environment. Brainstem stroke or spinal cord injury, cerebral palsy, muscular dystrophies, multiple sclerosis and numerous other diseases impair the neural pathways leading to communication and control which make the victims intellectually or physically disabled. Most often, the communication for paralyzed people is established by using a Brain Control Interface (BCI). Most of the existing systems had experimented Brain Computer Interface either with animals or healthy human beings. But, this paper focuses on movements of the mouse cursor controlled by a person with multiple disabilities. The mouse cursor movement would further be used by the disabled person to have a communication with his caretaker by means of the software developed by us. The proposed system uses discrete wavelet transforms for de-noising the muscular and cardiac signals. An independent component analysis is performed in order to extract the beta rhythms from the EEG signal. The mouse control is achieved by interfacing the mouse with a microcontroller which receives the operating voltages from the Data Acquisition System (DAS) which acquires and conditions the EEG signals coming from the user brain. The proposed system is tested on several young and elderly persons and is found to be working with more than 95% accuracy

An Adaptive Neuro-Fuzzy Inference System-Based Lung Cancer Detection System

In the last forty years, there have been dramatic growths in the area of medical and healthcare systems. During this period, the true causes of several illnesses were uncovered, fresh diagnostic techniques were created, and new medications were created. Despite these advances, illnesses like cancer continue to plague us because people remain susceptible to the system. Cancer is the second-biggest reason of mortality worldwide, accounting for around one in every six deaths. Therefore, early illness detection considerably increases the likelihood of survival. Among all cancers, lung cancer has the highest risk. Using the capabilities of Artificial Intelligence (AI), tumour diagnosis may be automated to analysis larger capacity in lesser time and at a lesser cost. A Machine Learning based Lung Cancer Detection System (ML-LCDS) is suggested in this research. The automated identification and localization of tumour locations in lung imaging are increasingly crucial for saving patients' lives via prompt medical therapy. In this study, a lung tumour detection, categorization, and segmentation method based on machine learning are suggested. The tumour categorization stage first applies an adaptive median filtration to the original lung computerised tomography picture and then applies Discrete-Timing Complicated Wavelet Transformation (DT-CWT) to divide the whole picture into several sub-bands. In addition to the deconstructed sub-bands, Discrete Wavelet Transformation (DWT), and co-occurrence characteristics are calculated and identified using an ANFIS. The tumour segmentation stage detects tumour locations on this identified abnormal lung image using morphological features. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, False positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The outcomes of the simulation show that the proposed system for finding and predicting lung cancer is accurate and precise. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others