Parkinson disease detction through ensemble of Voice patterns and Key stroke dynamics #PhD #researchtopic

What is Parkinson disease?

Parkinson's, is a progressive neurodegenerative disorder that primarily affects movement and can also cause a wide range of non-motor symptoms. It is a chronic condition that worsens over time, and while there is no cure, there are treatments available to manage its symptoms.

What are the symptoms of Parkinson disease ?

The primary symptoms of parkinson includes, 

• tremers, Bradykinesia we call as slow in doing any activity,
•  Muscle Rigidity that creates challenging muscle stiffness, 
• Postural Instability unable to balance the co-ordination. 

These symptoms are called as motorists symptoms since we can see the changes in movements. on the other hand, other symptoms are

• Depression and Anxiety: Many individuals with Parkinson's experience mood disorders, including depression and anxiety.
• Cognitive Changes: Cognitive impairment can occur, and some people may develop dementia in advanced stages.
• Sleep Disturbances: Sleep problems such as insomnia and restless leg syndrome are common.
• Autonomic Dysfunction: This can lead to issues like constipation, urinary problems, and orthostatic hypotension (a drop in blood pressure when standing up).
• Loss of Smell: A reduced ability to smell is often an early non-motor symptom.
• Speech and Swallowing Difficulties: Parkinson's can affect the muscles involved in speech and swallowing, leading to issues in these areas

How Voice data is helpful in analyzing Parkinson's?

Voice data analysis can be a valuable tool in diagnosing and monitoring Parkinson's disease. Parkinson's disease is a neurodegenerative disorder that affects motor function and can also impact speech.

Early Detection: Changes in speech patterns can be one of the early indicators of Parkinson's disease. Analyzing voice data for subtle changes in pitch, tone, and articulation can help in early diagnosis.

Monitoring Progression: As Parkinson's disease progresses, it can lead to more pronounced speech issues. Voice data analysis can be used to monitor how speech changes over time, which can be valuable for tracking the progression of the disease.

Objective Assessment: Unlike traditional clinical assessments, voice data analysis can provide objective data about speech patterns. This can be useful for both patients and healthcare professionals to track the impact of the disease.

Treatment Efficacy: Voice data analysis can also be used to assess the effectiveness of treatments and interventions. It can help determine if medication or therapy is improving speech quality.

How to Overcome Parkinson's?

There is currently no cure for Parkinson's disease, but various treatments are available to help manage its symptoms. Medications, physical therapy, occupational therapy, and, in some cases, surgical interventions like deep brain stimulation can be used to improve the quality of life and manage the condition. Parkinson's disease is a chronic condition that requires ongoing management and support, often involving a healthcare team that includes neurologists, physical therapists, and other specialists.

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Optimized SAD (OSAD) algorithm for Image similarity analysis

Optimized SAD (OSAD) algorithm

The Sum of Absolute Differences (SAD) algorithm is a simple and widely used image processing technique used for various computer vision tasks, including template matching and motion estimation. It measures the similarity between two images by calculating the absolute differences between corresponding pixel values and then summing up these differences. The SAD algorithm is particularly useful in finding similarities between a small template image and a larger target image.

 Here's how the SAD algorithm works step by step:

1. Template and Target Images: You have a template image (usually smaller) and a target image (usually larger). The goal is to find where the template image best matches the target image.
2. Sliding Window: Place the template image at the top-left corner of the target image.
3. Pixel-wise Absolute Differences: For each corresponding pixel in the template and target images, calculate the absolute difference in pixel values. This is done by subtracting the value of the corresponding pixel in the template from the value of the pixel in the target image and taking the absolute value of the result.
4. Sum of Absolute Differences: Sum up all the absolute differences calculated in step 3 to get a single value representing the dissimilarity or "error" between the template and the portion of the target image it currently covers.
5. Move the Window: Slide the template one pixel to the right (or in any desired direction) and repeat steps 3 and 4 to calculate the SAD value for the new position.
6. Repeat: Continue sliding the template over the target image until you have covered all possible positions or until you find the position with the lowest SAD value.
7. Matching Location: The position with the lowest SAD value represents the best match for the template within the target image.
8. Optimize : the results by comparing the obtained values with the reference images.

Applications of the SAD algorithm include object detection, facial recognition, motion estimation in video processing, and various pattern recognition tasks.

While the SAD algorithm is conceptually straightforward, it can be computationally intensive, especially for large images or when used in real-time applications. Therefore, optimizing the algorithm, as mentioned in the previous response, can be crucial for achieving good performance. This may involve using parallel processing, efficient data structures, and other optimization techniques to speed up the calculations.

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Expected Energy Optimization for Real-Time Multiprocessor SoCs Running Periodic Tasks with Uncertain Execution Time

Design of energy-saving optimization approach for Multi-processor SoC task scheduling

Power saving is a challeging task in VLSI design perhaps the multiple processing SOC carry out more power consumption through system on chip model. 

The low power VLSI architecture is capable of handling the leakage power within the circuit by altering the circuit level changes we call it as optimization.

Multi processor SOC architectures required optimized SOC systems with stable operating clock frequency.

The SoC blocks are composed of FSM enabled pipelines control mechanism. the prioritizing algorithm that holds various SOC modules put priority in the index thus handle the modules one by one without interrupting the other modules. 

The best SOC platform also capable of handling the uncertain exceutions in the system architecture by providing safegaurd module as error monitor. the dynamic error monitor keep tracks the glitches and stack the pulses to produce recycled clock with stable Duty Cycle

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