7 research outputs found
Early Detection of Parkinson's Disease using Motor Symptoms and Machine Learning
Parkinson's disease (PD) has been found to affect 1 out of every 1000 people,
being more inclined towards the population above 60 years. Leveraging
wearable-systems to find accurate biomarkers for diagnosis has become the need
of the hour, especially for a neurodegenerative condition like Parkinson's.
This work aims at focusing on early-occurring, common symptoms, such as motor
and gait related parameters to arrive at a quantitative analysis on the
feasibility of an economical and a robust wearable device. A subset of the
Parkinson's Progression Markers Initiative (PPMI), PPMI Gait dataset has been
utilised for feature-selection after a thorough analysis with various Machine
Learning algorithms. Identified influential features has then been used to test
real-time data for early detection of Parkinson Syndrome, with a model accuracy
of 91.9
FIELD PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF A VARIABLE LEAKY LEAST MEAN SQUARE ADAPTIVE ALGORITHM
Adaptive noise cancellation is an extensively researched area of signal processing. Many algorithms had been studied such as least mean square algorithm (LMS), recursive least square algorithm, and normalized LMS algorithm. The statistical characteristics of noise are fast in nature and the algorithms for noise cancellation should converge fast. Since LMS algorithm has slow convergence; in this paper, a variable leaky LMS (VLLMS) algorithm is explored. VLLMS is implemented using the concept of hardware-software cosimulation using Xilinx System Generator. The design is implemented on Virtex-6 ML605 field programmable gate array board. The implemented design is tested for sinusoidal signal added with an additivewhite Gaussian noise. The design summary and the utilization summary are presented.Â
i-VOTING USING TRIP
ABSTRACT: Data imputation aims at filling in missing attribute values in databases
FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF THE DYNAMIC TIME WARPING ALGORITHM FOR SPEECH RECOGNITION
Objective of this research is to implement a speech recognition algorithm in smaller form factor device. Speech recognition is an extensively used inmobile and in numerous consumer electronics devices. Dynamic time warping (DTW) method which is based on dynamic programming is chosen tobe implemented for speech recognition because of the latest trend in evolving computing power. Implementation of DTW in field-programmable gatearray is chosen for its featured flexibility, parallelization and shorter time to market. The above algorithm is implemented using Verilog on Xilinx ISE.The warping cost is less if the similarity is found and is more for dissimilar sequences which is verified in the simulation output. The results indicatethat real time implementation of DTW based speech recognition could be done in future
ADAPTED DTW JOINT WITH WAVELET TRANSFORM FOR ISOLATED DIGIT RECOGNITION
ABSTRACT Dynamic Time Warping (DTW) is a template matching approach based on dynamic programming algorithm. This paper proposes an adapted DTW algorithm for calculating the global distance matrix. Speech signals are decomposed using Discrete Wavelet Transform (DWT) into various frequency sub-bands and the resulted sub-bands of unknown; template digit utterances are compared using the adapted DTW. The performance of the proposed approach is tested with TIDIGITs data. The results indicate that there is a reduction in the order of complexity compared to DTW and increase in the performance