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Updates found with 'qrs wave'

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Updates found with 'qrs wave'

VLSI PROJECTS ABSTRACT 2016 -2017LOW-POWER SYSTEMS FOR DETECTION OF SYMPTOMATIC PATTERNS IN AUDIO BIOLOGICAL SIGNALSABSTRACT: In this paper, we present a low-power, efficacious, and scalable system for the detection of symptomatic patterns in biological audio signals. The digital audio recordings of various symptoms, such as cough, sneeze, and so on, are spectrally analyzed using a discrete wavelet transform. Subsequently, we use simple mathematical metrics, such as energy, quasi-average, and coastline parameter for various wavelet coefficients of interest depending on the type of pattern to be detected. Furthermore, a mel-frequency cepstrum-based analysis is applied to distinguish between signals, such as cough and sneeze, which have a similar frequency response and, hence, occur in common wavelet coefficients. Algorithm-circuit codesign methodology is utilized in order to optimize the system at algorithm and circuit levels of design abstraction. This helps in implementing a low-power system as well as maintaining the efficacy of detection. The system is scalable in terms of user specificity as well as the type of signal to be analyzed for an audio symptomatic pattern. We utilize multiplierless implementation circuit strategies and the algorithmic modification of mel cepstrum computation to implement low power system in the 65-nm bulk Si technology. It is observed that the pattern detection system achieves about 90% correct classification of five types of audio health symptoms. We also scale the supply voltage due to lower frequency of operation and report a total power consumption of ~184 µW at 700 mV supply.
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VLSI PROJECTS ABSTRACT 2016-2017 LOW-POWER SYSTEM FOR DETECTION OF SYMPTOMATIC PATTERNS IN AUDIO BIOLOGICAL SIGNALS ABSTRACT:In this paper, we present a low-power, efficacious, and scalable system for the detection of symptomatic patterns in biological audio signals. The digital audio recordings of various symptoms, such as cough, sneeze, and so on, are spectrally analyzed using a discrete wavelet transform. Subsequently, we use simple mathematical metrics, such as energy, quasi-average, and coastline parameter for various wavelet coefficients of interest depending on the type of pattern to be detected. Furthermore, a multi-frequency spectrum-based analysis is applied to distinguish between signals, such as cough and sneeze, which have a similar frequency response and, hence, occur in common wavelet coefficients. Algorithm-circuit codesign methodology is utilized in order to optimize the system at algorithm and circuit levels of design abstraction. This helps in implementing a low-power system as well as maintaining the efficacy of detection. The system is scalable in terms of user specificity as well as the type of signal to be analyzed for an audio symptomatic pattern. We utilize multiplierless implementation circuit strategies and the algorithmic modification of multi spectrum computation to implement low power system in the 65-nm bulk Si technology. It is observed that the pattern detection system achieves about 90% correct classification of five types of audio health symptoms. We also scale the supply voltage due to lower frequency of operation and report a total power consumption of ∼184 µW at 700 mV supply. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2. EXISTING SYSTEM: In the past decade, rapid advancements in the development of low-power design methodologies have resulted in feasible designs for various wearable and implantable medical systems. Numerous wearable health monitoring systems have been proposed in order to deliver early warning of an impending health condition. These systems monitor various internal as well as external parameters related to the human health, such as temperature, heart rate, and so on. Apart from these parameters, it is well known that acoustic symptoms, such as cough, sneeze, belching, and so on, are early markers of serious health issues, such as influenza, diarrhea, and whooping cough, especially among children. If repetitive occurrence of these symptoms is detected in advance, it is possible for the patient or the healthcare personnel to commence remedial action prior to aggravation of the problem. In the literature, most of the developed systems detect a single acoustic symptom (cough or sneeze). The Kids Health Monitoring System (KiMS) proposed in uses wearable sensors and acoustic signal processing in order to provide health monitoring in children. Using the neural network-based processing, the KiMS classifies various symptoms and activities and, subsequently, transmits the record to a parent or doctor for further analysis.PROPOSED SYSTEM: We describe the proposed algorithm and the methodology used to modify the various computational tools in order to make it implementable into low-power hardware. In Section II, we had described the basics and justified the basis for selecting specific computational techniques used in developing this algorithm. The application of these computations is dependent on the characteristic property of the symptom to be detected. The algorithm methodology is shown in Fig. 1. We also describe the details along with the mapping of algorithm to specific signals as follows. ADVANTAGES:• efficient low-power health monitoring system DISADVANTAGES• High power for monitoring system.SOFTWARE IMPLEMENTATION:• Modelsim• Xilinx ISE
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VLSI PROJECTS ABSTRACT 2016-2017 LOW-POWER ECG-BASED PROCESSOR FOR PREDICTING VENTRICULAR ARRHYTHMIA ABSTRACT: This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points. Those techniques are robust to any variations in the ECG signal with high sensitivity and precision. Two databases of the heart signal recordings from the MIT PhysioNet and the American Heart Association were used as a validation set to evaluate the performance of the processor. Based on application-specified integrated circuit (ASIC) simulation results, the overall classification accuracy was found to be 86% on the out-of-sample validation data with 3-s window size. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.EXISTING SYSTEM: Recently, due to the remarkable advancement in technology, the development of dedicated hardware for accurate ECG analysis and classification in real time has become possible. The main requirements are low-power consumption and low-energy operation in order to have longer battery lifetime along with the small area for wearability. Many attempts succeeded to implement ECG signal processing and classification systems in hardware. Shiu et al. implemented an integrated electrocardiogram signal processor (ESP) for the identification of heart diseases using the 90-nm CMOS technology. The system employed an instrumentation amplifier and a low-pass filter (LPF) to remove the baseline wander and the power line interference form the ECG and employed a time-domain morphological analysis for the feature extraction and classification based on the evaluation of the ST segment. The system was carried out in a field programmable gate array and consumed a total of 40.3-μW power and achieved an accuracy of 96.6%. The main disadvantage of the system is that it uses fixed search window with predefined size to locate S and T fiducial points, which is not suitable for real-time scenarios. PROPOSED SYSTEM: The architecture of the proposed ESP is shown in Fig. 1. The architecture includes the modules of the three stages along with a main FSM that controls the flow of the data between the different stages, as shown in Fig. 2. The processing of the data is done using fixed point representation.ADVANTAGES:• small area• low power• high performance DISADVANTAGES:• large area• high power• high performanceSOFTWARE IMPLEMENTATION• Modelsim• Xilinx ISE
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VLSI PROJECTS ABSTRACT 201-2017 A 0.1–3.5-GHZ DUTY-CYCLE MEASUREMENT AND CORRECTION TECHNIQUE IN 130-NM CMOS ABSTRACT: A duty-cycle correction technique using a novel pulse width modification cell is demonstrated across a frequency range of 100 MHz–3.5 GHz. The technique works at frequencies where most digital techniques implemented in the same technology node fail. An alternative method of making time domain measurements such as duty cycle and rise/fall times from the frequency domain data is introduced. The data are obtained from the equipment that has significantly lower bandwidth than required for measurements in the time domain. An algorithm for the same has been developed and experimentally verified. The correction circuit is implemented in a 0.13-µm CMOS technology and occupies an area of 0.011 mm2. It corrects to a residual error of less than 1%. The extent of correction is limited by the technology at higher frequencies. The proposed architecture of this paper area and power consumption analysis using tanner tool.EXISTING SYSTEM: In radio frequency (RF) transceivers for modern communication standards, the clocks with a precise 50% duty cycle are necessary. In a receiver, even order harmonics, which arise out of non-50% duty-cycle clocks, result in additional noise folding, and thus degrading the sensitivity. They also cause the second-order nonlinearity and carrier feed-through in some mixer topologies. With the emergence of digitally intensive transmitter architectures, the switching power amplifiers are being used in many cutting-edge applications. The presence of even order harmonics causes performance degradation in terms of linearity, efficiency, and additional out-of-band components. High-speed wireline communication systems also demand very precise clocks. In implementations where a fine phase separation is generated on-chip, non-50% duty-cycle clocks result in direct performance degradation. DRAMs are another application, which require 50% duty-cycle clocks. PROPOSED SYSTEM: The DCC technique is based on a negative feedback loop, as shown in Fig. 1. The incoming differential waveforms (V + in and V − in) are processed by a pulse width modification cell (PMC), which performs pulse width expansion and contraction. ADVANTAGES:• operate over a very wide frequency range• high-speed digital links DISADVANTAGES• Use limited range of frequency
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