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Updates found with 'whooping cough'

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Updates found with 'whooping cough'

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 -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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