Paper Battery

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  • Topic: Signal processing, Digital signal processing, Noise
  • Pages : 7 (2481 words )
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  • Published : October 8, 2011
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A PAPER ON URINE ACTIVATED PAPER BATTERIES FOR BIOSYSTEMS

Author 1 B.Amruthavalli,ΙΙΙ B.Tech ECE JNTU College of Engineering Kakinada, Plot No:3-E,Vamsi Krishna Apartments, Vidyuth Nagar, Kakinada, Andhra Pradesh, India. Ph No:9885589402, E- mail Id:amruthavalli11@yahoo.co.in

Author 2 D.Santosh Kumar,ΙΙΙ B.Tech ECE JNTU College of Engineering ,Kakinada

Dr no: 2-13-13, Venkat Nagar,
Kakinada, Andhra Pradesh,India Ph No:9440917270 E- Mail Id: durga_santosh@yahoo.co.in

ABSTRACT

A spatial noise shaping (SNS) method based on human visual sensitivity is presented. The method exploits the capability of frequency domain linear prediction for spatial envelope retrieval. It effectively shapes (or hides) the noise of an image in areas which are not sensitive to human vision so that the resultingimage is more pleasant to human eyes. The noise comes from the processing of the image, and it can be either separable like the additive noise pattern in image watermarking or non separable like the quantization noise in image coding. An application of the algorithm is demonstrated in the paper by using it to enhance image coders. Images decoded from the SNS incorporated coders have superior perceived quality than those without using SNS.

I. INTRODUCTION
NOISE appearance has always been unwelcome in many speech, audio, and image applications. Many studies have been done in the field of noise reduction, ranging from conventional median or Wiener filter types of algorithms [1] to recent wavelet denoising techniques [2], [3]. Although these methods are able to eliminate or reduce the amount of noise, some useful information in the host signal may be damaged by them as well, and the damage is usually proportional to the amount of noise reduced. This tradeoff constitutes the major challenge for these methods, and limits their usage. Noise

shaping techniques are another approach of removing perceptible noise, and have been widely employed in many applications, such as coding (compression) [4], data hiding, and watermarking [5]. Unlike noise reduction methods, the purpose of all these techniques is not to reduce the noise but rather to shape the structure of the noise so that it becomes less perceptible in the final signal. Most of them shape noise by altering its spectrum, and hence called spectral noise shaping methods. These methods may be called spectral noise shaping methods. Many of them are application specific, and not applicable to other methods. Recently, a novel temporal noise shaping (TNS) method [6] was proposed, which adapts the temporal structure of the quantization noise to that of the host signal, therefore the masking effects of the human auditory system can be exploited. As a result, this new approach effectively reduces the pre-echo problem caused by the spread of quantization noise in the time domain within a transform window. It has been shown that TNS has contributed to the high performance of MPEG advanced audio coder (AAC) [7]. A few attempts [8]– [10] were reported with some success in shaping quantization noise spatially for image compression. A larger part of the noise could be spatially distributed to the textured part of the image by using filtered noise distribution makes it difficult to control the noise distribution accurately. The problem appeared to be resolved by optimizing the perceptual weighted quantization noise feedback [9], [10]. In addition, all these methods [8]–[10] were specifically developed for transform image coders, especially distributed to the textured

part of the image by using filtered quantization noise feedback. However, the lack of known relationship between filter coefficients and noise distribution makes it difficult to control the noise distribution accurately. The problem appeared to be resolved by optimizing the perceptual weighted quantization noise feedback [9], [10]. The algorithm was shown to be effective in reducing mosquito noise in a JPEG image. However,...
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