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There are several voice communication systems that are used nowadays which are capable of maintain voice calls between two users in real time. Telephones are widely used all around the world in an unlimited kind of situations. All of these situations expose the microphone (or microphones) of the phones to different and unpredictable noises, as street noise, sea noise, rain noise, wind noise, unwanted voices, car motors, etc. As microphones capture all the sounds around it, including the wanted voice and the unwanted noises, it is necessary to implement digital real time filters capable of attenuate as much as possible all the surrounding noises. It exists a large quantity of noise reduction methods that have been used in the calling algorithms of phones. Even if these methods have had, in general, a good performance, there is still a research being done in this area in order to improve the current results. Because of this, the multichannel methods were created (using multiple microphones) as well as new algorithms that pretend to have a better noise reduction than the single channel methods. Most of these methods require a speech presence probability (SPP) method to achieve the noise reduction. The following document presents a research about different SPP methods as well as a comparison between these. This includes an explanation on how theses algorithm work, a Matlab implementation using real voice and noise recordings and objective tests of the filter.

A second project for Calculus 1 at Fitchburg State. Explore the proofs of some of the derivative rules and derive new rules from old.

LaTeX Template for my academic homework assignments. It has been created by using the fphw class. Github link: https://github.com/rafisics/My_HW-Assignment_Template

A template for submissions to the HDSR

The template for BTech Thesis submission for IIT Patna students.

本模板来源于https://github.com/TheNetAdmin/zjuthesis 具体使用说明参见 GitHub 主页，本Overleaf 模板不保证与 GitHub 模板同步更新，需要使用最新版本请参考 GitHub 主页。

The Loewner Equation and the derivative of its solution

The preparation of these files was supported by Schlumberger Palo Alto Research, AT&T Bell Laboratories, and Morgan Kaufmann Publishers. Shirley Jowell, of Morgan Kaufmann Publishers, and Peter F. Patel-Schneider, of AT&T Bell Laboratories collaborated on their preparation. These instructions can be modified and used in other conferences as long as credit to the authors and supporting agencies is retained, this notice is not changed, and further modification or reuse is not restricted. Neither Shirley Jowell nor Peter F. Patel-Schneider can be listed as contacts for providing assistance without their prior permission. To use for other conferences, change references to files and the conference appropriate and use other authors, contacts, publishers, and organizations. Also change the deadline and address for returning papers and the length and page charge instructions. Put where the files are available in the appropriate places.

Image reconstruction is observed to be one of the most common problem because of it's large data movement and non-trivial data dependencies. In the past, these problems were tackled by many high performance hardware such as FPGA's and GPGPU's. This also reflects the investemts to be made in these supercomputers for real time reconstruction of clinical computed tomography (CT) applications. Medical imaging systems are employing high performance computing (HPC) technology to meet their time constraints. This paper presents different optimizations to the volume reconstruction and implement it on a commodity hardware such as x86 based multicore system. This paper chooses to perform its implementaion on Intel Xeon X5365 multicore processor. We perform different levels of parallelization and analyse each of them and report their results with respect to serial implementation. The objective of this paper is to understand the constraints of volume reconstruction in multicore architecture and optimize them while preserving the quality of the reconstructed image.