#### IEEE SEMINAR BY PROF. (DR.) S.D. JOSHI

#### 8th April, 2016

** An IEEE seminar** on Statistical Signal Processing was delivered by

*on*

**Prof. (Dr.) S.D Joshi***at*

**8th April 2016***A total of 60 students (40 from ECE and 20 from EEE department) attended the seminar.*

**Dronacharya College of Engineering, Gurgaon.***(HOD EEE),*

**Dr. K. K. Singh***(HOD ECE) with faculty members*

**Mrs. Dimple Saproo***(EEE),*

**Mrs. Tanushree Bhattacharjee***(ECE),*

**Mrs. Amnindar Kaur***(ECE) also attended the seminar.*

**Mrs. Sanghamitra V. Arora**** Dr. S D Joshi** began his lecture with a brief introduction on statistical signal processing. Statistical signal processing is an area covering Applied Mathematics and Signal Processing. It treats signals as stochastic processes and deals with their statistical properties (e.g., mean, covariance, etc.). Because of its broad range of applications statistical signal processing is taught in either Electrical Engineering, Applied Mathematics, Pure Mathematics / Statistics, or Biomedical Engineering and Physics departments around the world. Never the less it’s an important application that exist in almost all scientific fields.

Many engineering applications require extension of a signal or parameter of interest from degraded measurements. To accomplish this, it is useful to deploy fine - grained statistical models; diverse sensors which acquire extra spatial, temporal, or polarization information; or multi - dimensional signal representations, e.g. time - frequency or time - scale. When applied in combination, these approaches can be used to develop highly sensitive signal estimation, detection, or tracking algorithms, which can exploit small differences between signals, interferences, and noise. Conversely, these approaches can be used to develop algorithms to identify a channel or system producing a signal in additive noise and interference, even when the channel input is unknown but has known statistical properties. Broadly stated, statistical signal processing is concerned with the reliable estimation, detection and classification of signals which are subject to random fluctuations. Statistical signal processing has its roots in probability theory, mathematical statistics and, more recently, systems theory and statistical communications theory.

There was a detail discussion on Fourier series and Fourier Transform in the area of signal processing. Fourier series is a way to represent a (wave - like) function as the sum of simple sine waves. Formally, it decomposes any periodic function or periodic signal into the sum of a (possibly infinite) set of simple oscillating functions, namely sines and cosines (or, equivalently, complex exponentials), whereas discrete - time Fourier transform is a periodic function, often defined in terms of a Fourier series. The Fourier transform decomposes a function of time (a signal) into the frequencies that make it up, similarly to how a musical chord can be expressed as the amplitude (or loudness) of its constituent notes. The Fourier transform is a function of time itself is a complex - valued function of frequency, whose absolute value represents the amount of that frequency present in the original function, and whose complex argument is the phase offset of the basic sinusoid in that frequency. The Fourier transform is called the frequency domain representation of the original signal. The term Fourier transform refers to both the frequency domain representation and the mathematical operation that associates the frequency domain representation to a function of time.

The seminar was very effective with respect to understanding of Fourier series, Fourier Transform, Signal processing basics and its practical applications. Students and faculty members learned about new ideas and areas in this field. This has helped the students to become aware of advances in this field and make different types of projects on their own.

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