What is ‘SETUP and HOLD’ time concept?

Gates is referred as the basic building blocks of combinational logic circuits. However there are XOR, NAND, NOR, XNOR gates too but particularly AND, OR, and NOT gates are used. Similarly, Flip flops are referred as the basic building blocks of sequential circuits. Flip flops are clock based devices. One bit is stored by each flip flop.

There are restrictive time regions around the clock for every flip flop. Input should not change in these time regions. These regions are called restrictive because by changing the input in this region the output is not sure, it may or may not be the one you expected. 

Output is derived from either the new input, the old input, or may be in between these two. The two most important terms in the digital clocking are defined below.

Setup and hold time.

• The setup time is the time interval just before the clock where the data must be remain stable. Other definition is, “SETUP time is the minimum time before the clock's active edge that the data must be stable to be latched correctly in this period of time. It may cause incorrect data to be captured, if there is any violation, which is known as setup time violation.



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• The hold time is the time interval after the clock where the data must be remain stable. It may cause incorrect data to be latched if there is any violation, which is known as a hold time violation.

• Remedies for setup time violation:
  •    Optimize the combinational logic between the flip-flops to get minimum delay.
  •    To get lesser setup time, redesign the flip-flops.
  •    Play with clock skew (useful skews).
  
  Remedies for hold time violation :

          •    Use buffers to add delays

         .•    lockup-latches can be added (basically to avoid data slip).  

                                                                                                              Author - Poornima Sharma

                                                                                                              (Intern Design Engineer)                                            

Understanding the process of Image Segmentation

Image segmentation is the process in which we do portioning of digital image into multiple segments. We convert the image information into more meaningful and easy to analyze data.  Segmentation is not so easy in image processing and it is still in research.

In image segmentation we extract the information from the image. We use segmentation in object detection and feature extraction, in this we firstly do the edge segmentation. 

Edge segmentation is the process in which when the intensity changes abruptly it can create edges in an image. Second we use segmentation by Thresholding, in thresholding process we use grey scale image and composed dark intensity image on light background.

Image segmentation

Thresholding is the way to separate out your information from image and it easy to analyze. We use histogram, histogram is the technique improving the contrast of an image for thresholding. 

It is the common step of image processing when we are going to do recognition, object detection, region estimation, feature extraction.

Whenever we do segmentation our goal is to extract the information clearly because we want the information easy to analyse and understand.


Author - Rahul Bhardwaj
(Research Associate at Silicon Mentor)

Doing Research in image and signal processing by M.Tech and PhD students

Doing research in signal and image processing is having a fight with time domain and frequency domain methods. The algorithm and complexity theory put a hard delay to complete his/her M.Tech/PhD thesis within the allotted time (2 year for M.Tech and 3-4 year for PhD). The student is subjected to do his/her practical implementation in real system by using MATLAB or other DSP tools. 

The M.Tech and PhD in DSP is aimed at capturing a conceptual understanding of mathematical algorithm, theoretical and implementation aspects of signal and image.

Here are the few steps in the journey of a research project:

Base Paper selection:

Most students’ do his/her research or project based on an IEEE Paper (conference or transaction) or from a good impact factor journal. So, that student is expected to implement the complete idea presented in that particular “base paper”. After proving this paper he/she expected to do somewhat new in terms of concept or results.

Finish this research within allotted time (2 year for M.Tech and 3-4 year for PhD):

It depends upon the student’s ability and complexity of the Base Paper. Because we all know signal and image processing are typical and mathematical areas. However, some of the results given in these particular papers are not to be expected mean these results are fake. The major problem in India is that the curriculum certainly not providing enough research environment.

How to check the complexity and quality of “Base paper “

A student can understand the quality and complexity of the particular journal by impact factor, citation, manuscript submission and publication process, an acceptance percentage of that particular journal.

Searching for Guidance in Research or Project:

When student didn’t get any support from their campus they will search for guidance in their M.Tech or PhD projects. In such case they could find some project centers. These project centers provide them list of IEEE projects in signal and image processing and claim that they are having all concept and algorithms of these papers.

Students who sink into any of one get low quality paper and thesis. And submit this low quality paper in his/her university which is not considered real research.

How to find a good research partner and research platform?

There is a balanced and novel way to find a research partner, for this there should be following qualities:

Past work done by research entity.

Research guides profile in terms of their published work in International and national level.

•  Total Published work quality
•  Research environment
•  Available Tools

Research Projects in VLSI

Tags : VLSI Research projects   VLSI M.tech projects       IEEE VLSI Projects         Phd. VLSI Projects 

 

Speech Recognition : A concept for Creative Generation

Speech Recognition is the process in which words of a speaker will be automatically recognized as text or some predefined instruction or code based upon the information included in individual speech waves. A robust speech-recognition system combines accuracy of speech identification with the ability to filter out noise and adapt to other acoustic conditions, such as the speaker’s speech rate and accent. Speech-recognition technology is nowadays embedded in voice-activated routing systems at customer call centers, voice dialing on mobile phones, transcription (voice to text), managing stuff  (creating voice commands),web search, GPS navigation, vending machines, smart homes and many other everyday applications.

ASR System can be: Speaker dependent, Speaker independent, Isolated Word, Limited Vocabulary, Continuous Speech, Unlimited Vocabulary.

Products Used include:

■ MATLAB^©

■ Data Acquisition Toolbox™

■ Signal Processing Toolbox™

■ Statistics Toolbox™

■    ASR System Overview:

 The basic workflow is demonstrated considering an isolated; speaker dependent digit recognition system. It comprise of three steps:

■     Speech acquisition

For training, speech is acquired from a microphone and brought into the development environment for offline analysis. For testing, speech is continuously streamed into the environment for online processing. Data Acquisition Toolbox™ is used to set up continuous acquisition of the speech signal and for simultaneous extraction of frames of data for processing. Speech processing includes: Pre-emphasis (Flatten the magnitude spectrum), Frame Blocking (Speech is short term predictable), Windowing (Remove the discontinuities at the beginning and the end of each frame).  

 ■   Speech analysis

  Developing a Speech-Detection Algorithm : The speech-detection algorithm is developed by processing the prerecorded speech frame by frame within a simple loop.

 Developing the Acoustic Model : A good acoustic model should be derived from speech characteristics that will enable the system to distinguish between the different words in the dictionary.

 

       ■    User interface development

After developing the isolated digit recognition system in an offline environment with pre-recorded speech, we migrate the system to operate on streaming speech from a microphone input. We use MATLAB GUIDE tools to create an interface that displays the time domain plot of each detected word as well as the classified digit (Figure1).

Speech Recognition

                                                                                                                   Author - Sushant shama

                                                                                              (Research Associate at Sillicon Mentor)