technical writing presentation (february 16, 2013)

8/11/2019 Technical Writing Presentation (February 16, 2013)

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Digital Signal ProcessiIts Applications and Benefits to the Mo

World

DEAN MARK D. MOISES

JUSTIN ROMEO A. MALICDEM


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Digital Signal Processing

is the mathematical manipulation of an inforsignal to modify or improve it in some way.

It refers to manipulating analog information, susound or photographs that has been converteddigital form. DSP also implies the use of acompression technique.

It is also a special type of coprocessor design

performing the mathematics involved in DSP. Moare programmable, which means that they cused for manipulating different types of informincluding sound, images, and video.


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It is characterized by: audio and speech signal processing,

sonar and radar signal processing,

sensor array processing,

spectral estimation,

statistical signal

processing,

digital image processing,

signal processing for communications, control of systems,

biomedical signal processing,

seismic data processing,

etc.


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Goal of DSP to measure, filter and/or compress continuous real-world analog signa

The goal of DSP is usually to measure, filter and/or compress continuou

real-world analog signals.

The first step is usually to convert the signal from an analog to a digitalform, by sampling and then digitizing it using an analog-to-digitalconverter (ADC), which turns the analog signal into a stream ofnumbers.

However, often, the required output signal is another analog output

signal, which requires a digital-to-analog converter (DAC). Even if thisprocess is more complex than analog processing and has a discretevalue range, the application of computational power to digital signalprocessing allows for many advantages over analog processing inmany applications, such as error detection and correction intransmission as well as data compression.


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Assembly

DSP algorithms have long been run on standard computers, on

specialized processors called digital signal processor on purpose-built hardware such as application-specific integrated circuit(ASICs). Today there are additional technologies used for digitalsignal processing including more powerful general purposemicroprocessors, field-programmable gate arrays (FPGAs), digitalsignal controllers (mostly for industrial apps such as motor control),and stream processors, among others.


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Case Study

On Speech Recognition On Digital Image Processing


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Case Study 1

Industry: Beverage Distribution

Background: A liquor distributor in the Northwest was looking for away to increase picking productivity and accuracy. Picking frompaper orders was slow and error prone. With bottles of liquor varyinggreatly in price, picking errors were very expensive and retailersreceiving incorrect orders were frustrated. The distributor

implemented a 3rd

party Warehouse Management System(WMS) toreplace the paper-based system, The WMS improved inventoryvisibility and efficiency, but the customer was still looking foradditional accuracy and productivity gains.


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Time Productivity an

Picking Accuracy fothe customers.


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Speech Recognition

Goals: Increase Accuracy and Productivity

Solution: The WMS vendor identified speech recognition as a way timprove productivity. In partnership with Genesta, the WMS vendorintegrated the SyVox Client speech recognition software, whichallowed pickers to work hands and eyes free, Genestarecommended that the WMS vendor pair the SyVox Client with

Intermecs SF51 wireless scanner, requiring pickers to scan the UPCon the bottles/cases being picked. This allowed the WMS to confirmthat the correct product was selected, The SF51s unique designprovided quick access to the scanner, ensuring that the addition oscanning did not reduce the productivity gains realized by addingspeech recognition.


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Genesta Genesta is a supply chain systems development and technology

deployment firm that specializes in warehouse inventorymanagement and customized solutions to optimize warehousingand distribution. We are a leading provider of data collectionsolutions for industrial, transportation and logistics applications, withextensive expertise in the design, installation and support of multi-modal data collection systems and supply chain managementsoftware. Genesta's deep knowledge of bar code data collection,industrial speech recognition, wireless and RFID inventory trackinghelps clients improve business performance.

SyVox The origins of SyVox date back to 1981 when the founders set o

speech recognition technology to allow users to control their compuspeech.

SyVox developed one of the early speaker independent engines bu

any usersnatural speech.


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Speech Recognition Device by Syvo


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Speech Recognition

-In computer science, it is the translation of spoken words into text. It is alsknown as automatic speech recognition, ASR. An individual speakerreads sections of text into the speech recognition system.

-These systems analyze the persons specific voice and use it to fine tunethe recognition of that persons speech, resulting in more accuratetranscription.

Value Delivered: The solution increase accuracy by assuring that theappropriate product is picked when scanned. Along with this product theSyVox voice picking program increase productivity by providing a paper-free picking system that allowed paralled information processing and amore ergonomically efficient system.


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Case Study 2

The Challenge: Imsense is a software start-up focused on improvingimages through dynamic range correction. Their proprietaryalgorithms had been developed for use on a standard PCcomputing platform. The challenge was for Argon Design to makeuse of their in depth graphics, imaging and semiconductorknowledge to determine whether it would be practical toimplement Imsensesalgorithms in hardware, making them suitable

for use in handheld and video applications.


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Digital Image Processing

The Approach: The project was addressed in two phases, first toanalyse the existing algorithms and explore alternative options,before creating the optimal architecture design to implement thefinal algorithm.


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Digital Image Processing

The Outcome: The output from the project provided significantdetail on how the algorithms could be implemented, along withadditional ideas for Imsense to further optimise their algorithms formobile use.


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Case Study 2: Digital Image Processin The Outcome: The split image below shows the contrast and quality

using Dynamic Range Correction (DRC). On the left hand side DRChas been applied to the image, on the right hand side the imageremains un-processed.


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References

http://www.genesta.com/assets/pdf/speech.pdf

http://www.argondesign.com/case-studies/2012/sep/18/Imsense/
http://www.genesta.com/assets/pdf/speech.pdfhttp://www.argondesign.com/case-studies/2012/sep/18/Imsense/http://www.argondesign.com/case-studies/2012/sep/18/Imsense/http://www.argondesign.com/case-studies/2012/sep/18/Imsense/http://www.argondesign.com/case-studies/2012/sep/18/Imsense/http://www.argondesign.com/case-studies/2012/sep/18/Imsense/http://www.genesta.com/assets/pdf/speech.pdfhttp://www.genesta.com/assets/pdf/speech.pdf

technical writing presentation (february 16, 2013)

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