Engineering Seminar Topics and Seminar Topics

Download Full Article  AFFECTIVE_COMPUTING

        Affective computing is computing that relates to, arises from or deliberately influences emotions. Neurological studies indicate that the role of emotions in human cognition is essential and that emotions play a critical role in rational decision-making, perception, human interaction and human intelligence. In the view of increased human computer interaction or HCI it has become important that for proper and full interaction between Humans and Computers, computers should be able to at least recognize and react to different user emotion states.

        Emotion is a difficult thing to classify and study fully. Therefore to replicate or to detect emotions in agents is a challenging task. In Human-Human interaction it is often easy to see if a person is angry, happy, or frustrated etc. It is not easy to replicate such an ability in an agent. In this seminar I will be dealing with different aspects of affective computing including a brief study of human emotions, theory and practice related to affective systems, challenges to affective computing and systems which have been developed which and support this type of interaction. I will also be doing a tryst into the area of ethics related to this field as well as implication of computers which will have emotions of their own.

        Affective computing is an emerging, interdisciplinary area, addressing a variety of research, methodological, and technical issues pertaining to the integration of affect into human-computer interaction. The specific research areas include recognition of distinct affective states, user interface adaptation and function integration due to changes in user¹s affective state, supporting technologies such as wearable computing for improved affective state detection and adaptation.

INTRODUCTION

         Affective computing aims at developing computers with understanding capabilities vastly beyond today’s computer systems. Affective computing is computing that relates to, or arises from, or deliberately influences emotion. Affective computing also involves giving machines skills of emotional intelligence: the ability to recognize and respond intelligently to emotion, the ability to appropriately express (or not express) emotion, and the ability to manage emotions. The latter ability involves handling both the emotions of others and the emotions within one self.

Today, more than ever, the role of computers in interacting with people is of importance. Most computer users are not engineers and do not have the time or desire to learn and stay up to date on special skills for making use of a computer’s assistance. The emotional abilities imparted to computers are intended to help address the problem of interacting with complex systems leading to smoother interaction between the two. Emotional intelligence that is the ability to respond to one’s own and others emotions is often viewed as more important than mathematical or other forms of intelligence. Equipping computer agents with such intelligence will be the keystone in the future of computer agents.

         Emotions in people consist of a constellation of regulatory and biasing mechanisms, operating throughout the body and brain, modulating just about everything a person does. Emotion can affect the way you walk, talk, type, gesture, compose a sentence, or otherwise communicate. Thus, to infer a person’s emotion, there are multiple signals you can sense and try to associate with an underlying affective state. Depending on which sensors is available (auditory, visual, textual, physiological, biochemical, etc.) one can look for different patterns of emotion’s influence. The most active areas for machine motion recognition have been in automating facial expression recognition, vocal inflection recognition, and reasoning about emotion given text input about goals and actions. The signals are then processed using pattern recognition techniques like hidden Markov models (HMM’s), hidden decision trees, auto-regressive HMM’s, Support Vector Machines and neural networks.

Download Full Article  AFFECTIVE_COMPUTING

Download Full Article Dense wavelength division multiplexing

Definition

Dense wavelength division multiplexing (DWDM) is a fiber-optic transmission technique that employs light wavelengths to transmit data parallel-by-bit or serial-by-character.

Overview

The role of scalable DWDM systems in enabling service providers to accommodate consumer demand for ever-increasing amounts of bandwidth is important. DWDM is discussed as a crucial component of optical networks that allows the transmission of e-mail, video, multimedia, data, and voice—carried in Internet protocol (IP), asynchronous transfer mode (ATM), and synchronous optical network/synchronous digital hierarchy (SONET/SDH), respectively, over the optical layer.

Fundamentals of DWDM Technology

The emergence of DWDM is one of the most recent and important phenomena in the development of fiber optic transmission technology. The functions and components of a DWDM system, including the enabling technologies, and a description of the operation of a DWDM system are discussed below.

Development of DWDM Technology

Early WDM began in the late 1980s using the two widely spaced wavelengths in the 1310 nm and 1550 nm (or 850 nm and 1310 nm) regions, sometimes called wideband WDM. Figure below shows an example of this simple form of WDM. One of the fiber pair is used to transmit and the other is used to receive. This is the most efficient arrangement and the one most found in DWDM systems.

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Download Full Article HOLOGRAPHIC MEMORY

This paper describes holographic data storage as a viable alternative to magnetic disk data storage. Currently data access times are extremely slow for magnetic disks when compared to the speed of execution of CPUs so that any improvement in data access speeds will greatly increase the capabilities of computers, especially with large data and multimedia files. Holographic memory is a technology that uses a three dimensional medium to store data and it can access such data a page at a time instead of sequentially, which leads to increases in storage density and access speed. Holographic data storage systems are very close to becoming economically feasible. Obstacles that limit holographic memory are hologram decay over time and with repeated accesses, slow recording rates, and data transfer rates that need to be increased. Photorefractive crystals and photopolymers have been used successfully in experimental holographic data storage systems. Such systems exploit the optical properties of these photosensitive materials along with the behavior of laser light when it is used to record an image of an object. Holographic memory lies between main memory and magnetic disk in regards to data access times, data transfer rates, and data storage density.

INTRODUCTION

As processors and buses roughly double their data capacity every three years (Moore’s Law), data storage has struggled to close the gap. CPUs can perform an instruction execution every nanosecond, which is six orders of magnitude faster than a single magnetic disk access. Much research has gone into finding hardware and software solutions to closing the time gap between CPUs and data storage. Some of these advances include cache, pipelining, optimizing compilers, and RAM. As the computer evolves, so do the applications that computers are used for. Recently large binary files containing sound or image data have become commonplace, greatly increasing the need for high capacity data storage and data access. A new high capacity form of data storage must be developed to handle these large files quickly and efficiently. Holographic memory is a promising technology for data storage because it is a true three dimensional storage system, data can be accessed an entire page at a time instead of sequentially, and there are very few moving parts so that the limitations of mechanical motion are minimized. Holographic memory uses a photosensitive material to record interference patterns of a reference beam and a signal beam of coherent light, where the signal beam is reflected off of an object or it contains data in the form of light and dark areas. The nature of the photosensitive material is such that the recorded interference pattern can be reproduced by applying a beam of light to the material that is identical to the reference beam. The resulting light that is transmitted through the medium will take on the recorded interference pattern and will be collected on a laser detector array that encompasses the entire surface of the holographic medium. Many holograms can be recorded in the same space by changing the angle or the wavelength of the incident light. An entire page of data is accessed in this way.

The three features of holographic memory that make it an attractive candidate to replace magnetic storage devices are redundancy of stored data, parallelism, and multiplexing. Stored data is redundant because of the nature of the interference pattern between the reference and signal beams that is imprinted into the holographic medium. Since the interference pattern is a plane wave front, the stored pattern is propagated throughout the entire volume of the holographic medium, repeating at intervals. The data can be corrupted to a certain level before information is lost so this is a very safe method of data storage. Also, the effect of lost data is to lower the signal to noise ratio so that the amount of data that can be safely lost is dependent on the desired signal to noise ratio. Stored holograms are massively parallel because the data is recorded as an optical wave front that is retrieved as a single page in one access. Since light is used to retrieve data and there are no moving parts in the detector array, data access time is on the order of 10 ms and data transfer rate approaches 1.0 GB/sec. Multiplexing allows many different patterns to be stored in the same crystal volume simply by changing the angle at which the reference beam records the hologram.

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Download Full Article ELECTRONIC NOSE

The harnessing of electronics to measure odor is greatly to be desired. Human panels backed up by gas chromatography and mass spectrometry are helpful in quantifying smells, but they time are consuming, expensive and seldom performed in real time in the field. So it is important that these traditional methods give way to a speedier procedure using and electronic nose composed of gas sensors. Electronic nose or E-noses are the systems that detect and identify odours and vapours, typically linking chemical sensing devices with signal processing, pattern recognition and artificial intelligence techniques which enable uses to readily extract relevant and reliable information.

INTRODUCTION

            The electronics field is developing at a fast rate. Each day the industry is coming with new technology and products. The electronic components play a major role in all fields of life. The scientists had started to mimic the biological world. The development of artificial neural network (ANN), in which the nervous system is electronically implemented is one among them.

                The scientists realized the importance of the detection and identification of odor in many fields. In human body it is achieved with the help of one of the sense organ, the nose. So scientists realized the need of imitating the human nose. The concept of the electronic nose appeared for the first time in a nature paper by Persuade and Dodd (1982). The authors suggested and demonstrated with a few examples that gas sensor array responses could be analyzed with artificial neural networks thereby increasing sensitivity and precision in analysis significantly. This first publication was followed by several methodological papers evaluating different sensor types and combinations.

                The scientists saw the last advances in the electronic means of seeing and hearing. Witnessing this fast advances they scent a marker for systems mimicking the human nose. The harnessing of electronics to measure odor is greatly desired. Human panels backed by gas chromatography (GC)/ mass spectroscopy (MS) are helpful in quantifying smells. The human panels are subject to fatigue and inconsistencies. While classical gas chromatography (GC)/ mass spectrograph (MS) technique separate quantify and identify individual volatile chemicals, they cannot tell us if the components have an odour. Also they are very slow. So it is important that faster methods must give way to speedier procedure using an electronic nose composed of gas sensory. The E-nose was developed not to replace traditional GC/MS and sensory techniques. The E-nose was sensitive and as discriminating as the human nose, and it also correlates extremely with GC/MS data. The electronic nose allows to transfer expert know ledged from highly trained sensory panels and very sophisticated R&D analytical techniques to the production floor for the control of quality. Although the human nose is very sensitive, it is highly subjective. The E – nose offers objectivity and reproducibility.

                The electronic nose technology goes several steps ahead of the conventional gas sensors. The electronics nose system detects and sensing devices with pattern recognition sub system. The electronic nose won quickly considerable interest in food analysis for rapid and reliable quality classification in manufacturing testing. Later, the electronic noses have also been applied to classification of micro organisms and bio-reactor monitoring. Even though the electronic nose resembles its biological counter part nose too closely the label “electronic nose” or “E-nose” has been widely accepted around the world.

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