Browsing by Author "Hossain, Gahangir"

Now showing 1 - 5 of 5
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    Developing Intelligent Negotiation System
    (Office of the Vice Chancellor for Research, 2015-04-17) Singh, Amandeep; Dhaliwal, GaganPal Singh; Dudani, Raviraj; Patil, Suyog; Hossain, Gahangir
    Abstract Negotiation has become an important aspect of our daily lives. Humans negotiate over a phone, face to face meeting and verbal and non-verbal activities. With the advent of intelligence system research, the requirement of efficient negotiation system became a prime issue. It has a number of applications including collaborating cyber human interaction, e-commerce negotiation and intelligent shared behavior study. Adapting game theory of mind concept in intelligent negotiation protocol implementation may make the future cyber system robust, social and adaptive. Keeping in mind the user’s policies and an intention to gain the maximum profit, we introduced the hybrid negotiation system to make a system robust and more useful. User’s intention to gain the maximum profit is considered important to figure out the opponent’s policies so that the system can make a right automatic decision. Based on our initial literature review, game theory of mind can be a good choice in sub-optimal intelligent negotiation system design. Therefore, a system based on game theory of mind is under design process that is being evaluated on Yahoo marketing data set.
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    Multinomial Processing Models in Visual Cognitive Effort Diagnostics
    (IEEE, 2015-06) Elkins, Joshua D.; Hossain, Gahangir; Department of Electrical and Computer Engineering, School of Engineering and Technology
    The pupillary response has been used to measure mental workload because of its sensitivity to stimuli and high resolution. The goal of this study was to diagnose the cognitive effort involved with a task that was presented visually. A multinomial processing tree (MPT) was used as an analytical tool in order to disentangle and predict separate cognitive processes, with the resulting output being a change in pupil diameter. This model was fitted to previous test data related to the pupillary response when presented a mental multiplication task. An MPT model describes observed response frequencies from a set of response categories. The parameter values of an MPT model are the probabilities of moving from latent state to the next. An EM algorithm was used to estimate the parameter values based on the response frequency of each category. This results in a parsimonious, causal model that facilitates in the understanding the pupillary response to cognitive load. This model eventually could be instrumental in bridging the gap between human vision and computer vision.
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    Neural Mechanisms of Pupillary Dynamics and Cognitive Effort
    (Office of the Vice Chancellor for Research, 2015-04-17) Elkins, Joshua; Hossain, Gahangir; Yoshida, Ken
    The pupillary response has been used to measure mental workload because of its sensitivity to stimuli and high resolution. The goal of this study was to understand interconnections between the visual or auditory pathway and the resulting pupillary response relative to cognitive effort. A multinomial processing tree was used as a diagnostic tool in order to disentangle and measure separate cognitive processes, with the resulting output being a change in pupil diameter. This model was fitted to previous test data related to the pupillary response when presented a mental multiplication task. Two models were derived as a result: a subject response category tree and a pupil dilation response category tree. One tree compares the visual and aural pathways, while the other compares latent processes within the visual pathway. This results in a parsimonious model that facilitates in the understanding of the neural interconnections involved with the pupillary response to cognitive load.
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    Robust Understanding of Motor Imagery EEG Pattern in Voice Controlled Prostatic Arm Design
    (Office of the Vice Chancellor for Research, 2015-04-17) Ghane, Parisa; Maridi, Divya; Hossain, Gahangir
    Introduction: Understanding neural mechanism of communication between human and machine has become more interesting research issue in last few decades. One of the most motivating purposes is to help the people with motor disabilities. This excites researchers to work on the interaction between brain-computer-interfacing (BCI) systems, which in turn needs a fast and accurate algorithm to decode the commands in the brain or electroencephalogram (EEG) signals. EEG signals are very noisy and contain several types of artifacts, so it would be very important to use efficient methods to train the BCI system. Aims and Goals: The goal of this project is to train an intelligent system based on the information in the sample EEG data. This system is going to predict the person’s intention in future experiments with new EEG data. Finally, this project can be used in controlling a moving object like a robot, a wheelchair, or many other devices. Data Acquisition and methods: In this project, we are working with the EEG signals taken from 20 subjects thinking about English vowels \a\, \e\, \i\, \o\, and \u\. This means we can define only 5 clusters, which contain all signals with similar features. We are going to use part of the signals for training and the rest for testing. In training section, we have to first preprocess the data, and then categorize it into 5 clusters. Robust Principle Component Analysis (PCA) helps us to analyze the data to extract the features. Afterwards based on principle component features of signals, we employ a Hidden Markov Model (HMM) classifier to send similar signals to the same cluster. As EEG data is a randomly variant signal, we are using Hybrid HMM classifier for classification of EEG pattern. Our Initial results are promising in robust understanding of auditory command, which is been explored from EEG pattern analysis.
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    Smart Unit Care for Pre Fall Detection and Prevention
    (IEEE, 2016-07) Thella, Ashok Kumar; Suryadevara, Vinay Kumar; Rizkalla, Maher; Hossain, Gahangir; Electrical and Computer Engineering, School of Engineering and Technology
    Generally falls may occur from moving or resting postures. This may include slipping from bed and fall from a sitting, or from running or walking. The pre-fall is a non-equilibrium state of human position that may lead to serious injuries, and may negatively impact the quality life condition, particularly for elders. Physical disabilities resulting from the fall incidences may lead to high costs involved with the healing process. In this work, an embedded sensor system using Arduino micro-controller was utilized to coordinate the data received from accelerometer and gyroscope. For a given threshold voltage and fall pattern, the fall decision is made by the microcontroller, citing an incoming fall. The study addresses the number of sensors to be coordinated for enhancing probability of receiving a real fall. Sensors are suggested to be placed on the human body within a belt, and safety devices at human body as well as incorporated in a smart room will be coordinated with the processor commands. Near 150 ms time frame was detected from the simulation results, suggesting a safety device to be triggered and activated for protection within this time frame. This paper discusses the research parameters such as response time for the device activation and interfacing the microcontroller to airbag switch, and means of activating the safety devices within the sharp edges in the smart unit care to minimize the impact of the fall injuries.