A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Expiry Prediction and Reducing Food Wastage using IoT and ML

This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) & K-Nearest Neighbours (KNN) classifiers were the binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing food wastage

Myoelectric Human Computer Interaction Using Reliable Temporal Sequence-based Myoelectric Classification for Dynamic Hand Gestures

To put a computerized device under human control, various interface techniques have been commonly studied in the realm of Human Computer Interaction (HCI) design. What this dissertation focuses on is a myoelectric interface, which controls a device via neuromuscular electrical signals. Myoelectric interface has advanced by recognizing repeated patterns of the signal (pattern recognition-based myoelectric classification). However, when the myoelectric classification is used to extract multiple discrete states within limited muscle sites, there are robustness issues due to external conditions: limb position changes, electrode shifts, and skin condition changes. Examined in this dissertation is the robustness issue, or drop in the performance of the myoelectric classification when the limb position varies from the position where the system was trained. Two research goals outlined in this dissertation are to increase reliability of myoelectric system and to build a myoelectric HCI to manipulate a 6-DOF robot arm with a 1-DOF gripper. To tackle the robustness issue, the proposed method uses dynamic motions which change their poses and configuration over time. The method assumes that using dynamic motions is more reliable, vis-a-vis the robustness issues, than using static motions. The robustness of the method is evaluated by choosing the training sets and validation sets at different limb positions. Next, an HCI system manipulating a 6-DOF robot arm with a 1-DOF gripper is introduced. The HCI system includes an inertia measurement unit to measure the limb orientation, as well as EMG sensors to acquire muscle force and to classify dynamic motions. Muscle force and the orientation of a forearm are used to generate velocity commands. Classified dynamic motions are used to change the manipulation modes. The performance of the myoelectric interface is measured in terms of real-time classification accuracy, path efficiency, and time-related measures. In conclusion, this dissertation proposes a reliable myoelectric classification and develops a myoelectric interface using the proposed classification method for an HCI application. The robustness of the proposed myoelectric classification is verified as compared to previous myoelectric classification approaches. The usability of the developed myoelectric interface is compared to a well-known interface

INTELLIGENT SENSOR SYSTEM FOR SELECTED ENVIRONMENTAL SAFETY APPLICATION

Song, Chen-Lin. M.S., Purdue University, August 2015. Intelligent Sensor System for Selected Environmental Safety Applications. Major Professor: Suranjan Panigrah