Pattern perception

The gripping action as performed by an average person is developed over their life and
changes over time. The initial learning is based on trial and error and becomes a natural
action which is modified as the physiology of the individual changes. Each grip type is a
personal expression and as the grip changes over time to accommodate physiologically
changes, it can be considered to be a grip-signature. lt is postulated that an ANN can deliver a classification mechanism that is able to make
sense of the varying gripping inputs that are linearly inseparable and uniquely attributed
to user physiology. Succinctly, in this design, the stifnulus is characterized by a voltage
that represents the applied force in a grip. This signature of forces is then used to train an
ANN to recognize the grip that produced the signature, the ANN in turn is used to
successfully classify three unique states of grip-signatures collected from the gripping
action of various individuals as they hold, lift and crush a paper coffee-cup. A comparative study is done for three types of classification: K-Means, Backpropagation
Feedforward Neural Networks and Recurrent Neural Networks, with recommendations
made in selecting more effective classification methods.

Molecular dynamics is a computer simulation technique for expressing the
ultimate details of individual particle motions and can be used in many fields, such as
chemical physics, materials science, and the modeling of biomolecules. In this thesis, we
study visualization and pattern mining in molecular dynamics simulation. The molecular
data set has a large number of atoms in each frame and range of frames. The features of
the data set include atom ID; frame number; position in x, y, and z plane; charge; and
mass. The three main challenges of this thesis are to display a larger number of atoms and
range of frames, to visualize this large data set in 3-dimension, and to cluster the
abnormally shifting atoms that move with the same pace and direction in different frames.
Focusing on these three challenges, there are three contributions of this thesis. First, we
design an abnormal pattern mining and visualization framework for molecular dynamics
simulation. The proposed framework can visualize the clusters of abnormal shifting atom
groups in a three-dimensional space, and show their temporal relationships. Second, we propose a pattern mining method to detect abnormal atom groups which share similar
movement and have large variance compared to the majority atoms. We propose a
general molecular dynamics simulation tool, which can visualize a large number of atoms,
including their movement and temporal relationships, to help domain experts study
molecular dynamics simulation results. The main functions for this visualization and
pattern mining tool include atom number, cluster visualization, search across different
frames, multiple frame range search, frame range switch, and line demonstration for atom
motions in different frames. Therefore, this visualization and pattern mining tool can be
used in the field of chemical physics, materials science, and the modeling of
biomolecules for the molecular dynamic simulation outcomes.

The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies.