Theory and Algorithms Commons^™

Image Processing – I: Automated System For Fingerprint Image Enhancement Using Improved Segmentation And Gabor Wavelets, Amna Saeed, Anam Tariq, Usman Jawaid

International Conference on Information and Communication Technologies

This research revolves around the fingerprint image enhancement. It is used for automated fingerprint identification systems (AFIS) for extracting the best quality fingerprint images. Accurate feature extraction and identification is the basic theme of this enhancement. This paper is on the fingerprint image enhancement using wavelets. Wavelets are famous for their special localization property and orientation flow estimation. The proposed technique is basically comprises of three main steps: segmentation followed by image sharpening and then Gabor wavelet filtering. Segmentation distinguishes between image background and foreground which in turn reduces processing time. Our sharpening stage of enhancement algorithm sharpens the edges …

Application Of Ict: Fram Based Tmr (Triple Modular Redundancy) For Fault Tolerance Implementation, Kashif Sagheer Siddiqui, Mirza Altamash Baig

International Conference on Information and Communication Technologies

The main objective of this paper is to design a Triple modular redundancy test bench using FRAM (Ferroelectric RAM) based memory module for main driver of OBDH (On Board Data Handling) system of LEO (Lower Earth Orbit) Satellite that enables the fast detection of error in driver data when implied with FPGA (Field Programmable Gate Array) and provides more realistic and tolerant way of fault finding for Single Event Upset (SEU) in highly radiated space environment. The scope of paper embraces development of TMR test bench, software algorithms, functional simulations, timing simulations and conclusion of comparison of FRAM based memory …

Artificial Intelligence - I: Adaptive Automated Teller Machines - Part Ii, Ghulam Mujtaba, Tariq Mahmood

International Conference on Information and Communication Technologies

Nowadays, the banking sector is increasingly relying on Automated Teller Machines (ATMs) in order to provide services to its customers. Although thousands of ATMs exist across many banks and different locations, the GUI and content of a typical ATM interface remains, more or less, the same. For instance, any ATM provides typical options for withdrawal, electronic funds transfer, viewing of mini-statements etc. However, such a static interface might not be suitable for all ATM customers, e.g., some users might not prefer to view all the options when they access the ATM, or to view specific withdrawal amounts less than, say, …

Automatic Content Generation For Video Self Modeling, Ju Shen, Anusha Raghunathan, Sen-Ching S. Cheung, Ravi R. Patel

Computer Science Faculty Publications

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him or herself. Its effectiveness in rehabilitation and education has been repeatedly demonstrated but technical challenges remain in creating video contents that depict previously unseen behaviors. In this paper, we propose a novel system that re-renders new talking-head sequences suitable to be used for VSM treatment of patients with voice disorder. After the raw footage is captured, a new speech track is either synthesized using text-to-speech or selected based on voice similarity from a database of clean speeches. …

Online Auc Maximization, Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbo Yang

Research Collection School Of Computing and Information Systems

Most studies of online learning measure the performance of a learner by classification accuracy, which is inappropriate for applications where the data are unevenly distributed among different classes. We address this limitation by developing online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions. The key challenge of online AUC maximization is that it needs to optimize the pairwise loss between two instances from different classes. This is in contrast to the classical setup of online learning where the overall loss is a sum …

Heuristic Algorithms For Balanced Multi-Way Number Partitioning, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Balanced multi-way number partitioning (BMNP) seeks to split a collection of numbers into subsets with (roughly) the same cardinality and subset sum. The problem is NP-hard, and there are several exact and approximate algorithms for it. However, existing exact algorithms solve only the simpler, balanced two-way number partitioning variant, whereas the most effective approximate algorithm, BLDM, may produce widely varying subset sums. In this paper, we introduce the LRM algorithm that lowers the expected spread in subset sums to one third that of BLDM for uniformly distributed numbers and odd subset cardinalities. We also propose Meld, a novel strategy for …

Algorithms Analysis System: Recurrences, Anchit Sharma

Master's Projects

Algorithms which are recursive have running times which can be described by

recurrence equations or recurrences. These equations determine the overall running time complexity of the algorithm. This project intends to create a mechanism for

• auto generating recurrence equations of the form T(n) = a(T(n)/b) + f(n)

• creating a computational method for solving them and generating running times

  i.e. O (f(n)) or Ω (f(n)).

• presenting students with a way to verify their manually computed answers with

  the solution generation by the project

• generating grading and feedback for their solution

  The exercises will utilize the ‘Substitution Method’ and the ‘Master Method’ …

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Estimating Anthropometric Marker Locations From 3-D Ladar Point Clouds, Matthew J. Maier

Theses and Dissertations

An area of interest for improving the identification portion of the system is in extracting anthropometric markers from a Laser Detection and Ranging (LADAR) point cloud. Analyzing anthropometrics markers is a common means of studying how a human moves and has been shown to provide good results in determining certain demographic information about the subject. This research examines a marker extraction method utilizing principal component analysis (PCA), self-organizing maps (SOM), alpha hulls, and basic anthropometric knowledge. The performance of the extraction algorithm is tested by performing gender classification with the calculated markers.

Resizable, Scalable, Concurrent Hash Tables, Josh Triplett, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow the hash table to maintain constant-time performance as the number of entries grows, and reclaim memory as the number of entries decreases, without delaying or disrupting readers.

We implemented our algorithms in the Linux kernel, to test their performance and scalability. Benchmarks show lookup scalability improved 125x over readerwriter locking, and 56% over the current state-of-the-art for Linux, with no performance degradation for lookups during a resize.

To achieve this performance, this hash table implementation uses a new concurrent …

Smoothly Varying Affine Stitching, Wen-Yan Lin, Siying Liu, Yasuyuki Matsuhita, Tian-Tsong Ng, Loong-Fah Cheong

Research Collection School Of Computing and Information Systems

Traditional image stitching using parametric transforms such as homography, only produces perceptually correct composites for planar scenes or parallax free camera motion between source frames. This limits mosaicing to source images taken from the same physical location. In this paper, we introduce a smoothly varying affine stitching field which is flexible enough to handle parallax while retaining the good extrapolation and occlusion handling properties of parametric transforms. Our algorithm which jointly estimates both the stitching field and correspondence, permits the stitching of general motion source images, provided the scenes do not contain abrupt protrusions.

A Feature Based Frequency Domain Analysis Algorithm For Fault Detection Of Induction Motors, Zhaoxia Wang, C. S. Chang, Zhang Yifan

Research Collection School Of Computing and Information Systems

This paper studies the stator currents collected from several inverter-fed laboratory induction motors and proposes a new feature based frequency domain analysis method for performing the detection of induction motor faults, such as the broken rotor-bar or bearing fault. The mathematical formulation is presented to calculate the features, which are called FFT-ICA features in this paper. The obtained FFT-ICA features are normalized by using healthy motor as benchmarks to establish a feature database for fault detection. Compare with conventional frequency-domain analysis method, no prior knowledge of the motor parameters or other measurements are required for calculating features. Only one phase …

Hardware Acceleration Of Inference Computing: The Numenta Htm Algorithm, Dan Hammerstrom

Systems Science Friday Noon Seminar Series

In this presentation I will describe the latest version of the Numenta HTM Cortical Learning Algorithm and why it is interesting for doing research into radical new computer architectures. Then I will discuss the hardware acceleration research we are doing, and briefly look at some preliminary applications development.

Algebraic Solutions To Overdefined Systems With Applications To Cryptanalysis, Eric Crockett

Mathematical Sciences Technical Reports (MSTR)

Cryptographic algorithms are based on a wide variety of difficult problems in mathematics. One of these problems is finding a solution to a system of multivariate quadratic equations (MQ). A generalization of this problem is to find a solution to a system of higher order non-linear equations. Both of these problems are NP-hard over any field. Many cryptosystems such as AES, Serpent, Toyocrypt, and others can be reduced to some form of the MQ problem. In this paper we analyze the relinearization and XL algorithms for solving overdetermined systems of non-linear equations, as well as two variations of the XL …

Finding Acronyms And Their Definitions Using Hmm, Lakshmi Vyas

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this thesis, we report on design and implementation of a Hidden Markov Model (HMM) to extract acronyms and their expansions. We also report on the training of this HMM with Maximum Likelihood Estimation (MLE) algorithm using a set of examples.

Finally, we report on our testing using standard recall and precision. The HMM achieves a recall and precision of 98% and 92% respectively.

Cloud Shadow Detection And Removal From Aerial Photo Mosaics Using Light Detection And Ranging (Lidar) Reflectance Images, Glover Eugene George

Dissertations

The process of creating aerial photo mosaics can be severely affected by clouds and the shadows they create. In the CZMIL project discussed in this work, the aerial survey aircraft flies below the clouds, but the shadows cast from clouds above the aircraft cause the resultant mosaic image to have sub-optimal results. Large intensity variations, caused both from the cloud shadow within a single image and the juxtaposition of areas of cloud shadow and no cloud shadow during the image stitching process, create an image that may not be as useful to the concerned research scientist. Ideally, we would like …

Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato

Research Collection School Of Computing and Information Systems

In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale and time constraints. We argue that this is due to existing methods not exploiting the inherent structure of the organizations which solve these problems. We propose a new model called the OrgPOMDP (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: …

Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

In most kernel based online learning algorithms, when an incoming instance is misclassified, it will be added into the pool of support vectors and assigned with a weight, which often remains unchanged during the rest of the learning process. This is clearly insufficient since when a new support vector is added, we generally expect the weights of the other existing support vectors to be updated in order to reflect the influence of the added support vector. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short, that explicitly addresses this problem. …

Implementation Of Numerically Stable Hidden Markov Model, Usha Ramya Tatavarty

UNLV Theses, Dissertations, Professional Papers, and Capstones

A Hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. HMM is an extremely flexible tool and has been successfully applied to a wide variety of stochastic modeling tasks. One of the first applications of HMM is speech recognition. Later they came to be known for their applicability in handwriting recognition, part-of-speech tagging and bio-informatics.

In this thesis, we will explain the mathematics involved in HMMs and how to efficiently perform HMM computations using dynamic programming (DP) which makes it easy to implement …

Information Extraction In An Optical Character Recognition Context, Ramon Pereda

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this dissertation, we investigate the effectiveness of information extraction in the presence of Optical Character Recognition (OCR). It is well known that the OCR errors have no effects on general retrieval tasks. This is mainly due to the redundancy of information in textual documents. Our work shows that information extraction task is significantly influenced by OCR errors. Intuitively, this is due to the fact that extraction algorithms rely on a small window of text surrounding the objects to be extracted.

We show that extraction methodologies based on the Hidden Markov Models are not robust enough to deal with extraction …

Topic Detection And Tracking Using Hidden Markov Models, Aditya S. Tatavarty

UNLV Theses, Dissertations, Professional Papers, and Capstones

There is a continuous progress in automatic recording of broadcast speech using speech recognition. With the increasing use of this technology, a new source of data is added to the pool of information available over web. This has increased the need to categorize the resulting text, based on their topic for the purpose of information retrieval.

In this thesis we present an approach to automatically assign a topic or track a change of topic in a stream of input data. Our approach is based on the use of Hidden Markov Models and language processing techniques. We consider input text as …