Hybrid constant factor incremental delta modulators

The paper presents a new adaptive delta modulator, called the hybrid constant factor incremental delta modulator (HCFIDM), which uses instantaneous as well as syllabic adaptation of the step size. Three instantaneous algorithms have been used: two new instantaneous algorithms (CFIDM-3 and CFIDM-2) and the third, Song's voice ADM (SVADM). The quantisers have been simulated on a digital computer and their performances studied. The figure of merit used is the SNR with correlated, /?C-shaped Gaussian signals and real speech as the input. The results indicate that the hybrid technique is superior to the nonhybrid adaptive quantisers. Also, the two new instantaneous algorithms developed have improved SNR and fast response to step inputs as compared to the earlier systems.

Algorithm for incremental compaction of geometrical layouts

In this paper, a new incremental algorithm for layout compaction is proposed. In addition to its linear time performance in terms of the number of rectangles in the layout, we also describe how incremental compaction can form a good feature in the design of a layout editor. The design of such an editor is also described. In the design of the editor, we describe how arrays can be used to implement quadtrees that represent VLSI layouts. Such a representation provides speed of data access and low storage requirements.

Constant factor incremental delta modulator

This paper presents a new algorithm for the step-size change of instantaneous adaptive delta modulator. The present strategy is such that the step-size at any sampling instant can increase or decrease by either of the two constant factors or can remain the same, depending upon the combination of three or four most recent output bits. The quantizer has been simulated on a digital computer, and its performance compared with other quantizers. The figure of merit used is the SNR with gaussian signals as the input. The results indicate that the new design can give an improved SNR over a wider dynamic range and fast response to step inputs, as compared to the earlier systems.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Microbial aspects of acid generation and bioremediation with relevance to indian mining

The role of Acidithiobacillus group of bacteria in acid generation and heavy metal dissolution was studied with relevance to some Indian mines. Microorganisms implicated in acid generation such as Acidithiobacillus Acidithicibacillus thiooxidans and Leptospirillum ferrooxidans were isolated from abandoned mines, waste rocks and tailing dumps. Arsenite oxidizing Thiomonas and Bacillus group of bacteria were isolated and their ability to oxidize As (111) to As (V) established. Mine isolated Sulfate reducing bacteria were used to remove dissolved copper, zinc, iron and arsenic from solutions.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

Incremental attribute evaluation through recursive procedures

Incremental semantic analysis in a programming environment based on Attribute Grammars is performed by an Incremental Attribute Evaluator (IAE). Current IAEs are either table-driven or make extensive use of graph structures to schedule reevaluation of attributes. A method of compiling an Ordered Attribute Grammar into mutually recursive procedures is proposed. These procedures form an optimal time Incremental Attribute Evaluator for the attribute grammar, which does not require any graphs or tables.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).

The complexity of certain incremental code generation problems

This paper looks at the complexity of four different incremental problems. The following are the problems considered: (1) Interval partitioning of a flow graph (2) Breadth first search (BFS) of a directed graph (3) Lexicographic depth first search (DFS) of a directed graph (4) Constructing the postorder listing of the nodes of a binary tree. The last problem arises out of the need for incrementally computing the Sethi-Ullman (SU) ordering [1] of the subtrees of a tree after it has undergone changes of a given type. These problems are among those that claimed our attention in the process of our designing algorithmic techniques for incremental code generation. BFS and DFS have certainly numerous other applications, but as far as our work is concerned, incremental code generation is the common thread linking these problems. The study of the complexity of these problems is done from two different perspectives. In [2] is given the theory of incremental relative lower bounds (IRLB). We use this theory to derive the IRLBs of the first three problems. Then we use the notion of a bounded incremental algorithm [4] to prove the unboundedness of the fourth problem with respect to the locally persistent model of computation. Possibly, the lower bound result for lexicographic DFS is the most interesting. In [5] the author considers lexicographic DFS to be a problem for which the incremental version may require the recomputation of the entire solution from scratch. In that sense, our IRLB result provides further evidence for this possibility with the proviso that the incremental DFS algorithms considered be ones that do not require too much of preprocessing.

Incremental-angle and angular velocity estimation using a star sensor

A method is described for estimating the incremental angle and angular velocity of a spacecraft using integrated rate parameters with the help of a star sensor alone. The chief advantage of this method is that the measured stars need not be identified, whereas the identification of the stars is necessary in earlier methods. This proposed estimation can be carried out with all of the available measurements by a simple linear Kalman filter, albeit with a time-varying sensitivity matrix. The residuals of estimated angular velocity by the proposed spacecraft incremental-angle and angular velocity estimation method are as accurate as the earlier methods. This method also enables the spacecraft attitude to be reconstructed for mapping the stars into an imaginary unit sphere in the body reference frame, which will preserve the true angular separation of the stars. This will pave the way for identification of the stars using any angular separation or triangle matching techniques applied to even a narrow field of view sensor that is made to sweep the sky. A numerical simulation for inertial as well as Earth pointing spacecraft is carried out to establish the results.

Dynamic subspace clustering for very large high-dimensional databases

Emerging high-dimensional data mining applications needs to find interesting clusters embeded in arbitrarily aligned subspaces of lower dimensionality. It is difficult to cluster high-dimensional data objects, when they are sparse and skewed. Updations are quite common in dynamic databases and they are usually processed in batch mode. In very large dynamic databases, it is necessary to perform incremental cluster analysis only to the updations. We present a incremental clustering algorithm for subspace clustering in very high dimensions, which handles both insertion and deletions of datapoints to the backend databases.