A Jacobian-Based Algorithm for Planning Attitude Maneuvers Using Forward and Reverse Rotations

Algorithms for planning quasistatic attitude maneuvers based on the Jacobian of the forward kinematic mapping of fully-reversed (FR) sequences of rotations are proposed in this paper. An FR sequence of rotations is a series of finite rotations that consists of initial rotations about the axes of a body-fixed coordinate frame and subsequent rotations that undo these initial rotations. Unlike the Jacobian of conventional systems such as a robot manipulator, the Jacobian of the system manipulated through FR rotations is a null matrix at the identity, which leads to a total breakdown of the traditional Jacobian formulation. Therefore, the Jacobian algorithm is reformulated and implemented so as to synthesize an FR sequence for a desired rotational displacement. The Jacobian-based algorithm presented in this paper identifies particular six-rotation FR sequences that synthesize desired orientations. We developed the single-step and the multiple-step Jacobian methods to accomplish a given task using six-rotation FR sequences. The single-step Jacobian method identifies a specific FR sequence for a given desired orientation and the multiple-step Jacobian algorithm synthesizes physically feasible FR rotations on an optimal path. A comparison with existing algorithms verifies the fast convergence ability of the Jacobian-based algorithm. Unlike closed-form solutions to the inverse kinematics problem, the Jacobian-based algorithm determines the most efficient FR sequence that yields a desired rotational displacement through a simple and inexpensive numerical calculation. The procedure presented here is useful for those motion planning problems wherein the Jacobian is singular or null.

An interpreter for slips-An applicative language based on LAMBDA-Calculus

An applicative language based on the LAMBDA-Calculus is presented. The language, SLIPS (Small Language for Instruction Purposes), is described using the LAMBDA-Calculus as a metalanguage. A call-by-need mechanism of function invocation eliminates the drawbacks of both call-by-name and call-by-value. The system has been implemented in PASCAL.

Edge-based connected component approach for skew correction of complex document images

Skew correction of complex document images is a difficult task. We propose an edge-based connected component approach for robust skew correction of documents with complex layout and content. The algorithm essentially consists of two steps - an 'initialization' step to determine the image orientation from the centroids of the connected components and a 'search' step to find the actual skew of the image. During initialization, we choose two different sets of points regularly spaced across the the image, one from the left to right and the other from top to bottom. The image orientation is determined from the slope between the two succesive nearest neighbors of each of the points in the chosen set. The search step finds succesive nearest neighbors that satisfy the parameters obtained in the initialization step. The final skew is determined from the slopes obtained in the 'search' step. Unlike other connected component based methods, the proposed method does not require any binarization step that generally precedes connected component analysis. The method works well for scanned documents with complex layout of any skew with a precision of 0.5 degrees.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Mass Spectrometry-Based Systems Approach for Identification of Inhibitors of Plasmodium falciparum Fatty Acid Synthase{triangledown}

The emergence of strains of Plasmodium falciparum resistant to the commonly used antimalarials warrants the development of new antimalarial agents. The discovery of type II fatty acid synthase (FAS) in Plasmodium distinct from the FAS in its human host (type I FAS) opened up new avenues for the development of novel antimalarials. The process of fatty acid synthesis takes place by iterative elongation of butyryl-acyl carrier protein (butyryl-ACP) by two carbon units, with the successive action of four enzymes constituting the elongation module of FAS until the desired acyl length is obtained. The study of the fatty acid synthesis machinery of the parasite inside the red blood cell culture has always been a challenging task. Here, we report the in vitro reconstitution of the elongation module of the FAS of malaria parasite involving all four enzymes, FabB/F (β-ketoacyl-ACP synthase), FabG (β-ketoacyl-ACP reductase), FabZ (β-ketoacyl-ACP dehydratase), and FabI (enoyl-ACP reductase), and its analysis by matrix-assisted laser desorption-time of flight mass spectrometry (MALDI-TOF MS). That this in vitro systems approach completely mimics the in vivo machinery is confirmed by the distribution of acyl products. Using known inhibitors of the enzymes of the elongation module, cerulenin, triclosan, NAS-21/91, and (–)-catechin gallate, we demonstrate that accumulation of intermediates resulting from the inhibition of any of the enzymes can be unambiguously followed by MALDI-TOF MS. Thus, this work not only offers a powerful tool for easier and faster throughput screening of inhibitors but also allows for the study of the biochemical properties of the FAS pathway of the malaria parasite.

Mass Spectrometry-Based Systems Approach for Identification of Inhibitors of Plasmodium falciparum Fatty Acid Synthase{triangledown}

The emergence of strains of Plasmodium falciparum resistant to the commonly used antimalarials warrants the development of new antimalarial agents. The discovery of type II fatty acid synthase (FAS) in Plasmodium distinct from the FAS in its human host (type I FAS) opened up new avenues for the development of novel antimalarials. The process of fatty acid synthesis takes place by iterative elongation of butyryl-acyl carrier protein (butyryl-ACP) by two carbon units, with the successive action of four enzymes constituting the elongation module of FAS until the desired acyl length is obtained. The study of the fatty acid synthesis machinery of the parasite inside the red blood cell culture has always been a challenging task. Here, we report the in vitro reconstitution of the elongation module of the FAS of malaria parasite involving all four enzymes, FabB/F (β-ketoacyl-ACP synthase), FabG (β-ketoacyl-ACP reductase), FabZ (β-ketoacyl-ACP dehydratase), and FabI (enoyl-ACP reductase), and its analysis by matrix-assisted laser desorption-time of flight mass spectrometry (MALDI-TOF MS). That this in vitro systems approach completely mimics the in vivo machinery is confirmed by the distribution of acyl products. Using known inhibitors of the enzymes of the elongation module, cerulenin, triclosan, NAS-21/91, and (–)-catechin gallate, we demonstrate that accumulation of intermediates resulting from the inhibition of any of the enzymes can be unambiguously followed by MALDI-TOF MS. Thus, this work not only offers a powerful tool for easier and faster throughput screening of inhibitors but also allows for the study of the biochemical properties of the FAS pathway of the malaria parasite.

Task assignment in a multiprocessor system

One of the key problems in the design of any incompletely connected multiprocessor system is to appropriately assign the set of tasks in a program to the Processing Elements (PEs) in the system. The task assignment problem has proven difficult both in theory and in practice. This paper presents a simple and efficient heuristic algorithm for assigning program tasks with precedence and communication constraints to the PEs in a Message-based Multiple-bus Multiprocessor System, M3, so that the total execution time for the program is minimized. The algorithm uses a cost function: “Minimum Distance and Parallel Transfer” to minimize the completion time. The effectiveness of the algorithm has been demonstrated by comparing the results with (i) the lower bound on the execution time of a program (task) graph and (ii) a random assignment.

A FIFO-based multicast network and its use in multicomputers

We present an implementation of a multicast network of processors. The processors are connected in a fully connected network and it is possible to broadcast data in a single instruction. The network works at the processor-memory speed and therefore provides a fast communication link among processors. A number of interesting architectures are possible using such a network. We show some of these architectures which have been implemented and are functional. We also show the system software calls which allow programming of these machines in parallel mode.

A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) lambda-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The lambda-coverage problem is concerned with finding a set of k key nodes having minimal size that can influence a given percentage lambda of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the lambda-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. Note to Practitioners-In recent times, social networks have received a high level of attention due to their proven ability in improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market using viral marketing techniques, etc. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. This problem is called the target set selection problem and has two variants: 1) the top-k nodes problem, where we are required to identify a set of k influential nodes that maximize the number of nodes being influenced in the network and 2) the lambda-coverage problem which involves finding a set of influential nodes having minimum size that can influence a given percentage lambda of the nodes in the entire network. There are many existing algorithms in the literature for solving these problems. In this paper, we propose a new algorithm which is based on a novel interpretation of information diffusion in a social network as a cooperative game. Using this analogy, we develop an algorithm based on the Shapley value of the underlying cooperative game. The proposed algorithm outperforms the existing algorithms in terms of generality or computational complexity or both. Our results are validated through extensive experimentation on both synthetically generated and real-world data sets.

Tool for Identification of Duplicate Records Downloaded from Multiple Cd-Roms. A Case Study with Spirs Based Databases

As research becomes more and more interdisciplinary, literature search from CD-ROM databases is often carried out on more than one CD-ROM database. This results in retrieving duplicate records due to same literature being covered (indexed) in more than one database. The retrieval software does not identify such duplicate records. Three different programs have been written to accomplish the task of identifying the duplicate records. These programs are executed from a shell script to minimize manual intervention. The various fields that have been used (extracted) to identify the duplicate records include the article title, year, volume number, issue number and pagination. The shell script when executed prompts for input file that may contain duplicate records. The programs identify the duplicate records and write them to a new file.

Transaction-based QoS management in a Hybrid Wireless Superstore Environment

Hybrid wireless networks are extensively used in the superstores, market places, malls, etc. and provide high QoS (Quality of Service) to the end-users has become a challenging task. In this paper, we propose a policy-based transaction-aware QoS management architecture in a hybrid wireless superstore environment. The proposed scheme operates at the transaction level, for the downlink QoS management. We derive a policy for the estimation of QoS parameters, like, delay, jitter, bandwidth, availability, packet loss for every transaction before scheduling on the downlink. We also propose a QoS monitor which monitors the specified QoS and automatically adjusts the QoS according to the requirement. The proposed scheme has been simulated in hybrid wireless superstore environment and tested for various superstore transactions. The results shows that the policy-based transaction QoS management is enhance the performance and utilize network resources efficiently at the peak time of the superstore business.

Register File Energy Optimization for Snooping Based Clustered VLIW Architectures

Frequent accesses to the register file make it one of the major sources of energy consumption in ILP architectures. The large number of functional units connected to a large unified register file in VLIW architectures make power dissipation in the register file even worse because of the need for a large number of ports. High power dissipation in a relatively smaller area occupied by a register file leads to a high power density in the register file and makes it one of the prime hot-spots. This makes it highly susceptible to the possibility of a catastrophic heatstroke. This in turn impacts the performance and cost because of the need for periodic cool down and sophisticated packaging and cooling techniques respectively. Clustered VLIW architectures partition the register file among clusters of functional units and reduce the number of ports required thereby reducing the power dissipation. However, we observe that the aggregate accesses to register files in clustered VLIW architectures (and associated energy consumption) become very high compared to the centralized VLIW architectures and this can be attributed to a large number of explicit inter-cluster communications. Snooping based clustered VLIW architectures provide very limited but very fast way of inter-cluster communication by allowing some of the functional units to directly read some of the operands from the register file of some of the other clusters. In this paper, we propose instruction scheduling algorithms that exploit the limited snooping capability to reduce the register file energy consumption on an average by 12% and 18% and improve the overall performance by 5% and 11% for a 2-clustered and a 4-clustered machine respectively, over an earlier state-of-the-art clustered scheduling algorithm when evaluated in the context of snooping based clustered VLIW architectures.

Self Assessment-Based Decision Making for Multiagent Cooperative Search

This paper addresses a search problem with multiple limited capability search agents in a partially connected dynamical networked environment under different information structures. A self assessment-based decision-making scheme for multiple agents is proposed that uses a modified negotiation scheme with low communication overheads. The scheme has attractive features of fast decision-making and scalability to large number of agents without increasing the complexity of the algorithm. Two models of the self assessment schemes are developed to study the effect of increase in information exchange during decision-making. Some analytical results on the maximum number of self assessment cycles, effect of increasing communication range, completeness of the algorithm, lower bound and upper bound on the search time are also obtained. The performance of the various self assessment schemes in terms of total uncertainty reduction in the search region, using different information structures is studied. It is shown that the communication requirement for self assessment scheme is almost half of the negotiation schemes and its performance is close to the optimal solution. Comparisons with different sequential search schemes are also carried out. Note to Practitioners-In the futuristic military and civilian applications such as search and rescue, surveillance, patrol, oil spill, etc., a swarm of UAVs can be deployed to carry out the mission for information collection. These UAVs have limited sensor and communication ranges. In order to enhance the performance of the mission and to complete the mission quickly, cooperation between UAVs is important. Designing cooperative search strategies for multiple UAVs with these constraints is a difficult task. Apart from this, another requirement in the hostile territory is to minimize communication while making decisions. This adds further complexity to the decision-making algorithms. In this paper, a self-assessment-based decision-making scheme, for multiple UAVs performing a search mission, is proposed. The agents make their decisions based on the information acquired through their sensors and by cooperation with neighbors. The complexity of the decision-making scheme is very low. It can arrive at decisions fast with low communication overheads, while accommodating various information structures used for increasing the fidelity of the uncertainty maps. Theoretical results proving completeness of the algorithm and the lower and upper bounds on the search time are also provided.