Showing papers by "Lakhmi C. Jain published in 2004"
Book•
17 Sep 2004
TL;DR: Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition, and design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks.
Abstract: Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction between topdown expectation and bottom-up input, thereby creating a focus of attention which, in turn, determines the nature of coded memories. Sites of early and ongoing transfer of ART-based technologies include industrial venues such as the Boeing Corporation [6] and government venues such as MIT Lincoln Laboratory [7]. A recent report on industrial uses of neural networks [8] states: “[The] Boeing ... Neural Information Retrieval System is probably still the largest-scale manufacturing application of neural networks. It uses [ART] to cluster binary templates of aeroplane parts in a complex hierarchical network that covers over 100,000 items, grouped into thousands of self-organised clusters. Claimed savings in manufacturing costs are in millions of dollars per annum.” At Lincoln Lab, a team led by Waxman developed an image mining system which incorporates several models of vision and recognition developed in the Boston University Department of Cognitive and Neural Systems (BU/CNS). Over the years a dozen CNS graduates (Aguilar, Baloch, Baxter, Bomberger, Cunningham, Fay, Gove, Ivey, Mehanian, Ross, Rubin, Streilein) have contributed to this effort, which is now located at Alphatech, Inc. Customers for BU/CNS neural network technologies have attributed their selection of ART over alternative systems to the model's defining design principles. In listing the advantages of its THOT technology, for example, American Heuristics Corporation (AHC) cites several characteristic computational capabilities of this family of neural models, including fast on-line (one-pass) learning, “vigilant” detection of novel patterns, retention of rare patterns, improvement with experience, “weights [which] are understandable in real world terms,” and scalability (www.heuristics.com). Design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks, including fuzzy ARTMAP [9], ART-EMAP [10], ARTMAP-IC [11],
1,745 citations
TL;DR: A temporal neuro-fuzzy system named ANFIS_unfolded_in_time which is designed to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data by using fuzzy rules.
19 citations
Book•
01 Jan 2004
TL;DR: The implementation of the EGAP-based multi-valued argument models, which allow agents to make arguments with self or other agents under uncertain knowledge, is described.
Abstract: We studied the many-valued argumentation frameworks. They allow agents to make arguments with self or other agents under uncertain knowledge which is to be represented in the expressive EGAP (Extended Generalized Annotated Logic Programming). In this paper, we describe the implementation of the EGAP-based multi-valued argument models. The versatility of our many-valued argument models is shown through convincing argument examples.
13 citations
01 Jan 2004
TL;DR: The innovations in intelligent systems that will be your best choice for better reading book and how to find the best thing of book that you can read.
Abstract: Use of multi-category proximal SVM for data set reduction.- Bayesian control of dynamic systems.- AppART: a hybrid neural network based on adaptive resonance theory for universal function approximation.- An algorithmic approach to the main concepts of rough set theory.- Automated case selection from databases using similarity-based rough approximation.- An induction algorithm with selection significance based on a fuzzy derivative.- Model and fixpoint semantics for fuzzy disjunctive programs with weak similarity.- An automated report generation tool for the data understanding phase.- Finding trigonometric identities with tree adjunct grammar guided genetic Modeling a distributed knowledge management for autonomous cooperative agents with knowledge migration programming.- Intelligent information systems based on paraconsistent logic programs.- Neuro-fuzzy paradigms for intelligent energy management.- Information space optimization for inductive learning.- Detecting, tracking, and classifying human movement using active contour Fuzzy sets in investigation of human cognition processes models and neural networks.- A full explanation facility for an MLP network that classifies .- Automatic translation to controlled medical vocabularies low-back-pain patients and for predicting MLP reliability.- A genetic programming for the induction of natural language parser.
11 citations
Posted Content•
TL;DR: In this paper, a hybrid neuro-genetic learning approach for the adaptation of a Mamdani fuzzy inference system for the Tactical Air Combat Decision Support System (TACDSS) is presented.
Abstract: Normally a decision support system is build to solve problem where multi-criteria decisions are involved. The knowledge base is the vital part of the decision support containing the information or data that is used in decision-making process. This is the field where engineers and scientists have applied several intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a hybrid neuro-genetic learning approach for the adaptation a Mamdani fuzzy inference system for the Tactical Air Combat Decision Support System (TACDSS). Some simulation results demonstrating the difference of the learning techniques and are also provided.
11 citations
20 Sep 2004
TL;DR: The problem of continuing accidents in contemporary aircraft landings is discussed and three autonomous agents whose task it is to jointly monitor the aircraft and its flight crew are proposed.
Abstract: We discuss the problem of continuing accidents in contemporary aircraft landings and propose three autonomous agents whose task it is to jointly monitor the aircraft and its flight crew. Two of these agents are monitoring the path of the aircraft, one armed with prior knowledge of how planes tend to land at that airport, the other with the ability to project forward from the plane’s current position in order to identify potential dangers. The third agent monitors the flight crew’s behavior. These three agents act together to improve safety in the specific process of landing the aircraft.
10 citations
TL;DR: This paper presents a concurrent fuzzy-neural network approach combining unsupervised and supervised learning techniques to develop the Tactical Air Combat Decision Support System (TACDSS).
Abstract: Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing technologies that underlie the conception, design and utilization of intelligent systems. Several works have been done where engineers and scientists have applied intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a concurrent fuzzy-neural network approach combining unsupervised and supervised learning techniques to develop the Tactical Air Combat Decision Support System (TACDSS). Experiment results clearly demonstrate the efficiency of the proposed technique.
10 citations
20 Sep 2004
TL;DR: This paper describes how to harvest results and proposes a team for situations where human-agent collaboration is crucial to its success, and is designed with the Prometheus agent design methodology as a guide.
Abstract: Previous research on human-machine teaming[1] argued that a formal specification in human-agent systems could prove beneficial in a hostile environment, and proposed an initial demonstration which has been successfully implemented[2] using a test-bed based on JACK[3] and Unreal Tournament (UT) [4]. This paper describes how to harvest these results and proposes a team for situations where human-agent collaboration is crucial to its success. A specific game type within Unreal Tournament is utilised, called Capture The Flag[5]. The proposed team is designed with the Prometheus agent design methodology[6] as a guide and is comprised of humans and agents each having a specific role.
Book•
01 Jan 2004
TL;DR: In this paper, the three-dimensional Conformal Field Theories with Abelian Symmetry (3DCF) were studied in the context of the TASI 2003 lecture on AdS/CFT.
Abstract: SL(2,Z) Action on Three-Dimensional Conformal Field Theories with Abelian Symmetry (E Witten) The Search for a Holographic Dual to AdS3xS3xS3xS1 Chern-Simons Gauge Theory and the AdS3/CFT2 Correspondence (S Gukov et al.) Spinning Strings and AdS/FT Duality (A A Tseytlin) TASI 2003 Lectures on AdS/CFT (J Maldacena) The Three-String Vertex for a Plane-Wave Background (J H Schwarz) A New Hat or the c=1 Matrix Model (M R Douglas et al.) Small Instantons, Little Strings and Free Fermions (A Losev et al.) AdS and dS Entropy from String Junctions or the Function of Junction Conjunctions (E Silverstein) Low-Dimensional Sisters of Seiberg-Witten Effective Theory (A V Smilga) From Super-Yang-Mills Theory to QCD: Planar Equivalence and Its Implications (A Armoni et al.) This is How It Was ... (I Kogan).
27 Jun 2004
TL;DR: This work presents an efficient and secure algorithm for embedding a gray-level watermark into the original image with VQ, and proves the effectiveness of the proposed schemes in VQ-based image-in-image data hiding.
Abstract: Vector quantization (VQ)-based data hiding and watermarking techniques form a newly developed branch in the digital watermarking research field. We propose a novel watermarking scheme for image-in-image data hiding based on VQ. Unlike existing schemes in the literature for embedding a binary watermark into the original image with VQ, we present an efficient and secure algorithm for embedding a gray-level watermark into the original. The offered scheme is robust to attacks such as VQ compression and JPEG compression. Moreover, it greatly expands the capacity of the watermarking system. Experimental results demonstrate the superiority of our scheme in watermark robustness and watermark capacity. The results also prove the effectiveness of our proposed schemes in VQ-based image-in-image data hiding.
04 Dec 2004
TL;DR: A fast visual landmark search and recognition mechanism for real-time robotics applications that models two stages of visual perception named preattentive and attentive stages and shows validity and applicability to autonomous robot applications.
Abstract: Robot navigation relies on a robust and real-time visual perception system to understand the surrounding environment This paper describes a fast visual landmark search and recognition mechanism for real-time robotics applications The mechanism models two stages of visual perception named preattentive and attentive stages The pre-attentive stage provides a global guided search by identifying regions of interest, which is followed by the attentive stage for landmark recognition The results show the mechanism validity and applicability to autonomous robot applications.
30 Nov 2004
TL;DR: Experimental results show that the proposed vector quantisation (VQ) based watermarking scheme for hiding the gray watermark possesses advantages over other related methods in literature.
Abstract: A vector quantisation (VQ) based watermarking scheme for hiding the gray watermark is presented. It expands the watermark size, and employs VQ index assignment procedure with genetic algorithm, called genetic index assignment (GIA), for watermarking. The gray watermark is coded by VQ, and obtained indices are translated into a binary bitstream with a much smaller size. We partition the codebook into two sub-codebooks, and use either one of them based on the value of the bit for embedding. Next, GIA is employed to find better imperceptibility of watermarked image. Experimental results show that the proposed method possesses advantages over other related methods in literature.
Posted Content•
TL;DR: Different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems are presented.
Abstract: Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies that underlie the conception, design and utilization of intelligent systems. In this paper, we present different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems. We demonstrate the efficiency of the different algorithms by developing a decision support system for a Tactical Air Combat Environment (TACE). Some empirical comparisons between the different algorithms are also provided.
TL;DR: The concept of intelligence is complex, and thus many theories, de'nitions and taxonomies has emerged to explain its essence, but their limited success has given rise to the idea that such a multidimensional concept cannot be explained by a single theory.
Book•
01 Jan 2004
TL;DR: This paper presents Adaptive Technical Analysis in the Financial Markets Using Machine Learning: a Statistical View, a Brief Background, and Selected ICA Resources, a Guide for Applications of ICA.
Abstract: 1 Adaptive Technical Analysis in the Financial Markets Using Machine Learning: a Statistical View.- 1.1 'Technical Analysis' in Finance: a Brief Background.- 1.2 The 'Moving Windows' Paradigm.- 1.3 Post-Hoc Performance Assessment.- 1.3.1 The Effect of Dividends.- 1.3.2 Transaction Costs Approximations.- 1.4 Genetic programming.- 1.5 Support-Vector Machines.- 1.6 Neural Networks.- 1.7 Discussion.- References.- 2 Higher Order Neural Networks for Satellite Weather Prediction.- 2.1 Introduction.- 2.2 Higher Order Neural Networks.- 2.2.1 Polynomial Higher-Order Neural Networks.- 2.2.2 Trigonometric Higher-Order Neural Networks.- Output Neurons in THONN Model#1.- Second Hidden Layer Neurons in THONN Model#1.- First Hidden Layer Neurons in THONN Model#1.- 2.2.3 Neuron-Adaptive Higher-Order Neural Network.- 2.3 Artificial Neural Network Groups.- 2.3.1 ANN Groups.- 2.3.2 PHONN, THONN & NAHONN Groups.- 2.4 Weather Forecasting & ANNs.- 2.5 HONN Models for Half-hour Rainfall Prediction.- 2.5.1 PT-HONN Model.- 2.5.2 A-PHONN Model.- 2.5.3 M-PHONN Model.- 2.5.4 Satellite Rainfall Estimation Results.- 2.6 ANSER System for Rainfall Estimation.- 2.6.1 ANSER Architecture.- 2.6.2 ANSER Operation.- 2.6.3 Reasoning Network Based on ANN Groups.- 2.6.4 Rainfall Estimation Results.- 2.7 Summary.- Acknowledgements.- References.- Appendix-A Second Hidden Layer (multiply) Neurons.- Appendix-B First Hidden Layer Neurons.- 3 Independent Component Analysis.- 3.1 Introduction.- 3.2 Independent Component Analysis Methods.- 3.2.1 Basic Principles and Background.- 3.2.2 Mutual Information Methods.- 3.2.3 InfoMax ICA Algorithm.- 3.2.4 Natural/Relative Gradient Methods.- 3.2.5 Extended InfoMax.- 3.2.6 Adaptive Mutual Information.- 3.2.7 Fixed Point ICA Algorithm.- 3.2.8 Decorrelation and Rotation Methods.- 3.2.9 Comon Decorrelation and Rotation Algorithm.- 3.2.10 Temporal Decorrelation Methods.- 3.2.11 Molgedey and Schuster Temporal Correlation Algorithm.- 3.2.12 Spatio-temporal ICA Methods.- 3.2.13 Cumulant Tensor Methods.- 3.2.14 Nonlinear Decorrelation Methods.- 3.3 Applications of ICA.- 3.3.1 Guidelines for Applications of ICA.- 3.3.2 Biomedical Signal Processing.- 3.3.3 Extracting Speech from Noise.- 3.3.4 Unsupervised Classification Using ICA.- 3.3.5 Computational Finance.- 3.4 Open Problems for ICA Research.- 3.5 Summary.- References.- Appendix - Selected ICA Resources.- 4 Regulatory Applications of Artificial Intelligence.- 4.1 Introduction.- 4.2 Solution Spaces, Data and Mining.- 4.3 Artificial Intelligence in Context.- 4.4 Anomaly Detection: ANNs for Prediction/Classification.- 4.4.1 Training to Classify on Spare Data Sets.- 4.4.2 Training to Predict on Dense Data Sets.- 4.4.3 Feature Selection for and Performance of Anomaly Detection Suites.- 4.4.4 Interpreting Anomalies.- 4.4.5 Other Approaches to Anomaly Detection.- 4.4.6 Variations of BackProp' ANNs for Use with Complex Data Sets.- 4.5 Formulating Expert Systems to Identify Common Events of Interest.- A Note on the Software.- Acknowledgements.- References.- 5 An Introduction to Collective Intelligence.- 5.1 Collective Intelligence.- 5.1.1 A Simple Example of Stigmergy at Work.- 5.2 The Power of Collective Action.- 5.3 Optimisation.- 5.3.1 Optimisation in General.- 5.3.2 Shades of Optimisation.- 5.3.3 Exploitation versus Exploration.- 5.3.4 Example of Common Optimisation Problems.- Minimum Path Length.- Function Optimisation.- Sorting.- Multi-Component Optimisation.- 5.4 Ant Colony Optimisation.- 5.4.1 Ant Systems - the Basic Algorithm.- The Problem with AS.- 5.4.2 Ant Colony Systems.- 5.4.3 Ant Multi-Tour System (AMTS).- 5.4.4 Limiting the Pheromone Density - the Max-Min Ant System.- 5.4.5 An Example: Using Ants to Solve a (simple) TSP.- 5.4.6 Practical Considerations.- 5.4.7 Adding a Local Heuristic.- 5.4.8 Other Uses for ACO.- 5.4.9 Using Ants to Sort.- An Example of Sorting Using ACO.- 5.5 Particle Swarm Optimisation.- 5.5.1 The Basic Particle Swarm Optimisation Algorithm.- 5.5.2 Limitations of the Basic Algorithm.- 5.5.3 Modifications to the Basic PSO Algorithm.- Choosing the Position S.- The Problem of a finite t.- Aggressively Searching Swarms.- Adding Memory to Each Particle.- 5.5.4 Performance.- 5.5.5 Solving TSP Problems Using PSO.- PSO Performance on a TSP.- 5.5.6 Practical Considerations.- 5.5.7 Scalability and Adaptability.- References.- 6 Where are all the Mobile Robots?.- 6.1 Introduction.- 6.2 Commercial Applications.- 6.2.1 Robot Couriers.- 6.2.2 Robot Vacuum Cleaners.- 6.2.3 Robot Lawn Mowers.- 6.2.4 Robot Pool Cleaners.- 6.2.5 Robot People Transporter.- 6.2.6 Robot Toys.- 6.2.7 Other Applications.- 6.2.8 Getting a Robot to Market.- 6.2.9 Wheeled Mobile Robot Research.- 6.3 Research Directions.- 6.4 Conclusion.- A Note on the Figures.- References.- 7 Building Intelligent Legal Decision Support Systems: Past Practice and Future Challenges.- 7.1 Introduction.- 7.1.1 Benefits of Legal Decision Support Systems to the Legal Profession.- 7.1.2 Current Research in AI and Law.- 7.2 Jurisprudential Principles for Developing Intelligent Legal Knowledge-Based Systems.- 7.2.1 Reasoning with Open Texture.- 7.2.2 The Inadequacies of Modelling Law as a Series of Rules.- 7.2.3 Landmark and Commonplace Cases.- 7.3 Early Legal Decision Support Systems.- 7.3.1 Rule-Based Reasoning.- 7.3.2 Case-Based Reasoning and Hybrid Systems.- 7.3.3 Knowledge Discovery in Legal Databases.- 7.3.4 Evaluation of Legal Knowledge-Based Systems.- 7.3.5 Explanation and Argumentation in Legal Knowledge-Based Systems.- 7.4 Legal Decision Support on the World Wide Web.- 7.4.1 Legal Knowledge on the WWW.- 7.4.2 Legal Ontologies.- 7.4.3 Negotiation Support Systems.- 7.5 Conclusion.- Acknowledgements.- References.- 8 Forming Human-Agent Teams within Hostile Environments.- 8.1 Introduction.- 8.2 Background.- 8.3 Cognitive Engineering.- 8.4 Research Challenge.- 8.4.1 Human-Agent Teaming.- 8.4.2 Agent Learning.- 8.5 The Research Environment.- 8.5.1 The Concept of Situational Awareness.- 8.5.2 The Unreal Tournament Game Platform.- 8.5.3 The Jack Agent.- 8.6 The Research Application.- 8.6.1 The Human Agent Team.- 8.6.2 The Simulated World Within Unreal Tournament.- 8.6.3 Interacting With Unreal Tournament.- 8.6.4 The Java Extension.- 8.6.5 The Jack Component.- 8.7 Demonstration System.- 8.7.1 Wrapping Behaviours in Capabilities.- 8.7.2 The Exploring Behaviours.- 8.7.3 The Defending Behaviour.- 8.8 Conclusions.- Acknowledgements.- References.- 9 Fuzzy Multivariate Auto-Regression Method and its Application.- 9.1 Introduction.- 9.2 Fuzzy Data Analysis.- 9.2.1 Fuzzy Regression.- 9.2.2 Fuzzy Time Series Analysis.- 9.2.3 Fuzzy Linear Regression (FLR).- Basic Definitions.- Linear Programming Problem.- 9.3 Fuzzy Multivariate Auto-Regression Algorithm.- Example - Gas Furnace Data Processed by MAR.- 9.3.1 Model Selection.- 9.3.2 Motivation for FLR in Fuzzy MAR.- 9.3.3 Fuzzification of Multivariate Auto-Regression.- 9.3.4 Bayesian Information Criterion in Fuzzy MAR.- 9.3.5 Obtaining a Linear Function for a Variable.- 9.3.6 Processing of Multivariate Data.- 9.4 Experimental Results.- 9.4.1 Experiments with Gas Furnace Data.- 9.4.2 Experiments with Interest Rate Data.- 9.4.3 Discussion of Experimental Results.- 9.5 Conclusions.- References.- 10 Selective Attention Adaptive Resonance theory and Object Recognition.- 10.1 Introduction.- 10.2 Adaptive Resonance Theory (ART).- 10.2.1 Limitations of ART's Attentional Subsystem with Cluttered Inputs.- 10.3 Selective Attention Adaptive Resonance Theory.- 10.3.1 Neural Network Implementation of SAART.- Postsynaptic Cellular Activity.- Excitatory Postsynaptic Potential.- Lateral Competition.- Transmitter Dynamics.- 10.3.2 Translation-invariant 2D Shape Recognition.- 10.4 Conclusions.- References.
TL;DR: A digital watermarking scheme based on vector quantisation (VQ) for gray watermark is proposed, which embeds the encoded indices into the cover image in VQ domain then and a genetic index assignment (GIA) procedure is proposed to improve the performance of the watermarked scheme.
Abstract: A digital watermarking scheme based on vector quantisation (VQ) for gray watermark is proposed. It begins with the procedure of encoding the gray watermark, and embeds the encoded indices into the cover image in VQ domain then. To improve the performance of the watermarking scheme, a genetic index assignment (GIA) procedure, which modifies the signal of the watermark to suit the signal of the cover image, is proposed. The proposed gray watermark embedding scheme is easy to implement, requires no original cover image to be presented during extraction, expands the size of the used watermark, and provides better watermarked results. Experimental results will show the novelty and effectiveness of it.
01 Jan 2004
TL;DR: In this Chapter, a fuzzy multivariate auto-regression method is introduced which is based on fuzzy linear regression and the aim of the study is to model multivariate time series data.
Abstract: In this Chapter, a fuzzy multivariate auto-regression method is introduced which is based on fuzzy linear regression. The aim of the study is to model multivariate time series data.
30 Jun 2004
TL;DR: For a multi-user-based watermarking system, a new scheme for providing the function of secret sharing is proposed and a user-key generating procedure is introduced to generate one master key and several normal keys.
Abstract: For a multi-user-based watermarking system, a new scheme for providing the function of secret sharing is proposed. A user-key generating procedure is introduced to generate one master key and several normal keys. By using either of these normal keys, a secret watermark is obtained from the cover image. By referring to the original watermark and all the generated secret watermarks, a public watermark is created and embedded into the cover image. The proposed scheme does not require the original image to be presented during extracting. To reveal the genuine watermark from the watermarked image, except for the super-user who can extract it directly by using the master key, the normal users who share the secret can only achieve it by presenting the shadow watermarks extracted by using their own keys
01 Jan 2004
TL;DR: The concept of selective attention as a useful mechanism in Artificial Neural Network models of visual pattern recognition has received a lot of attention recently, particularly since it was found that such a mechanism influences the receptive field profiles of cells in the primate visual pathway by filtering out non-relevant stimuli.
Abstract: The concept of selective attention as a useful mechanism in Artificial Neural Network models of visual pattern recognition has received a lot of attention recently, particularly since it was found that such a mechanism influences the receptive field profiles of cells in the primate visual pathway by filtering out non-relevant stimuli [28, 29]. It is believed that the massive feedback pathways in the brain play a role in attentional mechanisms by biasing the competition amongst the neural populations that are activated by different parts of a scene [8, 14, 15].
20 Sep 2004
TL;DR: It is shown that this robust system identification method using the neural network can be applied to successfully identify the nonlinear system in the presence of comparatively loud noise.
Abstract: The robust system identification method using the neural network is developed based on the canonical variate analysis (CVA). The main contribution of this algorithm is using CVA to obtain the k-step optimal prediction value. Therefore, the method to obtain the comparatively accurate estimate is introduced without iteration calculations. We show that this algorithm can be applied to successfully identify the nonlinear system in the presence of comparatively loud noise. Results from several simulation studies have been included to the effectiveness of this method.
Journal Article•
22 Nov 2004
TL;DR: This paper investigates the use of Generalised Regression Neural Network to create and train agents capable of detecting face images and shows how the detection agent fits into the overall system.
Abstract: This paper investigates the use of Generalised Regression Neural Network (GRNN) to create and train agents capable of detecting face images. This agent would make up the ‘Detection Agent’ in an architecture comprising of several different agents that collaborate together to detect and then recognise certain images. The overall agent architecture will operate as an Automatic Target Recognition’ (ATR) system. The architecture of ATR system is presented in this paper and it is shown how the detection agent fits into the overall system. Experiments and results using the detection agent are also presented.
06 Dec 2004
TL;DR: A new watermarking approach based on lapped orthogonal transform (LOT) is proposed, which provides better imperceptibility by reducing the blocking effects which exist in most block-based image coding transforms like JPEG.
Abstract: A new watermarking approach based on lapped orthogonal transform (LOT) is proposed. It provides better imperceptibility by reducing the blocking effects which exist in most block-based image coding transforms like JPEG. The proposed scheme performs the LOT to the cover image, modifies the LOT coefficients to hide the watermark bits according to the given user-key, and executes the inverse LOT to generate a watermarked image. It requires no original cover image to be introduced during extraction, and it has strong robustness under the JPEG attacks.
01 Jan 2004
TL;DR: The syntactic and semantic aspects of the formal language of General System Logical Theory (GSLT), also known as Abstract Theory of Adaptive Agents is presented in order to improve the formal description of the action of the adaptive agents.
Abstract: The syntactic and semantic aspects of the formal language of General System Logical Theory (GSLT), also known as Abstract Theory of Adaptive Agents is presented in order to improve the formal description of the action of the adaptive agents. Following an introduction to the language of GSLT, we show the possibility of the action of the Adaptive Agents in a number of areas. The category theory will be the abstract structure to model the adaptive agent at different orders. Every semantic unity which describes an action of the adaptive agent is expressed by an elementary unity denoted Elementary Logical Systems (ELS) which are the basic components of GSLT. GSLT uses the input-output paradigm to represent any action. The static classical form of categorical structures is transformed as a dynamic transformation of objects. The input-output paradigm of system theory is well known and is extensively developed in the literature.
01 Jan 2004
TL;DR: With the definition of the Adaptive Field, one may assign a particular degree of significance to any possible world in modal logic, and the attention can be focused on more significant worlds or on the comparison between two different worlds.
Abstract: With the definition of the Adaptive Field we may assign a particular degree of significance to any possible world in modal logic. In this way, our attention can be focused on more significant worlds or on the comparison between two different worlds. The information is one of the main sources able to show the logic structure of information. A guide is obtained from this on the use of information. It is also able to discover and measure the degree of uncertainty contained. The space of worlds is thus useful in its ability to divide information into the important parts. An accessibility relation exists between the most significant worlds with respect to the least significant worlds. Logical models of information are possible using the adaptive fields.