Utilization of multiple-input multiple-output (MIMO) systems as a means of increasing channel capacity has been an area of increasing consideration in radio communications. However, less study has been devoted to MIMO in the high-frequency band. This research is important because high-frequency communication using MIMO allows for international communication at long distances using lower power consumption than many other approaches. The inter-symbol interference caused by the selective fading of multiple received signals and the randomness of the ionospheric conditions means there is a need for a novel solution. The purpose of this research is to introduce two machine learning approaches that can adaptively apply equalization algorithms to address fading and optimize equalization parameters. The novelty of our approach lies in two main factors. The first is that our approach allows for a software-defined radio to switch equalization algorithms depending on conditions during run-time. The second is that we optimize this selected algorithm further by using two machine-learning approaches. The first proposed cognitive engine model, which utilizes a genetic algorithm, demonstrates the validity and advantage of using a cognitive engine to select optimal equalization parameters at the receiver under the problems created by utilizing the high-frequency band. This approach acts as a comparison for our second. We then propose a second cognitive engine, the adaptive manipulator, which optimizes not only by selecting equalization parameters but also continually changes the equalization algorithm. Finally, we compare the performance of the proposed cognitive engine models with state-of-the-art algorithms.

In this paper, we provide an overview of how cognitive radio (CR) technology is making its way into the current wireless standards, and we discuss further opportunities for more CR-oriented standardization efforts. Specifically, we discuss how a cognitive engine (CE) can be potentially standardized, and we offer our latest innovations on metacognitive engine (meta-CE) design. The intelligent techniques of a CR are implemented in an agent commonly referred to as a CE. A CE manages the CR's operation. Typically, a CE is based on a primary learning and optimization algorithm that is utilized within the CE. Each of these algorithms has its own strengths and deficiencies depending on the operating scenario that makes some of them more appropriate for specific operating conditions. A meta-CE is an engine that learns which CE is more appropriate to provide the adaptation needed for the operating conditions. To make a meta-CE possible, first, we need a method for characterizing the CE algorithm's performance. Second, we should classify the operating conditions based on the CE algorithm's characteristics in order to determine the most appropriate operating condition. Our results show that, when we use meta-CE techniques, we have about 30% increase in performance compared with using the best individual CE.

As CMOS technology enters the nanometer regime, the increasing process variation is bringing manifest impact on circuit performance. To accurately consider both global and local process variations, a large number of random variables (or parameters) have to be incorporated into circuit models. This in turn raises the complexity of the circuit models. In this paper, we propose a principle Hessian direction-based parameter-reduction approach. This new approach relies on the impact of each parameter on circuit performance to decide whether keeping or reducing the parameter. Compared with the existing principle component analysis method, this performance based property provides us a significantly smaller set of parameters after reduction. The experimental results also support our conclusions. In interconnect cases, the proposed method reduces 70% of parameters. In some cases, for example, the mesh circuit in the current paper, the new approach leads to an 85% reduction. We also tested ISCAS benchmarks. In all cases, an average of 53% of reduction is observed with less than 3% error in the mean value and less than 8% error in the variation.

With semiconductor fabrication technologies scaled below 100 nm, the design-manufacturing interface becomes more and more complicated. The resultant process variability causes a number of issues in the new generation IC design. One of the biggest challenges is the enormous number of process variation related parameters. These parameters represent numerous local and global variations, and pose a heavy burden in today's chip verification and design. This paper proposes a new way of reducing the statistical variations (which include both process parameters and design variables) according to their impacts on the overall circuit performance. The new approach creates an effective reduction subspace (ERS) and provides a transformation matrix by using the mean and variance of the response surface. With the generated transformation matrix, the proposed method maps the original statistical variations to a smaller set of variables with which we process variability analysis. Thus, the computational cost due to the number of variations is greatly reduced. Experimental results show that by using new method we can achieve 20% to 50% parameter reduction with only less than 8% error on average.

As CMOS technology enters the nanometer regime, the increasing process variation is bringing manifest impact on circuit performance. In this paper, we propose a Principle Hessian Direction (PHD) based parameter reduction approach. This new approach relies on the impact of each parameter on circuit performance to decide whether keeping or reducing the parameter. Compared with the existing principle component analysis (PCA) method, this performance based property provides us a significantly smaller set of parameters after reduction. The experimental results also support our conclusions. In all cases, an average of 53% of reduction is observed with less than 3% error in the mean value and less than 8% error in the variation.

It has been found that the sensor nodes dissipate a significant proportion of their energy in redundant sensing and idle listening. The former occurs when multiple sensor nodes perform their sensing activity in the same region. The latter occurs when sensor nodes have their radios switched on awaiting incoming communication from other sensor nodes, but none is received since no sensor node is wanting to communicate with it. Researchers have proposed putting the sensors and/or the radios of sensor nodes to sleep (switch them off) so as to conserve energy. The task of scheduling when the sensors and/or radios need to be in sleep/active mode is referred to as sleep scheduling. Sensor sleeping may result in interesting events being missed by the network or may lead to a lower quality of data being sensed. Radio sleeping may lead to communication delays in the network. In this work we propose several distributed algorithms to perform sensor and radio sleep scheduling in wireless sensor networks while trying to minimize its negative impact

The fact that a lot of time in a manufacturing industry is spent in searching for similarly shaped designs from a database of previously manufactured parts and the fact that most of the new designs are modified versions of previous designs, resulted in the need for an efficient system for retrieval and reuse of CAD models. Features and the spatial relationships between the features, form a signature, which can be used to uniquely identify each part. Using this signature we develop an indexing scheme to speed up database searching. In this work we propose a content based search technique to retrieve similar parts from a database of old parts. We also perform a running time analysis of the search and retrieve algorithm.

In this paper, we analyze the error in the dimensional measurement due to localization errors in the image plane of a parallel stereo setup. The mean square error of the dimensional measurements is formulated as the optimality criterion for determining camera poses. To determine optimal poses, a nonlinear program that minimizes the MSE while satisfying the sensor constraints of resolution, focus, field of view, visibility, workspace, and incidence angle constraints is presented. The probability that the measurement errors are within a specified tolerance for this setup is formulated. Finally, we evaluate the performance of the model against real data.

A new approach to motion planning to achieve contact state transitions in robotic assembly of polyhedral parts is presented. The contact state of a pair of spatial polyhedra is represented by qualitative contact models, which include Feature Interaction Matrices (FIM’s) representation is adopted. Given the desired contact transition (i.e., the current contact state and the next desired contact state) and the current moving object configuration, we want to generate the compliant motion parameters for the robot system to guide the work piece to the next desired contact state. In this work, optimization method is used to derive the compliant motion parameters. Four motion control conditions are defined to provide constraints; a cost function representing the moving distance is also defined. Minimizing the cost function, the compliant motion parameters can be generated. The method is demonstrated by both translation and rotation examples.

The reuse of multiple cases to solve a single planning problem presents a promise of better utilization of past experience over single‐reuse planning, which can lead to better planning performance. In this paper, we present the theory and implementation of CBPOP, and show how it addresses the multi‐reuse planning problems. In particular, we present novel approaches to retrieval and refitting. We also explore the difficult issue of when to retrieve in multi‐reuse scenarios, and we empirically compare the results of several solutions we propose. Results from our experiments show that the best ranking function for pure generative planning is not necessarily the best ranking function for multi‐reuse planning. The surprising result in the reuse scenarios is that the single‐goal case library performed better than larger case libraries consisting of solutions to multi‐goal problems.

A methodology that can intelligently search a CAD database of 2.5D machinable components and retrieve components and sub-pieces with similar shape characteristics has been presented. The proposed methodology has the potential to reduce both the development time and cost of new designs by effectively re-using existing designs more efficiently. This is accomplished by searching a database of parts with their associated plans and extracting parts of those plans that are required for the query part. These plan parts can subsequently be merged to develop the entire plan for the query part. The methodology utilizes qualitative matrices to represent the spatial relationship between the maximal features of the query component. These feature-spatial relationship pairs are matched against those present in existing components within the database. The query can be relaxed based upon TAH structures that allow for generalizing of features and spatial relationships to allow for the best match to be found if no exact match is found.

A review of the basic methods used to model a learning agent, such as instance-based learning, artificial neural networks and reinforcement learning, suggests that they either lack flexibility (can only be used to solve a small number of problems) or they tend to converge very slowly to the optimal policy. This paper describes and illustrates a set of processes that address these two shortcomings. The resulting learning agent is able to "adapt fairly well" to a much larger set of environments and is capable of doing this in a reasonable amount of time. In order to address the lack of flexibility and slow convergence to the optimal policy, the new learning agent becomes a hybrid between a learning agent based on instance-based learning and one based on reinforcement learning. To accelerate its convergence to its optimal policy, this new learning agent incorporates the use of a new concept we call propagation of good findings. Furthermore, to make a better use of the learning agent's memory resources,, and therefore increase its flexibility, we make use of another new concept we call moving prototypes.

Recent research uses qualitative contact models for compliant motion planning and control. Although these methods have great potential, generation of qualitative contact models of nonconvex polyhedra is difficult. Qualitative contact models could be generated in a generate-and-test fashion if a predicate were available for verifying contact hypotheses. In general, a contact hypothesis does not fully constrain the relative configuration of the objects making verification of the hypothesis difficult. This paper presents an optimization-based method to verify geometric feasibility of contact hypotheses that do not fully constrain the relative configuration. This method has been implemented and applied to test contact hypotheses of nontrivial object pairs.

Introduces a sensor placement approach for dimensional inspection with active visual inspection systems. For a given set of entities, an effective sensor plan includes parameter values that allow some entities to be observed simultaneously. Two kinds of approaches have generally been used for sensor planning in visual inspection systems: the generate-and-test method and the synthesis method. In order to have high performance and satisfactory accuracy, uncertainty must be considered in the sensing model of the visual system. Furthermore, in order to avoid sharp deterioration of the performance of the visual inspection system for very small disturbances of the parameters, an appropriate definition of robustness is proposed. In an experiment a new sensor planning approach based on robustness resolves some problems with earlier approaches.

Recent works in domain-independent plan merging have shown that the optimal plan merging problem is NP-hard, and heuristic algorithms have been proposed to generate near-optimal solutions. We have developed a plan merging methodology that merges partial-order plans based on the notion of plan fragments. In contrast to previous works, mergeability classes no longer necessarily form a partition over the set of actions in the input plans. This methodology applies to a class of planning domains which are decomposable. We also investigate the previously unexplored notion of alternative actions in domain-independent plan merging, which can improve the quality of the resulting merged plan, and a novel approach is presented for removing cyclic dependencies that may result during the plan merging process. We provide theoretical analyses of several algorithms for computing the minimum cost cover of plan fragments, a central component of the methodology. We support the theoretical analysis of these algorithms with experimental data to show that a greedy approach is near-optimal and efficient.

The increased use of Computer Aided Design (CAD) tools have resulted in the obvious need to efficiently retrieve specific and groups of similar parts from CAD databases for group technology (GT) applications; Current classification and retrieval methods from CAD databases are typically code based. The disadvantage of such a retrieval scheme is application dependency. We propose an application independent scheme and utilize it in retrieving similarly shaped parts on the basis of form-features and the inter-feature relationships. We address the issues of completeness of spatial interactions vocabulary and multiple interpretations for a part.

Recent works in domain‐independent plan merging have shown that the optimal plan‐merging problem is NP‐hard, and heuristic algorithms have been proposed to generate near‐optimal solutions. These works have shown heuristic algorithms which assume that the mergeability of two actions can be determined by considering only the actions themselves. In this paper, we show that it is often that case that the surrounding actions in the plan must also be considered when determining the mergeability of two or more actions; therefore, the context in which the actions are used affects their mergeability. To address this problem, we have developed a plan‐merging methodology that merges partial‐order plans based on the the notion of plan fragments, which encapsulate actions with the context in which they are being utilized. This methodology applies to a class of planning domains, called decomposable domains, which consist of actions that follow all or some portion of a type sequence. Merging is performed hierarchically by action type. We also investigate the previously unexplored notion of alternative actions in domain‐independent plan merging, which can improve the quality of the resulting merged plan. A novel approach is presented for removing cyclic dependencies that may occur during the plan‐merging process. A key part of the methodology is the computation of a minimum cost cover of plan fragments. We provide theoretical analyses of two optimal algorithms and a greedy‐based algorithm for computing the minimum cost cover. We support the theoretical analysis of these algorithms with experimental data to show that the greedy approach is near‐optimal and efficient.

This paper discusses the effect of spatial quantization errors and displacement errors on the precision dimensional measurements for an edge segment. Probabilistic analysis in terms of the resolution of the image is developed for 2D quantization errors. Expressions for the mean and variance of these errors are developed. The probability density function of the quantization error is derived. The position and orientation errors of the active head are assumed to be normally distributed. A probabilistic analysis in terms of these errors is developed for the displacement errors. Through integrating the spatial quantization errors and the displacement errors, we can compute the total error in the active vision inspection system. Based on the developed analysis, we investigate whether a given set of sensor setting parameters in an active system is suitable to obtain a desired accuracy for specific dimensional measurements, and one can determine sensor positions and view directions which meet the necessary tolerance and accuracy of inspection.

An important aspect of inspection planning involves determining camera poses based on some criterion. We seek to find camera poses where the effects of displacement and quantization errors are minimal. The mean squared error is formulated, including all dependencies, and minimized to determine an optimal camera pose that satisfies the sensor constraints of resolution, focus, field-of-view, and visibility. Dimensional tolerances for line entities are also formulated and exploited to determine the acceptability of a given camera pose for all entities observed.

This paper presents a qualitative spatial model for manufactured components capable of supporting systematic spatial inferencing about the component. The model incorporates the features of the component as well as mathematical spatial relationships between the features. With the incorporation of these spatial relationships, the model is capable of supporting spatial inferencing tasks of benefit to the planning phases of manufacturing. As one example, spatial inferencing task, this paper describes how to find the multiple interpretations of a component.

This paper proposes interval constraint network and interval propagation techniques for automatic tolerance design. A hierarchical representation is utilized in the interval constraint network. The consistency of a constraint is defined for the purpose of tolerance design. Forward and backward propagation techniques are introduced in the interval constraint network for tolerance analysis and synthesis, respectively. Both a propagation technique for a single constraint and a parallel propagation technique for multiple constraints between two adjacent levels in the network are introduced. Experiments conducted to illustrate the procedures of tolerance analysis and synthesis for the tank problem are described.

This paper introduces a new uncertainty reasoning-based method for identification and extraction of manufacturing features from solid model description of objects. A major difficulty faced by previously proposed methods for feature extraction has been the interaction between features. In interacting situations, the representation for various primitive features is nonunique making their recognition very difficult. We develop an approach based on generating, propagating, and combining geometric and topological evidences in a hierarchical belief network for identifying and extracting features. The methodology combines and propagates evidences to determine a set of correct virtual links to be augmented to the cavity graph representing a depression of the object so that the resulting supergraph can be partitioned to obtain the features of the object. The hierarchical belief network is constructed based on the hypotheses for the potential virtual links, the evidences which are topological and geometric relationships at different abstraction levels impact the belief network through their (amount of) support for different hypotheses. The propagation of the impact of different evidences updates the beliefs in the network in accordance with the Bayesian probability rules.

Displacement error is inherent in automated visual inspection systems. This paper discusses the effect of displacement error of the end-effector on the precision measurement of the dimension of an edge line segment. The position and orientation errors of the end-effector are assumed to be normal distributed. A probabilistic analysis in terms of the these errors is developed for the displacement errors. Given that the nominal orientation and position of the sensor are known, the effect of this error on the dimension measurement is also analyzed. Based on this analysis, we investigate whether a given set of sensor setting parameters in an active system is suitable to obtain a desired accuracy for specific line segment dimensional measurements. In addition, based on this approach, one can determine sensor positions and view directions which meet specific targets for tolerance and accuracy of inspection. Developing these mechanisms is central to achieving effective, economic, and accurate inspection systems.

The feature recognition problem is formulated as a geometric constraint satisfaction problem (GCSP), in which variables are the faces in the delta volume, the possible feature classifications determine values for the variables, and the constraints are the geometric and topological properties between the variables. A novel representation language is proposed to encode more, and somewhat redundant, information about geometric and topological properties in form features. The redundant information adds more constraints amongst objects in feature interactions and thus helps in feature recognition. Moreover, the language can be extended to represent new features.

Introduces a new uncertainty reasoning-based method for the identification and extraction of manufacturing features from solid model descriptions of objects. A major difficulty faced by previously proposed methods for feature extraction has been the interaction between features. In interacting situations, the representation for various primitive features is non-unique, making their recognition very difficult. We develop an approach based on generating, propagating and combining geometrical and topological evidences in a hierarchical belief network for identifying and extracting features. The methodology combines and propagates evidences to determine a set of correct virtual links to be augmented in the cavity graph representing a depression of the object, so that the resulting supergraph can be partitioned to obtain the features of the object.>

This paper is concerned with problems in automated visual inspection of manufactured (particularly machined) components based on their (CAD) design models. In order to achieve the integrated intelligent inspection goals, the authors address several interrelated problems. These problems include: (i) developing hierarchical representation mechanisms to effectively capture the knowledge about geometric entities, their relationships, sensors, and plans, (ii) reasoning mechanisms to determine the different attributes of the different features of an object to be inspected, and the alternative strategies which can be used for inspection of each attribute, (iii) strategies for automated generation of position and viewing angles of the cameras in an active vision system, and for determining the visible entities in each configuration, and (iv) optimization of the constructed plan including minimizing the number of sensor settings and the total distance traveled by an active visual sensor.>

Describes a knowledge-based inspection planning system for solid objects. The goals of intelligent inspection planning include determining which entities (edges, etc.) of the object should be measured, what camera locations and viewing directions should be used to perform inspection, and how the search for these entities and their inspection can be robustly and efficiently done once the sensor data is obtained. An effective solution to this problem is clearly very important not only from the automation point of view but also in order to gather and use sensor information effectively given the volume and complexity of required information for adequate performance.>

Methods and an implemented system for automatic generation of process plans from the CAD boundary representation of a part are presented. Two problems are addressed: one is the machine understanding of the shape of a part from its low-level boundary representation, and the other is developing a plan based on this semantic information. The architecture of the system at the highest level consists of a solid modeler, a shape description system, an automatic process planner, and the interfaces between them. Cooperative reasoning and combining geometric evidences are used to automatically extract the shape primitives of a part and determine the relations between these primitives from the boundary representation CAD data, thus producing the higher-level semantic information. A case-based planner uses the information produced by the shape description system to construct a conceptual plan for machining the part. Examples illustrate the reasoning performed by the system and show its advantages.>

An object-oriented kernel that integrates design and process planning in the domain of mechanical parts is presented. The kernel has identified and separated the integral elements of its domain from the elements that are associated with a particular design/manufacturing environment. Consequently, it can be customized to a wide range of environments and be used as a rapid prototyping tool. A prototype has been developed and implemented in Smalltalk-80.

A method for extracting manufacturing shape features from the boundary representation of a polyhedral object is presented. In this approach, the depressions of the part are represented as cavity graphs, which are in turn used as a basis for hypothesis generation-elimination. The proposed cavity graphs are an extended representation in which the links reflect the concavity of the intersection between two faces, and the node labels reflect the relative orientation of the faces comprising the depression. Because previous methods have limited success in handling interactions, emphasis is put on automatic analysis of depressions which are formed by the interactions of primitive features. It is shown that although there is a unique subgraph for each primitive feature, every cavity graph does not correspond to a unique set of primitive features. Consequently, since the cavity graph of a depression may not be the union of the representations for the involved primitives, the concept of virtual links for the formal analysis of the depressions based on cavity graphs is introduced. Finally, a suitable method for automatic determination of the virtual links is presented. This method is based on combining topologic and geometric evidences, and uses a combination of Dempster-Shafer decision theory and clustering techniques to reach its conclusions. Experimental results are presented for a number of examples.>