This paper proposes a novel link quality estimation method for IEEE 802.15.4 based IoT and WSN devices. The method aims to estimate the link quality in noisy environments in a low complexity manner. The effect of signal to noise ratio on the link quality is investigated for Additive White Gaussian Noise and Rayleigh Fading models. Symbol error rates which determine the link quality are extracted from Monte Carlo simulations for a wide range of noise levels in the environment. A relationship model between chip error rate and symbol error rate is established by a curve fitting method. Considering constrained hardware of IoT and WSN devices, the model should have a reasonable time complexity. Suitability of the model is discussed, and the real-time usage is explained. In the experiments, chip error occurrences are analyzed for both noise models. By means of experimental data, a number of fitting functions are compared in the respect of their fitting performances and complexities. Eventually, an efficient and reliable method for the link quality estimation is proposed to provide a low-complexity solution for IoT and WSN devices.

In this paper, we propose a modular approach to construction of multi-layer spiking neural networks with a Coulomb energy function based learning algorithm for training each module. In this approach, a single-layer spiking neural network is constructed and trained with the Coulomb energy function based learning algorithm. If the learning result is not sufficiently good, another layer is added, and the input is the output of the previous layer. The process continues until a desired learning result is achieved. The approach eliminates the need for advance determination of the number of hidden layers and the need for error-backpropagation training in multi-layer spiking neural networks. Experimental results of classifying a two-ring-shaped dataset and segmenting an aerial image show that our proposed modular multi-layer spiking neural network requires a simple learning algorithm and achieves better results compared with other approaches.

One important application of wireless sensor networks (WSNs) is intruder detection for protection of a region of interest, and k-barrier coverage is a good way to achieve the necessary level of security using WSNs. One of the biggest problems with WSNs is their limited lifetime, which is directly related to the deployment and maintenance costs, and many approaches have been proposed to improve network lifetime. In this paper, a new approach to extending network lifetime through strategic deployment of additional sensors is presented. We propose two strategies for deployment of additional sensors: random and targeted. We also analyze their performance through extensive simulation. The key concept for the targeted additional sensor deployment is minimal bottleneck, which limits the network lifetime. We develop an algorithm for finding a bottleneck for 1-barrier coverage. We apply the strategies for deployment of additional sensors to a WSN initially randomly deployed. The targeted additional sensor deployment strategy extends the network lifetime 24% on average with only additional 1.4% of the initial randomly deployed sensors added to the WSN.

The widespread deployment of multicore systems over the last decade has brought about major changes in the software and hardware landscape. The resulting importance of parallel computing is reflected in the 2013 Curriculum Guidelines developed by the joint ACM/IEEE taskforce. The document recommends increased coverage of parallel computing and describes a new Knowledge Area on this topic. These recommendations have already been adopted by several universities in the form of new parallel programming courses. Implementing the recommendations in a complete curriculum, however, poses many challenges, including deciding on existing material to be removed, complying with administrative and ABET requirements, and maintaining caps on graduation credit hours. This paper describes an alternative approach for adopting the 2013 curricular recommendations on parallel computing. Specifically, we use a module based approach that introduces parallel computing concepts and re-iterates them through a series of short, self-contained modules taught across several lower-division courses. Most of these concepts are then combined into a new senior-level capstone course on parallel programming. Each module covers parallelism aspects in the context of a conventional computer science topic, thus enabling us to include parallel computing without a major overhaul of the curriculum. Evaluations conducted during the first year show encouraging results for this early-and-often approach in terms of learning outcomes, student interest, and confidence gains.

Wireless Sensor Networks (WSNs) can be used effectively to form barriers that monitor strips of land for applicaions such as homeland security and critical infrastructure protection. Sensors are highly constrained in their energy usage, so when they harvest energy, it is neccessary that this energy be used effectively. When WSNs are used as barriers, they are typically deployed for extended period of time, so it is of great importance that the network has a long lifetime. The lifetime problem for WSNs has been studied extensively. In this paper we give an exact solution to the problem of achieving maximum lifetime for the application of k-barrier coverage in energy harvesting wireless sensor networks.

The lifetime of wireless sensor networks (WSNs) has been studied extensively. Lifetime is application specific, and long lifetime is desirable in general. In barrier coverage, it is desirable to have a large number of barriers active as well. Energy harvesting is an active research area in WSNs. In this paper we study the barrier coverage problem in energy harvesting WSNs. We develop an algorithm that finds a repeating sleep/wakeup schedule that can provide the continuous operation of the maximum number of barriers in an energy harvesting WSN.

Due to different requirements in applications, sensors with different capacities are deployed. How to design efficient, reliable and scalable routing protocols in such wireless heterogeneous sensor networks (WHSNs) with intermittent asymmetric links is a challenging task. In this paper, we propose ProHet: a distributed probabilistic routing protocol for WHSNs that utilizes asymmetric links to reach assured delivery rate with low overhead. The ProHet protocol first produces a bidirectional routing abstraction by finding a reverse path for every asymmetric link. Then, it uses a probabilistic strategy to choose forwarding nodes based on historical statistics using local information. Analysis shows that ProHet can achieve assured delivery rate ρ if ρ is set within its upper-bound. Extensive simulations are conducted to verify its efficiency.

Due to requirements in different applications, sensors with various capacities are deployed. Data routing in such heterogeneous wireless sensor networks (HWSNs) poses challenges. First, the heterogeneous features create asymmetric links in the communication graphs which are not dealt with by conventional routing algorithms using undirected graphs. Second, it is important to provide an assured delivery rate for mission critical applications since sensors communicate with each other through lossy asymmetric links. In this paper, we propose LayHet: a layer-based routing protocol for HWSNs, which takes advantage of the asymmetric links to deliver messages to the sink with an assured delivery rate. Simulation results show that LayHet outperforms previous routing methods in terms of average delivery ratio, average hops, average packet replication and average control message overhead.

The VoIDP algorithm is the first optimal algorithm for efficiently selecting the subset of observations in chain graphical models. The original VoIDP algorithm has a mistake in the process of recovering the optimal selections, and fails to produce correct outputs. In this paper, we present an improved version of the algorithm; which fixes the mistakes and verifies the solutions in experiments. Further more, we discuss some recent works in the area of subset selection problems, and present a simplified solution for computing the maximum expected total reward for a sub chain under certain circumstances.

Constructing sensor barriers to detect intruders crossing restricted regions, such as country borders, is one of the major application categories for wireless sensor networks. In this paper, we present a distributed algorithm to find the maximum number of disjoint sensor barriers in wireless sensor networks. Our solution works for any sensor deployment, for any size and shape of a covered region, and even for heterogeneous sensor nodes. In particular, our algorithm is distributed and works perfectly in an asynchronous communication environment. It utilizes the property of wireless channel and has lower complexity compared with other algorithms. For a deployment of n sensors, our algorithm spends O(n2) messages and O(n2) time.

It is one of the major application categories for wireless sensor networks to detect intruders entering protected areas. Early research has studied the problem of barrier coverage for intruder detections. However, it is an open problem to set up the sensor barriers with the minimum number of sensor nodes in wireless sensor networks. It is a critically important problem, and its solution can be widely utilized in the sensor barrier deployment. In this paper, we present a complete solution to this minimum-node barrier coverage problem. Our algorithm works in an asynchronous and distributed fashion for protected areas of any size and any shape. In our algorithm, each node is only required to communicate to its neighbors and is not necessary to be homogeneous or to know its location. For a deployment of n sensors, the new approach has O(n2log(n)) message and time complexities. Our algorithm is the first and the best to provide a distributed solution to the minimum-node barrier coverage problem in asynchronous wireless sensor networks.

Since sensors do not have a sophisticated hardware architecture or an operating system to manage code for safety, attacks injecting code to exploit memory-related vulnerabilities can present threats to sensor applications. In a sensor's simple memory architecture, injected code can alter the control flow of a sensor application to either misuse existing routines or download other malicious code to achieve attacks. To protect the control flow, this paper proposes a self-healing scheme that can detect attacks attempting to alter the control flow and then recover sensor applications to normal operations with minimum overhead. The self-healing scheme embeds diversified protection code at particular locations to enforce access control in program memory. Both the access control code and the recovery code are designed to be resilient to control flow attacks that attempt to evade the protection. Furthermore, the self-healing scheme directly processes application code at the machine instruction level, instead of performing control or data analysis on source code. The implementation and evaluation show that the self-healing scheme is lightweight in protecting sensor applications.

The world we live in is becoming smarter. Miniature sensors and actuators integrated with processors are being embedded in our environments to monitor and control the physical processes. These sensor and actuator systems are evolving towards a physically heterogeneous and distributed but functionally coherent environment consisting of both wired and wireless communication components. Battery-powered networked devices are increasingly more common. Mobile ad hoc and wireless sensor and actuator networks are receiving considerable research attention. Applications of intelligent networked sensor and actuator systems are tremendous and have the potential to dwarf the 20th century computing revolution. They include intelligent traffic control and safety, advanced automotive systems, process control, energy conservation, environmental control, advanced medical and health care devices and systems, critical infrastructure control, communications systems, distributed robotics, and security surveillance and protection. For example, transportation systems could benefit considerably from better embedded intelligence in automobiles and in transportation infrastructures, which could improve safety and efficiency. Embedded sensor and actuator systems are not new. Yet most existing systems are special purpose, closed systems with tightly coupled components. Networking and managing a large number of embedded sensors and actuators poses significant technical challenges. For example, in a network environment, the behaviors of sensor and actuator systems are much more unpredictable, and it is impossible to test software under all possible conditions. The technical challenges are not only efficiency, but also robustness, scalability, and predictability. These unique characteristics in the design of distributed systems of sensors and actuators have motivated a growing interest and a considerable amount of research activities in this emerging area. This special issue includes a collection of 13 outstanding research papers which cover diverse topics on the design, analysis, experimental evaluation, and applications of distributed systems of sensors and actuators, particularly the ones connected through wireless networks. This special issue begins with three survey papers on wireless sensor networks (WSN): applications in intelligent transportation systems, radio power management, and mobility-aware MAC protocols. In “Wireless sensor networks in intelligent transportation systems,” Tubaishat et al. reviewed existing sensor network technologies and applications for intelligent transportation systems and proposed new methods for using WSN in traffic modeling and estimation and traffic control that outperform existing methods. In “Mobility handling in MAC for wireless ad hoc networks,” Raja and Su reviewed the current state of the arts in handling mobility in sensor networks, presented simulation results to analyze existing mobility-aware MAC protocols, and discussed open issues in dealing with mobility in sensor networks. In “Towards unified radio power management for wireless sensor networks,” Xing et al. reviewed existing transmission power control and sleep scheduling approaches to reducing radio power consumption in the transmission state and the idle state and proposed a Unified Radio Power Management framework for the design and implementation of holistic radio power management solutions in WSN. The next three papers address architecture, middleware, and data storage issues in WSN. In “Lifetime-maximized cluster association in two-tiered wireless sensor networks,” Song et al. studied the two-tiered WSN architecture and proposed an optimal cluster association algorithm to maximize the overall network lifetime. In “Embedding standard distributed object-oriented middlewares in wireless sensor networks,” Moya et al. analyzed the design space available to middleware developers in the context of WSN and presented a development framework based on standard distributed objects and a set of specific services designed to support highly dynamic and scalable WSN applications. In “Improving data centric storage with diffuse caching in wireless sensor networks,” Ma et al. studied data-centric storage problems in a sensor network and proposed new diffuse caching techniques to alleviate the hot-spot problem and reduce the message overheads. The following three papers present new methods for data aggregation and image delivery in sensor networks. In “Isolines: Efficient spatio-temporal data aggregation in sensor networks,” Solis and Obraczka introduced an energy-efficient data collection technique that takes advantage of spatial and temporal data correlation for monitoring continuously changing conditions. In “Reward oriented packet filtering algorithm for wireless sensor networks,” Shu et al. studied packet filtering for reducing energy consumption in sensor networks and proposed a two-tier buffer model and two packet filtering algorithms for real time and non-real time transmissions, respectively. In “An adaptive approach for image encryption and secure transmission over multirate wireless sensor networks,” Wang et al. proposed a new adaptive secure image delivery approach to achieve effective and efficient image encryption based on an optimal transmission rate in terms of energy efficiency in WSN. The final four papers propose new methods for routing and topology control in wireless networks. In “Energy-efficient geographic multicast routing for error-prone wireless sensor networks,” Sanchez and Ruiz proposed a new protocol, Localized Energy-Efficient Multicast Algorithm, to address the error-prone nature of wireless links when designing routing protocols for WSN. In “Receiver-oriented load-balancing and reliable routing in wireless sensor networks,” Chen et al. proposed a novel receiver-oriented load-balancing and reliable routing protocol that achieves relatively longer network lifetime and higher reliability than other existing schemes. In “QuESt: A QoS-based energy efficient sensor routing protocol,” Saxena et al. used multi-objective genetic algorithm to design a QoS-based energy-efficient sensor routing protocol that determines application-specific, near-optimal sensory-routes demand by optimizing multiple QoS parameters and energy consumption. In “A novel distributed MCDS approximation algorithm for wireless sensor networks,” Xie et al. proposed a novel distributed approximation algorithm to construct the minimal connected dominating set for WSN. This algorithm generates smaller connected dominating sets while requiring less communication overhead compared with the existing distributed approximation algorithms. We thank all the authors who submitted their papers to this special issue. Owing to the limitation of space, we can only include limited number of papers in the special issue. We are grateful to all the reviewers for their time and efforts in carefully reviewing all the papers and providing valuable review comments. We also thank the Editor-in-Chief, Mohsen Guizani, for his continuous support for this special issue, and all the publication staff for their support during the publishing process.

Real-time traffic light controllers (TLCs) play a major role in optimizing traffic flow. We uses wireless sensor network (WSN) to decrease vehicles' average trip waiting time (ATWT) on the road. We studied the performance of using one sensor and two sensors and designed corresponding controllers. In the case of one sensor we developed two models; a non-occupancy detection (NOD) and an occupancy detection (OD). NOD detects passing vehicles only whereas, OD detects vehicles that pass the sensor or stop at it. In both methods, we changed the sensor location relative to the traffic light's location. We then used two sensors to calculate number of vehicles waiting or approaching a traffic light. We tested different distances between these two sensors. Two sensors-based controller outperform the one sensor controller and produced results comparable to the ideal control that knows exact number of waiting vehicles. In analyzing our simulation, we found that the distance between the two sensors does not affect the performance of our design. Hence, placing both the sensors close to each others produce the best performance in terms of quality of the data and reduce energy consumption which leads to extending the life time of the WSN.

Measuring traffic flow plays an important role in intelligent transportation systems. In recent years, the technology of sensor network has been brought into the field due to their reliability and non-intrusiveness. In this paper, we propose a framework to reduce the installation and maintenance cost of traffic measuring sensor networks. The key to the solutions lies on predicting the complete measurements using the readings at a limited number of observing locations. We describe two correlation-based prediction methods and show that the Gaussian method is more informative and achieves better accuracy. We propose an analytical approach that eases the procedure of acquiring the Gaussian parameters. We demonstrate through experimental results that the model is correct and achieves prediction very close to the model learned over a large set of training data.

This paper proposes a new anchor placement algorithm for improving localization performance. Motivated from the result obtained on the anchor placement error analysis (Shi et al., 2005), an near-exhaustive search is done on anchor placement for localization variance. Based on the best anchor area found in the search, we design a selective mobile anchor placement scheme (SAP). Simulation result shows that the SAP anchor placement scheme is effective in a sensor network with a small anchor ratio. Localization simulation result shows both the position variance and accuracy are improved by more than 10% of the radio range.

In this paper, we propose a novel decentralized traffic light control using wireless sensor network. The system architecture is classified into three layers; the wireless sensor network, the localized traffic flow model policy, and the higher level coordination of the traffic lights agents. The wireless sensors are deployed on the lanes going in and out the intersection. These sensors detect vehicles' number, speed, etc. and send their data to the nearest Intersection Control Agent (ICA) which, determines the flow model of the inter- section depending on sensors' data (e.g., number of vehicles approaching a specific intersection). Coping with dynamic changes in the traffic volume is one of the biggest challenges in intelligent transportation system (ITS). Our main contribution is the real-time adaptive control of the traffic lights. Our aim is to maximize the flow of vehicles and reduce the waiting time while maintaining fairness among the other traffic lights. Each traffic light controlled intersection has an intersection control agent that collects information from the sensor nodes. An intersection control agent manages its intersection by controlling its traffic lights. Multiple intersection agents can exchange information among themselves to control a wider area.

Out of its importance to various applications and services, the geographical location of the sensed event is to be associated with the event itself being reported. Despite the numerous number of localization algorithms proposed, very few of them are really ad-hoc methods that are appropriate for sensor networks. In this paper, our contribution is double-folded. First, we design an experimental framework to evaluate localization methods for sensor networks. We use this framework to evaluate three localization methods: ad-hoc positioning system (APS), multi-dimensional scaling (MDS), and semi-definite programming (SDP). Using this evaluation, we identify five network properties that affect the localization accuracy. Second, we propose an adaptive localization method that we refer to as: network-aware positioning (NAP). NAP starts by assuming known network properties. Given these properties, NAP determines the best localization algorithm to use. Simulation results show that NAP performs the best among the three algorithms under all network conditions.

Summary form only given. Knowing the positions of nodes in sensor networks are essential for many applications. We propose a new method, MDS-MAP(R), that determines the relative positions of nodes based on local distance information. Given a communication path between a starting node and a remote node, the nodes on the path first compute their local relative maps based on local distance estimates in a distributed fashion, using the MDS-MAP method. Then, the relative maps of adjacent nodes along the path are aligned based on their common nodes and their optimal linear transformation parameters are computed. Finally, the relative position of the remote node in the coordinate system of the starting node is determined by applying the sequence of linear transformations. The method can easily be extended from the single-path peer-to-peer communication situation to other types of communications such as multicasting or broadcasting. The accuracy of relative position estimation is affected by several factors, including the network connectivity, the errors of local distance measures, the length of the path, and the number of common nodes of two adjacent relative maps. Through simulation, we analyze the effect of these factors on the position estimation errors. We show that the new method performs well on both regular and irregular networks when there are enough connectivity and the distance measurement errors are small.

In dense sensor networks, density control is an important technique for prolonging network lifetime while providing sufficient sensing coverage. We present a new density control algorithm called NODC (non-overlapping density control). The goal of NODC is maximizing coverage while avoiding the overlap of sensing areas of active sensors. We derive the optimality conditions and develop a distributed algorithm. Experimental results show that NODC can use up to 20% fewer active nodes than OGDC with less than 5% reduction in coverage.

In ad-hoc wireless sensor networks, one approach for estimating the positions of sensor nodes is to use connectivity information of the network or local distance measurement. We compare two representative methods, multilateration and MDS-MAP, and study some of their key issues that affect the performance. First, we study the various optimization techniques and the effect of anchor selection for multilateration and the effects of the sizes of local maps and the refinement techniques for MDS-MAP. Secondly, we investigate the error property of localization by deriving the Cramer-Rao bound for different topologies and compare the bounds with the results of the two methods on different anchor placements. Previous results suggest that anchors should be placed on the perimeter of the network to get more accurate solutions. Our results show that MDS-MAP tolerates "ill placement" of anchors much better than multilateration. Finally, we compare the performance of the two methods through extensive simulation and identify the situations when one method is better than the other.

Parallel machine translation (PMT) is a new machine translation paradigm that takes advantage of high-speed and large-memory computers and existing machine translation systems with different characteristics to solve the difficult machine translation problem. PMT is based on technologies of parallel computing, machine translation, and artificial intelligence. A PMT system consists of many machine translation procedures running in parallel, coordinated by a controller to dissolve various ambiguities in machine translation. We have designed and implemented a PMT system based on the above approach at a coarse parallel level. The system consists of four independent machine translation subsystems. Each subsystem is implemented using an existing machine translation technique and has its own characteristics. We present the principles and practice of PMT. We also present some results of experiments with our experimental PMT system and point out some future research on PMT.

No abstract available.

In this paper, we present a new machine translation (MT) approach using MT engines and sentence partitioning. A multiple engine MT system consists of several MT engines running in parallel, coordinated by a controller. Each engine is implemented using an existing MT technique and has its own characteristics. When translating a sentence, each engine translates it independently. If more than one engine translates the sentence successfully, the controller chooses the best translation according to a combining algorithm implemented using translation statistics. If no engine succeeds in translating the sentence, the, controller partitions the sentence, coordinates the engines to translates its constituent simple sentences, and combines the partial translation results into a translation result for the whole input sentence. A complex sentence is partitioned based on conjunctives and punctuation marks such as comma: and semicolon. We have developed a multiple engine MT system based on the above approach. The system consists of four independent MT engines. The experiments show that the proposed approach is effective for implementing practical MT systems.

Automatic Chinese text checking and error correction is an important and difficult problem. Compared with automatic checking and error correction of Western text automatic checking and error correction of Chinese text faces more challenges. The Chinese language has many characters and no delimiters separating words. It is impossible to detect. and correct errors by penetrating into the inner composition of a character. In this paper, we describe some special features of Chinese characters and text and some statistical information obtained from a real world Chinese text corpus, and we present a hybrid approach that combines a rule-based method and a probability-based method to automatic checking and error correction of Chinese text. We also present an experimental system, HSACCCT (Hybrid System of Automatic Checking and Correction for Chinese Text), that implements this hybrid approach and some experimental results on real world Chinese text.

As the traditional relational databases and the new XML document repositories are being widely used on the Web as information storage, there is a great need for easy access to the information sources, particularly through natural language interactions. In this paper, we present the design and implementation of an intelligent system that interacts with users using a natural language, English, and retrieves information from sources, for the users. The system consists of four major parts: part of speech tagging, query knowledge base, query formation, and answer synthesis. In implementation, the system first uses QTAG, a Hidden Markov Model based speech part tagger, to tag each word in the input sentence. Then, important words in the main phrase axe identified. A thesaurus is applied to reduce the important words to basic keywords, which are used to query the database. The query is formed based on the query knowledge stored in the query knowledge base. Finally, the query result is synthesized into an English sentence, which is presented to the user as the answer. With an efficient part of speech tagger, intelligent subsystems for query formation and synthesis of query result, and user-friendly interface, the intelligent system can answer questions effectively.

Active learning is an effective learning approach. In this article we present an intelligent agent-assisted environment for active learning to better support the student-centered, self-paced, and highly interactive learning approach. The environment uses the student’s learning-related profile such as learning style and background knowledge in selecting, organizing, and presenting learning material, and it adopts a new approach to course content organization and delivery based on smart instructional components that can be integrated into a wide range of courses. The environment is being implemented using the prevalent Internet, Web, digital library, and multiagent technologies.

Active learning is an effective learning approach. In this article we present an intelligent agent-assisted environment for active learning to better support the student-centered, selfpaced, and highly interactive learning approach. The environment uses the students learningrelated profile such as learning style and background knowledge in selecting, organizing, and presenting learning material, and it adopts a new approach to course content organization and delivery based on smart instructional components that can be integrated into a wide range of courses. The environment is being implemented using the prevalent Internet, Web, digital library, and multiagent technologies.

In recent years, agent technology has been used increasingly in information management and distributed computing. A CSE curriculum that cultivates the knowledge of agent technology will increase the likelihood that the next generation of IT professionals have the background needed to design and develop software systems that are scalable, reliable, adaptable, and secure. In this paper, we present the rationale and our practice in incorporating agent technology into the CSE curriculum. We develop agent-based teaching materials and software modules and apply them to existing CSE courses including artificial intelligence, parallel and distributed processing, networking, and software engineering. Promising results have been obtained in teaching two graduate level courses using agent components.

In this paper, we present a multi-agent system for supporting student-centered, self-paced, and highly interactive learning in undergraduate computer science education. The system is based on a hybrid problem-based and case-based learning model, for both creative problem solving and mechanical experience simulation. It aims at enhancing the effectiveness of the undergraduate learning experience in computer science. Implemented using the prevalent Internet, Web, and digital library technologies, the system adopts an open architecture design and targets at large-scale, distributed operations. In the initial implementation of the system, a number of prototypes using different Java-based software environments have been developed. They offer tradeoffs in system performance and design complexity.

A large variety of information processing applications deal with natural language texts. Many of these applications require extracting and processing the meanings of natural language texts, in addition to processing their syntactic forms. In order to extract meanings from texts and manipulate them, a natural language processing system must have a significant amount of knowledge about the world and the domain of discourse. However, different kinds of knowledge are required for different languages, for different functions, and for different media. We design a new general ontology based natural language processing system referred to as the multi-lingual multi-function multimedia intelligent system (MMM-IS). MMM-IS is a complex system with multiple functions that can deal with multiple languages and multiple media. The general ontology consists of a static layer and a dynamic layer, and provides a real world knowledge model for the natural language processing system. The paper presents the concept of MMM-IS and how to build MMM-IS for real applications.

In the standard segmentation-based approach to handwritten word recognition, individual character-class confidence scores are combined via averaging to estimate confidences in the hypothesized identities for a word. We describe a methodology for generating optimal linear combination of order statistics operators for combining character class confidence scores. Experimental results are provided on over 1000 word images.

This paper proposes a general model for bidirectional associative memories that associate patterns between the X-space and the Y-space. The general model does not require the usual assumption that the interconnection weight from a neuron in the X-space to a neuron in the Y-space is the same as the one from the Y-space to the X-space. We start by defining a supporting function to measure how well a state supports another state in a general bidirectional associative memory (GBAM). We then use the supporting function to formulate the associative recalling process as a dynamic system, explore its stability and asymptotic stability conditions, and develop an algorithm for learning the asymptotic stability conditions using the Rosenblatt perceptron rule. The effectiveness of the proposed model for recognition of noisy patterns and the performance of the model in terms of storage capacity, attraction, and spurious memories are demonstrated by some outstanding experimental results.

A new parallel binary image shrinking algorithm that can be considered as an improvement of Levialdi's (1972) parallel shrinking algorithm and can be used in many image labeling algorithms as a basic operation in order to reduce the storage requirements for local memory and speed up the labeling process is presented. This new parallel shrinking algorithm shrinks an nxn binary image to an image with no black pixels in O(n) parallel steps with a multiplicative constant of 1.5, preserving the 8-connectivity in the shrinking process.

Convolutions and correlations are fundamental operations in computer vision and image processing. The image-template product expresses convolutions and correlations. In this paper, the authors present an efficient algorithm for the image-template product on SIMD mesh connected computers. The image-template product is computed along disjoint convolution paths of the template. For an M /spl times/ N image and a template of T elements, the simple algorithm for the image-template product on an M /spl times/ N mesh connected computer takes O(T) time and requires O(1) local memory per processing element. Compared with the previously proposed algorithms, the algorithm works for both invariant and variant templates without any restrictions on the template shapes. Furthermore, it does not broadcast template weights and does not have partial result movement.>

A new parallel algorithm for image component labeling with local operators on SIMD mesh connected computers is presented, providing a positive answer to the open question posed by several researchers whether there exists an O(n)-time and O(log n)-space local labeling algorithm on SIMD mesh connected computers. The algorithm uses a pipeline mechanism with stack-like data structures to achieve the lower bound of O(n) in time complexity and O(log n) in space complexity. Furthermore, the algorithm has very small multiplicative constants in its complexities.