We present a predictive closed-loop power control scheme for delay-prone burst transmission systems. The scheme has a sample-by-sample predictor compensating burst delay and a built-in channel encoder reducing power control command bit error.

The availability of IP networks has increased its importance due to the evolving use of real-time and mission-critical applications on IP networks. Methods for preparing alternate routing tables that can be used for fast restoration from link failures have been investigated. In such methods, each node has to compute a number of alternate routing tables in advance since they have to prepare for each potential failure. The resulting huge number of alternate routing tables has prevented these methods from being deployed. In this paper, we propose a method for reducing the number of alternate routing tables for link failure. It analyzes three types of shortest path trees on the basis of link-state information. We show that the number of alternate routing tables can be reduced to 1/100, on average, from that with the conventional method, and that they are small enough to be stored in the memory of IP routers.

This paper presents an adaptive maximum power point tracking (MPPT) algorithm. The aim is to dynamically adjust the step length for updating duty ratio (or operating voltage) so as to make full utilization of the output power of photovoltaic (PV) systems, even under the rapidly changing atmospheric conditions. To this end, the average slope in terms of voltage and power is exploited for reducing the harmful effect of noise and error (incurred in measurement or quantization) on the slope. Also, a statistical decision-making scheme is employed for reliably deciding the time instant at which atmospheric conditions actually change. Empirical study has adduced grounds for its dominance over existing references.

Continuous pen pressure can be used to operate multi-state widgets such as menus in pen based user interfaces. The number of levels into which the pen pressure space is divided determines the number of states in the multi-state widgets. To increase the optimal number of divisions of the pen pressure space and achieve greater pen pressure usability, we propose a new discretization method which divides the pen pressure space according to a personal pen pressure use profile. We present here four variations of the method: discretization according to personal/aggregation pen pressure use profile with/without visual feedback of uniform level widths and the traditional even discretization method. Two experiments were conducted respectively to investigate pen pressure use profile and to comparatively evaluate the performance of these methods. Results indicate that the subjects performed fastest and with the fewest errors when the pen pressure space was divided according to personal profile with visual feedback of uniform level widths (PU) and performed worst when the pen pressure space was divided evenly. With PU method, the optimal number of divisions of the pen pressure space was 8. Visual feedback of uniform level widths enhanced performance of uneven discretization. The findings of this study have implications for human-oriented pen pressure use in pen pressure based user interface designs.

System implementation for e-Government initiatives should be reliable. Unreliable system implementation could, on the one hand, be insufficient to fulfill basic system requirements, and more seriously on the other hand, break the trust of citizens on governments. The objective of this paper is to advocate the use of formal methods in general, the OTS/CafeOBJ method in particular in this paper, to help develop reliable system implementation for e-Government initiatives. An experiment with the OTS/CafeOBJ method on an e-Government messaging framework proposed for providing citizens with seamless public services is described to back up our advocation. Two previously not well-clarified problems of the framework and their potential harm realized in this experiment are reported, and possible ways of revisions to the framework are suggested as well. The revisions are proved to be sufficient for making the framework satisfy certain desired properties.

Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

ISO/IEC 18000-7 Active RFID standard, a single channel system operating at 433 MHz, faces technical difficulties in supporting some recently introduced application demands because of its low transmission rates and vulnerability to radio interference between the readers. We propose a new multi-channel active RFID system operating at the 2.4 GHz band. The special feature of the proposed system is that a reader makes use of multiple interfaces to improve its performance. However, if only a small part of the interfaces is actually used, the performance improvement would not meet expectations. To overcome this problem, a multi-channel multi-interface active RFID protocol that balances the loads among all available interfaces is necessary. Three protocols, "Aggregated," "LP-Combined" and "AP-Balanced" are proposed in this paper. We carry out simulations to compare them under various conditions by changing numbers of tags, numbers of interfaces and tag data size. The AP-Balanced shows the best and the most stable performance and its performance increases almost linearly in proportion to the number of interfaces, which meets our expectation.

We present a technology based on Nb/NbxSi1-x/Nb junctions, with barriers near the metal-insulator transition, for applications in superconducting electronics (SCE) as an alternative to Nb/AlOx/Nb tunnel junctions. Josephson junctions with co-sputtered amorphous Nb-Si barriers can be made with a wide variety of electrical properties: critical current density (Jc), capacitance (C), and normal resistance (Rn) can be reliably selected within wide ranges by choosing both the barrier thickness and Nb concentration. Nonhysteretic Nb/NbxSi1-x/Nb junctions with IcRn products greater than 1 mV, where Ic is the critical current, and Jc values near 100 kA/cm2 have been fabricated and are promising for superconductive digital electronics. These barriers have thicknesses of several nanometers; this improves fabrication reproducibility and junction uniformity, both of which are necessary for complex digital circuits. Recent improvements to our deposition system have allowed us to obtain better uniformity across the wafer.

In most cases in wireless networks, a user has a two-way communication that consists of two sessions: uplink and downlink sessions, and its overall satisfaction to the communication depends on the quality of service of both sessions. However, in most previous approaches in wireless resource allocation, the satisfactions of a user for its uplink and downlink sessions are modeled separately and treated independently, which fails to accurately model user's overall satisfaction to its communication. Hence, in this paper we model user's overall satisfaction to its communication considering both its uplink and downlink sessions. To this end, we propose a novel concept for a utility function to model user's overall satisfaction to its communication, which is called a user-level utility function, considering user's satisfaction to uplink and downlink sessions jointly. To show the appropriateness of our approach, we apply our user-level utility functions to scheduling problems in TDMA wireless networks and show the performance improvement of our approach over the traditional approach that does not treat uplink and downlink sessions of a user jointly.

Motivated by the recent research in crosslayer design of cooperative wireless network, we propose a distributed cooperative routing algorithm for a multihop multi-relay wireless network to achieve selection diversity. We propose two algorithms, rate optimal path selection and outage optimal path selection, to satisfy the different requirements of the systems. Both algorithms work on distributed processing without requiring any centralized controller. Simulations are conducted to evaluate the performance of the proposal. The results of the simulations show that the proposed routing algorithms significantly improve the end-to-end data rate and outage performance compared with noncooperative routing protocols.

This paper proposes a technique for detecting the occurrences of refactoring from source code revisions. In a real software development process, a refactoring operation may sometimes be performed together with other modifications at the same revision. This means that detecting refactorings from the differences between two versions stored in a software version archive is not usually an easy process. In order to detect these impure refactorings, we model the detection within a graph search. Our technique considers a version of a program as a state and a refactoring as a transition between two states. It then searches for the path that approaches from the initial to the final state. To improve the efficiency of the search, we use the source code differences between the current and the final state for choosing the candidates of refactoring to be applied next and estimating the heuristic distance to the final state. Through case studies, we show that our approach is feasible to detect combinations of refactorings.

In this paper, we propose a novel Web service composition framework which dynamically accommodates various failure recovery requirements. In the proposed framework called Adaptive Failure-handling Framework (AdaFF), failure-handling submodules are prepared during the design of a composite service, and some of them are systematically selected and automatically combined with the composite Web service at service instantiation in accordance with the requirement of individual users. In contrast, existing frameworks cannot adapt the failure-handling behaviors to user's requirements. AdaFF rapidly delivers a composite service supporting the requirement-matched failure handling without manual development, and contributes to a flexible composite Web service design in that service architects never care about failure handling or variable requirements of users. For proof of concept, we implement a prototype system of the AdaFF, which automatically generates a composite service instance with Web Services Business Process Execution Language (WS-BPEL) according to the users' requirement specified in XML format and executes the generated instance on the ActiveBPEL engine.

In this paper, we propose a novel target acoustic signal detection approach which is based on non-negative matrix factorization (NMF). Target basis vectors are trained from the target signal database through NMF, and input vectors are projected onto the subspace spanned by these target basis vectors. By analyzing the distribution of time-varying normalized projection error, the optimal threshold can be calculated to detect the target signal intervals during the entire input signal. Experimental results show that the proposed algorithm can detect the target signal successfully under various signal environments.

In this letter, a statistical uncorrelated near class discriminant (SUNCD) approach is proposed for face recognition. The optimal discriminant vector obtained by this approach can differentiate one class and its near classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices and using the Fisher criterion. In this manner, SUNCD acquires all discriminant vectors class by class. Furthermore, SUNCD makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding class and part of its most neighboring classes. Experiments on the public AR face database demonstrate that the proposed approach outperforms several representative discriminant methods.

Previous research shows that the IEEE 802.11 DCF channel contention mechanism is not capable of providing throughput fairness among nodes in different locations of the wireless mesh network. The node nearest the gateway will always strive for the chance to transmit data, causing fewer transmission opportunities for the nodes farther from the gateway, resulting in starvation. Prior studies modify the DCF mechanism to address the fairness problem. This paper focuses on the fairness study when TCP flows are carried over wireless mesh networks. By not modifying lower layer protocols, the current work identifies TCP parameters that impact throughput fairness and proposes adjusting those parameters to reduce frame collisions and improve throughput fairness. With the aid of mathematical formulation and ns2 simulations, this study finds that frame transmission from each node can be effectively controlled by properly controlling the delayed ACK timer and using a suitable advertised window. The proposed method reduces frame collisions and greatly improves TCP throughput fairness.

This paper presents a fault-tolerance scheme based on mobile agents for the reliable mobile computing systems. Mobility of the agent is suitable to trace the mobile hosts and the intelligence of the agent makes it efficient to support the fault tolerance services. This paper presents two approaches to implement the mobile agent based fault tolerant service and their performances are evaluated and compared with other fault-tolerant schemes.

We provide an efficient transmission scheme which embeds a pilot signal in the data signal for channel state information (CSI) based on the configuration of a multiple-input multiple-output (MIMO) system using space-time block coding (STBC) with an adaptive array (AA). A computer simulation and analysis show that the proposed scheme, which combines the advantage of an Alamouti-like STBC scheme and the pilot-based AA, can suppress the irreducible error due to random FM noise. The proposed scheme using a pilot minimizes the decoding delay, and is highly robust against fast fading. We show that the proposed scheme can significantly increase the data transmission rate by using the transmitter diversity based on STBC, and the accuracy of the proposed technique is exemplified by a computer simulation.

The rate-distortion optimization (RDO) method in the H.264/AVC encoder is an informative technology that improves the coding efficiency, but increases the computational complexity. In this letter, a fast Intra mode decision algorithm using DCT (Discrete Cosine Transform) coefficients distribution is proposed to reduce the H.264 encoder complexity. The proposed method reduces the encoder complexity on average 63.44%, while the coding efficiency is slightly decreased compared with the H.264/AVC encoder.

The bit error rate (BER) performance of multicode multi-carrier code division multiple access (MC-CDMA) severely degrades due to the inter-code interference (ICI) in a strong frequency-selective channel. Recently a spreading code group construction method was proposed for MC-CDMA. The Walsh-Hadmard (WH) codes are divided into a number of code groups such that the code orthogonality can be maintained within each group even in a strong frequency-selective channel; any code pair taken from different groups is not orthogonal. The number of spreading codes in each group is determined by the maximum time delay difference of the channel. In this paper, we point out that the number of codes in each group is determined by the distribution of time delay differences among the propagation paths of the channel, not the maximum time delay difference. Based on that observation, we show that more orthogonal spreading codes can exist in each code group. The conditional BER is derived taking into account the interference from other code groups and the achievable downlink BER performance using the proposed spreading code group construction is numerically evaluated in a frequency-selective Rayleigh fading channel.

This paper investigates the autonomous decision-making process of the selection of alternative countermeasures against threats in electronic warfare settings. We introduce a threat model, which represents a specific threat pattern, and a methodology that decides the best countermeasure against real-time threats using the decision theory. To determine the optimal countermeasure, we model the probabilities of the effects of countermeasures, if executed, and combine the probabilities with their utilities. This methodology based upon the inductive threat model calculates the expected utilities of countermeasures which are applicable given a situation, and provide an intelligent command and control agent with the best countermeasure to threats. We present empirical results that demonstrate the agent's capabilities of choosing countermeasures to threats in simulated electronic warfare settings.