This paper presents a new incremental approach for robot navigation using associative memory. We defined the association as node→action→node where node is the robot position and action is the action of a robot (i.e., orientation, direction). These associations are used for path planning by retrieving a sequence of path fragments (in form of (node→action→node) → (node→action→node) →…) starting from the start point to the goal. To learn such associations, we applied the associative memory using Self-Organizing Incremental Associative Memory (SOIAM). Our proposed method comprises three layers: input layer, memory layer and associative layer. The input layer is used for collecting input observations. The memory layer clusters the obtained observations into a set of topological nodes incrementally. In the associative layer, the associative memory is used as the topological map where nodes are associated with actions. The advantages of our method are that 1) it does not need prior knowledge, 2) it can process data in continuous space which is very important for real-world robot navigation and 3) it can learn in an incremental unsupervised manner. Experiments are done with a realistic robot simulator: Webots. We divided the experiments into 4 parts to show the ability of creating a map, incremental learning and symbol-based recognition. Results show that our method offers a 90% success rate for reaching the goal.

A wideband beamformer with mainlobe control is proposed. To make the beamformer robust against pointing errors, inequality rather than equality constraints are used to restrict the mainlobe response, thus more degrees of freedom are saved. The constraints involved are nonconvex, therefore are linearly approximated so that the beamformer can be obtained by iterating a second-order cone program. Moreover, the response variance element is introduced to achieve a frequency invariant beamwidth. The effectiveness of the technique is demonstrated by numerical examples.

Question and Answering (Q&A) sites are recently gaining popularity on the Web. People using such sites are like a community-anyone can ask, anyone can answer, and everyone can share, since all of the questions and answers are public and searchable immediately. This mechanism can reduce the time and effort to find the most relevant answer. Unfortunately, the users suffer from answer quality problem due to several reasons including limited knowledge about the question domain, bad intentions (e.g. spam, making fun of others), limited time to prepare good answers, etc. In order to identify the credible users to help people find relevant answer, in this paper, we propose a ranking algorithm, InfluenceRank, which is basis of analyzing relationship in terms of users' activities and their mutual trusts. Our experimental studies show that the proposed algorithm significantly outperforms the baseline algorithms.

We investigate a sparsely encoded Hopfield model with unit replacement by using a statistical mechanical method called self-consistent signal-to-noise analysis. We theoretically obtain a relation between the storage capacity and the number of replacement units for each sparseness a. Moreover, we compare the unit replacement model with the forgetting model in terms of the network storage capacity. The results show that the unit replacement model has a finite value of the optimal sparseness on an open interval 0 (1/2 coding) < a < 1 (the limit of sparseness) to maximize the storage capacity for a large number of replacement units, although the forgetting model does not.

Ultra-wideband (UWB) technology has attracted much attention recently due to its high data rate and low emission power. Its media access control (MAC) protocol, WiMedia MAC, promises a lot of facilities for high-speed and high-quality wireless communication. However, these benefits in turn involve a large amount of computational load, which challenges the traditional uniprocessor architecture based implementation method to provide the required performance. However, the constrained cost and power budget, on the other hand, makes using commercial multiprocessor solutions unrealistic. In this paper, a low-cost and energy-efficient multiprocessor system-on-chip (MPSoC), which tackles at once the aspects of system design, software migration and hardware architecture, is presented for the implementation of UWB MAC layer. Experimental results show that the proposed MPSoC, based on four simple RISC processors and shared-memory infrastructure, achieves up to 45% performance improvement and 65% power saving, but takes 15% less area than the uniprocessor implementation.

A new training algorithm for the chaotic Adachi Neural Network (AdNN) is investigated. The classical training algorithm for the AdNN and it's variants is usually a “one-shot” learning, for example, the Outer Product Rule (OPR) is the most used. Although the OPR is effective for conventional neural networks, its effectiveness and adequateness for Chaotic Neural Networks (CNNs) have not been discussed formally. As a complementary and tentative work in this field, we modified the AdNN's weights by enforcing an unsupervised Hebbian rule. Experimental analysis shows that the new weighted AdNN yields even stronger dynamical associative memory and pattern recognition phenomena for different settings than the primitive AdNN.

This paper describes latest RF Automated Test Equipment (RF ATE) technologies that include device under test (DUT) connections, a calibration method, and an RF test module mainly focusing on low cost of test (COT). Most important respect for low COT is how achieve a number of simultaneous measurements and short test time as well as a plain calibration. We realized these respects by a newly proposed calibration method and a drastically downsized RF test module with multiple resources and high throughput. The calibration method is very convenient for RF ATE. Major contribution for downsizing of the RF test module is RF circuit technology in form of RF functional system in package (RF-SIPs), resulting in very attractive test solutions.

This letter proposes an efficient Location-Aware Social Routing (LASR) scheme for Delay Tolerant Networks (DTNs). LASR makes forwarding decisions based on a new metric which uses location information to reflect the node relations and community structure. Simulation results are presented to support the effectiveness of our scheme.

Classification based on predictive association rules (CPAR) is a widely used associative classification method. Despite its efficiency, the analysis results obtained by CPAR will be influenced by missing values in the data sets, and thus it is not always possible to correctly analyze the classification results. In this letter, we improve CPAR to deal with the problem of missing data. The effectiveness of the proposed method is demonstrated using various classification examples.

Clickstreams in users' navigation logs have various data which are related to users' web surfing. Those are visit counts, stay times, product types, etc. When we observe these data, we can divide clickstreams into sub-clickstreams so that the pages in a sub-clickstream share more contexts with each other than with the pages in other sub-clickstreams. In this paper, we propose a method which extracts more informative rules from clickstreams for web page recommendation based on genetic programming and association rules. First, we split clickstreams into sub-clickstreams by contexts for generating more informative rules. In order to split clickstreams in consideration of context, we extract six features from users' navigation logs. A set of split rules is generated by combining those features through genetic programming, and then informative rules for recommendation are extracted with the association rule mining algorithm. Through experiments, we verify that the proposed method is more effective than the other methods in various conditions.

In this letter, a novel association method called ELBA (efficient load balancing association) is proposed for improved load balancing in IEEE 802.15.4-based WSNs (wireless sensor networks). ELBA adds new nodes to the network in an efficient load-balancing manner by exploiting not only RSSI (received signal strength indicator), which is used in the standard, but also traffic-load, the number of allocated GTSs (guaranteed time slots), and the number of parent nodes and child nodes. Simulation results show that ELBA offers better performance in load balancing and preventing congestion.

Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

An H.264/AVC encoder LSI (named “SARA”) that supports High422 profile, as well as 422 profile of MPEG-2, has been developed for HDTV broadcasting infrastructures. It contains three motion estimation and compensation (ME/MC) engines with wide search ranges of -217.75 to +199.75 horizontally, -109.75 to +145.75 vertically, which can utilize almost all H.264/AVC ME/MC coding tools, such as multiple reference frame, variable block size, quarter-pel prediction, macroblock adaptive field/frame prediction (MBAFF), spatial/temporal direct mode, and weighted prediction. Our evaluations show that it can encode fast moving scenes with 1.2 dB to 1.7 dB higher than the JM. It was successfully fabricated in a 90-nm technology, and integrates 140 million transistors.

Hybrid wired/wireless on-chip network is a promising communication architecture for multi-/many-core SoC. For application-specific SoC design, it is important to design a dedicated on-chip network architecture according to the application-specific nature. In this paper, we propose a heuristic wireless link allocation algorithm for creating hybrid on-chip network architecture. The algorithm can eliminate the performance bottleneck by replacing multi-hop wired paths by high-bandwidth single-hop long-range wireless links. The simulation results show that the hybrid on-chip network designed by our algorithm improves the performance in terms of both communication delay and energy consumption significantly.

To measure the detrusor pressure for diagnosing lower urinary tract symptoms, we designed a small-area and low-power System on a Chip (SoC). The SoC should be small and low power because it is encapsulated in tiny air-tight capsules which are simultaneously inserted in the urinary bladder and rectum for several days. Since the SoC is also required to be programmable, we designed an Application Specific Instruction set Processor (ASIP) for pressure measurement and wireless communication, and implemented almost required functions on the ASIP. The SoC was fabricated using a 0.18 µm CMOS mixed-signal process and the chip size is 2.5 2.5 mm2. Evaluation results show that the power consumption of the SoC is 93.5 µW, and that it can operate the capsule for seven days with a tiny battery.

Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

Recently, social network services are rapidly growing and this trend is expected to continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When social network data are published, however, the privacy of some people may be disclosed. The most straightforward manner to preserve privacy in social network data is to remove the identifiers of persons from the social network data. However, an adversary can infer the identity of a person in the social network by using his/her background knowledge, which consists of content information such as the age, sex, or address of the person and structural information such as the number of persons having a relationship with the person. In this paper, we propose a privacy protection method for social network data. The proposed method anonymizes social network data to prevent privacy attacks that use both content and structural information, while minimizing the information loss or distortion of the anonymized social network data. Through extensive experiments, we verify the effectiveness and applicability of the proposed method.

The message passing interface (MPI) broadcast communication commonly causes a severe performance bottleneck in multicore system that uses distributed memory. Thus, in this paper, we propose a novel algorithm and hardware structure for the MPI broadcast communication to reduce the bottleneck situation. The transmission order is set based on the state of each processing node that comprises the multicore system, so the novel algorithm minimizes the performance degradation caused by conflict. The proposed scoreboard MPI unit is evaluated by modeling it with SystemC and implemented using VerilogHDL. The size of the proposed scoreboard MPI unit occupies less than 1.03% of the whole chip, and it yields a highly improved performance up to 75.48% as its maximum with 16 processing nodes. Hence, with respect to low-cost design and scalability, this scoreboard MPI unit is particularly useful towards increasing overall performance of the embedded MPSoC.

The social network model has been regarded as a promising mechanism to defend against Sybil attack. This model assumes that honest peers and Sybil peers are connected by only a small number of attack edges. Detection of the attack edges plays a key role in restraining the power of Sybil peers. In this paper, an attack-resisting, distributed algorithm, named Random walk and Social network model-based clustering (RSC), is proposed to detect the attack edges. In RSC, peers disseminate random walk packets to each other. For each edge, the number of times that the packets pass this edge reflects the betweenness of this edge. RSC observes that the betweennesses of attack edges are higher than those of the non-attack edges. In this way, the attack edges can be identified. To show the effectiveness of RSC, RSC is integrated into an existing social network model-based algorithm called SOHL. The results of simulations with real world social network datasets show that RSC remarkably improves the performance of SOHL.

An infinitely long monopole antenna driven by a coaxial cable is revisited. The associated Weber transform and the mode-matching method are used to obtain simple simultaneous equations for the modal coefficients. Computations are performed to illustrate the behavior of current distribution and antenna admittance in terms of antenna geometries.