A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.

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.

Clustering technology is very important in ad hoc networks and sensor networks from the view point of reducing the traffic load and energy consumption. In this paper, we propose a new structure formation mechanism as a tool for clustering. It meets the key clustering requirements including the use of an autonomous decentralized algorithm and a consideration of the situation of individual nodes. The proposed mechanism follows the framework of autonomous decentralized control based on local interaction, in which the behavior of the whole system is indirectly controlled by appropriately designing the autonomous actions of the subsystems. As an application example, we demonstrate autonomous decentralized clustering for a two-dimensional lattice network model, and the characteristics and adaptability of the proposed method are shown. In particular, the clusters produced can reflect the environmental situation of each node given by the initial condition.

This paper presents a novel method for improving the readability of automatic speech recognition (ASR) results for classroom lectures. Because speech in a classroom is spontaneous and contains many ill-formed utterances with various disfluencies, the ASR result should be edited to improve the readability before presenting it to users, by applying some operations such as removing disfluencies, determining sentence boundaries, inserting punctuation marks and repairing dropped words. Owing to the presence of many kinds of domain-dependent words and casual styles, even state-of-the-art recognizers can only achieve a 30-50% word error rate for speech in classroom lectures. Therefore, a method for improving the readability of ASR results is needed to make it robust to recognition errors. We can use multiple hypotheses instead of the single-best hypothesis as a method to achieve a robust response to recognition errors. However, if the multiple hypotheses are represented by a lattice (or a confusion network), it is difficult to utilize sentence-level knowledge, such as chunking and dependency parsing, which are imperative for determining the discourse structure and therefore imperative for improving readability. In this paper, we propose a novel algorithm that infers clean, readable transcripts from spontaneous multiple hypotheses represented by a confusion network while integrating sentence-level knowledge. Automatic and manual evaluations showed that using multiple hypotheses and sentence-level knowledge is effective to improve the readability of ASR results, while preserving the understandability.

This paper investigates a new method for fusion of scores generated by multiple classification sub-systems that help to further reduce the classification error rate in Spoken Language Recognition (SLR). In recent studies, a variety of effective classification algorithms have been developed for SLR. Hence, it has been a common practice in the National Institute of Standards and Technology (NIST) Language Recognition Evaluations (LREs) to fuse the results from several classification sub-systems to boost the performance of the SLR systems. In this work, we introduce a discriminative performance measure to optimize the performance of the fusion of 7 language classifiers developed as IIR's submission to the 2009 NIST LRE. We present an Error Corrective Fusion (ECF) method in which we iteratively learn the fusion weights to minimize error rate of the fusion system. Experiments conducted on the 2009 NIST LRE corpus demonstrate a significant improvement compared to individual sub-systems. Comparison study is also conducted to show the effectiveness of the ECF method.

Cognitive radio is a wireless technology aimed at improving the efficient use of the radio-electric spectrum, thus facilitating a reduction in the load on the free frequency bands. Cognitive radio networks can scan the spectrum and adapt their parameters to operate in the unoccupied bands. To avoid interfering with licensed users operating on a given channel, the networks need to be highly sensitive, which is achieved by using cooperative sensing methods. Current cooperative sensing methods are not robust enough against occasional or continuous attacks. This article outlines a Group Fusion method that takes into account the behaviour of users over the short and long term. On fusing the data, the method is based on giving more weight to user groups that are more unanimous in their decisions. Simulations of a dynamic environment with interference are performed. Results prove that when attackers are present (both reiterative or sporadic), the proposed Group Fusion method has superior sensing capability than other methods.

This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.

One typical phonotactic system for language recognition is parallel phone recognition followed by vector space modeling (PPRVSM). In this system, various phone recognizers are applied in parallel and fused at the score level. Each phone recognizer is trained for a known language, which is assumed to extract complementary information for effective fusion. But this method is limited by the large amount of training samples for which word or phone level transcription is required. Also, score fusion is not the optimal method as fusion at the feature or model level will retain more information than at the score level. This paper presents a new strategy to build and fuse parallel phone recognizers (PPR). This is achieved by training multiple acoustic diversified phone recognizers and fusing at the feature level. The phone recognizers are trained on the same speech data but using different acoustic features and model training techniques. For the acoustic features, Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) are both employed. In addition, a new time-frequency cepstrum (TFC) feature is proposed to extract complementary acoustic information. For the model training, we examine the use of the maximum likelihood and feature minimum phone error methods to train complementary acoustic models. In this study, we fuse phonotactic features of the acoustic diversified phone recognizers using a simple linear fusion method to build the PPRVSM system. A novel logistic regression optimized weighting (LROW) approach is introduced for fusion factor optimization. The experimental results show that fusion at the feature level is more effective than at the score level. And the proposed system is competitive with the traditional PPRVSM. Finally, the two systems are combined for further improvement. The best performing system reported in this paper achieves an equal error rate (EER) of 1.24%, 4.98% and 14.96% on the NIST 2007 LRE 30-second, 10-second and 3-second evaluation databases, respectively, for the closed-set test condition.

Spectrum sensing is a fundamental function for cognitive radio network to protect transmission of primary system. Cooperative spectrum sensing, which can help increasing sensing performance, is regarded as one of the most promising methods in realizing a reliable cognitive network. In such cooperation system, however the communication resources such as sensing time delay, control channel bandwidth and consumption energy for reporting the cognitive radio node's sensing results to the fusion center may become extremely huge when the number of cognitive users is large. In this paper, we propose an ordered sequential cooperative spectrum sensing scheme in which the local sensing data will be sent according to its reliability order to the fusion center. In proposed scheme, the sequential fusion process is sequentially conducted based on Dempster Shafer theory of evidence's combination of the reported sensing results. Above all, the proposed scheme is highly feasible due to the proposed two ordered sequential reporting methods. From simulation results, it is shown that the proposed technique not only keeps the same sensing performance of non-sequential fusion scheme but also extremely reduces the reporting resource requirements.

This letter proposes a novel censor-based scheme for cooperative spectrum sensing on Cognitive Radio Sensor Networks. A Takagi-Sugeno's fuzzy system is proposed to make the decision on the presence of the licensed user's signal based on the observed energy at each cognitive sensor node. The local spectrum sensing results are aggregated to make the final sensing decision at the fusion center after being censored to reduce transmission energy and reporting time. Simulation results show that significant improvement of the spectrum sensing accuracy, and saving energy as well as reporting time are achieved by our scheme.

Processing structures required in sensing are designed to convert real-world information into useful information, and there are various restrictions and performance goals depending on physical restrictions and the target applications. On the other hand, network technologies are mainly designed for data exchange in the information world, as is seen in packet communications, and do not go well with sensing structures from the viewpoints of real-time properties, spatial continuity, etc. This indicates the need for understanding the architectures and restrictions of sensor technologies and network technologies when aiming to fuse these technologies. This paper clarifies the differences between these processing structures, proposes some issues to be addressed in order to achieve real fusion of them, and presents future directions toward real fusion of sensor technologies and network technologies.

Design of high gain and high efficiency antennas is one of the key challenges in antenna engineering and especially in millimeter wave communication systems. Various types of planar waveguide arrays with series-fed traveling wave operation have been developed in Tokyo Tech with the special focus upon efficiency enhancement as well as reduction of fabrication cost. In this review, four kinds of single layer waveguide arrays characterized with the series fed travelling wave operation are surveyed first. To cope with the bandwidth narrowing effects due to long line effects associated with the series fed operation, authors have introduced partially corporate feed embedded in the single layer waveguide. They further extended the study to cover fully corporate feed arrays with multiple layer waveguide as well; a new fabrication technique of diffusion bonding of laminated thin plates has the potential to realize the low cost mass production of multi-layer structures for the millimeter wave application. Secondly, the novel methods for loss evaluation of copper plate substrate are established for the design of post-wall waveguide arrays where dielectric loss and conductor loss is determined in wide range of millimeter wave band, by using the Whispering gallery mode resonator. This enables us to design the planar arrays with the loss taken into account. Finally, the planar arrays are now applied to two kinds of systems in the Tokyo Tech millimeter wave project; the indoor short range file-transfer systems and the outdoor communication systems for the medium range backhaul links. The latter has been field-tested in the model network built in Tokyo Tech Ookayama campus. Early stage progress of the project including unique propagation data is also reported.

Introducing diffusion bonding of laminated thin metal plates to the fabrication of slotted waveguide arrays enlightens the high potential and the feasibility of multi-layer antennas with high-performance. It is a promising process with low cost even for a double-layer antenna, because the number of etching patterns for thin metal plates is only five. In this paper, a double-layer antenna for broadband characteristics is designed in 39 GHz band as demonstration. A 20 20-element antenna is composed of 2 2 sub-arrays by installing a partially-corporate feed circuit in the bottom layer underneath radiating waveguides in the top layer. The five-element sub-arrays in both the feeding and radiating parts are designed first. A new structure for the last slot coupler with shortened termination is also proposed to avoid an extra slot-free region when assembling the neighbor sub-arrays. As the simulation results by HFSS, the maximum gain of 34.55 dBi with the antenna efficiency of 85.5% is estimated at 38.5 GHz. The test antenna is fabricated by the diffusion bonding of thin copper plates. As the measurement results, a very high aperture efficiency of 83.2% with the directivity of 34.5 dBi is realized at the center frequency of 38.75 GHz, where the antenna gain of 34.4 dBi with the high antenna efficiency of 81.4% is achieved. The bandwidth of 5.0% defined as 1 dB down from the maximum gain is achieved.

The error diffusion filter in this paper is optimized with respect to the ideal blue noise pattern corresponding to a single tone level. The filter coefficients are optimized by the minimization of the squared error norm between the Fourier power spectra of the resulting halftone and the blue noise pattern. During the process of optimization, the binary pattern power spectrum matching algorithm is applied with the aid of a new blue noise model. The number of the optimum filters is equal to that of different tones. The visual fidelity of the bilevel halftones generated by the error diffusion filters is evaluated in terms of a weighted signal-to-noise ratio, Fourier power spectra, and others. Experimental results have demonstrated that the proposed filter set generates satisfactory bilevel halftones of grayscale images.

Cooperation is an attractive approach to improving the spectrum sensing performance of cognitive systems experiencing deep shadowing and fading. In this letter, an efficient weight-based cooperative spectrum sensing scheme is proposed. Simulation results show that the proposed scheme has better accuracy than "AND," "OR," and "half-voting" combination schemes and has similar spectrum sensing accuracy but with lower computational and communication complexity in comparison to the "optimal data fusion" rule.

In this paper, we propose a file replication method to achieve load balancing in terms of write access to storage device ("write storage access load balancing" for short) in unstructured peer-to-peer (P2P) file-sharing networks in which the popularity trend of queried files varies dynamically. The proposed method uses a write storage access ratio as a load balance index value in order to stabilize dynamic P2P file-sharing environments adaptively. In the proposed method, each peer autonomously controls the file replication ratio, which is defined as a probability to create the replica of the file in order to uniform write storage access loads in the similar way to thermal diffusion phenomena. Theoretical analysis results show that the behavior of the proposed method actually has an analogy to a thermal diffusion equation. In addition, simulation results reveal that the proposed method has an ability to realize write storage access load balancing in the dynamic P2P file-sharing environments.

This paper describes diffusion of electric vehicles and novel social infrastructure from the viewpoint of systems innovation theory considering both human society aspects and elemental technological aspects. Firstly, fundamentals of the systems innovation theory and the platform theory are mentioned. Secondly, discussion on mobility from the viewpoint of the human-society layer and discussion of electrical vehicles from the viewpoint of the elemental techniques are carried out. Thirdly, based on those, R & D, measures are argued such as establishment of the ubiquitous noncontact feeding and authentication payment system is important. Finally, it is also insisted that after the establishment of this system the super smart grid with temporal and spatial control including demand itself with the low social cost will be expected.

In this paper we propose a file replication scheme inspired by a thermal diffusion phenomenon for storage load balancing in unstructured peer-to-peer (P2P) file sharing networks. The proposed scheme is designed such that the storage utilization ratios of peers will be uniform, in the same way that the temperature in a field becomes uniform in a thermal diffusion phenomenon. The proposed scheme creates replicas of files in peers probabilistically, where the probability is controlled by using parameters that can be used to find the trade-off between storage load balancing and search performance in unstructured P2P file sharing networks. First, we show through theoretical analysis that the statistical behavior of the storage load balancing controlled by the proposed scheme has an analogy with the thermal diffusion phenomenon. We then show through simulation that the proposed scheme not only has superior performance with respect to balancing the storage load among peers (the primary objective of the present proposal) but also allows the performance trade-off to be widely found. Finally, we qualitatively discuss a guideline for setting the parameter values in order to widely find the performance trade-off from the simulation results.

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.