Transmission performance of carrier superposed signals for frequency reuse are significantly degraded when transmitted through a satellite channel containing a nonlinear device. The extent to which the signals are degraded depends on the operating level (back off) of the transponder. This paper proposes a method to compensate for the effects of nonlinearity in the interference canceller by giving the same nonlinearity to a replica with the capability to automatically track the back off of the satellite transponder. Computer simulations show that the proposed technique significantly enhances system performance at all transponder operating levels even though it can be simply implemented in the canceller by digital signal processing circuits.

We demonstrate an all-optical picosecond pulse duration-tunable nonreturn-to-zero (NRZ)-to-return-to-zero (RZ) data format conversion using a Raman amplifier-based compressor and a fiber-based four-wave mixing (FWM) switch. A NRZ data signal is injected into the fiber-based FWM switch (AND gate) with a compressed RZ clock by the Raman amplifier-based compressor, and convert to RZ data signal by the fiber-based FWM switch. The compressed RZ clock train acts as a pump signal in the fiber-based FWM switch to perform the NRZ-to-RZ data format conversion. By changing the Raman pump power of the Raman amplifier-based compressor, it is possible to tune the pulse duration of the converted RZ data signal from 15 ps to 2 ps. In all the tuning range, the receiver sensitivity at bit error rate (BER) of 10-9 for the converted RZ data signal was about 1.31.7 dB better than the receiver sensitivity of the input NRZ data signal. Moreover, the pulse pedestal of the converted RZ data signals is well suppressed owing to the FWM process in the fiber-based FWM switch.

An adaptive selective retransmission algorithm for video communications based on packet importance value is proposed. The algorithm can adaptively select the retransmission threshold in realtime and efficiently manage the retransmission process in heavy loaded networks while guaranteeing acceptable video quality at the receiver.

Digital RF solutions have been shown to be advantageous in various design aspects, such as accurate modeling, design reuse, and scaling when migrating to the next CMOS process node. Consequently, the majority of new low-cost and feature cell phones are now based on this approach. However, another equally important aspect of this approach to wireless transceiver SoC design, which is instrumental in allowing fast and low-cost productization, is in creating the inherent capability to assess performance and allow for low-cost built-in calibration and compensation, as well as characterization and final-testing. These internal capabilities can often rely solely on the SoCs existing processing resources, representing a zero cost adder, requiring only the development of the appropriate algorithms. This paper presents various examples of built-in measurements that have been demonstrated in wireless transceivers offered by Texas Instruments in recent years, based on the digital-RF processor (DRPTM) technology, and highlights the importance of the various types presented; built-in self-calibration and compensation, built-in self-characterization, and built-in self-testing (BiST). The accompanying statistical approach to the design and productization of such products is also discussed, and fundamental terms related with these, such as 'soft specifications', are defined.

Recently, global positioning system (GPS)-enabled mobile units have been popular in wireless mobile communications systems, and thus it becomes possible for mobile units to estimate the velocity before a random access for initiating communications. Motivated by this, we propose a new random access scheme establishing two or more access slot groups corresponding to velocity ranges of mobile units, where each mobile unit attempts a random access only at the slot group corresponding to its current velocity. It gives advantages that access slots can be flexibly grouped according to vehicle traffic conditions and detection algorithms can be optimized to each velocity range.

Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.

As the demand for spectrum for future wireless communication services increases, cognitive radio technology has been developed for dynamic and opportunistic spectrum access, which enables the secondary users to use the underutilized licensed spectrum of the primary users. In particular, the recent studies on the MAC protocol for dynamic and opportunistic access have focused on sensing and using the vacant spectrum efficiently. Under the ad-hoc network environment, how the secondary users use the unused channels by the primary users affects the efficient utilization of channels and a cognitive radio system is required to follow the rapid and frequent changes in channel status. In this paper, we propose a self-scheduling multi-channel cognitive MAC (SMC-MAC) protocol, which allows multiple secondary users to transmit data though the sensed idle channels by two cooperative channel sensing algorithms, i.e., fixed channel sensing (FCS) and adaptive channel sensing (ACS), and by slotted contention mechanism to exchange channel request information for self-scheduling. The performance of the proposed SMC-MAC protocol is investigated via analysis and simulations. According to the results, the proposed SMC-MAC protocol is effective in allowing multiple secondary users to transmit data frames effectively on multi-channels and adaptively in response to the primary users' traffic dynamics.

It is well known that the problem to solve a set of randomly chosen multivariate quadratic equations over a finite field is NP-hard. However, when the number of variables is much larger than the number of equations, it is not necessarily difficult to solve equations. In fact, when n ≥ m(m+1) (n,m are the numbers of variables and equations respectively) and the field is of even characteristic, there is an algorithm to find one of solutions of equations in polynomial time (see [Kipnis et al., Eurocrypt '99] and also [Courtois et al., PKC '02]). In the present paper, we propose two new algorithms to find one of solutions of quadratic equations; one is for the case of n ≥ (about) m2-2m 3/2+2m and the other is for the case of n ≥ m(m+1)/2+1. The first one finds one of solutions of equations over any finite field in polynomial time, and the second does with O(2m) or O(3m) operations. As an application, we also propose an attack to UOV with the parameters given in 2003.

Hop-timing detection is of extreme importance for the reception of frequency hopping (FH) signals. Any error in the hop-timing detection has a deleterious effect on the performance of the receiver in frequency hopping (FH) communication systems. However, it is not easy to detect the hop-timing under low signal to noise power ratio (SNR) environments. Adaptive array antennas (AAA) have been expected to improve the performance of FH communication systems by beamforming for the direction of arrival of the desired signal. Since the conventional AAA exploits at least the coarse synchronization for dehopping of FH signals before achieving the beamforming, any fault in the hop-timing detection causes the deterioration of the performance of AAA. Using AAA based on the constant modulus algorithm (CMA), this paper proposes a new method for blind beamforming and hop-timing detection for FH signals. The proposed method exploits both the spatial and temporal characteristics of the received signal to accomplish the beamforming and detect the hop-timing without knowing any a priori information such as fine/coarse time synchronization and training signal. The performance verifications of the proposed method based on pertinent simulations are presented.

CMOS SoCs can reduce power consumption while maintaining performance by adopting voltage scaling (VS) technologies. The operating speed of the level converter (LC) strongly affects the effectiveness of VS technologies. However, PVT variations can cause serious problems to the LC, because the state-of-the-art LC designs do not give enough attention to this issue. In this work, we proposed to analyze the impact of PVT variations on the performance of the LC using a previously developed heuristic sizing methodology. Based on the evaluation results from different operating corners with different offset voltages and temperatures, we proposed a variation-tolerant LC that achieves both high performance and low energy with a high tolerability for PVT variations.

This paper discusses the possibility of deploying a short-range cognitive radio (secondary communication system) within the service area of a primary system. Although the secondary system interferes with the primary system, there are certain locations in the service area of the primary system where the cognitive radio can reuse the frequency of the primary system without causing harmful interference to it and being disturbed by the primary system. These locations are referred to as having a spatial opportunity for communications in the secondary system, since it can reuse the frequency of the primary system. Simulation results indicate that the antenna gain, beamwidth, and propagation path loss greatly affect the spatial opportunity of frequency reuse for the secondary users. The results show that spatial spectrum reuse can be significantly increased when the primary system users are equipped with directional antennas. An important component in this study is the heterogeneous path loss model, i.e., the path loss model within the primary system is different from the model used to calculate the interference between the primary and the secondary systems. Our results show that the propagation models corresponding to the actual antenna heights in the primary/secondary system can largely impact the possibilities for spectrum reuse by the cognitive radios.

On uniformly convex real Banach spaces, a fixed point theorem in weak topology for successively recurrent system of fuzzy-set-valued nonlinear mapping equations and its application to ring nonlinear network systems are theoretically discussed in detail. An arbitrarily-level likelihood signal estimation is then established.

With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.

Recently, to track and relate news documents from several sources, association rule mining has been applied due to its performance and scalability. This paper presents an empirical investigation on how term representation basis, term weighting, and association measure affects the quality of relations discovered among news documents. Twenty four combinations initiated by two term representation bases, four term weightings, and three association measures are explored with their results compared to human judgment of three-level relations: completely related, somehow related, and unrelated relations. The performance evaluation is conducted by comparing the top-k results of each combination to those of the others using so-called rank-order mismatch (ROM). The experimental results indicate that a combination of bigram (BG), term frequency with inverse document frequency (TFIDF) and confidence (CONF), as well as a combination of BG, TFIDF and conviction (CONV), achieves the best performance to find the related documents by placing them in upper ranks with 0.41% ROM on top-50 mined relations. However, a combination of unigram (UG), TFIDF and lift (LIFT) performs the best by locating irrelevant relations in lower ranks (top-1100) with 9.63% ROM. A detailed analysis on the number of the three-level relations with regard to their rankings is also performed in order to examine the characteristic of the resultant relations. Finally, a discussion and an error analysis are given.

We report on the enhancement of system detection efficiency in a superconducting nanowire single-photon detector (SNSPD) by applying the optical cavity structure. The nanowire was made using 4-nm-thick NbN thin films and covered with an SiO cavity and Au mirror designed for 1300-1600 nm wavelengths. The device is mounted into fiber-coupled packages, and installed in a practical multichannel system based on GM cryocoolers. System detection efficiency depends on the absorptance of cavity structure, and reached 28% and 40% at 1550 nm and 1310 nm wavelengths, respectively. These values were considerably higher than an SNSPD without optical cavity.

In this study, we extracted articles describing problems, articles describing their solutions, and articles describing their causes from a Japanese Q&A style Web forum using a supervised machine learning with 0.70, 0.86, and 0.56 F values, respectively. We confirmed that these values are significantly better than their baselines. This extraction will be useful to construct an application that can search for problems provided by users and display causes and potential solutions.

In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a "stakeholder" is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of RelationshipWordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource SentiWordNet. As a result of analysis, we construct a relations graph of stakeholders to group stakeholders sharing mutual interests and to represent the interests of stakeholders. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents some experimental results to validate the proposed methods.

We describe the fabrication processes and electrical characteristics of two types of NbN junctions. One is a self-shunted NbN/NbNx/AlN/NbN Josephson junction, which is expected to improve the density of integrated circuits; the other is an underdamped NbN/AlNx/NbN tunnel junction with radical-nitride AlNx barriers, which has highly controllable junction characteristics. In the former, the junction characteristics were changed from underdamped to overdamped by varying the thickness of the NbNx layer. Overdamped junctions with a 6-nm-thick NbNx film exhibited a characteristic voltage of Vc = 0.8 mV and a critical current density of Jc = 22 A/cm2 at 4.2 K. In the junctions with radical-nitride AlNx barriers, Jc could be controlled in the range 0.01-3 kA/cm2 by varying the process conditions, and good uniformity of the junction characteristics was obtained.

In many real-world face recognition applications, there might be only one training image per person available. Moreover, the test images may vary in facial expressions and illuminations, or may be partially occluded. However, most classical face recognition techniques assume that multiple images per person are available for training, and they are difficult to deal with extreme expressions, illuminations and occlusions. This paper proposes a novel block-based bag of words (BBoW) method to solve those problems. In our approach, a face image is partitioned into multiple blocks, dense SIFT features are then calculated and vector quantized into different visual words on each block respectively. Finally, histograms of codeword distribution on each local block are concatenated to represent the face image. Our method is able to capture local features on each block while maintaining holistic spatial information of different facial components. Without any illumination compensation or image alignment processing, the proposed method achieves excellent face recognition results on AR and XM2VTS databases. Experimental results show that only using one neutral expression frame per person for training, our method can obtain the best performance ever on face images of AR database with extreme expressions, variant illuminations, and partial occlusions. We also test our method on the standard and darkened sets of XM2VTS database, and achieve the average rates of 100% and 96.10% on the standard and darkened sets of XM2VTS database, respectively.

The nanoporosity installed in conjugated polymer films prepared by electrophoretic deposition makes it difficult to measure the amount of polymer deposited on a substrate. Here, an alternative approach, the estimation of material efficiency of the electrophoretic deposition from the optical absorption spectra of the residual suspensions has been studied. The ultimate recovery rate, which becomes smaller in suspensions with lower acetonitrile content, does not depend on the deposition voltage. The light scattering by the colloidal particles seems to be absent in residual suspensions after a deposition long enough to reach the ultimate recovery rate, indicating the exhaustion of the colloidal particles. Although the deposition rate of the polymer markedly lowers upon coating of the deposition electrode with PEDOT, the ultimate recovery rate remains unchanged. These results suggest that the material efficiency in this deposition method is limited by the generation rate of the colloidal particles in the suspension.