After receiving emitted signals from various radars by electronic support measures (ESM) system, several processes are applied to signals such as: deinterleaving, recognition of pulse repetition interval (PRI) modulation, PRI estimation and etc. Indeed, recognition of PRI modulation is an essential task of ESM system. In this paper a novel and robust method for recognition of complicated PRI Modulations is presented. This method uses specifications such as distribution related to members of sequences obtained from first and second order derivation of TOAs around a constant value and continuity of these sequences to recognize the PRI modulation. Some numerical simulations are performed to illustrate the effectiveness of proposed method. Simulation results show high robustness of proposed method against noise (spurious and missing pulses) and unwanted jitter.

This paper describes a novel technique to replace some of the driven elements in an array antenna with parasitic elements. First, the antenna characteristics are studied by simulation for a basic unit array with one driven and two parasitic elements. The entire antenna is backed with a flat reflector to conform to practical applications. The parasitic elements are excited by the neighboring driven elements through the electromagnetic coupling effect. It is shown that at the optimal coupling condition, the radiation patterns are almost identical with those of an array antenna whose elements are all driven without coupling. The simulation result is confirmed by performing an experiment at 5.8GHz (λ =51.7mm). Finally, a 12-element array is formed by combining four unit arrays. The simulation results show that the maximum antenna gain is 19.4dBi, indicating that there is no penalty with respect to the antenna gain of a fully driven 12-element array. Therefore, the array antenna can be considerably simplified by replacing 67% of its elements with parasitic elements.

In this paper, we propose a chromatic adaptation model based on the adapting degree according to the level of adapting luminance and chromaticity in various surround illuminants. In the proposed model, first maximum adapted cone responses are calculated through the estimation of adapting degree for viewing conditions then corresponding colors are reproduced from original colors using the ratio of maximum adapted cone responses between different viewing conditions. The purpose of this study is to produce chromatic adaptation transform applied to environment-adaptive color display system. As a result, our proposed model can give better estimation performance than prior models and be embodied easily as a linear model in display systems. So it is confirmed that the implemented system can predict corresponding-color data very well under a variety of viewing conditions.

Cloud computing is broadly recognized as as the prevalent trend in IT. However, in cloud computing mode, customers lose the direct control of their data and applications hosted by the cloud providers, which leads to the trustworthiness issue of the cloud providers, hindering the widespread use of cloud computing. This paper proposes a trustworthiness verification and audit mechanism on cloud providers called T-YUN. It introduces a trusted third party to cyclically attest the remote clouds, which are instrumented with the trusted chain covering the whole architecture stack. According to the main operations of the clouds, remote verification protocols are also proposed in T-YUN, with a dedicated key management scheme. This paper also implements a proof-of-concept emulator to validate the effectiveness and performance overhead of T-YUN. The experimental results show that T-YUN is effective and the extra overhead incurred by it is acceptable.

Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.

In this paper, we propose a novel technique for film grain noise removal and synthesis that can be adopted in high fidelity video coding. Film grain noise enhances the natural appearance of high fidelity video, therefore, it should be preserved. However, film grain noise is a burden to typical video compression systems because it has relatively large energy levels in the high frequency region. In order to improve the coding performance while preserving film grain noise, we propose film grain noise removal in the pre-processing step and film grain noise synthesis in the post processing step. In the pre-processing step, the film grain noise is removed by using temporal and inter-color correlations. Specifically, color image denoisng using inter color prediction provides good denoising performance in the noise-concentrated B plane, because film grain noise has inter-color correlation in the RGB domain. In the post-processing step, we present a noise model to generate noise that is close to the actual noise in terms of a couple of observed statistical properties, such as the inter-color correlation and power of the film grain noise. The results show that the coding gain of the denoised video is higher than for previous works, while the visual quality of the final reconstructed video is well preserved.

In secret sharing scheme, Tompa and Woll considered a problem of cheaters who try to make another participant reconstruct an invalid secret. Later, some models of such cheating were formalized and lower bounds of the size of shares were shown in the situation of fixing the minimum successful cheating probability. Under the assumption that cheaters do not know the distributed secret, no efficient scheme is known which can distribute bit strings. In this paper, we propose an efficient scheme for distributing bit strings with an arbitrary access structure. When distributing a random bit string with threshold access structures, the bit length of shares in the proposed scheme is only a few bits longer than the lower bound.

Localization in wireless sensor networks is the problem of estimating the geographical locations of wireless sensor nodes. We propose a framework to utilizing multiple data sources for localization scheme based on support vector machines. The framework can be used with both classification and regression formulation of support vector machines. The proposed method uses only connectivity information. Multiple hop count data sources can be generated by adjusting the transmission power of sensor nodes to change the communication ranges. The optimal choice of communication ranges can be determined by evaluating mutual information. We consider two methods for integrating multiple data sources together; unif method and align method. The improved localization accuracy of the proposed framework is verified by simulation study.

We estimated the dependence of the perceived depth on luminance ratio by increasing the distance between the front and rear planes of a depth-fused 3-D (DFD) display. When the distance is great, the perceived depth has the tendency of nonlinear dependence on luminance ratio, which is very different from the almost linear dependence in a short-distance conventional DFD display. In a long-distance DFD display, the perceived depth is split to near the front plane at 0-40% of the rear luminance, near the rear plane at 70-100%, and the midpoint of the front and rear planes at 40-60%. Thus, the luminance-ratio dependence of perceived depth changes widely with the distance.

1 × N active multi-mode interferometer laser diode (MMI LD) is proposed and demonstrated to realize single-wavelength edge-emitter without using grating configuration. As the 1 × N active-MMI LDs are based on longitudinal mode interference, they have a potential of single-wavelength emission without incorporating any grating layer on/beneath active layer. The fabricated devices showed single-wavelength emission with a side mode suppression ratio (SMSR) of 12dB at a wavelength of 1.57µm.

This paper presents a new scalable method to considerably reduce the rollback propagation effect of the conventional optimistic message logging by utilizing positive features of reliable FIFO group communication links. To satisfy this goal, the proposed method forces group members to replicate different receive sequence numbers (RSNs), which they assigned for each identical message to their group respectively, into their volatile memories. As the degree of redundancy of RSNs increases, the possibility of local recovery for each crashed process may significantly be higher. Experimental results show that our method can outperform the previous one in terms of the rollback distance of non-faulty processes with a little normal time overhead.

Pyramid is a solitaire game, where the object is to remove all cards from both a pyramidal layout and a stock of cards. Two exposed cards can be matched and removed if their values total 13. Any exposed card of value 13 and the top card of the stock can be discarded immediately. We prove that the generalized version of Pyramid is NP-complete.

This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.

In this paper, we introduce a new multi-operator pico eNodeB (eNB) concept for cellular networks. It is expected that mobile data offloading will be performed effectively after installing the pico eNBs in cellular networks, owing to the rapid increase in mobile traffic. However, when several different operators independently install the pico eNBs, high costs and large amounts of space will be required for the installation. In addition, when several different operators accommodate their own user equipments (UEs) in the pico eNBs, not enough UEs can be accommodated. This is because the UEs are not evenly distributed in the coverage area of the pico eNBs. In this paper, the accommodation of the UEs of different operators in co-sited pico eNB is discussed as one of the solutions to these problems. For the accommodation of the UEs of different operators, wireless resources should be allocated to them. However, when each operator independently controls his wireless resources, the operator is not provided with an incentive to accommodate the UEs of the other operators in his pico eNBs. For this reason, an appropriate rule for appropriate allocation of the wireless resources to the UEs of different operators should be established. In this paper, by using the concepts of game theory and mechanism design, a resource allocation rule where each operator is provided with an incentive to allocate the wireless resources to the UEs of different operators is proposed. With the proposed rule, each operator is not required to disclose the control information like link quality and the number of UEs to the other operators. Furthermore, the results of a throughput performance evaluation confirm that the proposed scheme improves the total throughput as compared with individual resource allocation.

Considering the real existent fact of the ideal edge and the learning style of image analogy without reference parameters, a blind image recovery algorithm using a self-adaptive learning method is proposed in this paper. We show that a specific local image patch with degradation characteristic can be utilized for restoring the whole image. In the training process, a clear counterpart of the local image patch is constructed based on the ideal edge assumption so that identification of the Point Spread Function is no longer needed. Experiments demonstrate the effectiveness of the proposed method on remote sensing images.

Protecting the confidentiality and integrity of a configuration bitstream is essential for the dynamic partial reconfiguration (DPR) of field-programmable gate arrays (FPGAs). This is because erroneous or falsified bitstreams can cause fatal damage to FPGAs. In this paper, we present a high-speed and area-efficient bitstream protection scheme for DPR systems using the Advanced Encryption Standard with Galois/Counter Mode (AES-GCM), which is an authenticated encryption algorithm. Unlike many previous studies, our bitstream protection scheme also provides a mechanism for error recovery and tamper resistance against configuration block deletion, insertion, and disorder. The implementation and evaluation results show that our DPR scheme achieves a higher performance, in terms of speed and area, than previous methods.

The abundance of information published on the Internet makes filtering of hazardous Web pages a difficult yet important task. Supervised learning methods such as Support Vector Machines (SVMs) can be used to identify hazardous Web content. However, scalability is a big challenge, especially if we have to train multiple classifiers, since different policies exist on what kind of information is hazardous. We therefore propose two different strategies to train multiple SVMs for personalized Web content filters. The first strategy identifies common data clusters and then performs optimization on these clusters in order to obtain good initial solutions for individual problems. This initialization shortens the path to the optimal solutions and reduces the training time on individual training sets. The second approach is to train all SVMs simultaneously. We introduce an SMO-based kernel-biased heuristic that balances the reduction rate of individual objective functions and the computational cost of kernel matrix. The heuristic primarily relies on the optimality conditions of all optimization problems and secondly on the pre-calculated part of the whole kernel matrix. This strategy increases the amount of information sharing among learning tasks, thus reduces the number of kernel calculation and training time. In our experiments on inconsistently labeled training examples, both strategies were able to predict hazardous Web pages accurately (> 91%) with a training time of only 26% and 18% compared to that of the normal sequential training.

In this letter, by using Whiteman's generalized cyclotomy of order 2 over Zpq, where p, q are twin primes, we construct new perfect Gaussian integer sequences of period pq.

This article introduces the Coincidence Algorithm (COIN) to solve several multimodal puzzles. COIN is an algorithm in the category of Estimation of Distribution Algorithms (EDAs) that makes use of probabilistic models to generate solutions. The model of COIN is a joint probability table of adjacent events (coincidence) derived from the population of candidate solutions. A unique characteristic of COIN is the ability to learn from a negative sample. Various experiments show that learning from a negative example helps to prevent premature convergence, promotes diversity and preserves good building blocks.

To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.