Group signature (GS) schemes guarantee anonymity of the actual signer among group members. Previous GS schemes assume that randomness in signing is never exposed. However, in the real world, full randomness exposure can be caused by implementation problems (e.g., using a bad random number generator). In this paper, we study (im)possibility of achieving anonymity against full randomness exposure. First, we formulate a new security model for GS schemes capturing full randomness exposure. Next, we clarify that it is impossible to achieve full-anonymity against full randomness exposure without any secure component (e.g., a tamper-proof module or a trusted outside storage). Finally, we show a possibility result that selfless-anonymity can be achieved against full randomness exposure. While selfless-anonymity is weaker than full-anonymity, it is strong enough in practice. Our transformation is quite simple; and thus, previous GS schemes used in real-world systems can be easily replaced by a slight modification to strengthen the security.

In this paper, we present a method of analyzing relationships between items in specific health examination data, as one of the basic researches to address increases of lifestyle-related diseases. We use self-organizing maps, and pick up the data from the examination dataset according to the condition specified by some item values. We then focus on twelve items such as hemoglobin A1c (HbA1c), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (γ-GTP), and triglyceride (TG). We generate training data presented to a map by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. We label neurons in the map on condition that one of the item values of training data is employed as a parameter. We finally examine the relationships between items by comparing results of labeling (clusters formed in the map) to each other. From experimental results, we separately reveal the relationships among HbA1c, AST, ALT, γ-GTP and TG in the unfavorable case of HbA1c value increasing and those in the favorable case of HbA1c value decreasing.

Self-Aligned Quadruple Patterning (SAQP) is an important manufacturing technique for sub 14nm technology node. Although various routing algorithms for SAQP have been proposed, it is not easy to find a dense SAQP compliant routing pattern efficiently. Even though a grid for SAQP compliant routing pattern was proposed, it is not easy to find a valid routing pattern on the grid. The routing pattern of SAQP on the grid consists of three types of routing. Among them, third type has turn prohibition constraint on the grid. Typical routing algorithms often fail to find a valid routing for third type. In this paper, a simple directed grid-graph for third type is proposed. Valid SAQP compliant two dimensional routing patterns are found effectively by utilizing the proposed directed grid-graph. Experiments show that SAQP compliant routing patterns are found efficiently by our proposed method.

This paper presents a single image super-resolution (SR) algorithm based on self-similarity using non-local-mean (NLM) metric. In order to accurately find the best self-example even under noisy environment, NLM weight is employed as a self-similarity metric. Also, a pixel-wise soft-switching is presented to overcome an inherent drawback of conventional self-example-based SR that it seldom works for texture areas. For the pixel-wise soft-switching, an edge-oriented saliency map is generated for each input image. Here, we derived the saliency map which can be robust against noises by using a specific training. The proposed algorithm works as follows: First, auxiliary images for an input low-resolution (LR) image are generated. Second, self-examples for each LR patch are found from the auxiliary images on a block basis, and the best match in terms of self-similarity is found as the best self-example. Third, a preliminary high-resolution (HR) image is synthesized using all the self-examples. Next, an edge map and a saliency map are generated from the input LR image, and pixel-wise weights for soft-switching of the next step are computed from those maps. Finally, a super-resolved HR image is produced by soft-switching between the preliminary HR image for edges and a linearly interpolated image for non-edges. Experimental results show that the proposed algorithm outperforms state-of-the-art SR algorithms qualitatively and quantitatively.

We investigate a finite-sized self-complementary bow-tie antenna (SC-BTA) integrated with a semiconductor mesa with respect to radiation characteristics such as the peak radiation frequency and bandwidth around the fundamental radiation mode. For this investigation, we utilize an equivalent circuit model of the SC-BTA derived in our previous work and a finite element method solver. Moreover, we derive design guidelines for the radiation characteristics in the form of size scaling-rules with respect to the antenna outer size for a terahertz transmitter.

The paper presents a small reversible language R-CORE, a structured imperative programming language with symbolic tree-structured data (S-expressions). The language is reduced to the core of a reversible language, with a single command for reversibly updating the store, a single reversible control-flow operator, a limited number of variables, and data with a single atom and a single constructor. Despite its extreme simplicity, the language is reversibly universal, which means that it is as powerful as any reversible language can be, while it is linear-time self-interpretable, and it allows reversible programming with dynamic data structures. The four-line program inverter for R-CORE is among the shortest existing program inverters, which demonstrates the conciseness of the language. The translator to R-CORE, which is used to show the formal properties of the language, is clean and modular, and it may serve as a model for related reversible translation problems. The goal is to provide a language that is sufficiently concise for theoretical investigations. Owing to its simplicity, the language may also be used for educational purposes.

The increase in network access devices and demand for high quality of service (QoS) by the users have led to insufficient capacity for the network operators. Moreover, the existing control equipment and mechanisms are not flexible and agile enough for the dynamically changing environment of heterogeneous cellular networks (HetNets). This non-agile control plane is hard to scale with ever increasing traffic demand and has become the performance bottleneck. Furthermore, the new HetNet architecture requires tight coordination and cooperation for the densely deployed small cell base stations, particularly for interference mitigation and dynamic frequency reuse and sharing. These issues further complicate the existing control plane and can cause serious inefficiencies in terms of users' quality of experience and network performance. This article presents an SDN control framework for energy efficient downlink/uplink scheduling in HetNets. The framework decouples the control plane from data plane by means of a logically centralized controller with distributed agents implemented in separate entities of the network (users and base stations). The scheduling problem consists of three sub-problems: (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. Moreover, these sub-problems are coupled and must be solved simultaneously. We formulate the DL/UL scheduling in HetNet as an optimization problem and use the Markov approximation framework to propose a distributed economical algorithm. Then, we divide the algorithm into three sub-routines for (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. These sub-routines are then implemented on different agents of the SDN framework. We run extensive simulation to validate our proposal and finally, present the performance analysis.

Improvements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the time-varying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

In this paper we study the structure of self-dual cyclic codes over the ring $Lambda= Z_4+uZ_4$. The ring Λ is a local Frobenius ring but not a chain ring. We characterize self-dual cyclic codes of odd length n over Λ. The results can be used to construct some optimal binary, quaternary cyclic and self-dual codes.

Theoretical maximum radiation efficiency of electrically small spherical surface antennas has been derived in this study. The current on the antenna surface is described in terms of vector spherical harmonics, and the radiated and the dissipated powers are calculated to obtain the radiation efficiency. It has been found that non-resonant TM1m mode shows the best radiation efficiency, and a proper combination of TM10 and TE10 modes establishes a resonant spherical surface antenna whose radiation efficiency is bounded by those values of non-resonant TM10 and TE10 modes. As a practical example of the spherical surface antennas, the radiation efficiency of the spherical helix antennas has also been computed to check the validity of our formulation.

Self-updating encryption (SUE) is a new cryptographic scheme produced in the recent work of Lee, Choi, Lee, Park and Yung (Asiacrypt 2013) to achieve a time-updating mechanism for revocation. In SUE, a ciphetext and a private key are associated with the time and a user can decrypt a ciphertext only if its time is earlier than that of his private key. But one drawback is the encryption computational overhead scales with the size of the time which makes it a possible bottleneck for some applications. To address this problem, we provide a new technique for the SUE that splits the encryption algorithm into two phases: an offline phase and an online phase. In the offline phase, an intermediate ciphertext header is generated before it knows the concrete encryption time. Then an online phase is implemented to rapidly generate an SUE ciphertext header when the time becomes known by making use of the intermediate ciphertext header. In addition, two different online encryption constructions are proposed in view of different time level taking 50% as the boundary. At last, we prove the security of our scheme and provide the performance analysis which shows that the vast majority of computational overhead can be moved to the offline phase. One motivating application for this technique is resource-constrained mobile devices: the preparation work can be done when the mobile devices are plugged into a power source, then they can later rapidly perform SUE operations on the move without significantly consuming the battery.

This paper proposes an on-line rigid object tracking framework via discriminative object appearance modeling and learning. Strong classifiers are combined with 2D scale-rotation invariant local features to treat tracking as a keypoint matching problem. For on-line boosting, we correspond a Gaussian mixture model (GMM) to each weak classifier and propose a GMM-based classifying mechanism. Meanwhile, self-organizing theory is applied to perform automatic clustering for sequential updating. Benefiting from the invariance of the SURF feature and the proposed on-line classifying technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experiments show that the proposed method achieves better performance than previously reported trackers.

This article describes an approximate model of a group of cells in the wireless 4G network with implemented load balancing mechanism. An appropriately modified model of Erlang's Ideal Grading is used to model this group of cells. The model makes it possible to take into account limited availability of resources of individual cells to multi-rate elastic and adaptive traffic streams generated by Erlang and Engset sources. The developed solution allows the basic traffic characteristics in the considered system to be determined, i.e. the occupancy distribution and the blocking probability. Because of the approximate nature of the proposed model, the results obtained based on the model were compared with the results of a digital simulation. The present study validates the adopted assumptions of the proposed model.

In the self-similarity super resolution (SR) approach, similar examples are searched across down-scales in the image pyramid, and the computations of searching similar examples are very heavy. This makes it difficult to work in a real-time way under common software implementation. Therefore, the search process should be further accelerated at an algorithm level. Cauchy-Schwarz inequality has been used previously for fast vector quantization (VQ) encoding. The candidate patches in the search region of SR are analogous to the code-words in the VQ, and Cauchy-Schwarz inequality is exploited to exclude implausible candidate patches early. Consequently, significant acceleration of the similar patch search process is achieved. The proposed method can easily make an optimal trade-off between running speed and visual quality by appropriately configuring the bypass-threshold.

In this paper, we investigate multi-service forwarding in selfish wireless networks (SeWN) with selfish relay nodes (RN). The RN's node-selfishness is characterized from the perspectives of its residual energy and the incentive paid by the source, by which the degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) are defined. Meanwhile, a framework of the node-selfishness management is conceived to extract the RNs' node-selfishness information (NSI). Based on the RN's NSI, the expected energy cost and expected service profit are determined for analyzing the effect of the RN's node-selfishness on the multi-service forwarding. Moreover, the optimal incentive paid by the source is obtained for minimizing its cost and, at the same time, effectively stimulating the multi-service delivery. Simulation validate our analysis.

Let Fq be a finite field of cardinality q, R=Fq[u]/=Fq+uFq+u2Fq+u3Fq (u4=0) which is a finite chain ring, and n be a positive integer satisfying gcd(q,n)=1. For any $delta,alphain mathbb{F}_{q}^{ imes}$, an explicit representation for all distinct (δ+αu2)-constacyclic codes over R of length n is given, and the dual code for each of these codes is determined. For the case of q=2m and δ=1, all self-dual (1+αu2)-constacyclic codes over R of odd length n are provided.

In this paper, a self optimization beamforming null control (SOBNC) scheme is proposed. There is a need of maintaining signal to interference plus noise ratio (SINR) threshold to control modulation and coding schemes (MCS) in recent technologies like Wi-Fi, Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A). Selection of MCS depends on the SINR threshold that allows maintaining key performance index (KPI) like block error rate (BLER), bit error rate (BER) and throughput at certain level. The SOBNC is used to control the antenna pattern for SINR estimation and improve the SINR performance of the wireless communication systems. The nulling comes with a price; if wider nulls are introduced, i.e. more number of nulls are used, the 3dB beam-width and peak side lobe level (SLL) in antenna pattern changes critically. This paper proposes a method which automatically controls the number of nulls in the antenna pattern as per the changing environment based on adaptive-network based fuzzy interference system (ANFIS) to maintain output SINR level higher or equal to the required threshold. Finally, simulation results show a performance superiority of the proposed SOBNC compared with minimum mean square error (MMSE) based adaptive nulling control algorithm and conventional fixed null scheme.

We developed an electrically driven near-infrared broadband light source based on self-assembled InAs quantum dots (QDs). By combining emissions from four InAs QD ensembles with controlled emission center wavelengths, electro-luminescence (EL) with a Gaussian-like spectral shape and approximately 85-nm bandwidth was obtained. The peak wavelength of the EL was blue-shifted from approximately 1230 to 1200 nm with increased injection current density (J). This was due to the state-filling effect: sequential filling of the discrete QD electron/hole states by supplied carriers from lower (ground state; GS) to higher (excited state; ES) energy states. The EL intensities of the ES and GS emissions exhibited different J dependence, also because of the state-filling effect. The point-spread function (PSF) deduced from the Fourier-transformed EL spectrum exhibited a peak without apparent side lobes. The half width at half maximum of the PSF was 6.5 µm, which corresponds to the estimated axial resolution of the optical coherence tomography (OCT) image obtained with this light source. These results demonstrate the effectiveness of the QD-based device for realizing noise-reduced high-resolution OCT.

Event-triggered and self-triggered control methods are an important control strategy in networked control systems. Event-triggered control is a method that the measured signal is sent to the controller (i.e., the control input is recomputed) only when a certain condition is satisfied. Self-triggered control is a method that the control input and the (non-uniform) sampling interval are computed simultaneously. In this paper, we propose new methods of event-triggered control and self-triggered control from the viewpoint of online optimization (i.e., model predictive control). In self-triggered control, the control input and the sampling interval are obtained by solving a pair of a quadratic programming (QP) problem and a mixed integer linear programming (MILP) problem. In event-triggered control, whether the control input is updated or not is determined by solving two QP problems. The effectiveness of the proposed methods is presented by numerical examples.

In this paper, a new single soft switched forward converter with a self driven synchronous rectification (SDSR) is introduced. In the proposed converter, a soft switching condition (ZCS turn on and ZVS turn off) is provided for the switch, by an auxiliary circuit without any extra switch. In additional, this auxiliary circuit does not impose high voltage or current stresses on the converter. Since the proposed converter uses SDSR to reduce conductive loss of output rectifier, the rectifier switches are switched under soft switching condition. So, the conductive and switching losses on the converter reduce considerably. Also, implementing control circuit of this converter is very simple, due to the self-driven method employed in driving synchronous rectification and the converter is controlled by pulse width modulation (PWM). The experimental results of the proposed converter are presented to confirm the theoretical analysis.