This letter studies the price-based power control algorithm for the spectrum sharing cognitive radio networks. The primary user (PU) profits from the secondary users (SUs) by pricing the interference power made by them. The SUs cooperate with each other to maximize their sum revenue with the signal-to-interference plus noise ratio (SINR) balancing condition. The interaction between the PU and the SUs is modeled as a Stackelberg game. Closed-form expressions of the optimal price for the PU and power allocation for the SUs are given. Simulation results show the proposed algorithm improves the revenue of both the PU and fairness of the SUs compared with the uniform pricing algorithm.

For some applications, it has been known that stochastic computing (SC) has many potential advantages compared with conventional computation on binary radix encoding. Thus, there has been proposed many design methodologies to realize SCs. Recently, a general design method to realize SC operations by designing Boolean circuits (functions) has been proposed. As a central part of the method, we need to design a logic circuit such that its output becomes 1 with a certain desired probability with respect to random inputs. Also, to realize an SC arithmetic operation with a constant value, in some situations we need to prepare a random bit-stream that becomes 1 with a desired probability from a set of predetermined physical random sources. We call such a bit-stream as a stochastic number (SN). We can utilize the above-mentioned previous method to prepare stochastic numbers by designing Boolean circuits. The method assumes all the random sources become 1 with the same probability 1/2. In this paper, we investigate a different framework where we can prepare different probabilities of each stochastic number in the physical random sources. Then, this paper presents the necessary and sufficient condition of given random inputs in order to produce a stochastic number with a given specified precision. Based on the condition, we can propose a method to generate a stochastic number by using the minimum number of random inputs. Indeed our method uses much less number of inputs than the previous method, and our preliminary experiment shows that the generated circuits by our method also tend to be smaller than the ones by the previous method.

Long-distance wireless local area networks (WLANs) are the key enablers of wide-area and low-cost access networks in rural areas. In a WLAN, the long propagation delay between an access point (AP) and stations (STAs) significantly degrades the throughput and creates a throughput imbalance because the delay causes unexpected frame collisions. This paper summarizes the problems caused in the medium access control (MAC) mechanism of the WLAN by a long propagation delay. We propose a MAC protocol for solving the delay-induced throughput degradation and the throughput imbalance between the uplink and the downlink in WLANs to address these problems. In the protocol, the AP extends NAV duration of CTS frame to protect an ACK frame and transmits its data frame to avoid delay induced frame collisions by piggybacking on the ACK frame transmission. We also provide a throughput model for the proposed protocol based on the Bianchi model. A numerical analysis using the proposed throughput model and simulation evaluation demonstrate that the proposed protocol increases the system throughput by 150% compared with that obtained using the conventional method, and the uplink throughput can be increased to the same level as the downlink throughput.

Code division multiple access (CDMA) based on direct sequence (DS) spread spectrum modulation using spreading codes is one of standard technologies for multiple access communications. In asynchronous DS/CDMA communications, spreading codes with appropriate negative auto-correlation can reduce bit error rate (BER) compared with uncorrelated sequences. In this letter, we design new binary functions for generating chaotic binary sequences with negative auto-correlation using Bernoulli chaotic map. Such binary functions can be applied to the generation of spreading codes with negative auto-correlation based on existing spreading codes (e.g., shift register sequences).

This paper investigates open-loop Stackelberg games for a class of stochastic systems with multiple players. First, the necessary conditions for the existence of an open-loop Stackelberg strategy set are established using the stochastic maximum principle. Such conditions can be represented as solvability conditions for cross-coupled forward-backward stochastic differential equations (CFBSDEs). Second, in order to obtain the open-loop strategy set, a computational algorithm based on a four-step scheme is developed. A numerical example is then demonstrated to show the validity of the proposed method.

In this letter, efficient closed-form formulas for the exact and asymptotic average bit error probability (ABEP) of space shift keying (SSK) systems are derived over Rayleigh fading channels with imperfect channel state information (CSI). Specifically, for a generic 2×NR multiple-input multiple-output (MIMO) system with the maximum likelihood (ML) detection, the impact of imperfect CSI is taken into consideration in terms of two types of channel estimation errors with the fixed variance and the variance as a function of the number of pilot symbols and signal-to-noise ratio (SNR). Then, the explicit evaluations of the bit error floor (BEF) and asymptotic SNR loss are carried out based on the derived asymptotic ABEP formula, which accounts for the impact of imperfect CSI on the SSK system. The numerical results are presented to validate the exactness of our theoretical analysis.

A comprehensive model is presented for estimating the bit error rate (BER) of write disturbance in a resistive memory composed of a cross-point array. While writing a datum into the selected address, the non-selected addresses are biased by word-line (WL) and bit-line (BL). The stored datum in the non-selected addresses will be disturbed if the bias is large enough. It is necessary for the current flowing through the non-selected address to be calculated in order to estimate the BER of the write disturbance. Since it takes a long time to calculate the current flowing in a large-scale cross-point array, several simplified circuits have been utilized to decrease the calculating time. However, these simplified circuits are available to the selected address, not to the non-selected one. In this paper, new simplified circuits are proposed for calculating the current flowing through the non-selected address. The proposed and the conventional simplified circuits are used, and on that basis the trade-off between the write disturbance and the write error is discussed. Furthermore, the error correcting code (ECC) is introduced to improve the trade-off and to provide the low-cost memory chip matching current production lines.

The solution of the standard 2-norm-based multiple kernel regression problem and the theoretical limit of the considered model space are discussed in this paper. We prove that 1) The solution of the 2-norm-based multiple kernel regressor constructed by a given training data set does not generally attain the theoretical limit of the considered model space in terms of the generalization errors, even if the training data set is noise-free, 2) The solution of the 2-norm-based multiple kernel regressor is identical to the solution of the single kernel regressor under a noise free setting, in which the adopted single kernel is the sum of the same kernels used in the multiple kernel regressor; and it is also true for a noisy setting with the 2-norm-based regularizer. The first result motivates us to develop a novel framework for the multiple kernel regression problems which yields a better solution close to the theoretical limit, and the second result implies that it is enough to use the single kernel regressors with the sum of given multiple kernels instead of the multiple kernel regressors as long as the 2-norm based criterion is used.

Spectrum sharing effectively improves the spectrum usage by allowing secondary users (SUs) to dynamically and opportunistically share the licensed bands with primary users (PUs). The concept of cooperative spectrum sharing allows SUs to use portions of the PUs' radio resource for their own data transmission, under the condition that SUs help the PUs' transmission. The key issue with designing such a scheme is how to deal with the resource splitting of the network. In this paper we propose a relay-based cooperative spectrum sharing scheme in which the network consists of one PU and multiple SUs. The PU asks the SUs to relay its data in order to improve its energy efficiency, in return it rewards the SUs with a portion of its authorized spectrum. However each SU is only allowed to transmit its data via the rewarded channel at a power level proportional to the contribution it makes to the PU. Since energy cost is considered, the SUs must carefully determine their power level. This scheme forms a non-cooperative Stackelberg resource allocation game where the strategy of PU is the bandwidth it rewards and the strategy of each SU is power level of relay transmission. We first investigate the second stage of the sub-game which is addressed as power allocation game. We prove there exists an equilibrium in the power allocation game and provide a sufficient condition for the uniqueness of the equilibrium. We further prove a unique Stackelberg equilibrium exists in the resource allocation game. Distributed algorithms are proposed to help the users with incomplete information achieve the equilibrium point. Simulation results validate our analysis and show that our proposed scheme introduces significant utility improvement for both PU and SUs.

IDMs are getting more effective and secure with biometric recognition and more privacy-preserving with advanced cryptosystems. In order to meet privacy and security needs of an IDM, the cryptographic background should rely on reliable random number generation. In this study, a Biometric Random Number Generator (BRNG) is proposed which plays a crucial role in a typical cryptosystem. The proposed novel approach extracts the high-frequency information in biometric signal which is associated with uncertainty existing in nature of biometrics. This bio-uncertainty, utilized as an entropy source, may be caused by sensory noise, environmental changes, position of the biometric trait, accessories worn, etc. The filtered nondeterministic information is then utilized by a postprocessing technique to obtain a random number set fulfilling the NIST 800-22 statistical randomness criteria. The proposed technique presents random number sequences without need of an additional hardware.

In recent years, applications of neural networks with Clifford algebra have become widespread. Hyperbolic numbers are useful Clifford algebra to deal with hyperbolic geometry. It is difficult when Hopfield neural network is extended to hyperbolic versions, though several models have been proposed. Multistate or continuous hyperbolic Hopfield neural networks are promising models. However, the connection weights and domain of activation function are limited to the right quadrant of hyperbolic plane, and the learning algorithms are restricted. In this work, the connection weights and activation function are extended to the entire hyperbolic plane. In addition, the energy is defined and it is proven that the energy does not increase.

Algorithms requiring fast manipulation of multiple-length numbers are usually implemented in hardware. However, hardware implementation, using HDL (Hardware Description Language) for instance, is a laborious task and the quality of the solution relies heavily on the designer expertise. The main contribution of this work is to present a flexible-length-arithmetic processor based on FDFM (Few DSP slices and Few Memory blocks) approach that supports arithmetic operations on multiple-length numbers using FPGAs (Field Programmable Gate Array). The proposed processor has been implement on the Xilinx Virtex-6 FPGA. Arithmetic instructions of the proposed processor architecture include addition, subtraction, and multiplication of integer numbers exceeding 64-bits. To reduce the burden of implementing algorithm directly on the FPGA, applications requiring multiple-length arithmetic operations are written in a C-like language and translated into a machine program. The machine program is then transferred and executed on the proposed architecture. A 2048-bit RSA encryption/decryption implementation has been used to assess the goodness of the proposed approach. Experimental results shows that the computing time, using the proposed architecture, of a 2048-bit RSA encryption takes only 2.2 times longer than a direct FPGA implementation. Furthermore, by employing multiple FDFM cores for the same task, the computing time reduces considerably.

We investigate the influence of launching conditions on misalignment tolerance of pluggable ballpoint-pen interconnects, where graded-index plastic optical fibers (GI POFs) are coupled with ball lenses mounted on their end faces. The lateral-misalignment tolerance of the ballpoint-pen connector decreased with an increase in the driving current of a vertical cavity surface emitting laser (VCSEL) under the center launching condition. This was attributed to the VCSEL multimode oscillation, which increased the connector coupling loss through the higher-order guided mode launching in the GI POF and the resulting output beam expansion in the ballpoint-pen connector. The driving-current dependence of the connector coupling loss could be decreased using offset launchings. For a radial launching offset of 20µm, we could obtain coupling losses below 1dB for lateral coupling offsets of ±50µm with little dependence on the driving current. This suggests that data transmission quality for misaligned connection of the GI POFs can be improved further by optimizing launching systems for the ballpoint-pen interconnects.

The segmentation of Mycobacterium tuberculosis images forms the basis for the computer-aided diagnosis of tuberculosis. The segmented objects are often broken due to the low-contrast objects and the limits of segmentation method. This will result in decreasing the accuracy of segmentation and recognition. A simple and effective post-processing method is proposed to connect the broken objects. The broken objects in the segmented binary images are connected based on the information obtained from their skeletons. Experimental results demonstrate the effectiveness of our proposed method.

There are often low contrast Mycobacterium tuberculosis (MTB) objects in the MTB images. Based on improved histogram equalization (HE), a framework of contrast enhancement is proposed to increase the contrast of MTB images. Our proposed algorithm was compared with the traditional HE and the weighted thresholded HE. The experimental results demonstrate that our proposed algorithm has better performance in contrast enhancement, artifacts suppression, and brightness preserving for MTB images.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

Antenna radiation patterns have side-lobes that add to ambiguity in the form of ghosting and object repetition in SAR images. An L-band 1.27GHz, 2×5 element proximity-coupled corner-truncated patch array antenna synthesized using the Dolph-Chebyshev method to reduce side-lobe levels is proposed. The designed antenna was sim-ulated, optimized, and fabricated for antenna performance parameter measurements. Antenna performance characteristics show good agree-ment with simulated results. A set of antennas were fabricated and then used together with a custom synthetic aperture radar system and SAR imaging performed on a point target in an anechoic chamber. Imaging results are also discussed in this paper showing improvement in image output. The antenna and its connected SAR systems developed in this work are different from most previous work in that this work is utilizing circular polarization as opposed to linear polarization.

In this paper, an infinite-horizon team-optimal incentive Stackelberg strategy is investigated for a class of stochastic linear systems with many non-cooperative leaders and one follower. An incentive structure is adopted which allows for the leader's team-optimal Nash solution. It is shown that the incentive strategy set can be obtained by solving the cross-coupled stochastic algebraic Riccati equations (CCSAREs). In order to demonstrate the effectiveness of the proposed strategy, a numerical example is solved.

Estimation of the time delay of arrival (TDOA) problem is important to acoustic source localization. The TDOA estimation problem is defined as finding the relative delay between several microphone signals for the direct sound. To estimate TDOA, the generalized cross-correlation (GCC) method is the most frequently used, but it has a disadvantage in terms of reverberant environments. In order to overcome this problem, the adaptive eigenvalue decomposition (AED) method has been developed, which estimates the room transfer function and finds the direct-path delay. However, the algorithm does not take into account the fact that the room transfer function is a sparse channel, and so sometimes the estimated transfer function is too dense, resulting in failure to exact direct-path and delay. In this paper, an enhanced AED algorithm that makes use of a proportionate step-size control and a direct-path constraint is proposed instead of a constant step size and the L2-norm constraint. The simulation results show that the proposed algorithm has enhanced performance as compared to both the conventional AED method and the phase-transform (PHAT) algorithm.

Since many cyber-physical systems (CPSs) manipulate security-sensitive data, enhancing the quality of security in a CPS is a critical and challenging issue in CPS design. Although there has been a large body of research on securing general purpose PCs, directly applying such techniques to a CPS can compromise the real-time property of CPSs since the timely execution of tasks in a CPS typically relies on real-time scheduling. Recognizing this property, previous works have proposed approaches to add a security constraint to the real-time properties to cope with the information leakage problem that can arise between real-time tasks with different security levels. However, conventional works have mainly focused on non-preemptive scheduling and have suggested a very naive approach for preemptive scheduling, which shows limited analytical capability. In this paper, we present a new preemptive fixed-priority scheduling algorithm incorporating a security constraint, called lowest security-level first (LSF) and its strong schedulability analysis to reduce the potential of information leakage. Our simulation results show that LSF schedulability analysis outperforms state-of-the-art FP analysis when the security constraint has reasonable timing penalties.