We propose an adaptive reversible data hiding method with superior visual quality and capacity in which an adaptive generalized difference expansion (AGDE) method is applied to an integer-to-integer subband transform (I2I-ST). I2I-ST performs the reversible subband transform and the AGDE method is a state-of-the-art method of reversible data hiding. The results of experiments we performed objectively and perceptually show that the proposed method has better visual quality than conventional methods at the same embedding rate due to low variance in the frequency domain.

N-Shift Zero Correlation Zone (NS-ZCZ) sequence is defined with the N-shift zero correlation zone in the correlation function. Namely, the N-shift zero only appears within the correlation zone symmetrically distributed in the center of the correlation function. Moreover, the traditional ZCZ sequences can be considered as the N-shift ZCZ sequence with N=1. Similar to ZCZ sequence, NS-ZCZ sequences can be applied in sequence design for co-channel interference mitigation with more sequences in the sequence set compared with the traditional N-shift sequences. In this letter, the definition and construction algorithms are proposed. The corresponding theoretical bounds are analyzed.

An effective interlaced-to-progressive scanning format conversion method is presented for the interpolation of interlaced images. On the basis of the weight assignment algorithm, the proposed method is composed of three stages: (1) straightforward interpolation with pre-determined six-tap filter, (2) fuzzy metric-based weight assignment, (3) updating the interpolation results. We first deinterlace the missing line with six-tap filter in the working window. Then we compute the local weight among the adjacent pixels with a fuzzy metric. Finally we deinterlace the missing pixels using the proposed interpolator. Comprehensive simulations conducted on different images and video sequences have proved the effectiveness of the proposed method, with significant improvement over conventional methods.

Computation integrity is difficult to verify when mass data processing is outsourced. Current integrity protection mechanisms and policies verify results generated by participating nodes within a computing environment of service providers (SP), which cannot prevent the subjective cheating of SPs. This paper provides an analysis and modeling of computation integrity for mass data processing services. A third-party sampling-result verification method, named TS-TRV, is proposed to prevent lazy cheating by SPs. TS-TRV is a general solution of verification on the intermediate results of common MapReduce jobs, and it utilizes the powerful computing capability of SPs to support verification computing, thus lessening the computing and transmission burdens of the verifier. Theoretical analysis indicates that TS-TRV is effective on detecting the incorrect results with no false positivity and almost no false negativity, while ensuring the authenticity of sampling. Intensive experiments show that the cheating detection rate of TS-TRV achieves over 99% with only a few samples needed, the computation overhead is mainly on the SP, while the network transmission overhead of TS-TRV is only O(log N).

We proposes a method for determining the frequency for monitoring the activities of a malware download site used for malware attacks on websites. In recent years, there has been an increase in attacks exploiting vulnerabilities in web applications for infecting websites with malware and maliciously using those websites as attack platforms. One scheme for countering such attacks is to blacklist malware download sites and filter out access to them from user websites. However, a malware download site is often constructed through the use of an ordinary website that has been maliciously manipulated by an attacker. Once the malware has been deleted from the malware download site, this scheme must be able to unblacklist that site to prevent normal user websites from being falsely detected as malware download sites. However, if a malware download site is frequently monitored for the presence of malware, the attacker may sense this monitoring and relocate that malware on a different site. This means that an attack will not be detected until the newly generated malware download site is discovered. In response to these problems, we clarify the change in attack-detection accuracy caused by attacker behavior. This is done by modeling attacker behavior, specifying a state-transition model with respect to the blacklisting of a malware download site, and analyzing these models with synthetically generated attack patterns and measured attack patterns in an operation network. From this analysis, we derive the optimal monitoring frequency that maximizes the true detection rate while minimizing the false detection rate.

To characterize an antenna, the acquisition of its three-dimensional radiation pattern is the fundamental requirement. Spherical antenna measurement is a practical approach to measuring antenna patterns in spherical geometry. However, due to the limitations of measurement range and measurement time, the measured samples may either be incomplete on scanning sphere, or be inadequate in terms of the sampling interval. Therefore there is a need to extrapolate and interpolate the measured samples. Spherical wave expansion, whose band-limited property is derived from the sampling theorem, provides a good tool for reconstructing antenna patterns. This research identifies the limitation of the conventional algorithm when reconstructing the pattern of an antenna which is not located at the coordinate origin of the measurement set-up. A novel algorithm is proposed to overcome the limitation by resampling between the unprimed and primed (where the antenna is centred) coordinate systems. The resampling of measured samples from the unprimed coordinate to the primed coordinate can be conducted by translational phase shift, and the resampling of reconstructed pattern from the primed coordinate back to the unprimed coordinate can be accomplished by rotation and translation of spherical waves. The proposed algorithm enables the analytical and continuous pattern reconstruction, even under the severe sampling condition for deviated AUT. Numerical investigations are conducted to validate the proposed algorithm.

In analysis of general-purpose problems which involves many factors from different viewpoints, an important challenge is to acquire different opinions and distributed modeling templates from multiple remote experts, and to aggregate these templates. In order to deal with this problem, we developed the Distributed Cooperative Modeling System (DCMS) by integrating our achievements [1]-[5]. The paper introduces how to analyze a complex problem using DCMS, with distributed templates from multiple experts, historical templates based on statistical data, and trend templates deduced from historical data, with the example of analyzing “diversification of Macao industries”

How do global warming and agriculture influence each other? It is possible to answer the question by searching knowledge about the relationship between global warming and agriculture. As exemplified by this question, strong demands exist for searching relationships between objects. Mining knowledge about relationships on Wikipedia has been studied. However, it is desired to search more diverse knowledge about relationships on the Web. By utilizing the objects constituting relationships mined from Wikipedia, we propose a new method to search images with surrounding text that include knowledge about relationships on the Web. Experimental results show that our method is effective and applicable in searching knowledge about relationships. We also construct a relationship search system named “Enishi” based on the proposed new method. Enishi supplies a wealth of diverse knowledge including images with surrounding text to help users to understand relationships deeply, by complementarily utilizing knowledge from Wikipedia and the Web.

Checkpoint-rollback recovery, which is a universal method for restoring distributed systems after faults, requires a sophisticated snapshot algorithm especially if the systems are large-scale, since repeatedly taking global snapshots of the whole system requires unacceptable communication cost. As a sophisticated snapshot algorithm, a partial snapshot algorithm has been introduced that takes a snapshot of a subsystem consisting only of the nodes that are communication-related to the initiator instead of a global snapshot of the whole system. In this paper, we modify the previous partial snapshot algorithm to create a new one that can take a partial snapshot more efficiently, especially when multiple nodes concurrently initiate the algorithm. Experiments show that the proposed algorithm greatly reduces the amount of communication needed for taking partial snapshots.

Recently, fuzzy set theory has been widely employed in building portfolio selection models where uncertainty plays a role. In these models, future security returns are generally taken for fuzzy variables and mathematical models are then built to maximize the investment profit according to a given risk level or to minimize a risk level based on a fixed profit level. Based on existing works, this paper proposes a portfolio selection model based on fuzzy birandom variables. Two original contributions are provided by the study: First, the concept of technical analysis is combined with fuzzy set theory to use the security returns as fuzzy birandom variables. Second, the fuzzy birandom Value-at-Risk (VaR) is used to build our model, which is called the fuzzy birandom VaR-based portfolio selection model (FBVaR-PSM). The VaR can directly reflect the largest loss of a selected case at a given confidence level and it is more sensitive than other models and more acceptable for general investors than conventional risk measurements. To solve the FBVaR-PSM, in some special cases when the security returns are taken for trapezoidal, triangular or Gaussian fuzzy birandom variables, several crisp equivalent models of the FBVaR-PSM are derived, which can be handled by any linear programming solver. In general, the fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO) is designed to find the approximate optimal solution. To illustrate the proposed model and the behavior of the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide a discussion of some existing approaches.

This paper deals with carrier frequency offset (CFO) estimation based on the minimum variance distortionless response (MVDR) criterion without using specific training sequences for interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. In the presence of large CFOs, the estimator is proposed to find a new CFO vector based on the first-order Taylor series expansion of the one initially given. The problem of finding the new CFO vector is formulated as the closed form of a generalized eigenvalue problem, which allows one to readily solve it. Since raising the accuracy of residual CFO estimation can provide more accurate residual CFO compensation, this paper also present a decision-directed MVDR approach to improve the CFO estimation performance. However, the proposed estimator can estimate CFOs with less computation load. Several computer simulation results are provided for illustrating the effectiveness of the proposed blind estimate approach.

To reduce perforamnce degradations of LR-WPANs due to interference from WLANs, this letter proposes a protocol to allow a piconet to switch an operating channel to an interference-free channel only for transmitting beacon frames. The proposed method does not only increase network performances because of hgh reliability of the beacon frames, but also increase overerall channel utilizations because of using even interfered-channels.

This paper presents indoor experimental results on 4-by-2 multi-user (MU)-MIMO transmission with carrier aggregation (90-MHz bandwidth) achieving real-time 1-Gbps data transmission using adaptive modulation and coding (AMC) in the LTE-Advanced downlink employing OFDMA radio access. In the experiments, eigenvalue decomposition (EVD)-based channel state information (CSI) feedback based on subband unit for MU-MIMO operation and inter-user interference whitening filter applied before maximum likelihood detection (MLD) are employed to achieve such a high data rate with realistic numbers of transmitter and receiver antennas. The indoor experiments are conducted in a conference room under line-of-sight conditions with multiple reflected waves where one mobile station (MS) travels at walking speed and the other MS is static. The experimental results show that the total throughput for the 2 MSs is greater than 1Gbps at the average received signal-to-interference plus noise power ratio (SINR) of approximately 25 and 17dB for the first and second streams of each MS, respectively, when the moving speed is up to approximately 1km/h. The results also show that a centralized transmitter antenna arrangement is more effective in order to achieve a high data rate such as 1Gbps compared to a distributed antenna arrangement for the measurement environment.

In modern hardware security applications, silicon physical unclonable functions (PUFs) are of interest for their potential use as a unique identity or secret key that is generated from inherent characteristics caused by process variations. However, arbiter-based PUFs utilizing the relative delay-time difference between equivalent paths have a security issue in which the generated challenge-response pairs (CRPs) can be predicted by a machine learning attack. We previously proposed the RG-DTM PUF, in which a response is decided from divided time domains allocated to response 0 or 1, to improve the uniqueness of the conventional arbiter-PUF in a small circuit. However, its resistance against machine learning attacks has not yet been studied. In this paper, we evaluate the resistance against machine learning attacks by using a support vector machine (SVM) and logistic regression (LR) in both simulations and measurements and compare the RG-DTM PUF with the conventional arbiter-PUF and with the XOR arbiter-PUF, which strengthens the resistance by using XORing output from multiple arbiter-PUFs. In numerical simulations, prediction rates using both SVM and LR were above 90% within 1,000 training CRPs on the arbiter-PUF. The machine learning attack using the SVM could never predict responses on the XOR arbiter-PUF with over six arbiter-PUFs, whereas the prediction rate eventually reached 95% using the LR and many training CRPs. On the RG-DTM PUF, when the division number of the time domains was over eight, the prediction rates using the SVM were equal to the probability by guess. The machine learning attack using LR has the potential to predict responses, although an adversary would need to steal a significant amount of CRPs. However, the resistance can exponentially be strengthened with an increase in the division number, just like with the XOR arbiter-PUF. Over one million CRPs are required to attack the 16-divided RG-DTM PUF. Differences between the RG-DTM PUF and the XOR arbiter-PUF relate to the area penalty and the power penalty. Specifically, the XOR arbiter-PUF has to make up for resistance against machine learning attacks by increasing the circuit area, while the RG-DTM PUF is resistant against machine learning attacks with less area penalty and power penalty since only capacitors are added to the conventional arbiter-PUF. We also attacked RG-DTM PUF chips, which were fabricated with 0.18-µm CMOS technology, to evaluate the effect of physical variations and unstable responses. The resistance against machine learning attacks was related to the delay-time difference distribution, but unstable responses had little influence on the attack results.

We propose a parallel pth powering method over an arbitrary finite field GF(pm). Using the proposed method, we present the explicit formulae for the computation of cubing over a ternary field GF(3m) which is defined by irreducible trinomials. We show that the field cubing computation for irreducible trinomials, which plays an important role in calculating pairing, can be implemented very efficiently.

In signal restoration problems, we expect to improve the restoration performance with a priori information about unknown target signals. In this paper, the parametric Wiener filter with linear constraints for unknown target signals is discussed. Since the parametric Wiener filter is usually defined as the minimizer of the criterion not for the unknown target signal but for the filter, it is difficult to impose constraints for the unknown target signal in the criterion. To overcome this difficulty, we introduce a criterion for the parametric Wiener filter defined for the unknown target signal whose minimizer is equivalent to the solution obtained by the original formulation. On the basis of the newly obtained criterion, we derive a closed-form solution for the parametric Wiener filter with linear constraints.

We introduce a new nonlinear control method to globally asymptotically stabilize a class of uncertain nonlinear systems. First, we provide a system reconfiguration method which reconfigures the nonlinear systems with smooth positive functions. Then, we provide a nonlinear controller design method to globally asymptotically stabilize the reconfigured systems by utilizing Lyapunov equations. As a result, a class of uncertain nonlinear systems which have not been treated in the existing results can be globally asymptotically stabilized by our control method. Examples are given for easy following and illustration.

A point-to-point fixed wireless access (FWA) system with a maximum throughput of 1Gbps has been developed in the 39GHz band. A double-layer plate-laminated waveguide slot array antenna is successfully realized with specific considerations of practical application. The antenna is designed so as to hold the VSWR under 1.5. The antenna input as well as feeding network is configured to reduce the antenna profile as well as the antenna weight. In addition, integrating the antenna into a wireless terminal is taken into account. A shielding wall, whose effectiveness is experimentally demonstrated, is set in the middle of the wireless terminal to achieve the spatial isolation of more than 65dB between two antennas on the H-plane. 30 test antennas are fabricated by diffusion bonding of thin metal plates, to investigate the tolerance and mass-productivity of this process. An aluminum antenna, which has the advantages of light weight and anti-aging, is also fabricated and evaluated with an eye to the future.

In recent years, there has been rapid developments in radio-frequency identification (RFID) systems, and their industrial applications include logistics management, automatic object identification, access and parking management, etc. Moreover, RFID systems have also been introduced for the management of medical instruments in medical applications to improve the quality of medical services. In recent years, the combination of such a system with a biological monitoring system through permanent implantation in the human body has been suggested to reduce malpractice events and ameliorate the patient suffering. This paper presents an implantable RFID tag antenna design that can match the conjugate impedance of most integrated circuit (IC) chips (9.3-j55.2Ω at 2.45GHz. The proposed antenna can be injected into the human body through a biological syringe, owing to its compact size of 9.3mm × 1.0mm × 1.0mm. The input impedance, transmission coefficient, and received power are simulated by a finite element method (FEM). A three-layered phantom is used to confirm antenna performance.

In this paper, a new digital true random number generator based on Cross Feedback Ring Oscillators (CFRO) is proposed. The random sources of CFRO lie in delay variations (jitter), unpredictable transition behaviors as well as metastability. The CFRO is proved to be truly random by restarting from the same initial states. Compared with the so-called Fibonacci Ring Oscillator (FIRO) and Galois Ring Oscillator (GARO), the CFRO needs less than half of their time to accumulate relatively high entropy and enable extraction of one random bit. Only a simple XOR corrector is used to reduce the bias of output sequences. TRNG based on CFRO can be run continuously at a constant high speed of 150Mbps. For higher security, the TRNG can be set in stateless mode at a cost of slower speed of 10Mbps. The total logical resources used are relatively small and no special placement and routing is needed. The TRNG both in continuous mode and in stateless mode can pass the NIST tests and the DIEHARD tests.