In this paper, a dual-polarized phased array based polarization state modulation method is proposed to enhance the physical-layer security in millimeter-wave (mm-wave) communication systems. Indeed, we utilize two polarized beams to transmit the two components of the polarized signal, respectively. By randomly selecting the transmitting antennas, both the amplitude and the phase of two beams vary randomly in undesired directions, which lead to the PM constellation structure distortion in side lobes, thus the transmission security is enhanced since the symbol error rate increases at the eavesdropper side. To enhance the security performance when the eavesdropper is close to the legitimate receiver and located in main beam, the artificial noise based on the orthogonal vector approach is inserted randomly between two polarized beams, which can further distort the constellation structure in undesired directions and improve the secrecy capacity in main beam as well. Finally, theoretical analysis and simulation results demonstrate the proposed method can improve the transmission security in mm-wave communication systems.

In this paper, we propose the novel low voltage CMOS current mode reference circuit. It reduces the minimum supply voltage by consisting the subthreshold two stage operational amplifier (OPAMP) which is regarded as the combination of the proportional to absolute temperature (PTAT) and the complementary to absolute temperature (CTAT) current generators. It makes possible to implement without extra OPAMP. This proposed circuit has been designed and evaluated by SPICE simulation using TSMC 65nm CMOS process with 3.3V (2.5V over-drive) transistor option. From simulation results, the line sensitivity is as good as 0.196%/V under the condition that the range of supply voltage (V_DD) is wide as 0.6V to 3.0V. The temperature coefficient is 71ppm/℃ under the condition that the temperature range is from -40℃ to 125℃ and V_DD=0.6V. The power supply rejection ratio (PSRR) is -47.7dB when V_DD=0.6V and the noise frequency is 100Hz. According to comparing the proposed circuit with prior current mode circuits, we could confirm the performance of the proposed circuit is better than that of prior circuits.

In this paper, operator-based reset control for a class of nonlinear systems with unknown bounded disturbance is considered using right coprime factorization approach. In detail, firstly, for dealing with the unknown bounded disturbance of the nonlinear systems, operator-based reset control framework is proposed based on right coprime factorization. By the proposed framework, robust stability of the nonlinear systems with unknown bounded disturbance is guaranteed by using the proposed reset controller. Secondly, under the reset control framework, an optimal design scheme is discussed for minimizing the error norm based on the proposed operator-based reset controller. Finally, for conforming effectiveness of the proposed design scheme, a simulation example is given.

Numerical inverse Laplace transform (NILT) methods are potential methods for time domain simulations, for instance the analysis of the transient phenomena in systems with lumped and/or distributed parameters. This paper proposes a numerical inverse Laplace transform method based originally on hyperbolic relations. The method is further enhanced by properly adapting several convergence acceleration techniques, namely, the epsilon algorithm of Wynn, the quotient-difference algorithm of Rutishauser and the Euler transform. The resulting accelerated models are compared as for their accuracy and computational efficiency. Moreover, an expansion to two dimensions is presented for the first time in the context of the accelerated hyperbolic NILT method, followed by the error analysis. The expansion is done by repeated application of one-dimensional partial numerical inverse Laplace transforms. A detailed static error analysis of the resulting 2D NILT is performed to prove the effectivness of the method. The work is followed by a practical application of the 2D NILT method to simulate voltage/current distributions along a transmission line. The method and application are programmed using the Matlab language.

Given a sequence of k convex polygons in the plane, a start point s, and a target point t, we seek a shortest path that starts at s, visits in order each of the polygons, and ends at t. We revisit this touring polygons problem, which was introduced by Dror et al. (STOC 2003), by describing a simple method to compute the so-called last step shortest path maps, one per polygon. We obtain an O(kn)-time solution to the problem for a sequence of pairwise disjoint convex polygons and an O(k²n)-time solution for possibly intersecting convex polygons, where n is the total number of vertices of all polygons. A major simplification is made on the operation of locating query points in the last step shortest path maps. Our results improve upon the previous time bounds roughly by a factor of log n.

MapReduce is an effective framework for processing large datasets in parallel over a cluster. Data locality and data skew on the reduce side are two essential issues in MapReduce. Improving data locality can decrease network traffic by moving reduce tasks to the nodes where the reducer input data is located. Data skew will lead to load imbalance among reducer nodes. Partitioning is an important feature of MapReduce because it determines the reducer nodes to which map output results will be sent. Therefore, an effective partitioner can improve MapReduce performance by increasing data locality and decreasing data skew on the reduce side. Previous studies considering both essential issues can be divided into two categories: those that preferentially improve data locality, such as LEEN, and those that preferentially improve load balance, such as CLP. However, all these studies ignore the fact that for different types of jobs, the priority of data locality and data skew on the reduce side may produce different effects on the execution time. In this paper, we propose a naive Bayes classifier based partitioner, namely, BAPM, which achieves better performance because it can automatically choose the proper algorithm (LEEN or CLP) by leveraging the naive Bayes classifier, i.e., considering job type and bandwidth as classification attributes. Our experiments are performed in a Hadoop cluster, and the results show that BAPM boosts the computing performance of MapReduce. The selection accuracy reaches 95.15%. Further, compared with other popular algorithms, under specific bandwidths, the improvement BAPM achieved is up to 31.31%.

The isomorphism of polynomials with two secret (IP2S) problem is one candidate of computational assumptions for post-quantum cryptography. The idea of identification scheme based on IP2S is firstly introduced in 1996 by Patarin. However, the scheme was not described concretely enough and no more details are provided on how to transcribe the idea into a real-world implementation. Moreover, the security of the scheme has not been formally proven and the originally proposed security parameters are no longer secure based on the most recent research. In this paper, we propose a concrete identification scheme based on IP2S with the idea of Patarin as the starting point. We provide formal security proof of the proposed scheme against impersonation under passive attack, sequential active attack, and concurrent active attack. We also propose techniques to reduce the implementation cost such that we are able to cut the storage cost and average communication cost to an extent that under parameters for the standard 80-bit security, the scheme is implementable even on the lightweight devices in the current market.

This paper addresses the issue of text matching for plagiarism detection. This task aims at identifying the matching plagiarism segments in a pair of suspicious document and its plagiarism source document. All the time, heuristic-based methods are mainly utilized to resolve this problem. But the heuristics rely on the experts' experiences and fail to integrate more features to detect the high obfuscation plagiarism matches. In this paper, a statistical machine learning approach, named the Ranking-based Text Matching Approach for Plagiarism Detection, is proposed to deal with the issues of high obfuscation plagiarism detection. The plagiarism text matching is formalized as a ranking problem, and a pairwise learning to rank algorithm is exploited to identify the most probable plagiarism matches for a given suspicious segment. Especially, the Meteor evaluation metrics of machine translation are subsumed by the proposed method to capture the lexical and semantic text similarity. The proposed method is evaluated on PAN12 and PAN13 text alignment corpus of plagiarism detection and compared to the methods achieved the best performance in PAN12, PAN13 and PAN14. Experimental results demonstrate that the proposed method achieves statistically significantly better performance than the baseline methods in all twelve document collections belonging to five different plagiarism categories. Especially at the PAN12 Artificial-high Obfuscation sub-corpus and PAN13 Summary Obfuscation plagiarism sub-corpus, the main evaluation metrics PlagDet of the proposed method are even 22% and 43% relative improvements than the baselines. Moreover, the efficiency of the proposed method is also better than that of baseline methods.

With the extensive use of location based devices, trajectories of various kinds of moving objects can be collected and stored. As time going on, the volume of trajectory data increases exponentially, which presents a series of problems in storage, transmission and analysis. Moreover, GPS trajectories are never perfectly accurate and sometimes with high noise. Therefore, how to overcome these problems becomes an urgent task in trajectory data mining and related applications. In this paper, an adaptive noise filtering trajectory compression and recovery algorithm based on Compressed Sensing (CS) is proposed. Firstly, a noise reduction model is introduced to filter the high noise in GPS trajectories. Secondly, the compressed data can be obtained by the improved GPS Trajectory Data Compression Algorithm. Thirdly, an adaptive GPS trajectory data recovery algorithm is adopted to restore the compressed trajectories to their original status approximately. Finally, comprehensive experiments on real and synthetic datasets demonstrate that the proposed algorithm is not only good at noise filtering, but also with high compression ratio and recovery performance compared to current algorithms.

In this paper, we present self-interference (SI) cancellation techniques in the digital domain for in-band full-duplex systems employing orthogonal frequency division multiple access (OFDMA) in the downlink (DL) and single-carrier frequency division multiple access (SC-FDMA) in the uplink (UL), as in the long-term evolution (LTE) system. The proposed techniques use UL subcarrier nulling to accurately estimate SI channels without any UL interference. In addition, by exploiting the structures of the transmitter imperfection and the known or estimated parameters associated with the imperfection, the techniques can further improve the accuracy of SI channel estimation. We also analytically derive the lower bound of the mean square error (MSE) performance and the upper bound of the signal-to-interference-plus-noise ratio (SINR) performance for the techniques, and show that the performance of the techniques are close to the bounds. Furthermore, by utilizing the SI channel estimates and the nonlinear signal components of the SI caused by the imperfection to effectively eliminate the SI, the proposed techniques can achieve SINR performance very close to the one in perfect SI cancellation. Finally, because the SI channel estimation of the proposed techniques is performed in the time domain, the techniques do not require symbol time alignment between SI and UL symbols.

Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the effective radius is set for every cluster based on positioning accuracy and the clusters are merged effectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33% and also the proposed method much improves F₁ measure compared to conventional state-of-the-art methods.

In this Letter, a robust variable step-size affine-projection subband adaptive filter algorithm (RVSS-APSAF) is proposed, whereby a band-dependent variable step-size is introduced to improve convergence and misalignment performances in impulsive noise environments. Specifically, the weight vector is adaptively updated to achieve robustness against impulsive noises. Finally, the proposed RVSS-APSAF algorithm is tested for system identification in an impulsive noise environment.

Direct-binary search method has been used for converting complex holograms into binary format. However, this algorithm is optimized to reconstruct monochromatic digital holograms and is accurate only in a narrow-depth range. In this paper, we proposed an advanced direct-binary search method to increase the depth of field of 3D scenes reconstructed in RGB by binary holograms.

This paper proposes a matched myriad filter based detector for MSK signal under symmetric alpha-stable (SαS) noise. As shown in the previous literatures, SαS distribution is more accurate to characterize the atmospheric noise, which is the main interference in VLF communication. MSK modulation is widely used in VLF communication for its high spectral efficiency and constant envelope properties. However, the optimal detector for MSK under SαS noise is rarely reported due to its memory modulation characteristic. As MSK signal can be viewed as a sinusoidal pulse weighted offset QPSK (OQPSK), a matched myriad filter is proposed to derive a near-optimal detection performance for the in-phase and quadrature components, respectively. Simulations for MSK demodulation under SαS noise with different α validate the effectiveness of the proposed method.

The concept of Gaussian integer sequence pair is generalized from a single Gaussian integer sequence. In this letter, by adopting cyclic difference set pairs, a new construction method for perfect Gaussian integer sequence pairs is presented. Furthermore, the necessary and sufficient conditions for constructing perfect Gaussian integer sequence pairs are given. Through the research in this paper, a large number of perfect Gaussian integer sequence pairs can be obtained, which can greatly extend the existence of perfect sequence pairs.

Bel-T is the national block cipher encryption standard of the Republic of Belarus. It operates on 128-bit blocks and uses either 128, 192 or 256-bit keys. Bel-T combines a Feistel network with a Lai-Massey scheme and it has a complex round function with 7 S-box layers. In this work, we use a Mixed Integer Linear Programming (MILP) approach to find a a related-key differential characteristic that extends for 4 rounds and 5 S-box layers ($4 rac{5}{7}$ rounds) with probability higher than 2^-128. To build an MILP model of Bel-T that a solver can practically handle, we use a partial Difference Distribution Table (DDT) based on the Hamming weight of the input and output differences. The identified differential characteristic is used to mount a key recovery attack on 5 rounds and 6 S-box layers ($5 rac{6}{7}$ out of 8 rounds) of Bel-T-256 with 2^123.28 chosen plaintexts and 2^228.4 encryptions. According to the best of our knowledge, this is the first public cryptanalysis of Bel-T in the black-box attack model.

At FSE 2014, Grosso et al. proposed LS-designs which are a family of bitslice ciphers aiming at efficient masked implementations against side-channel analysis. They also presented two specific LS-designs, namely the non-involutive cipher Fantomas and the involutive cipher Robin. The designers claimed that the longest impossible differentials of these two ciphers only span 3 rounds. In this paper, for the two ciphers, we construct 4-round impossible differentials which are one round more than the longest impossible differentials found by the designers. Furthermore, with the 4-round impossible differentials, we propose impossible differential attacks on Fantomas and Robin reduced to 6 rounds (out of the full 12/16 rounds). Both of the attacks need 2¹¹⁹ chosen plaintexts and 2^101.81 6-round encryptions.

A family of quaternary sequences over Z₄ is defined based on the Ding-Helleseth generalized cyclotomic classes modulo pq for two distinct odd primes p and q. The linear complexity is determined by computing the defining polynomial of the sequences, which is in fact connected with the discrete Fourier transform of the sequences. The results show that the sequences possess large linear complexity and are “good” sequences from the viewpoint of cryptography.

In this letter, a novel transmission scheme is proposed to eliminate the polarization dependent loss (PDL) effect in dual-polarized satellite systems. In fact, the PDL effect is the key problem that limits the performance of the systems based on the PM technique, while it is naturally eliminated in the proposed scheme since we transmit the two components of the polarized signal in turn in two symbol periods. Moreover, a simple and effective detection method based on the signal's power is proposed to distinguish the polarization characteristic of the transmit antenna. In addition, there is no requirement on the channel state information at the transmitter, which is popular in satellite systems. Finally, superiorities are validated by the theoretical analysis and simulation results in the dual-polarized satellite systems.

This letter considers a legitimate proactive eavesdropping scenario, where a half-duplex legitimate monitor hires a third-party jammer for jamming the suspicious communication to improve the eavesdropping performance. The interaction between the third-party jammer and the monitor is modeled as a Stackelberg game, where the jammer moves first and sets the price for jamming the suspicious communication, and then the legitimate monitor moves subsequently and determines the requested transmit power of the jamming signals. We derive the optimal jamming price and the optimal jamming transmit power. It is shown that the proposed price-based proactive eavesdropping scheme is effective in improving the successful eavesdropping probability compared to the case without jamming. It is also shown that the proposed scheme outperforms the existing full-duplex scheme when the residual self-interference cannot be neglected.