Solving linear equations is one of the fundamental problems in numerical calculation and has a wide application in many science and engineering fields. We present a family of P systems to solve linear equations in which the coefficients are real numbers. An instance is given to illustrate the feasibility and effectiveness of our designed P systems. Our work contributes to make P systems to solve more complicated computation problems effectively.

According to the Radio frequency identification (RFID) tag cloning attack problems of RFID system and the existing problems of intrusion detection research based on incomplete RFID traces, this paper proposes a method of intrusion detection by combining the Markov chain technology and probability statistics technology. We use this method to calculate the threshold that is used to determine whether the tag event is a cloning tag intrusion one. Current research focuses on RFID intrusion detection under the condition of complete RFID traces, such as encryption algorithm improvement, tag authentication protocol improvement, and track and trace technology in RFID supply chain. RFID traces are occasionally to be incomplete, which causes the above methods to be inefficient. Our method proposed in this paper is the solution to incomplete RFID traces which makes up for the study of RFID intrusion detection research.

The sharp expansion of the FIB table rapidly aggravates the hardware cost of Line cards (LCs) in the high-performance distributed routers. The storage optimization of FIB becomes a research hotspot. The traffic load of each LC is still very different in the current nonfull backup storage, which has a deep impact on the overall forwarding performance of the routers. An even FIB decompositionmodel was proposed, namely FEST, aiming for a two-dimension balance in both storage and traffic. Based on the splitting and distribution filters, FEST starts with splitting the root prefixes and utilizes the optimal adaptation algorithm to evenly distribute the routing entries and the traffic to LCs without the modification of the hardware designs of the current LCs. Eventually FEST uses the location routing to determine the location and the forwarding of every packet. The experiment results show that different LCs get very even numbers of routing entries and relatively even traffic in FEST.

S-Box based on Composite field arithmetic (CFA) technology is optimized by Genetic algorithm (GA) and Cartesian genetic programming (CGP) model for reducing the hardware complexity. After using the CFA technique to map Multiplicative inverse (MI) over GF(2⁸) into composite field GF((2⁴)²), the compact MI circuit over GF(2⁴) is selected from 100 evolved circuits, and same design method is applied to the compact multiplication circuit over GF(2²). Compared with the direct implementations, the areas of optimized circuits of MI over GF(2⁴) and multiplication over GF((2²)²) are reduced by 66% and 57.69%, respectively. The area reductions for MI over GF(2⁸) and the whole of S-Box are up to 59.23% and 56.14%, separately. In 180nm 1.8V COMS technology, compared to previous works, the S-Box proposed in this paper has the minimum area and minimum power, which are 11.27% and 6.65% smaller than that of the smallest area S-Box, respectively.

Secure multiparty computation (SMC) is a research focusing in the international cryptographic community. The protocols used to address the millionaires' problem are the basic building blocks of most SMC protocols and their efficiency dominates that of many other SMC protocols. To the best of our knowledge, almost all protocols used to address the millionaires' problem are based on integers, which means that their applications are limited. In this study, we propose precise and efficient protocols for rational numbers based on additively homomorphic encryptions. One of our protocols is inspired by computational geometry and it reduces the millionaires problem to computing the area of a triangle formed by three private points. This approach can determine whether the relationship between two private inputs is greater than, equal to or less than, and it has a much lower computational complexity compared with existing methods. We proved that these protocols are secure using simulation paradigm. Our approaches can be used in many SMC protocols that involve rational numbers and integers, and they can also be used directly to solve some secure multiparty computational geometry problem in rational number field.

The difficulty of maximizing the lifetime in directional sensor networks has gained increasing attention recently. Most of the existing studies are focused on directional sensors with single or several predefined sensing ranges. In the present study, directional sensors can change sensing ranges smoothly. We address the problem of maximizing the lifetime in directional sensor networks with such smoothly varying sensing ranges,and propose a hybrid approach that combines a column generation method with an immune genetic algorithm. We search for attractive columns with the genetic algorithm, and optimize them by designing dynamic vaccines. Computational results demonstrate the performance of the proposed approach. Meanwhile, the advantage of the mentioned sensors in terms of solution quality is also revealed.

Once a disk of storage system is failed, RAID mechanism will reconstruct to keep the reliability. The reconstruction leads to a sharp deterioration in I/O performance of RAID, and the storage system cannot provide high performance service for the applications. The Multi-TB disk capacity and high load of the application make the reconstruction last for a very long time. This paper proposes Rabbet, a method accelerating RAID reconstruction using data of backup. Rabbet includes a new RAID reconstruction frame, a RAID layout-aware mapping mechanism for backup, and a reconstruction algorithm. The result of experiments shows that, comparing to the classic DOR methods Rabbet shortens the RAID5 reconstruction process to 13.9%–42.8% and decreases the average response time for applications during reconstruction by 3.2%–14.6%. Rabbet also improves the reliability of storage systems.

In the design of passive Radio frequency (RF) tags' baseband processor, subthreshold timing and wide-range-Process, voltage and temperature (PVT) variation problems are the bottlenecks to extend the tag's working range. A sophisticated processor is presented based on the EPC and ISO protocol. Power-aware ideas are applied to the entire processor, including data link portions. Innovatively, a novel custom ratioed logic style is adopted in critical logic paths to fundamentally speed up the circuit operations at ultra-low-voltage. The proposed baseband processor was fabricated in 90nm CMOS, another baseband processor design by regular standard-cell-based design flow was also fabricated for comparison. In measurement the proposed design indicates good robustness in wide-range supply and frequency variation and much more competent for subthreshold operation. It can operate at minimum 0.28V supply with power consumption of 129nW.

In Wavelength modulation spectroscopy (WMS) system with a short open optical path to detect the oxygen concentration, the system parameter optimization is the preconditions for the accurate inversion of oxygen concentration. Absorption spectroscopy parameters of oxygen were obtained from the HIRTAN database, and the optimal center wavelength and its wavelength offset were confirmed. The laser's working temperature and direct current were determined according to the absorption peak position of photoelectric detection. Modulation parameters and time constant of lock-in amplifier were determined by analyzing intensity and standard deviation of the second harmonic signal. This method has been applied to detect the oxygen concentration in a glass vial, and the results indicate that the linear correlation coefficient of concentration and the second harmonic intensity is 0.9950. The resolution and stability of the detection system have been improved and it can meet the requirements of industrial field.

Differential evolution (DE) is a popular and powerful evolutionary algorithm for global optimization problems. However, the combination of mutation strategies and parameter settings of DE is problem dependent and choosing the suitable one is a challenge work and timeconsuming. In addition, the deficiency in local exploitation also has a significant influence on the performance of DE. In order to solve these problems, a DE variant with Commensal learning and uniform local search (CUDE) has been proposed in this paper. In CUDE, commensal learning is proposed to adaptively select optimal mutation strategy and parameter setting simultaneously under the same criteria. Moreover, uniform local search enhances exploitation ability. Comprehensive experiment results on all the CEC 2013 test suite and comparison with the state-of-the-art DE variants indicate that the CUDE is very competitive.

Feistel-PG structure is a new specific Generalized Feistel structure (GFS) adopted in DBlock and LHash. Its main feature is adding a sbox-size permutation before the round function. Different choices of the permutation may affect the security property of ciphers with Feistel-PG structure but how it effects is not clear. We evaluate the values of diffusion round for all possible parameters and summarize the characteristics of optimum shuffles. The results show that one special kind of Feistel-PG achieves full diffusion in less cost than the improved GFS. This advantage may attract the designers' interests and this kind of Feistel-PG ciphers are suggested to designers. We also evaluate the security of suggested ciphers against various byte-oriented attacks, including differential cryptanalysis, linear cryptanalysis, impossible differential attack and integral attack. Some permutations with optimum diffusion but relatively weaker security are filtered out and these permutations should be avoided by designers.

To reduce communication overhead on the premise of privacy protection, this study presents a novel secret Confusion based energy-saving and privacypreserving data aggregation algorithm (CESPT). In confusion phase, CESPT confuses real sensory data and their sources by positive-negative pairs and a confusion factor is introduced to determine the quantity of pairs generated by a sensor, the exchange rounds and the threshold of data exchange, which affect communication overhead and privacy intensity of a Wireless sensor network (WSN). In aggregation phase, CESPT adopts a positive-negative neutralization strategy and a well-designed time slice allocation mechanism to reduce network traffic and message collision. In a word, CESPT can greatly reduce data traffic and energy consumption and obtain accurate statistical results on the basis of data privacy.

A new Particle swarm optimisation (PSO) algorithm based on the Hénon chaotic map (hereafter HCPSO algorithm) is presented in this paper to deal with the premature convergence problem of the traditional PSO algorithm. The HCPSO algorithm changes the structure of the traditional PSO algorithm and deviates from the structures of conventional hybrid algorithms that merely introduce chaotic searching into PSO. Based on the convergence condition of PSO, the HCPSO algorithm can improve solution precision and increase the convergence rate by combing using the targeting technique of chaotic mapping. For validation, fourteen benchmark functions were used to compare the proposed algorithm with six other hybrid PSO algorithms. The experimental results indicated that the HCPSO algorithm is superior to the other algorithms in terms of convergence speed and solution accuracy.

As one of the most popular lightweight ciphers in recent years, LBlock has attracted great attention. Researchers have explored the security of LBlock against various attacks. We focus on fault attack–one of the most important implementation attacks. In the past two years, fault attacks under the random fault model have been successfully applied to LBlock, supposing faults were injected at the end of the 24th to the 31st round. If faults are injected at the end of the 23rd round, previous attacks only work under the semi-random fault model. For the first time, we address this issue and propose a 23rd round fault attack under the random fault model. Compared with the previous works, our attack extends the fault injection to earlier round, with reasonable time cost and no extra faults. Experiments show that it only takes 10 faults to recover the secret key.

We address the problem of adaptive time delay estimation with noisy measurements. We develop a computationally efficient adaptive time delay estimator based on Explicit time delay estimation (ETDE) algorithm, by applying the unbiased impulse response estimation approach. In this algorithm, a weighted error function is derived, and the time delay is explicitly parameterized in the filter coefficients and iteratively updated directly by utilizing the modified error function. Simulations validate the performance of the proposed algorithm for colored input and low signal-to-noise ratio scenarios.

In Direction-of-arrival (DOA) estimation, the real-valued sparse Bayesian algorithm degrades the estimation performance by decomposing the complex value into real and imaginary components and combining them independently.We directly use complex probability density functions to model the noise and complex-valued sparse direction weights. Based on the Multiple measurement vectors (MMV), block sparse structure for the direction weights is integrated into the variational Bayesian learning to provide accurate source direction estimates. The proposed algorithm can be used for arbitrary array geometries and does not need the prior information of the incident signal number. Simulation results demonstrate the better performance of the proposed method compared with the real-valued sparse Bayesian algorithm, the Orthogonal matching pursuit (OMP) and l₁ norm based complexvalued methods.

This paper is devoted to the study of quadratics residue codes and their extended codes over a finite non-chain ring. A class of Gray maps preserving the self-duality from the ring to the finite field are introduced. Some structural properties of quadratic residue codes and their extended codes are given. As an interesting application of these families of codes, some good linear codes are obtained by a special Gray map.

Generating high-resolution image from a set of degraded low-resolution images is a challenge problem in image processing. Due to the ill-posed nature of Super-resolution (SR), it is necessary to find an effective image prior model to make it well-posed. For this purpose, we propose a mixed non-local prior model by adaptively combining the non-local total variation and non-local H1 models, and establish a multi-frame SR method based on this mixed non-local prior model. The unknown Highresolution (HR) image, motion parameters and hyperparameters related to the new prior model and noise statistics are determined automatically, resulting in an unsupervised super-resolution method. Extensive experiments demonstrate the effectiveness of the proposed SR method, which can not only preserve image details better but also suppress noise better.

A sub-pixel disparity refinement algorithm based on Lagrange interpolation is proposed to calculate the sub-pixel disparity and the elevation of the target construction on the remote sensing image. The accurate integer matching feature points are obtained by improved affine scale invariant feature transform. A series of interpolation samples can be estimated through the three times magnification results of each feature matching point and its close neighbor pixels. The final sub-pixel deviation can be calculated by the interpolation samples. Experiment results show that a more accurate elevation information can be obtained on the remote sensing image with corrective epipolar line, and an accurate sub-pixel disparity result can be estimated without the epipolar line constraint as well.

With the recent advent of low-cost acquisition depth cameras, extracting 3D body skeleton has become relatively easier, which significantly lighten many difficulties in 2D videos including occlusions, shadows and background extraction, etc. Directly perceived features, for example points, lines and planes, can be easily extracted from 3D videos such that we can employ rigid motions to represent skeletal motions in a geometric way. We apply screw matrices, acquired by using rotations and translations in 3D space, to model single and multi-body rigid motion. Since screw matrices are members of the special Euclidean group SE(3), an action can be represented as a point on a Lie group, which is a differentiable manifold. Using Lie-algebraic properties of screw algebra, isomorphic to se(3), the classical algorithms of machine learning in vector space can be expanded to manifold space. We evaluate our approached on three public 3D action datasets: MSR Action3D dataset, UCF Kinect dataset and Florence3D-Action Dataset. The experimental results show that our approaches either match or exceed state-of-the-art skeleton-based human action recognition approaches.

This paper proposes a new object-based classification method for Polarimetric synthetic aperture radar (PolSAR) images, which considers scattering powers from an improved model-based polarimetric decomposition approach, as well as the spatial and textural features.With the decomposition, the scattering ambiguities between oriented buildings and vegetation are reduced. Furthermore, various contextual features are extracted from the object and incorporated into the K-nearest neighbors (k-NN) based classification. To reduce the feature redundancy, a new Supervised locally linear embedding (S-LLE) dimensionality reduction method is introduced to map the high dimensional polarimetric signatures into the most compact low-dimensional structure for classification. Experimental results with Airborne synthetic aperture rada (AIRSAR) C-band PolSAR image demonstrate the superior performance to other methods.

As for the research in the Free view television (FTV) and stereo video areas, the accurate multi-view video capture is a expensive precondition. We present a virtual multi-view video capture system based on OpenGL programming. By using this system, a researcher may create arbitrary 3D scenes, and capture images with depth maps by setting up multiple virtual cameras, whose intrinsic and extrinsic parameters can be imported from or exported to the outside.We conducted several experiments within it to perform camera calibration, depth estimation and view-points synthesis. The system has been demonstrated to be a powerful tool for evaluation of stereo video algorithms.

A novel matrix completion algorithm which iteratively minimizes the fitting error and the matrix rank is presented. Unlike conventional matrix completion algorithms, which usually require some relaxation technique to cope with the low rank constraints, the proposed algorithm does not require any such techniques, thus making the selection of the parameter q of the matrix q-norm (0 < q≤ 1) or the regularization parameter unnecessary. Simulation results of the random generated data and Jester joke data set verify our algorithm's effectiveness and superiority over the reported algorithms in literature.

Group authentication usually checks whether an individual user belongs to a pre-defined group each time but cannot authenticate all users at once without public key system. The paper proposes a Randomized component-based asynchronous (t,m,n) group authentication ((t,m,n)-RCAGA) scheme. In the scheme, each user employs the share of (t,n)-threshold secret sharing as the token, constructs a Randomized component (RC) with the share and verifies whether all users belong to a pre-defined group at once without requiring all users to release randomized components simultaneously. The proposed scheme is simple and flexible because each group member just uses a single share as the token and the scheme does not depend on any public key system. Analyses show the proposed scheme can resist up to t −1 group members conspiring to forge a token, and an adversary is unable to forge a valid token or derive a token from a RC.

Data leakage prevention (DLP) is very important for sensitive or unauthorized data protection, however, most current DLP technologies are based on content monitor, detection and filtering, which can be easily bypassed or cheated. We propose a thorough and highlevel Content protection secure scheme of DLP (CPSec DLP) based on kernel-level mandatory encryption, in which we proposed mutual authentication and key agreement method between client and server, and we adopted SM2 algorithm for session key management; and we propose kernel-level mandatory secure middleware for unstructured data protection, in which the secure middleware works in File system driver (FSD) layer supporting for “write-encryption, open-decryption” operation, once the data is written to storage space either in hard-disk or USB disk the data is mandatorily encrypted, while when the data is open the mandatory secure middleware decrypts the data to plain in system memory. Moreover we propose data share and delivery among domain internal users and external customers. In the CPSec DLP scheme, the encryption algorithms, security policy and rules can be dynamically parameterized when necessary, while in the lifecycle the data management can only be used according to its usage control rules, such as read-only, write, save, print, export, backup rights. Upon the proposed CPSec DLP, we implemented the CPSec DLP system in kernel-level driver layer based on FSD, which supports parameterized process and document format for unstructured data leakage protection. Large amount of experiments manifest the proposed scheme is secure, reliable, extendible and efficient for kinds of format unstructured data leakage protection.

Distributed antenna systems (DASs) using Radio-over-fiber (RoF) links is a promising candidate to provide flexible coverage and high data rates. For simulcast Wireless local-area network (WLAN) use in RoF systems, the stations covered by different Remote antenna units (RAUs) contend for the wireless medium using Media access control (MAC) mechanism. Severe inter-RAU hidden nodes problem will happen due to the stations in different RAUs cannot detect each other very well. In order to alleviate this inter-RAU collision problem, a promising detection-switching-assisted Distributed coordination function (DCF) mechanism is presented using in the uplink. The control delay issues of the proposed mechanism are also discussed in this paper.

In the heterogeneous Internet of things (IoT), the Signal to interference plus noise ratio (SINR) and delay constraint are two important factors that influence the throughput of IoT and the performance of users. Until recently, most network selection policy researches were based on either the Shannon theory or the signal strength, while the combined influence of the delay constraint and the SINR, which has a significant impact on resource utilization, is hardly considered. We therefore propose an SINR driven joint network selection policy, which incorporates the delay constraint and the signal strength into the SINR. This policy permits IoT users to access the network with the maximum of SINR from all the available networks under the delay and signal strength constraints. Theoretical analysis and the simulation results show that the joint network selection policy can obtain the higher throughput of IoT and average SINR comparing with other polices.

Time-triggered (TT), Rate-constrained (RC) and Best-effort (BE) traffics are included in Timetriggered ethernet (TTEthernet). For RC messages transmission is affected by TT messages, traditional scheduling policy cannot be well applied in TTEthernet. Dynamic programming priority (DPP) algorithm combines priority policy and dynamic programming algorithm for scheduling RC flows. The time slice for RC flows transmission is got by SMT solver YICES; RC flows are classified to different groups according to the priorities; Higher priority packets in one time slice are scheduled using First input first output (FIFO) policy and lower priority packets are scheduled by Dynamic programming policy. DPP policy guarantees different real-time requirements of heterogeneous RC flows, and make the best of time slice resource in aviation industries. The upper bound End-End of three methods and algorithm feasibility is analyzed. Simulation in aviation shows that DPP policy can obtain better real-time performance than other scheduling algorithms.

Channel handoff is a crucial function for Cognitive radio ad hoc networks (CRAHNs). The absence of centralized infrastructures and the limited power make the handoff design more challenging. A learningbased interference-aware handoff scheme is proposed for distributed CRAHNs. We model the channel handoff process as a Partially observable Markov decision process (POMDP) and adopt a Q-learning algorithm to find an optimal handoff strategy in a long term. The proposed algorithm obtains an efficient transmission performance by considering the interferences among SUs and PUs. To achieve PU awareness, the handoff scheme predicts the PU activities by using the historical channel usage statistics. In addition, we also propose a refined channel selection rule to compromise between learning speed and cumulative transmission reward. The simulation results show that the proposed handoff scheme can adapt to the PU activities and achieves a better performance in terms of high throughput and low collisions. And the learning process keeps a considerable balance between convergence time and cumulative reward.

Fast and effective identifications of a large number of items are required in many Radio-frequency identification (RFID) applications. Simultaneous responses from multiple tags are corrupted by collisions and thus result in low identification efficiency. To address the problem of low identification efficiency, many existing tag anticollision algorithms try to schedule the identification process to avoid collisions. A novel anti-collision scheme based on specific selection function is presented, in which tags pick a slot according to a selection function instead of being randomly assigned to slots within a frame. If collision occurs, the reader sends the custom command SETBM according to the collision information. When probed by the SETBM, the tags apply the selection function to its partial ID and send the mapped result to the reader. According to the mapped result, the reader assigns the time slot to the tags. All tags collided in the same slot will be identified by the tree traversal scheme. Compared to the most of existing anti-collision algorithms, simulation results show that our proposed algorithm can achieve better performance in terms of identification efficiency, time efficiency, and energy consumption.

We present a new adaptive subsample Explicit time delay estimator (ETDE) that is well suitable for determining and tracking the large-range time-varying delay between two noisy bandlimited signals only using low-order interpolators. Unlike conventional ETDE where a Fractional delay filter (FDF) is used, the new ETDE is based on a novel on-line interpolator which has the ability to optimally synthesize any continuously variable nonintegral sample delay. The theoretical derivations for our proposed on-line interpolator and the new ETDE algorithm are provided. Simulations are presented to evaluate the performance of the new estimator and to compare the performance with other estimators. Simulation results show that the new ETDE significantly outperforms FDF-based ETDE when the delay is large or varies in a large range, and the same filter orders are used.

A novel Synthetic aperture radar (SAR) signal processing technique has been proposed which refocused slow moving targets based on phase retrieval algorithm. After theoretical derivation, we can get that the raw data Fourier magnitude of slow moving targets is approximate to the stationary ones in the SAR system. By applying the Fourier magnitude of received data to phase retrieval algorithms, the blur and defocusing effect caused by the moving of the targets can be eliminated. The simulated results demonstrate the validity of this algorithm.

This paper proposes a new SAR image segmentation method based on graph and gray level reduction in Independent component analysis (ICA) space. Firstly, according to the grayscale information of SAR image, effective use of gray level reduction for initial segmentation can group the pixels with same or similar values to the same homogeneous region, which can address the problem of over-segmentation. Secondly, the features of regions are extracted in ICA space, and then the similarity degree can be calculated by Euclidean distance. The initial regions are merged in fully connected graph based on minimum spanning tree in ICA space. The process of region merging is divided into two phases; the first phrase is merging the different regions with the largest similarity degree, the second will focus on updating the fully connected graph for iteration. Finally, experimental and comparative results on synthetic and real SAR images verify the efficiency of the proposed algorithm.