Abstract: A mix-signal high precision capacitor mismatch error calibration method for charge domain pipelined ADCs is proposed. The calibration method calibrates the capacitors one by one based on binary search. Charge errors caused by the capacitor mismatch in and between pipelined sub-stage circuits can be compensated by the proposed calibration method. Based on the proposed calibration method, a 14bit 250MS/s charge domain pipelined Analog-to-digital converter (ADC) is designed and realized in a 1P6M 0.18m CMOS process. Test results show the 14bit 250MS/s ADC achieves the signal-to-noise ratio of 70.7dBFS and the spurious free dynamic range of 84.6dB, with 70.1MHz single-tone sine wave input at 250MS/s, while the ADC core consumes the power consumption of 235mW and occupies an area of 3.2mm2.
Abstract: An Adaptive voltage scaling (AVS) buck converter with Preset circuit (P-AVS), which can adaptively scale the output voltage fast, is presented. The voltage scaling loop in the proposed Pulse width modulation (PWM) buck with P-AVS is divided into coarse scaling loop and fine scaling loop, which can regulate the reference voltage adaptively according to load status. The coarse scaling loop can set quickly a coarse value of reference voltage according to the operation frequency of the load by a preset block, and the fine scaling loop is designed to scale voltage finely. The proposed P-AVS buck converter, with 3.3V input and 2MHz switching frequency, is fabricated in a 0.13mm standard CMOS process. The output voltage and the frequency of the load are designed from 0.7V to 1.5V and 30MHz to 120MHz respectively. Compared with a typical AVS buck, the proposed P-AVS converter can save 17ms scaling up from 0.7V to 1.1V.
Abstract: The servo motor's flexible acceleration/deceleration (acc/dec) control is an emerging research topic in the automation field. A velocity control algorithm based on trigonometric function is proposed in this paper. With required parameters, it transforms trigonometric calculations into elementary mathematical operations and calculates the velocity controlling values iteratively, which could avoid trigonometric calculation and reduce the computation time. Experimental results show that the proposed algorithm is suitable for implementation on field programmable gate arrays and achieves a flexible controlling, enhancing both the equipment's stability and reliability. It is promising to significantly improve the high-speed computerized numerical control equipment's controlling accuracy, without a huge hardware resource consumption.
Abstract: We show how Support vector machines (SVM) can be applied to the Satisfiability (SAT) problem and how their prediction results can be naturally applied to both incomplete and complete SAT solvers. SVM is used for the classification of the variables in the SAT problem and the classification results are the assignment of the variables. And we also present empirical results of applying SVM to instances of the SAT problem from the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) archive and compare them against the results of other incomplete and complete algorithms for the SAT problem.
Abstract: This paper proposes a Finite-time Zhang neural network (FTZNN) to solve time-varying quadratic minimization problems. Different from the original Zhang neural network (ZNN) that is specially designed to solve time-varying problems and possesses an exponential convergence property, the proposed neural network exploits a sign-bi-power activation function so that it can achieve the finite-time convergence. In addition, the upper bound of the finite convergence time for the FTZNN model is analytically estimated in theory. For comparative purposes, the original ZNN model is also presented to solve time-varying quadratic minimization problems. Numerical experiments are performed to evaluate and compare the performance of the original ZNN model and the FTZNN model. The results demonstrate that the FTZNN model is a more effective solution model for solving time-varying quadratic minimization problems.
Abstract: We introduce the concept of Complementary formula (COMF), which is a new and non-equivalent way for Knowledge compilation (KC). Based on the Hyper extension rule (HER) which is an expansion of Extension rule (ER), we design a compilation algorithm which can formula compile each Conjunctive normal form (CNF) formula to complementary Fully complementary connected diagram (c-FCCD), named as C2C (CNF formula to cFCCD). Theoretically, c-FCCD is a kind of complementary formulae of the input formulae and can support all queries and partial transformations in KC map. Experimentally, C2C is competitive with the EPCCL compilers KCER, C2E, UKCHER, DKCHER and IKCHER.
Abstract: Among post-quantum alternatives, latticebased cryptography is the most promising one, due to its simple operations, reduction from aver-age-case to worstcase hardness, and supporting of rich functionalities. Ring signature enables a user to sign anonymously on behalf of an adaptively chosen group, and has multiple applications in anonymous e-voting, anonymous authentication, whistle blowing etc. However, most lattice-based ring signature schemes were constructed in the random oracle model from lattice basis delegation and they suffer large verification key sizes as a common disadvantage. This work proposes an efficient ring signature scheme from lattice basis delegation without random oracle based on the extended split-SIS problem, whose security is approximately as hard as the worst-case SIVP problem. Our scheme is proved to be anonymous and existentially unforgeable under latticebased assumptions. Finally, the verification key size is significantly reduced to a small constant, instead of increasing linearly with the number of ring members.
Abstract: To publish social graphs with differential privacy guarantees for reproducing valuable results of scientific researches, we study a workflow for graph synthesis and propose an improved approach based on weighted Privacy integrated query (wPINQ). The workflow starts with a seed graph to fit the noisy degree sequence, which essentially is the 1K-graph. In view of the inaccurate assortativity coefficient, we truncate the workflow to replace the seed graph with an optimal one by doing target 1K-rewiring while preserving the 1K-distribution. Subsequently, Markov chain Monte Carlo employs the new seed graph as the initial state, and proceeds step by step guided by the information of Triangles by intersect to increase the number of triangles in the synthetic graphs. The experimental results show that the proposed algorithm achieves better performance for the published social graphs.
Abstract: Wireless sensor networks have some obvious characteristics, such as communication range is limited, computing power is limited and energy is limited. Group key agreement in this environment requires a cross-cluster, computation and communication overhead are lightweight and highly safe group key agreement protocol. Aiming at these demands, the paper proposes a Self-certified cross-cluster Asymmetric group key agreement (SC-AGKA). To establish a lightweight and efficient group communication channel among sensor nodes. According to the cluster head as the bridge node to realize the sensor nodes in different cluster have the same group key information, and negotiate a pair of asymmetric group keys to realize the cross cluster secure communication. The group communication adopts asymmetric encryption mechanism. It realizes the group security communication mechanism of message sender unconstraint. The asymmetric group key agreement has the key self-certified, which does not need additional rounds to verify the correctness of group key. Proven and analysis show that the proposed protocol has the advantages of in security and energy consumption.
Abstract: The value distribution of an exponential sum based on a class of Niho exponents is determined. As applications, we also completely determine the weight distribution of a class of four-weight cyclic codes and the correlation distribution among sequences in a sequence family, which extend some known results.
Abstract: Having the advantages of certificateless signature and the aggregate signature at the same time, certificateless aggregate signature has been widely applied in e-business, e-government and software security since it was proposed in 2007. Although a number of certificateless aggregate signature schemes have been proposed, all of them are based on the classic number theory problem, which are no longer secure in the quantum era. In this paper, a certificateless sequential aggregate signature over number theory research unit lattice is proposed, which is proven to be secure in random oracle model. Moreover, we extend the new scheme into an efficient certificatebased sequential aggregate signature which is also secure in quantum era.
Abstract: We study the periods of sequences produced by the cascade connection of two Feedback shift registers (FSRs). The period of the cascade connection is the period of the longest sequences it produces. An upper bound for the period of the cascade connection of a Nonlinear feedback shift register (NFSR) into a Linear feedback shift register (LFSR) is established. In addition, the cascade connection of an n-stage maximum-length LFSR into an n-stage NFSR is called an (n + n)-stage Grain-like NFSR, and we propose two families of (n + n)- stage Grain-like NFSRs such that the minimal period 2n-1 is achievable for a positive integer n.
Abstract: Translating computation tree logic formulas into Büchi tree automata has been proven to be an effective approach for implementing branching-time model checking. For a more generalized class of lattice-valued (L-valued, for short) computation tree logic formulas, the revelent study has not proceeded yet. We introduce the notion of L-valued alternating Büchi tree automata and achieve the goal of associating each L-valued computation tree logic formula with an L-valued Büchi tree automaton. We show that a satisfiability problem for an L-valued computation tree logic formula can be reduced to one for the language accepted by an L-valued Büchi tree automaton. L-valued alternating Büchi tree automata are the key to the automata-theoretic approach to L-valued computation tree logics, and we also study their expressive power and closure properties.
Abstract: Images/videos captured in low-light conditions often present low luminance and contrast. Although the existing low-light enhancement algorithms can improve the subjective perception, color distortion and over-enhancement are extremely obvious, which will disturb the subsequent intelligent analysis. Therefore, a naturalness-preserved low-light enhancement algorithm for intelligent analysis is proposed in this paper. An enhancement model is established in RGB color space. Images of ColorChecker color chart are captured under a series of light conditions. To preserve the naturalness, the factors of the proposed enhancement model are estimated by the images captured in practical illumination environment. Experimental results demonstrate that the proposed algorithm can produce natural enhanced results and improve the performance of vehicle license plate localization and skin color detection compared to the existing algorithms. Furthermore, the proposed algorithm can process the 720p videos at the speed of 28.3 fps on average.
Abstract: Among qualitative direction relation models, Oriented point relation algebra (OPRAm) is a remarkable model for robot navigation with uncertain direction information. It has great advantages in providing powerful expressions with very limited information compared with other point-based spatial relation models. The original OPRAm is defined in 2D space, and its model and reasoning algorithm are found not applicable in 3D space. We proposed a novel direction relation model named OPRA3Dm to extend the original OPRAm to 3D space, and presented a new reasoning algorithm on Oriented point relation algebra in three dimension (OPRA3Dm). A further study was carried out for composition reasoning on OPRA3Dm. The proposed reasoning algorithm will deduce new information which cannot be directly detected by hardware. The experiment showed the algorithm had some practical significance, it can be applied to the Unmanned aerial vehicle (UAV) navigation and similar scenarios.
Abstract: Reassembling fragmented image files is a useful technique to seize image evidence in digital forensics. A key problem of reassembly is how to measure the similarity between the fragments. Most of the measurements are based on the local similarity of the images. We analyze the impact of similarity patterns on the judgment of the adjacency of the fragments and conclude that the horizontal similarity has little help. According to this conclusion, we improve the median edge detector by replacing the horizontal similarity with the left and right diagonal similarity. Furthermore, we improve the sum of differences and Euclidean distance by replacing the mean/sum used in the two measurements with the median. Experimental results verify the analysis of similarity patterns and the improvements.
Abstract: S-Transform (ST) is a powerful timefrequency analysis tool with several useful performances. It has been found that ST is sensitive to the sudden change of signal phase and causes a spectrum spread phenomenon, which is often regarded as a shortage. However, a new interpretation of ST spectrum is proposed:the ST can provide a better property of phase hopping detection due to the sensitivity and mono-peak response of ST spectrum. It is a beneficial method to phase hopping estimation. The mathematical relation between the phase hopping angle and the instantaneous value changes of ST spectrum is derived in an explicit representation. This work helps to make a better understanding of the ST spectrum. Besides of the formula derivation, the characteristic is also proved and confirmed by the numerical simulations.
Abstract: This study proposes a novel short-range multitarget motion parameter estimation method based on Hough transform. The proposed method can be used for multitarget detection, motion parameter estimation, and data association. In our proposed method, the measured radial distance and Doppler frequency versus time data is mapped to the motion parameter space by Hough transform. The motion parameter space data is binarized to determine the number of targets. The unsupervised nearest neighbor clustering technique is used to determine the search space of targets. The maximum value in each search space is estimated as the motion parameter of the corresponding target. Simulation results show that the proposed method has higher parameter estimation accuracy than that of conventional methods.
Abstract: Because of the fluctuation and uncertainty characteristics of wind power, it is difficult to achieve a perfect wind power forecast. The forecast error may lead to an imbalance between the load demand and power supply. The object of recent research on forecast error is to achieve the probability distribution of forecast error based on the statistics of historical data. This statistical error achieved from a probability distribution cannot reveal the real-time condition of wind power. A real-time forecast Error estimate method based on dictionary learning (EEDL) was proposed. In EEDL, several coefficients that have strong relevance to the forecast error are computed. The dictionary learning method is used to extract the eigenvalues of forecast error from these coefficients. Based on the eigenvalues, a real-time error estimation model was built to obtain the forecast error. EEDL was compared to the estimation method based on a Probability distribution function (PDF). The performance of EEDL was also compared to the error estimation method based on a PDF while using different forecast techniques.
Abstract: The state-of-the-art speaker recognition system degrades performance rapidly dealing with shorttime utterances. It is known to all that identity vectors (i-vectors) extracted from short utterances have large uncertainties and standard Probabilistic linear discriminant analysis (PLDA) method can not exploit this uncertainty to reduce the effect of duration variation. In this work, we use Shared mixture of PLDA (SM-PLDA) to remodel the i-vectors utilizing their uncertainties. SM-PLDA is an improved generative model with a shared intrinsic factor, and this factor can be regarded as an identity vector containing speaker indentification information. This identity vector can be modeled by PLDA. Experimental results are evaluated by both equal error rate and minimum detection cost function. The results conducted on the National institute of standards and technology (NIST) Speaker recognition evaluation (SRE) 2010 extended tasks show that the proposed method has achieved significant improvements compared with ivector/PLDA and some other advanced methods.
Abstract: The complexity measures of chaotic or periodic signals are perpetual topics of interest to data scientists. This work adheres to the framework of the traditional 0-1 test for chaos and replaces sine and cosine functions by modified sign functions. The compressive mapping rules chosen are one-threshold of three-value or three-threshold of five-value. In new criteria for chaos in forms of the 3s plot and Ks metric compared with 0-1 test results, the periodic state of data features a short beeline instead of a big ring in the pq plot and signs the nearest zero mark, while the chaotic state signs a simple curve instead of a random-walking shape in the pq plot, and shows the nearest one mark. By computing the Lorenz equation evolution under the contrast tests of the Poincare section and Lyapunov index, we visualize a new chaoscriteria design in symbolic dynamics and data compression principles, and our work may lay the foundation for further expressing the chaotic appearance of novel signals deep into future brainets.
Abstract: Electromagnetic compromising emanations are potential threat to computer security. Computer emits energy in the form of electromagnetic wave which includes the processed information. The electromagnetic wave can be received and decoded in the distance, so the unintended information leakage occurs. In this paper, the automatic information reconstruction for computer electromagnetic eavesdropping is studied. This paper attempts to combine simple digital signal processing method for the first time to extract electromagnetic leakage information. The performance of the new method is presented numerically and experimentally. Compared with other algorithms, this method has strong practicability and reliability. Under complex electromagnetic environment, the synchronization parameters can be extracted and the information can be reconstructed automatically, quickly and reliably.
Abstract: Existing dynamic data possession verification schemes not only suffer from low efficiency of rebalancing its Merkle Hash tree (MHT) when executing data updating, but also lack effective mechanism to verify multi-version files. Aiming at these problems, this paper propose a new data structure called Rank-based multi-version Merkle AVL tree (RBMV-MAT) to achieve efficient batch updating verification for multi-version data. RBMV-MAT uses a special lock and relaxed balance to decrease the frequency of rebalacing operations. The experimental results show that our efficient scheme has better efficiency than those of existing methods.
Abstract: An Index geographic gossip (IGG) algorithm is proposed. Relay nodes participate in information exchange and updating. The cumulative number of times these nodes participate is characterized by an index number, which can be used to accelerate information updating. The convergence property of the IGG algorithm is theoretically analyzed in ring and grid network topologies. The IGG algorithm improves the standard gossip algorithm by a gain of O(n) in both convergence time and communication cost. Compared to the geographic gossip algorithm, the IGG algorithm has a gain on the order of O(n) and O(n1/2) in the average hop count for information exchange and communication cost, respectively. Finally, the proposed IGG algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.
Abstract: A new frequency standard comparison system is proposed based on the group quantization phase processing. By shortening the width of phase coincidence fuzzy area and capturing the best group phase coincidences, we reduce the randomness of the counting gate and improve the measurement precision of the comparison system. The method is based on the Fieldprogrammable gate array (FPGA), which not only retains the advantage using phase synchronization detection technology to overcome the ±1-word counting error, but also accelerates the system response time, simplifies the measurement equipment, and reduces the development cost and power consumption. The experimental results show that the method is reasonable and scientific, and the comparison accuracy of the system can reach the E-12 s-1 level, obviously superior to the measurement accuracy of the traditional frequency standard comparison method, which has a wide application and popularization value.
Abstract: Sensor networks contain a large number of nodes that can perceive changes of external environment, which makes sensor networks particularly suitable for target tracking and discovery. In practical applications, once the sensor nodes are arranged, it is difficult to move them. We focus on analysing the target discovery ability of static-sensor networks. We divided the target into two kinds, one is persistent target and the other is instantaneous target. We investigate target discovery probability and discovery delay with different nodes density, sensing range and duty cycle. Energy saving is still the most important problem for the applications of sensor networks. Balancing target discovery capability and lifetime of the whole sensor network is necessary. Based on the theoretical analysis, we propose a coverage adaptive optimization algorithm that significantly prolongs the life of sensor networks. Simulation results show the advantage of coverage adaptive optimization algorithm over previous proposed methods.
Abstract: Distributed Denial of Service (DDoS) attack is a difficult issue which needs to be addressed in Software defined networking (SDN). In order to help the controller to weather out the DDoS attack, an efficient controller scheduling method is proposed. The proposed controller scheduling method uses the normalized waiting time, length and extent of the switch being attacked to choose the request that needs to be processed by the controller. The evaluation results validate that compared with the polling based controller scheduling method, the proposed one can significantly reduce the connection failure ratio and delay.
Abstract: At present, fixing Android system vulnerabilities relies on official Android support and various equipment manufacturers, and it is mainly implemented by system upgrades. This situation causes many problems, such as high costs and delayed fixing of vulnerabilities. This study is performed to design a novel fixing policy construction model targeting Android system vulnerabilities, which can be used for vulnerability feature quantification and fixing policy customization. On this basis, a novel security vulnerability solution called DroidHFix is proposed and implemented. This solution constructs security policies and loads security policy files during the risky application startup. The system helps to fix Android system vulnerabilities dynamically and defend against attacks on the risky application depending on system vulnerability exploitation. Experimental results show that DroidHFix fixes the Android system vulnerabilities effectively, with good performance and compatibility.
Abstract: Network coding technology is always employed to improve the throughput of Wireless mesh networks (WMNs). However, traditional routing protocols based on network coding can only passively wait for coding opportunity, and the routing process is oblivious to coding operation. Taking into account the high throughput requirement in WMNs, a novel Coding awareness routing protocol with maximum benefit (CARMB) is proposed in this paper. The CARMB could actively create potential coding opportunities in the process of path establishment, which attempts to choose an appreciated route with more coding benefits among available path candidates. Simulations through NS-2 demonstrate that the CARMB performs better than traditional schemes in enhancing average end-to-end throughput and increasing coding opportunities as well as reducing average end-to-end delay. In particular, average end-to-end throughput and coding gain could be improved by 11% and 17% respectively compared with previous approaches.
Abstract: Convolutional neural network (CNN) has become a promising method for Synthetic aperture radar (SAR) target recognition. Existing CNN models aim at seeking the best separation between classes, but rarely care about the separability of them. We performs a separability measure by analyzing the property of linear separability, and proposes an objective function for CNN to extract linearly separable features. The experimental results indicate the output features are linearly separable, and the classification results are comparable with the other state of the art techniques.
Abstract: Given the orthogonal basis (or the projections) of no less than two subspaces in finite dimensional spaces, we propose two novel algorithms for computing the intersection of those subspaces. By constructing two matrices using cumulative multiplication and cumulative sum of those projections, respectively, we prove that the intersection equals to the null spaces of the two matrices. Based on such a mathematical fact, we show that the orthogonal basis of the intersection can be efficiently computed by performing singular value decompositions on the two matrices with much lower complexity than most state-of-the-art methods including alternate projection method. Numerical simulations are conducted to verify the correctness and the effectiveness of the proposed methods.
Abstract: Spoofing attacks for Global navigation satellite system (GNSS) would cause the abnormal changes of receiver measurements. Monitoring the abnormal changes is the main pattern of anti-spoofing techniques. Anomalies of Doppler shifts are important for GNSS spoofing detection. Most researchers focus on the consistency of Doppler such as the consistency of carrier Doppler and code rate. But the method is powerless in some cases. Doppler shift measurement is proportional to change rate of pseudorange, and the relationship would be broken in the case of spoofing attack. As both Doppler measurements and pseudoranges are related to velocity of receiver, two approaches of computing velocity can be used to check the consistency of Doppler shifts and pseudoranges measurements. An efficient spoofing detection method based on the consistency check of velocities is proposed in the paper. Principle of the method is given in detail and its performance evaluations are provided as well. Simulation results demonstrate the effectiveness of the solution.