Abstract: Compared with traditional endonasal endoscope, Virtual endonasal endoscope (VEE) is a computerized alternative solution with the advantages of being non-invasive, affordable, no perforation, low risk of infection, and high degree of sensitivity. A Central path extraction algorithm based on Pre-planning of the roaming area (CPE-PRA) is proposed to extract the central path of the nasal cavity model efficiently. And a B-spline curve fitting algorithm based on Relative center deviation (RCD) is introduced to fit a smooth curve to the extracted path, so that the virtual camera could have both a steady turning speed and a broad vision. A series of comparison tests were conducted to evaluate the effectiveness and efficiency of the proposed algorithms.
Abstract: Object detection is one of the essential tasks of computer vision. Object detectors based on the deep neural network have been used more and more widely in safe-sensitive applications, like face recognition, video surveillance, autonomous driving, and other tasks. It has been proved that object detectors are vulnerable to adversarial attacks. We propose a novel black-box attack method, which can successfully attack regression-based and region-based object detectors. We introduce methods to reduce search dimensions, reduce the dimension of optimization problems and reduce the number of queries by using the Covariance matrix adaptation Evolution strategy (CMA-ES) as the primary method to generate adversarial examples. Our method only adds adversarial perturbations in the object box to achieve a precise attack. Our proposed attack can hide the specified object with an attack success rate of 86% and an average number of queries of 5, 124, and hide all objects with a success rate of 74% and an average number of queries of 6, 154. Our work illustrates the effectiveness of the CMA-ES method to generate adversarial examples and proves the vulnerability of the object detectors against the adversarial attacks.
Abstract: Automated human facial image deidentification is a much-needed technology for privacy-preserving social media and intelligent surveillance applications. We propose a novel utility preserved facial image de-identification to subtly tinker the appearance of facial images to achieve facial anonymity by creating “averaged identity faces”. This approach is able to preserve the utility of the facial images while achieving the goal of privacy protection. We explore a decomposition of an Active appearance model (AAM) face space by using subspace learning where the loss can be modeled as the difference between two trace ratio items, and each respectively models the level of discriminativeness on identity and utility. Finally, the face space is decomposed into subspaces that are respectively sensitive to face identity and face utility. For the subspace most relevant to face identity, a k-anonymity de-identification procedure is applied. To verify the performance of the proposed facial image de-identification approach, we evaluate the created “ averaged faces” using the extended Cohn-Kanade Dataset (CK+). The experimental results show that our proposed approach is satisfied to preserve the utility of the original image while defying face identity recognition.
Abstract: Automatic identification of intracranial electroencephalogram (iEEG) signals has become more and more important in the field of medical diagnostics. In this paper, an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of iEEG epileptic seizures. Four kinds of entropy, Sample entropy, Approximate entropy, Shannon entropy, Log energy entropy are extracted from the database as the feature vectors of Neural network (NN) during the identification process. Four kinds of classification tasks, namely Pre-ictal v Post-ictal (CD), Pre-ictal v Epileptic (CE), Post-ictal v Epileptic (DE), Pre-ictal v Post-ictal v Epileptic (CDE), are used to test the effect of our classification method. The experimental results show that our algorithm achieves higher performance in all tasks than previous algorithms. The effect of hidden layer nodes number is investigated by a constructive approach named growth method. We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD, CE, DE, and the multitask classification problem CDE, respectively.
Abstract: In this paper, we propose a fully automatic mesh segmentation method, which divides meshes into sub-meshes recursively through spectral analysis. A common problem in the spectral analysis of geometric processing is how to choose the specific eigenvectors and the number of these vectors for analysis and processing. This method tackles this problem with only one eigenvector, i.e. Fiedler vector. In addition, using only one eigenvector drastically reduces the cost of computing. Different from the Fiedler vector commonly used in the bipartition of graphs and meshes, this method finds multiple parts in only one iteration, vastly reducing the number of iterations and thus the time of operation, because each iteration produces as many correct boundaries as possible, instead of only one. We have tested this method on many 3D models, the results of which suggest the proposed method performs better than many advanced methods of recent years.
Abstract: In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a Many-objective evolutionary algorithm based on a spatial division and angle-culling strategy (MaOEA-SDAC) is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed MaOEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of MaOEA-SDAC in solving high-dimensional optimization problems has been verified.
Abstract: Network on a chip (NoC) uses packet-switched network to implement interconnections in System on chip (SoC). In SoC design, performance and energy efficiency are respectively the first and second priorities, and optimal on-chip communication should decrease the power consumption and area overhead. In this work, a simplified BCH codec is proposed for reliable communication in NoC and SoC. It performs BCH error corrections without Berlekamp’s algorithm, only using reduced syndrome bits to determine error patterns. The error locations can be found by looking up tables, by which the possible errors are directly corrected. Only one matrix product and one ROM access are required in the BCH decoder. The proposed (20, 8, 2) and (31, 16, 3) decoders in the paper can be easily applied for error corrections of interconnects and buses for NoC and SoC. It is also beneficial to correct data lines without length definition and control lines without storage.
Abstract: We proposes an improved grasshopper algorithm for global optimization problems. Grasshopper optimization algorithm (GOA) is a recently proposed meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The original GOA has some drawbacks, such as slow convergence speed, easily falling into local optimum, and so on. To overcome these shortcomings, we proposes a grasshopper optimization algorithm based on a logistic Chaos maps opposition-based learning strategy and cloud model inertia weight (CCGOA). CCGOA is divided into three stages. The chaos opposition learning initialization strategy is used to initialize the population, so that the population can be evenly distributed in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial population distribution of the grasshopper algorithm. The inertia weight cloud model is introduced into the grasshopper algorithm, and different inertia weight strategies are used to adjust the convergence speed of the algorithm. Based on the principle of chaotic logistic maps, local depth search is carried out to reduce the probability of falling into local optimum. Fourteen benchmark functions and an engineering example are used for simulation verification. Experimental results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.
Abstract: Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution (DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.
Abstract: Local binary pattern (LBP) is sensitive to noise. Noise-resistant LBP (NRLBP) addresses this problem by thresholding local neighboring pixels into three-valued states (i.e., 0, 1 and uncertain bits) and recovering uncertain bits via an error-correction mechanism. In this paper, we extend NRLBP to deal with color images and propose a robust color image descriptor called Color context binary pattern (CCBP). In CCBP, we employ scale context and neighbor context to progressively correct the encoded bits. First, we encode intra-channel local neighboring pixels into three-valued states in scale space and use majority voting to correct all states across scales. Then, we compute inter-channel color feature distances and correct the uncertain bits via neighboring bit propagation. Finally, we construct the image descriptor by concatenating all histograms based on the corrected binary codes. Experiments on four benchmark databases demonstrate the robustness of CCBP for color image classification under very low signal-to-noise ratio levels.
Abstract: Inspired by the self-similar fractal properties of chaotic attractors and the heuristics of similarity filtering of images, a novel chaotic signal denoising algorithm is proposed. By grouping the chaotic signal with similar segments, the denoising of one-dimensional input is transformed into a two-dimensional joint filtering problem. Singular value decomposition is performed on the grouped signal segments and the transform coefficients are processed by thresholding to attenuate noise and finally undergo inverse transformation to recover the signal. Because the similar segments in the grouping have good correlation, the two-dimensional transformation of the grouping can obtain a more sparse representation of the original signal compared with the threshold value denoising in the direct one-dimensional transform domain, thereby having better noise suppression effect. Simulation results show that the algorithm can improve the reconstruction accuracy and has better signal-to-noise ratio than existing chaotic signal denoising algorithms.
Abstract: In recent decades, a number of protocols for Remote data integrity checking (RDIC) have been proposed. Identity (ID) based RDIC protocols are constructed to guarantee cloud data integrity and data privacy. The known protocols for RDIC always assume that the Private key generator (PKG) is a trusted one, but in real-world applications by corrupt PKG, malicious Cloud server (CS) can easily cheat the third party auditor that the data owner’s outsourced data are kept safe through the data has been deleted or altered. In this paper, we explore the novel model of RDIC with untrusted PKG and malicious CS, by employing the partial key method and Authentication, authorization, accounting (AAA) service. We construct a new ID-based RDIC, which provides the ID revocation and key updation. The experimental evaluations show that our scheme is more efficient than known ones.
Abstract: Power analysis methods are commonly used for evaluating the security of cryptographic devices. They are characteristically low-cost and display a high success rate and the ability to obtain important device information, e.g., keys. Given the current wide application of deep-learning technology, there is a growing tendency to incorporate power-analysis technology in development. This study investigates non-profiled deep-learning-based power analysis. The labels used in this attack are uncertain, and the attack conditions required are greatly reduced. We choose the Recurrent neural network (RNN), multilayer perceptron, and convolutional neural network algorithms, which use the same network structure, to recover the keys for the SM4 software and DES hardware implementations. We propose combining the RNN algorithm with power analysis, and validate the benefits experimentally. The experimental results show that they all successfully recover the correct key for the SM4 software implementation, although the RNN algorithm by itself achieves a better effect. This conclusion also applies to attacks on the DES hardware implementation but is limited to labels based on the bit model.
Abstract: A novel High-order extended Kalman Filter (HEKF) is designed for a class of complex dynamic systems with polynomial nonlinearities. The state and measurement models are represented by multi-dimensional high-order polynomials, respectively. All high-order polynomials in the state model are defined as implicit variables. By combining original variables with implicit variables, the state model is equivalently formulated to be a pseudo-linear form. By modeling dynamic relationship between implicit variables and combining original variables with all implicit variables, a new linear augmented state model is established correspondingly. The measurement model can be equivalently formulated as a linear form. On the basis of the new linear state and linear measurement models, the HEKF is designed and derived in detail. Simulation results demonstrate the effectiveness of the proposed estimator.
Abstract: Service-based architecture (SBA) is a profound advancement in the novel 5G Core network (5GC). Existing studies show that SBA can benefit from cloud computing to achieve extensibility, modularity, reusability, and openness. It also brings security problems (e.g., hypervisor hijacking, and malware injection). To provide secure 5G services, we propose a service-based cloud architecture called Mimicloud for 5GC based on dynamic and heterogeneous techniques. Mimicloud provides flexible reconfiguration mechanisms to protect containers and eliminate all attack knowledge obtained from adversaries. We use multiple containers to execute crucial services and ensure security with crosscheck. Mimicloud employs heterogeneous components to prevent multiple containers from being breached through the same vulnerabilities. Experimental results show that Mimicloud can effectively strengthen the security of the 5GC. The performance overhead is analyzed in order to demonstrate its scalability.
Abstract: In this paper, based on a result of Lidl and Mullen (Mathematical Journal of Okayama University, 1991), the maximum length and the second maximum length that can be attained by cycles of Dickson permutation polynomial (of the first kind) with parameter 1 are studied. Necessary and sufficient conditions for these two lengths to be attained are given, which are connected with Fermat primes and Mersenne primes, respectively. Furthermore, a class of coordinate sequences that maintains a large period is obtained, which is shown to be the coordinate sequences derived from cycles of the second maximum length. Explicit formulas for their periodicity and shift-equivalences are also presented.
Abstract: n this paper, we try to give a security evaluation of LIZARD stream cipher in regard to fault attacks, which, to the best of our knowledge, is the first fault analysis on LIZARD. We design a differential engine of LIZARD to track the differential trail of the keystreams. It is shown that the distributions of the keystream differences are heavily biased. Utilizing this characteristic, we propose an improved method to identify the fault location for LIZARD whose success probability approaches 1. Then we use the fault-free keystream and faulty keystreams to generate system of equations in internal state variables and solve it by SAT solver. The result shows that with 100 keystream bits, only 6 different faults are needed to recover the internal state. Finally, the comparison between LIZARD and Grain v1 shows that LIZARD is more resistable than Grain v1 in regard to fault attacks.
Abstract: An algorithm for output spectrum analysis of nonlinear system with correlative sources is proposed. First, the output terms of nonlinear system are analyzed based on the power series. Then, the algorithm is proposed. In the proposed method, different terms with the same frequency are picked out and added together by considering different phases, and the amplitudes of the output spectrum are obtained from the summarized results. Two numerical examples are used to verify the proposed algorithm. It is found that the amplitudes of output spectrum vary with different combinations of phases. Finally, the proposed algorithm is applied to control the output spectrum. Results show that the output amplitude of a specific frequency can be suppressed through changing both the amplitudes and the phases of the input signals.
Abstract: This paper presents the design and analysis of a distributed power amplifier with 6-dB bandwidth from 10MHz to 6GHz. To meet the stringent targeted specification, the concurrent design and analysis are carefully performed with optimizations in both passive and active devices. The gate capacitive division technique is proposed and proven theoretically of bandwidth extension effect and power efficiency enhancement. To validate the theory, a prototype is designed in a 0.25-μm GaAs technology. The fabricated amplifier chip is packaged in an evaluation cavity of SMA connectors. The measurement shows an average power gain of 15dB, OP1dB and PSAT of 31dBm and 32.6dBm at 3GHz, and PAE at OP1dB and PSAT points are 31.5% and 43.7% respectively. To the best of authors’ knowledge, the amplifier achieves the highest Power-added efficiency (PAE) among the similar GaAs amplifiers.
Abstract: This paper presents a Very large scale integration (VLSI) design method for Three-dimensional (3D) depth perception chip based on infrared coding structure light. The primary sub-modules on the chip contain the speckle pattern preprocessing module, block-matching disparity estimation, depth mapping and post-processing. The chip employs pipelining technology, and after Application specific integrated circuit (ASIC) verification, it proves that our chip has more advantages in performance of depth precision (12bits, 1mm @ 1m), image resolution (1280×960), time delay (less than 17ms), range limit (0.4~6m). It also can generate more stable and smooth depth map in real-time, which can be used in 3D recognition, motion capture or scene perception.
Abstract: Electric drive system with Insulated gate bipolar transistor (IGBT) power device is widely used in Electric vehicle (EV), which consists of inverter, cables and Permanent magnet synchronous motor (PMSM). Due to the fast switching in di/dt and dv/dt of IGBT device, the system produces serious radiated Electromagnetic interference (EMI) through the interconnection cables. Thus, modeling of EMI source, propagation path and load PMSM is the key to accurately evaluate the system’s radiation level. In addition, the system’s radiated EMI involves the integrated calculation of circuit, cable and electromagnetic field, which cannot be solved by using a single circuit or electromagnetic calculation method. Therefore, this paper develops an effective field-linecircuit coupling based method to investigate the radiated EMI problems for IGBT-PMSM drive system, which is validated by experimental measurement. Besides, the impact of power cable parameters on radiated EMI is discussed. The proposed approach has guiding significance for electromagnetic compatibility design of EV.
Abstract: In order to reduce the volume, weight and cost of conventional hybrid energy storage system (HESS) while properly exploring the complementary features of different energy storage devices for DC microgrid applications, this paper proposes a multiple-port three-level DC/DC converter. It possesses multiple ports sharing one front-end three-level DC/DC converter with an inductor and supercapacitor bank. Different types of batteries and/or multiple battery banks can be interfaced through the multiple terminals. Such a converter structure facilitates the cooperation of different energy storage devices to satisfy various power demands of DC microgrids with intermittent renewable generation plants. Moreover, the proposed structure allows power sharing among different energy storage devices, which enables more efficient cooperation of different battery banks or different types of batteries. Experimental results are presented to verify the efficacy of the proposed converter structure and its control.
Abstract: In the design of a BeiDou navigation satellite system(BDS) receiver, the carrier loop is largely influenced by the loop filter, and the selection of loop filter parameters is crucial to the performance of the carrier loop. In this paper, by analyzing the relationship between the parameters of the loop filter transfer function of the BDS receiver’s fourth-order carrier loop system and the time-domain index of the system, the loop filter transfer function parameters of the fourth-order system are obtained when the dynamic performance is best optimized under the condition that the maximum steady-state error is limited. The relationship between the phase margin, magnitude margin of the system and the parameters of the system transfer function is obtained through simulation experiment and theoretical derivation. The simultaneous equations of deriving the loop system transfer function parameters are given under the following conditions: the optimal phase margin and magnitude margin of the system are settled. These research results have important reference significance for the design of a high-performance BDS receiver.