In this paper our objective is to explore approaches of secure group-oriented communication with designation and revocation mechanisms simultaneously. We present a new scheme of Revocation-based broadcast encryption (RBBE) which is designed on Dan Boneh et al.'s scheme with the designation mechanism proposed in 2005. We combine two above-mentioned schemes into a new cryptosystem, called Dual-mode broadcast encryption (DMBE). Based on these work, we reach the following conclusions. We use the DMBE scheme as an example to show that it is feasible to construct a broadcast encryption scheme that supports designation and revocation mechanisms simultaneously. The cryptosystem with dual modes is more e.cient than that with single mode over computational costs, and the performance is improved to at most O(⌈N/2⌉), where N is the total number of users in the system. We prove completely that both the RBBE scheme and the DMBE scheme are semantically secure against chosen plaintext attack with full collusion under the decisional bilinear Di.e-Hellman exponent assumption.

Linear codes have been an interesting topic in both theory and practice for many years. Based on the theory of defining set, a class of three-weight and five-weight linear codes over prime field Fp are constructed, which is an extension of Wang et al.'s construction. Our construction include some optimal codes and almost optimal codes with respect to the Singleton bounds. We employ exponential sums to investigate the weight distributions of these linear codes. The results show that these codes can be used to construct secret sharing schemes.

In this paper, we consider the depth distribution of constacyclic codes over ℤ₄ with length even prime power. The depth distribution of negacyclic codes over ℤ₄ of length 2^e is completely determined. Furthermore, we determine the depth spectrum of cycilc codes over ℤ₄ of length 2^e, and the depth distribution of some cyclic codes over ℤ₄ of length 2^e is also given.

A theoretical Three dimensional (3D) multiuser High altitude platform (HAP) Multipleinput multiple-output (MIMO) channel model based on dynamic evolution of the Line-of-sight (LOS) components is proposed. In this paper, we consider that the LOS components could re-appear after they have vanished, and a two-state Continuous-time Markov chain (CTMC) is used to model the dynamic evolution of the LOS components. The survival probabilities of the LOS components are derived by using Chapman-Kolmogorov (C-K) equations, and the closed-form expressions are derived in this paper. Using the survival probabilities, the spatial correlation functions are derived. Measurements show that a two-state CTMC is indispensable to investigate the dynamic properties of the LOS components.

Emotions often facilitate interactions among human beings, but the big variation of human emotional states make a negative effect on the reliable emotion recognition. We propose a novel algorithm to extract common features for each type of emotional states which can reliably present human emotions. To uncover the common features from uncertain emotional states, the backward cloud generator is used to discover {Ex, En, He} by integrating randomness and fuzziness. Finally, the proposed method for emotion recognition is verified on the common facial expression datasets, the Extended Cohn-Kanade (CK+) dataset and the Japanese female facial expression (JAFFE). The results are satisfactory, which shows cloud model is potentially useful in pattern recognition and machines learning.

As a complex system, the topology of human's brain network can be analyzed by graph theory for the further study of brain's structural and functional mechanism. In order to construct and analyze the graph-based network efficiently and intuitively, it is necessary to develop flexible and independent visualization software. For this purpose, we developed an innovative software called VisConnectome. It runs on Windows system and does not rely on Matlab. It provides a friendly Graphical user interface (GUI) including the tool bar, the tool window, double slider, filter double slider, etc. It allows visualizing the brain network with the ball-and-stick geometric model, modifying its properties such as size and color, filtering nodes and connection as a simplification, blending with the brain surface as a context, etc. By experiments and comparison, we conclude that the VisConnectome is a flexible and independent visualization system with high speed and high quality.

Khudra is a lightweight block cipher proposed in SPACE 2014. The cipher is designed for Field programmable gate array (FPGA) based platforms. In this paper, we introduce the first biclique attack on full Khudra in the single key setting, with time complexity of 2^78:3 encryptions. The time complexity can be further reduced if the post-whitening key is omitted. Furthermore, based on the bicliques constructed, Meet-in-the-middle(MITM) attack is applied to 15-round Khudra, and the best result of Khudra in terms of attacked rounds against MITM attack is achieved.

The prevalence of deep learning has inspired innovations in numerous research fields including community detection, a cornerstone in the advancement of complex networks. We propose a novel community detection algorithm called the Deep auto-encoded clustering algorithm (DAC), in which unsupervised and sparse single autoencoders are trained and piled up one after another to embed key community information in a lower-dimensional representation, such that it can be handled easier by clustering strategies. Extensive comparison tests undertaken on synthetic and real world networks reveal two advantages of the proposed algorithm: on the one hand, DAC shows higher precision than the k-means community detection method benefiting from the integration of sparsity constraints. On the other hand, DAC runs much faster than the spectral community detection algorithm based on the circumvention of the time-consuming eigenvalue decomposition procedure.

A new parameter extraction method for Schottky barrier diodes is provided in this paper. Since the current model of Schottky barrier diodes is a nonlinear self-consistent equation, the nonlinear inconsistent equations set is composed of nonlinear model equations under different biases. The problem of solving nonlinear inconsistent equations set is transformed into an optimization problem. The global optimal solution of the parameters is obtained by genetic algorithm. Comparing with experimental data, the results indicate that the error of the method proposed by this paper is lower than that of Cheung method.

Variation in on-chip power supply continues to be a major challenge to limit circuit performance. To mitigate the impact of high-frequency voltage droop, a novel all-digital adaptive clock system is proposed. The adaptive clock system is composed of a droop detector and an adaptive clock generator, which directly stretch the clock period when the circuit suffers from the supply voltage droop. The droop detector circuit detects the VDD droop and the digital adaptive clock generator circuit selects the stretched clock to prevent timing-margin failures. The response time for droop detection and clock stretch can be as fast as one cycle. The whole scheme is used on a test circuit under SMIC 40nm CMOS process with a layout area of 900um*1100um. Postlayout simulation results demonstrate power reductions are 7% at 1.1V and 15% at 0.7V for a 10% VDD droop, respectively.

The existing construction methods of Quantum Boolean functions (QBFs) are extended and simplified. All QBFs with one qubit and all local QBFs with any qubits are constructed. And we propose the concept of Generalized quantum Boolean functions (GQBFs). We find all GQBFs with one qutrit and all kinds of local GQBFs with any qutrits. The number of each of the four kinds of functions above is uncountably infinitely many. By using diagonal matrices, we obtain uncountably infinitely many non-local QBFs with any qubits and GQBFs with any qutrits. Infinitely many families of GQBFs with any qudits are obtained from the properties of projection matrices of known saturated orthogonal arrays.

Proxy ring signature is a proxy signature designed to protect the privacy of the proxy signer. In this paper, we propose a new proxy ring signature based on the Schnorr's signature. Our scheme combines the idea of the Schnorr's signature with the method of existing structure of proxy ring signature. In order to improve the performance, we eliminate the use of bilinear pairing operation. We only use the hash function in the signature verification phase. Because of above features, the proxy ring signature scheme we proposed is more efficient in computation. We also prove that our scheme is unforgeable against all kinds of adversaries in the random oracle model.

The local crowd density and the crowd distribution estimation tasks are useful but challenging. There are two main problems which limit the performance of existing algorithms. The first one is that there are not enough labeled training samples to build the high-performance estimation model. Another one is that existing methods lack the supports of physical theories of the crowd. To remedy them, a novel crowd density field model is proposed, which is deduced by jointing crowd dynamics theory and social force model. A crowd counting method based on the proposed crowd density field model is introduced to measure the proposed crowd density field model. Extensive experiments confirm the effectiveness of the proposed plan.

For an odd prime p and positive integers r, d such that 0 < d ≤ p ^r , a generic construction of d-ary sequence based on Euler quotients is presented in this paper. Compared with the known construction, in which the support set of the sequence is fixed and d is usually required to be a prime, the support set of the proposed sequence is flexible and d could be any positive integer less then pr in our construction. Furthermore, the linear complexity of the proposed sequence over prime field GF(q) with the assumption of q ^p-1 ≢ 1 mod p ² is determined. An algorithm of computing the linear complexity of the sequence is also given. Our results indicate that, with some constrains on the support set, the new sequences possess large linear complexities.

Network analysis based on graph theory has greatly promoted the cognition of the human brain network. A detailed brain network function connection analysis was carried out for the brain of normal human brain and mental illness patients. We studied the Magnetoencephalography (MEG) of 9 normal subjects and 9 schizophrenics in left hemisphere temporal lobe and frontal lobe regions. And obtained the dynamic function connectivity matrix by calculating Pearson correlation coefficients that based on sliding time window and shorttime Fourier transform, and constructed weight and binary network by graph theory. Analyzed the small world properties of normal human brain networks, and compared the differences of network between normal subjects and patients with schizophrenia.

Reverberation is the major interference of active detection, discrimination between target echo and reverberation is difficult when the reverberation is very strong. For the distinguishing of underwater target echo with strong interference, the modulation feature of echo and reverberation spectrum are characterized by signal spectral flatness measure, and the relationship between signal spectral flatness and target physical properties are theoretically formed. A method of discrimination between broadband underwater target echo and reverberation that based on signal spectral flatness characteristics difference is proposed and studied. Sea experiment results of complex underwater target broadband acoustic scattering have shown that signal spectral flatness characteristics of underwater target broadband echo and reverberation have obvious differences. Target echo and reverberation can be well distinguished using the proposed method.

To deeply understand the essential relationship between two different-frequency signals and explore the phase synchronization law in frequency standard comparison, a novel modeling building method of Lissajous figure reversal period is proposed based on the group quantization phase processing. The relationships between the Lissajous figure reversal period and group period are revealed. The frequency deviation and nominal frequency of the measured signal are obtained using the group period and number of inflection point in the Lissajous figure. The results of a frequency standard comparison can accurately achieved using the frequency deviation. A lot of noise problems introduced by the frequency divider, frequency multiplier and frequency mixer are solved in the traditional frequency standard comparison, and the high-precision frequency standard comparison results can be quickly obtained using the proposed method. The experimental results show that a close relationship exists among the Lissajous figure reversal period, group period and frequency deviation. The measured frequency can be precisely calculated using the relationship, and the frequency stability of system can reach the E-112/s level.

Sand painting is a form of combination of arts and modern aesthetic, which relies on profound cultural heritage and cultural connotation. To provide the public and artists with an opportunity to better understand sand painting and make art creations surprisingly, this paper proposes a self-adaptive algorithm to simulate sand painting in a real-time way. Our simulation system exploits the height field to simulate sand flow to achieve a fast even real-time target. Seven frequently-used styles of painting techniques are elaborately defined and successfully simulated in our system, including pouring, seeping, dotting, stroking, sweeping, multi-stroking, and pinching. The procedure of sand flow is mainly consist of two key parts: sand accumulation and collapse. The direction field is introduced into the system to control a similar appearance of a normal distribution, which will be of benefit to sand accumulation algorithm. A selfadaptive approach is taken advantage of into sand collapse algorithm to present certain appearances with various details. A color factor is also considered for realistic simulation in this paper in two ways: one is the background color of sand table/canvas and the other is the natural color of sand particles themselves. User feedbacks and experimental results reveal that the algorithm of sand painting simulation in this paper can realize kinds of sand painting arts of creations easily, realistically, effectively and interactively.

The present study proposes an improved Quantum-behaved particle swarm optimization algorithm based on the two-body problem model (QTPSO) for solving the problem that other quantum-behaved particle swarm optimization algorithms easily converge on local optimal solutions when solving complex nonlinear problems. In the proposed QTPSO algorithm, particles are categorised as core particles and edge particles. Once the position of the core particle is determined, the edge particle appears in the vicinity of the attractor exhibiting a high probability, and the attractor is obtained through the random weighted sum of the core particle and the optimal mean position. Through simulation of the motion of these two particles by applying the interaction of the particles in the two-body problem, this mechanism not only improves the diversity of the population, but also enhances the local search capacity. To validate the proposed algorithm, three groups of experimental results were obtained to compare the proposed algorithm with other swarm intelligence algorithms. The experimental results indicate the superiority of the QTPSO algorithm.

Millions of smart meters have been installed all around the world so far. The reported meter readings are conducive for utility companies to provide high quality of service. To efficiently and securely make use of those readings, data aggregation has been widely studied. Lu et al. proposed an Efficient and privacypreserving aggregation (EPPA) scheme for secure smart grid communications in recent years. Unfortunately, we found a man-in-the-middle attack to the EPPA scheme in this paper. We then propose a new meter reading aggregation scheme, namely Universally composable meter reading aggregation (UCMRA) scheme, in order to resist against that attack. Moreover, we give a universally composable symbolic analysis to prove the security for UCMRA scheme. This proof also enables the UCMRA scheme to adopt an alternative cryptographic primitive arbitrarily, as long as the primitive meets the requirements of the corresponding ideal functionality. Finally, the experiment results show that the performance of the UCMRA scheme is almost as good as that of the EPPA scheme.

An important aspect of a Speech tutoring aimed talking-head system (STTS) is the accuracy of produced articulatory movements. Little work has been done for the Articulatory movements' accuracy (AMA) evaluation in STTSs. Although subjective evaluation is reliable, it is time consuming and inconvenient. The traditional objective evaluation is comparing the motion of several points on the surface of the synthetic articulator to the Electromagnetic articulography (EMA) data which describes the motion of corresponding points on the articulatory surface of a speaker. The EMA information is too limited to describe the whole shape changing of deformable articulators for a speech process. To solve this problem, we propose a substantially different objective evaluation method based on a separately recorded medical video. The synthetic articulatory shapes in a speech process are compared to the corresponding shapes tracked from the medical video. This method is translation, rotation, and scaling invariant which allows the comparison of the shapes from the synthetic tongue and the medical images. The time difference problem of synthesis results and medical video is solved by introducing Dynamic time warping (DTW) to the proposed method. Experimental results demonstrate that our method has the ability to evaluate the deformation shape accuracy from an entire articulation process. The comparison results suggest that our method is more accurate than the traditional method especially for deformable articulators.

The protection schemes of the entanglement in the system of two entangled atoms from nonMarkovian reservoirs via previous Weak measurement (pre-WM) and posterior Weak measurement reversal (post-WMR) are investigated. We firstly compare the effects of combination applying of pre-WM and post-WMR (scheme 1) and twice Weak measurements (WMs) (scheme 2) in the condition of different initial entangled states. The analysis indicates that the protection performance of scheme 1 is much better than that of scheme 2. The scheme 1 was utilized when the system of two entangled atoms suffers from the independent reservoirs. The numerical results show that the combination of pre-WM and post-WMR can effectively enhance entanglement and shorten the time of Entanglement sudden death (ESD) in non-Makovain environment. We have found the optimum of WM strengthens is drastically related to the initial entangled state.

It is difficult for existing dense or quasidense matching algorithms to obtain accurate matching results due to the lack of effective feature information in non-textured regions. This may affect the quality of subsequent 3D reconstruction and super-resolution reconstruction. We propose a method of non-textured regions matching. To reduce the impact of noise and illumination, vector sampling normalized cross correlation is proposed to directly measure coherence between two colour images by the effective information of multi-channel features. Three mathematical properties are used in affine transformation: 1) Centroid location is not affected by affine transformation; 2) Affine transformation transforms a straight line into another straight line; 3) Affine transformation maintains the linear relation invariant. In non-textured regions, we can construct texture regions which have affine invariant properties to improve the accuracy of template matching. This could provide more information for the diffusion of dense or quasi-dense matching. In experiments, we demonstrate that the method has high accuracy in cases where there is no big distortion between the two viewing angles from two aspects of simulated images and photographic images.

Inspired by the fact that back-off and interpolated smoothing algorithms have significant effect on statistical language modeling, this paper proposes a sentence-level Language model (LM) score regularization algorithm to improve the fault-tolerance of LMs for recognition errors. The proposed algorithm is applicable to both count-based LMs and neural network LMs. Instead of predicting the occurrence of a sequence of words under a fixed order Markov assumption, we use a composite model consisting of different order models with either n-gram or skip-gram features to estimate the probability of the sequence of words. In order to simplify implementations, we derive a connection between bidirectional neural networks and the proposed algorithm. Experiments were carried out on the Switchboard corpus. Results on N-best lists re-scoring show that the proposed algorithm achieves consistent word error rate reduction when it is applied to count-based LMs, Feedforward neural network (FNN) LMs, and Recurrent neural network (RNN) LMs.

Aiming at the drawback that the shape of Bézier triangles is fixed with respect to the control points, some blending functions with parameter and with similar properties to the bivariate Bernstein polynomials are presented. Few literatures introduce how do the blending functions are derived. This paper aims at providing the general construction method of adjustable Bézier triangles in polynomial space. With the help of degree elevation technique and based on the idea that the adjustable surfaces are defined by the adjustable control points, the shape adjustable Bézier triangles are defined. The construction process of the blending functions is demonstrated in detail.

An uplink multi-user massive Multi-input multi-output (MIMO) system is considered with a mixed Analog-to-digital converter (ADC) architecture under imperfect Channel state information (CSI). A closed-form approximation for the Spectral efficiency (SE) is derived for a general mixed ADC structure with any resolution profile. To achieve a balance between the SE and receive Energy efficiency (EE) of the system, the ADC resolution profile optimization problem that maximizes a linear combination of the SE and the Base station (BS) receive power consumption is formulated. An algorithm based on gradient search is proposed whose complexity is linear in the number of BS antennas. Numerical results verify that the proposed ADC resolution design largely outperforms the two-level structure especially in the lower SE region and provides more choices than the uniform-ADC architecture for resolving the SE-EE tradeoff.

The waveguide gate-type mechanism on micro-channel plate substrate was .rstly put forward for the complex photo detection in Free space optical communication (FSO). Based on the losses of energy coupling between both Micro-channel plates (MCP), we further proposed waveguide gate-type complex detecting mechanism, which was veri.ed in electron optic system by vacuum testing. With the application of the complex detecting mechanism, the device performance has greatly improved, including higher space optical transfer and wider dynamic detecting range.

When a big path di.erence exists between the multiple transmitter-receiver links, time dispersion is inevitable. A multipath channel model is proposed for Multiple-input multiple-output (MIMO) Visible light communications (VLC). To combat Intersymbol interference (ISI) as well as increase the channel capacity, a combination of MIMO and Direct-currentbiased Optical orthogonal frequency division multiplexing (DCO-OFDM) is studied. The performance analysis of MIMO-DCO-OFDM taking into account the limitation of the forward current of the o.-the-shelf LED is derived. The accuracy of the theoretical results is veri.ed by comparison with the Monte Carlo Bit error ratio (BER) simulation results under di.erent DC bias and di.erent clipping levels.

A multi-energy Demand response management (DRM) approach in Energy Internet is proposed by employing Stackelberg game theory. The multi-energy trading problem in DRM is formulated as a Stackelberg game, where an energy provider, as a leader, adjusts energy prices, and residential smart energy hubs, as followers, dynamically schedule multiple energy flows according to the prices. The existence and uniqueness of the equilibrium of the proposed game model are analyzed. A multi-energy DRM algorithm for reaching the game equilibrium is developed. Simulation results show that the proposed game-based approach can effectively reduce residential energy costs and improve revenues of the energy provider.

Energy-efficient cooperative spectrum sharing has become a new trend based on financial and environmental considerations. We focus on a novel energy-efficient cooperative strategy in a Radio frequency (RF) energy harvesting cognitive radio network, where the Secondary user (SU) provides energy supply and relaying assist for the Primary user (PU) in exchange for the transmission opportunities. On the basis of energy and information cooperation, an Energy efficiency (EE) maximization non-convex problem for SU is formulated under the PU's Quality of service (QoS) requirement and energy-causality constraint. With the aid of Dinkebalch's method and convex optimization technique, a joint time and power allocation scheme is proposed. Numerical results show that the proposed scheme achieves higher SU's EE and better PU's QoS guarantee compared to the scheme in which the SU only provides energy for the PU's transmission.

Node Localization is a fundamental issue for many critical applications in Wireless sensor networks (WSNs). Traditional DV-Hop localization algorithm and corresponding improved ones still cannot provide su.cient localization accuracy in such WSNs. To ensure accurate localization, this paper proposes an improved DistancevectorHop (DV-Hop) localization algorithm. Under such an algorithm, we determine a corrected average hopdistance of beacon nodes by employing the di.erences between actual and estimated distance among beacon nodes in WSNs. We propose a probability information based selective strategy for the selection of beacon nodes. Based on these selected beacon nodes, we adopt a two dimensional hyperbolic function to predict the locations of unknown nodes. Simulation results are provided to illustrate the localization accuracy of our algorithm compared with traditional DV-Hop algorithm and its two improved algorithms in WSNs.