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Voting is one of the most prominent components of democracy. The one-vote veto, which requires the voting result to be only “yes” or “no”, is a particularly noteworthy type of voting with widespread application. When obtaining a “no” result, no useful information about the number of “no” votes and who votes “no” is disclosed. In this paper, we introduce a protocol for anonymous one-vote veto utilizing qubits and local Pauli operations $\mathbb {Z}$ and $\mathbb {X}$ , building simple processes for easy implementation under the current technology. For better elaboration, we give two examples: all voters cast “yes” votes and only one voter casts a “no.” Then, the corresponding experiment tests are conducted on the simulated IBM quantum computer to verify their feasibility. We also show that the proposed protocol has the desirable properties of privacy, fairness, verifiability and robustness. Furthermore, we analyze the proposed protocol’s security against cheating from eavesdroppers, a semi-honest server and malicious voters. This work is the first attempt to illustrate how qubits can be useful for building a secure anonymous one-vote veto strategy.

Neighborhood rough set (NRS) is a classical extension model of Pawlak rough set, which has been used to evaluate the importance of attributes for attribute reduction. In addition to attribute evaluation, attribute search strategy is also a very important issue for attribute reduction. In this paper, we define the concentration, dispersion, stability degree of samples with respect to the single attribute to measure the significance of attributes, and use the stability of samples to pre-sort attributes. A fast attribute reduction algorithm with attribute pre-sort based on neighborhood rough set (APNRS) is designed to search a reduct, and the reduct is more conducive to classify learning tasks. Compared with the traditional greedy search algorithms, the APNRS algorithm greatly improves the computational efficiency under the condition of ensuring classification accuracy. Finally, a series of numerical experiments are carried out to verily the efficiency of the APNRS.

Wafer-level chip-scale package (WLCSP) has attract broad interest from fundamental sciences to industrial applications due to its advantages in ease of fabrication, low cost and real die size packaging, etc. However, in the advanced WLCSP process, the stress between the package and the die is the key challenge, especially in the reliability tests such as thermal shocking, the stress can lead to defects such as cracking. In this paper, several parameters including top metal (TM) and polyimide (PI) are studied both through simulations and board-level reliability testing. The result revealed that, by reducing local metal density gradient and increasing PI thickness, stress relief region can be constructed to extend the fatigue life of WLCSP. An enhancement of more than 2× in thermal shocking reliability was achieved. This work provides a design guideline for structure optimization to satisfactory WLCSP reliability.

Aiming at the problem of the fracture of the swing arm of a heavy-duty tow and hang separation type of wrecker, an improved design scheme for the reinforcement of the basic arm and swing arm beam structure is proposed. The finite element model of the arm bracket structure before and after the improvement was established, and the three working conditions of full shrinkage, semi-extension and full extension were selected. The maximum lift weight under different amplitude angles was selected as typical working conditions for checking calculation. On this basis, a comparative analysis of the structural strength of the arm bracket is carried out. The results show that the maximum stress of the arm bracket is reduced by 27.86 MPa after the improvement, and the stress distribution of some components is obviously improved.

To address the problem of high matrix computational cost in traditional diffusion process methods for large scale image retrieval, we propose a novel image retrieval algorithm based on hierarchical locally constrained diffusion process (HLCDP) by combining algebraic multigrid and diffusion ranking on manifold. In HLCDP, all retrieved images of the image database are built into a bottom-to-top hierarchical structure by selecting the representative images. Then the similarity among images on the top layer is diffused by using locally constrained diffusion process, and the affinities (i.e. ranking scores) between query images and top-layer representative images are interpolated to all images on the bottom layer to obtain the affinities between the query images and all of the images in database. Our method is evaluated on image retrieval tasks by comparing with locally constrained diffusion process (LCDP), self-smoothing operator (SSO) and self-diffusion (SD). The experimental results on MPEG7 data set and ImageCLEFmed2005 data set demonstrate that the proposed method improves the retrieval performance and reduces the retrieval time consumption.

Spectrum sensing is a key technology of cognitive radio network. Firstly, the paper introduced the latest research progress of the application of the stochastic resonance in cognitive radio spectrum sensing, including stochastic resonance in the local field of sensing (such as energy detection, covariance matrix spectrum sensing, cyclostationary feature detection) and the application of collaborative sensing, and then it pointed out some problems of the stochastic resonance to be solved in the cognitive radio spectrum sensing. Last but not the least, the next research directions were summarized.

Cloud computing infrastructure helps users to minimize cost by outsourcing data and computation on-demand. Due to the varying user needs in terms of computation power, storage capacity, etc., cloud providers offer various machines to choose from, to maximize the intended need. In this paper, we disprove several common conceptions regarding the performance and cost of cloud by experimenting on instances of two different families (compute and storage optimized) of the most popular cloud platform, Amazon Elastic Compute Cloud (EC2). Our analysis shows the interesting finding that, for the machines of the same configuration, storage optimized instances have lower disk read-write speed than compute optimized, which does not completely reflect the claim made by Amazon in all cases. Additionally, storage optimized instances have notable performance difference among them. We also identify that the I/O performance of same instance type varies over different time periods.

In this paper, we consider an impulsive nonlinear neural networks with variable time delays. Sufficient conditions ensuring the existence and global exponential stability of a unique almost periodic solution of the system are obtained, by the relation between the solutions of impulsive system and the corresponding non-impulsive system.

Insight occurs when problem solutions arise suddenly and is associated with an “Aha!” experience. While much research on insight has focused on event-related, goal-directed neural responses, there is growing interest in the spontaneous, resting-state brain activity. A recent study using high-density electroencephalograms (EEG) has demonstrated a dependence of task-related processing on the preceding resting state, but based on the power spectrum of individual electrodes in different frequency bands. In the current study, we sought to test the influence of resting-state functional networks on subsequent problem-solving strategy. We examined healthy subjects at rest who were subsequently directed to solve a series of anagrams to assess resting-state EEG network activity, as measured by coherence and Granger causality. Subjects were divided into two groups based on the proportion of anagram solutions derived with self-reported sudden insight versus analytic search. In this work, we mainly focused on gamma-band (30–40 Hz) activity, as it has been predominantly implicated in a wide variety of cognitive processes, and more importantly associated with insight solutions. Coherence analysis revealed that, within the gamma frequency band, there was a large-scale distributed resting-state network over the right hemisphere, with a network hub centering at the right anterior temporal lobe, for sudden insight relative to analytic search. Further analysis of the observed network with Granger causality indicated that there is a significant directional influence, originated from the network hub at the right anterior temporal lobe. These findings indicate that the spatial focus of the insight effect is at the right anterior temporal lobe, a result consistent with the hypothesis of right-lateralized hemispheric asymmetry.

By using Avery-Peterson fixed point theorem for cones, some criteria are established for the existence of three positive periodic solutions for a class of state-dependent delay dynamic system with feedback control on time scales.

To probe the perceptual awareness of a visual stimulus, we use the paradigm of generalized flash suppression (GFS) to dissociate physical stimulation from perceptual experience. This perceptual suppression approach has previously been used to investigate perceptual visibility correlates in visual cortex, mainly with linear methods, such as spectral analysis. While meaningful, the linear method alone may be insufficient for the full assessment of neural dynamics due to the fundamentally nonlinear nature of neural signal. In this contribution, we set forth to analyze local field potential (LFP) data collected from the multiple visual areas in V1, V2 and V4 of a macaque monkey while performing the GFS task using nonlinear method - adaptive multiscale entropy (AME) - to study the neural dynamics of perceptual suppression. We also propose a new cross-entropy measure at multiple scales, namely adaptive multiscale cross-entropy (AMCE), to assess the nonlinear interdependency between cortical areas. We show that: (1) multiscale entropy exhibits perception-related changes in all three areas, with higher entropy (i.e. higher complexity) observed during perceptual suppression; (2) the magnitude of the perception-related entropy changes increases systematically over successive hierarchical stages (i.e. from lower areas V1 to V2, up to higher area V4); and (3) cross-entropy between any two cortical areas reveals higher degree of asynchrony or dissimilarity during perceptual suppression, indicating decreased neuronal interdependency between areas. Our findings demonstrate that the adaptive multiscale entropy is a sensitive measure of perceptual visibility, and thus can be used to uncover perceptual awareness of a stimulus.

Saline irrigation decreased the evapotranspiration (ET) of Haloxylon Ammodendron Bunge (H. Ammodendron) and Caragana Karshiskii Kom (C. Karshiskii) via reducing transpiration water, and it also changed the proportion of transpiration in ET: the proportion of evaporation from soil surface increased, and the proportion of transpiration in ET decreased when irrigating with higher salt concentration. The ET, soil evaporation and transpiration of H. Ammodendron irrigated with salinity of 7g/L and 12g/L reduced by 20% and 42%, 1.97% and 12%, 24% and 50% respectively compared to the plants irrigated with freshwater, while the reductions for C Karshiskii were 27% and 38%, 17% and 18%, 31% and 44%, respectively.

Generalized flash suppression (GFS) is a visual illusion that produce perceptual suppression despite constant retinal input, which thereby provides a rare opportunity to study neural mechanisms directly related to perception [1]. Previous study has shown that local field potential (LFP) reflects perceptual suppression in monkey visual cortex with linear analysis techniques [2]. Yet, considering the fundamentally nonlinear nature of neural dynamics, we set forth to study the LFP difference of perceptual suppression with a nonlinear method. Specifically, we apply sample entropy to quantify the complexity of LFP activity. Our results showed that with sample entropy, the neural activity in visual area V1 revealed significantly perception-related changes. The higher entropy of LFP in the invisible condition indicates that neurons may produce more complex patterns to induce perceptual suppression.

This paper describes a low spurious and small step frequency synthesizer module based on PLL (Phase-Locked Loop)-DDS (Direct Digital Synthesis)-PLL structure, which is controlled by the parallel port of the computer. The module consists of four parts: the first PLL (PLL1), the DDS, the second PLL (PLL2) and the control part. The spurious of this module is ameliorated to some extent in comparison with traditional synthesizer technologies using single PLL. The experimental measurement of the actual module shows that the spurious is as low as -65 dBc. In addition, its frequency range is 2060 MHz-2160 MHz, the frequency step size 10 kHz, the phase noise -90 dBC/Hz@10 kHz and the harmonics -40 dBc.

Though the olfactory model entitled KIII has been widely used to pattern recognition, it only can give bare prediction. Combining EM model with the transductive confidence machine, a novel method to recognize hypoxia electroencephalogram (EEG) with a preset confidence level is proposed in this paper. This method can make prediction with confidence measure rather than bare prediction. The experimental results of classifying normal and hypoxia EEGs show that the method can set confidence level in advance for every prediction to control the risk of error effectively.

Graphs are widely used to model complex structured data such as XML documents, protein networks, and chemical compounds. One of the fundamental problems in graph databases is efficient search and retrieval of graphs using indexing techniques. In this paper, we study the problem of indexing graph databases using frequent subtrees as indexing structures. Trees can be manipulated efficiently while preserving a lot of structural information of the original graphs. In our proposed method, frequent subtrees of a database are selected as the feature set. To save memory, the set of feature trees is shrunk based on a support threshold function and their discriminative power. A tree-partition based query processing scheme is proposed to perform graph queries. The concept of center distance constraints is introduced to prune the search space. Furthermore, a new algorithm which utilizes the location information of indexing structures is used to perform subgraph isomorphism tests. We apply our method on a wide range of real and synthetic data to demonstrate the usefulness and effectiveness of this approach.

This paper reports a novel method to classify EEGs from subjects under normal and hypoxia conditions, which provides a potential efficient indicator to evaluate hypoxia in real time. The EEG data are collected from 3 healthy subjects while their neurobehaviors are evaluated to assess the degree of hypoxia. Together with Approximate entropy (ApEn), the specific energy in a sub-band of 30-60 Hz of the Welch power-spectral-density (PSD) is extracted as the features. Bayesian classifier, 3-layer perceptron established by back-propagation and SVM are utilized for classification, respectively. The accuracy of Bayesian classifier is over 90.8% on test set. We compared the performance in terms of changing the architecture of the net. The accuracy of BP network reaches 94.2% on test set. Meanwhile, a SVM with Polynomial kernel revealed an accuracy over 92.5% on the test set. The experimental results show that the hypoxia EEG can be distinguished from normal one for individuals remarkably.

This paper reports a novel method to classify EEGs from subjects under normal and hypoxia conditions, which provides a potential efficient method to evaluate hypoxia in real time. The EEG data are collected from 3 healthy subjects while their neurobehaviors are evaluated to assess the degree of hypoxia. The specific energy in a sub-band of 30-60 Hz of the Hilbert Huang transform is extracted as the features. A linear discriminant classifier is utilized for classification. The experimental results show that the hypoxia EEG can be distinguished from normal one for individuals and the classification accuracy varies with the degree of hypoxia

This study investigated hypoxia related changes in the human brain function through EEG analysis with the method of Lempel-Ziv complexity (LZC) and approximate entropy (ApEn). Meanwhile, multichannel human EEG signals were studied topographically in 3 healthy subjects under normal and different hypoxia conditions respectively. It was found that when subjects were at the simulated altitude of 3000 m, the measure of LZC and ApEn increased in almost all channels. The results also show that after 15 days hypoxic training, the LZC measure and ApEn decreased obviously and almost the same as those in normal condition. This suggests that after hypoxic training, the subjects were almost adapted to the hypoxia environment. In addition, the complexity measure was calculated and compared in each sub frequency band. We propose that LZC and ApEn can be used to evaluate the effect of brain functional changes at different levels of hypoxia and hypoxic training is an effective means to prevent hypoxic fatigue

This paper is to present the development and the verification of a compact optical sensor system for measuring the deflection of the cantilever in a tapping-mode atomic force microscope (AFM). An optical pickup head of commercial digital versatile disc read only memory (DVD-ROM) drive is applied in this sensor system. In order to satisfy the strict measuring requirements of sensibility and reliability, the build-in detection system is replaced with a four-quadrant photodiode of traditional optical-lever technique. In addition, an inertial motor composed by a piezo-actuator is used as the sample approaching mechanism and the scanner in the vertical direction. Thus, the volume of hardware structure is decreased, and the sensing variance due to temperature change will be minimized. The developed AFM system is well functionally verified by measuring a calibration grating with step height 19.5 nm, and the vertical resolution is plusmn 8 nm

We built an atomic force microscope (AFM) system in combination with a robotic manipulator to help users in two rigorous tasks: to load the probe and to align the optical detection path. A novel z-tracking optical design, which allows vertical probe scan is also proposed. This method enhances the scan speed for massive samples.

The modeling and control of a horizontally slewing inextensible Timoshenko-beam including the correct centrifugal stiffening effect and a tip payload are considered. Partial differential equations of motion and orthogonality conditions for the constrained modes are derived. A finite dimensional dynamic model simplified by using the orthogonality conditions is obtained. This nonlinear dynamic model places no restrictions on slenderness ratio, slewing angle, and rotational speed provided that the flexure deflections remain small. To achieve a joint angle trajectory tracking with rapid suppression of elastic vibrations, a nonlinear controller is designed using input-output linearization and elastic mode stabilization. The input-output linearization is used to generate a desired joint angle trajectory command. The elastic mode stabilization is designed based on the linear time-invariant part of the partially linearized system. Stability of the closed-loop system is investigated, and a sufficient condition for asymptotic stability is established. This stability condition justifies the existence of joint angle trajectory commands for accurate joint angle trajectory following and simultaneous vibration suppression.