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In order to efficiently manage the output power of the distributed generation system with multiple PV arrays, in this paper, a cooperative control strategy is used to control the output power ratio under load variation. Aiming at the real-time simulation requirements and real communication conditions, the validation process of the strategy is carried on a designed co-simulation platform with hardware-in-the-loop functionality. The power system model is built and simulated in RT-LAB. Cooperative control strategy is embedded in the developed hardware controllers based on digital signal processors, and the connected communication between them is accessed to a real-life switch equipment. The simulation results validate the effectiveness of the cooperative strategy and consider the real-world simulation conditions.

The asynchronous relative observations are incorporated in the cooperative localization system of multiple Autonomous Underwater Vehicles (AUVs) in unknown environment in this paper. First, the motion model of multi-AUV cooperative localization system is established. Then, the asynchronous relative observation is obtained when two AUVs meet and the corresponding measurement model is introduced. The state and the covariance matrix of this AUVs group can be easily distributed and calculated on each AUV platform node by using the distributed extend Kalman filtering (DEKF) algorithm. The cooperative localization with the proposed DEKF algorithm was implemented and tested in the simulated environment. The theoretical analysis and simulation results showed that the asynchronous relative observation between AUVs can be effectively fused by the DEKF method.

Teaching management personnel can use the database to deeply mine the comprehensive analysis of student performance data, further improve the level of education and teaching management, and use the improved Apriori correlation algorithm to conduct correlation analysis between courses. Students in Weifang Vocational College English lessons, computer lessons and computers. The grade test scores are used as mining objects. The improved Apriori correlation algorithm results show that the computer grade test scores are closely related to the calculated course scores and English scores, and scientific and rational suggestions are provided for the teaching arrangement of academic affairs. Improve the quality of education and teaching and make good decisions.

We present a new succinct zero knowledge argument scheme for layered arithmetic circuits without trusted setup. The prover time is O(C + nlogn) and the proof size is O(D logC +log2 n) for a D-depth circuit with n inputs and C gates. The verification time is also succinct, O(D logC + log2 n), if the circuit is structured. Our scheme only uses lightweight cryptographic primitives such as collision-resistant hash functions and is plausibly post-quantum secure. We implement a zero knowledge argument system, Virgo, based on our new scheme and compare its performance to existing schemes. Experiments show that it only takes 53 seconds to generate a proof for a circuit computing a Merkle tree with 256 leaves, at least an order of magnitude faster than all other succinct zero knowledge argument schemes. The verification time is 50ms, and the proof size is 253KB, both competitive to existing systems.Underlying Virgo is a new transparent zero knowledge verifiable polynomial delegation scheme with logarithmic proof size and verification time. The scheme is in the interactive oracle proof model and may be of independent interest.

Accurate channel estimation can greatly improve the performance of burst OFDM system by compensating channel fading. Channel estimation based on discrete Fourier transform (DFT) is suitable for burst OFDM systems due to its low complexity and short convergence time. Traditional DFT-based channel estimation algorithm does not consider the noise influence within the length of cyclic prefix, which greatly decreases its performance. A DFT-based channel estimation algorithm with noise elimination (DCEA/NE) is proposed to estimate the instantaneous signal-to-noise ratio (SNR) using the training sequence in the burst OFDM system, and based on this, eliminate the noise influence. Simulation results show that the proposed algorithm outperforms the traditional DFT-based channel estimation algorithm.

The water cloud plays an important role in the energy balance as well as the precipitation processes of the Earth system. The aerosol-cloud-precipitation interaction still contributes very large uncertainties for the climate prediction. Lidar is a powerful tool for cloud detection as the properties of cloud base can be obtained with high temporal-spatial resolution. Due to the effective radius of cloud droplet is quite large comparing with laser wavelength, the single scattering lidar equation cannot be used directly for cloud lidar as the multiple scattering effects cannot be ignored. Accurate forward model accounting for the multiple scattering effects is the foundation for the retrieval of cloud extinction coefficient and maybe also other micro-physical properties of cloud. In this paper, a semi-analytic Monte Carlo (MC) simulation program is developed for multiple scattering effects modeling in cloud lidar. And then MC simulations are performed for different lidar parameters (mainly different receiving field of view, FOV) with different cloud models and the simulation results are compared with the semi-analytical/analytical models based on the quasi-small-angle (QSA) approximation. The results demonstrate that the semi-analytical/analytical multiple scattering models based on QSA approximation have acceptable accuracy for ground-based cloud lidar applications in most cases. According to these simulations, we also examined the possibility for retrieving the extinction coefficients and the effective radius of water cloud simultaneously with multi-FOV lidar and optimal design of the multi-FOV lidar would be also talked.

When an IT company processes a large number of tasks in a high-concurrency scenario, it usually establishes one or more task queues for these tasks, and then uses multithreading to handle these tasks concurrently. As a pooling technology, the thread pool can greatly improve the performance of applications, especially when facing a large number of concurrent application scenarios. This paper presents a dynamic thread task scheduling algorithm based on thread pool (DWTSTP). The algorithm can not only perform efficient scheduling and execution of tasks in multiple task queues, but also dynamically adjust the capacity of the thread pool to maximize the performance of the thread pool. Experiments show that the algorithm presented in this paper is superior to the traditional task scheduling method based on thread pool in task processing efficiency

Collaborative filtering algorithm is one of the most successful recommendation algorithms in personalized recommendation system, but the traditional algorithm does not consider the user's interest changes in different time periods, resulting in the set of neighbors may not be the nearest neighbor set. what is more.because of data sparsity and computational complexity, the efficiency of the algorithm is poor. time-weighting and clustering appear as a nature solution to this problem. So this paper proposes a collaborative filtering algorithm based on users' interest in different time period. First.the algorithm performs sparse subspace clustering on users solving data sparsity problem and improving the accuracy for searching similar neighbors, then we assigns each item a score that gradually decreases with time using the weighted score and find the nearest neighbor of the target user. According to the history of similar friends' watching videos, recommend high rated videos to target users. We conduct experiments using real dates from movielens to verify our algorithm and evaluate its performance. Experiments demonstrate that the improved algorithm improves the recommendation quality of the collaborative filtering recommendation system.

We study the problem of verifiable computation (VC) for RAM programs, where a computationally weak verifier outsources the execution of a program to a powerful (but untrusted) prover. Existing efficient implementations of VC protocols require an expensive preprocessing phase that binds the parties to a single circuit. (While there are schemes that avoid preprocessing entirely, their performance remains significantly worse than constructions with preprocessing.) Thus, a prover and verifier are forced to choose between two approaches: (1) Allow verification of arbitrary RAM programs, at the expense of efficiency, by preprocessing a universal circuit which can handle all possible instructions during each CPU cycle; or (2) Sacrifice expressiveness by preprocessing an efficient circuit which is tailored to the verification of a single specific RAM program. We present vRAM, a VC system for RAM programs that avoids both the above drawbacks by having a preprocessing phase that is entirely circuit-independent (other than an upper bound on the circuit size). During the proving phase, once the program to be verified and its inputs are chosen, the circuit-independence of our construction allows the parties to use a smaller circuit tailored to verifying the specific program on the chosen inputs, i.e., without needing to encode all possible instructions in each cycle. Moreover, our construction is the first with asymptotically optimal prover overhead; i.e., the work of the prover is a constant multiplicative factor of the time to execute the program. Our experimental evaluation demonstrates that vRAM reduces the prover's memory consumption by 55-110x and its running time by 9-30x compared to existing schemes with universal preprocessing. This allows us to scale to RAM computations with more than 2 million CPU cycles, a 65x improvement compared to the state of the art. Finally, vRAM has performance comparable to (and sometimes better than) the best existing scheme with program-specific preprocessing despite the fact that the latter can deploy program-specific optimizations (and has to pay a separate preprocessing cost for every new program).

This paper takes the alert problem of the unified time controller as the point of penetration, and then, by analyzing the time comparison principle of the controller, it could be concluded that the time comparison mechanism is flawed. When there is not any reference source of “time comparison consensus”, two pieces of reference source time whose time flags are “reliable” are not equal, and correct discrimination could not be achieved, leading to the alert of all the time. By upgrading controller software, adding new discrimination conditions, When there is not any reference source of “time comparison consensus”, clearing all the comparison results of reference source time, comparing all the reference source time and generator time, and deciding if all the time is of consistency according to the principle of large number decision, the wrong discrimination of the correct time is avoided. This method improves the time comparison mechanism of the controller, increases the precision and correctness of time difference measurement, and is of highly popularizing value for the improvement of unified time synchronization.

Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic gradient descent method. Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who train various models on the joint data using secure two-party computation (2PC). We develop new techniques to support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions such as sigmoid and softmax that are superior to prior work. We implement our system in C++. Our experiments validate that our protocols are several orders of magnitude faster than the state of the art implementations for privacy preserving linear and logistic regressions, and scale to millions of data samples with thousands of features. We also implement the first privacy preserving system for training neural networks.

Cloud database systems such as Amazon RDS or Google Cloud SQLenable the outsourcing of a large database to a server who then responds to SQL queries. A natural problem here is to efficiently verify the correctness of responses returned by the (untrusted) server. In this paper we present vSQL, a novel cryptographic protocol for publicly verifiable SQL queries on dynamic databases. At a high level, our construction relies on two extensions of the CMT interactive-proof protocol [Cormode et al., 2012]: (i) supporting outsourced input via the use of a polynomial-delegation protocol with succinct proofs, and (ii) supporting auxiliary input (i.e., non-deterministic computation) efficiently. Compared to previous verifiable-computation systems based on interactive proofs, our construction has verification cost polylogarithmic in the auxiliary input (which for SQL queries can be as large as the database) rather than linear. In order to evaluate the performance and expressiveness of our scheme, we tested it on SQL queries based on the TPC-H benchmark on a database with 6 million rows and 13 columns. The server overhead in our scheme (which is typically the main bottleneck) is up to 120 times lower than previousapproaches based on succinct arguments of knowledge (SNARKs), and moreover we avoid the need for query-dependent pre-processing which is required by optimized SNARK-based schemes. In our construction, the server/client time and the communication cost are comparable to, and sometimessmaller than, those of existing customized solutions which only support specific queries.

We present a new construction of an expressive set accumulator. Unlike existing cryptographic accumulators, ours provides succinct proofs for a large collection of operations over accumulated sets, including intersection, union, set difference, SUM, COUNT, MIN, MAX, and RANGE, as well as arbitrary nestings of the above. We also show how to extend our accumulator to be zero-knowledge. The security of our accumulator is based on extractability assumptions and other assumptions that hold in the generic group model. Our construction has asymptotically optimal verification complexity and proof size, constant update complexity, and public verifiability/updatability—namely, any client who knows the public key and the last accumulator value can verify the supported operations and update the accumulator. The expressiveness of our accumulator comes at the cost of quadratic prover time. However, we show that the cryptographic operations involved are cheap compared to those incurred by generic approaches (e.g., SNARKs) that are equally expressive: our prover runs faster for sets of up to 5 million items. Our accumulator serves as a powerful cryptographic tool with many applications. For example, it can be applied to efficiently support verification of a rich collection of SQL queries when used as a drop-in replacement in existing verifiable database systems (e.g., IntegriDB, CCS 2015).

Processes and strengths of the ocean's biological carbon pump are significant to the study of ocean carbon cycle. However, the development and the utilization of the ocean's biological carbon pump are greatly limited because of human's limited knowledge of the ocean. Remote sensing methods have played important roles in understanding and exploring the ocean, and the lidar is one of the most outstanding representatives for its high spatial and temporal resolution, especially the ability of the vertical detection. High-spectral-resolution lidar (HSRL) employs an ultra-narrow spectral filter to separate scattering signals between particles and water molecules without assuming a lidar ratio, and obtains the optical properties of the ocean with a high accuracy. Nevertheless, the complexity of the seawater causes variable optical properties, which gives huge potentiality to develop the HSRL working at different wavelengths in order to promote the inversion accuracy and increase the detection depth. The field-widened Michelson interferometer (FWMI), whose central transmittance can be tuned to any wavelength, can be employed as the HSRL spectral filter and solve the problem that the operating wavelength of the iodine filter cannot change. Moreover, the FWMI has a very large field of view, which gives its superiority over Fabry-Perot interferometer that is an another popular HSRL filter. The principle of the HSRL based on the FWMI designing for the ocean remote sensing will be presented in detail. Furthermore, the availability of the application of the FWMI influenced by the disturbance of the states of Brillouin scattering is analyzed and the preliminary theory shows that the HSRL instrument basing on FWMI could be employed in the marine remote sensing with a high accuracy.

In this paper, we report a novel fiber-optic Fabry-Perot interferometric (FFPI) temperature sensor based on the difference of thermal expansion coefficient between fused silica and metallic materials. The sensor head is made by a single-mode fiber (SMF). A gold film and a nickel film are sputtered and electroplated on the surface of the SMF. Then, a microcavity is micromachined by focused ion beam (FIB) milling. Because the thermal expansion coefficient of nickel is about 20 times of fused silica, the different thermal expansions force the sensor head to bend when the temperature is high or low. Its temperature sensitivity is over 14 pm/°C in a wide range from -79°C to +70°C. And the coefficient of determination R2 is excellent (over 0.995). Moreover, the metallic cylinder can reinforce the cavity spot of the fiber sensor, so that this kind of sensor can work in harsh environments. For the first time to the best of our knowledge, we report this type of FFPI temperature sensor based on difference of thermal expansion coefficient between fused silica and metallic materials.

Privacy protection in the surveillance video data has received great attention. Although tremendous works have been proposed to provide effective privacy protection techniques, most of the algorithms are based on post-processing that deletes, obscures or encrypts the privacy information after privacy-included raw data are recorded. Consequently, they are vulnerable to raw data leak out, which may lead to unauthorized use. Therefore, it is imperative to develop a new privacy protection scheme which is capable of excluding any privacy information at the video recording phase. In this paper, we propose an anonymous camera aiming to protect the privacy of individuals at the video capturing phase by optical masking technique. It effectively reduces the risk of raw data leakage because no privacy information will be recorded by the camera. We implemented a prototype camera, which consists of an infrared camera, a RGB camera and a liquid crystal on silicon (LCoS) device. We introduce optical design and performance of the anonymous camera, the masking algorithm as well as the calibration methodology. Experimental results demonstrate that our prototype anonymous camera can perform accurate real time masking of the face for privacy protection.

Time Machine is an Augmented Reality (AR) system for enhancing tourists' traveling experience. Based on optimized panoramic images, we proposed a novel spatiotemporal interactive approach which imbues users the ability to interact with both the spatial world and its history. Time machine utilizes panorama of a specific place at different time for achieving interactions with temporal dimension. It also adopts interior panorama of landmark building which can be used by the user to interact with the physical world. Additionally, the panoramas optimized by aperture-coded camera can alleviate the noisy foreground information such as crowds and address defocus blurring to improve the quality of panorama.

In this paper, optimal design of a miniature AT-cut high-frequency quartz resonator is presented. This miniature AT-cut high-frequency quartz resonator is about only 25% of the AT-cut high-frequency resonator products in current market. It can be fabricated by our newly developed manufacturing process of MEMS quartz resonator, which cannot be realized by previous mechanical process. A three-dimensional finite element model using linear cuboid-type elements was established to carry out eigen-frequency analysis. To describe quantitatively the spurious coupling strength, we carried out the linear regression analysis to recover the ideal fundamental thickness-shear vibration without spurious vibration coupling, and introduced a parameter named coupling coefficient. To describe quantitatively the energy trapping performance of the resonator, we introduced a parameter named energy trapping rate defined by ratio of vibration energy inside and outside of electrode region. Optimal dimensions of resonator providing small coupling coefficient and large energy trapping rate were determined. The optimization method can certainly be applied in the development of the miniature high-frequency quartz resonators.

Ideally, many application systems for distributed users should be designed without requiring a centralized controller, for example cloud computing or wireless sensor networks. A fundamental challenge to developing distributed algorithms for these systems is load balancing, which is the focus of study in this paper. A common feature of these distributed algorithms is that routing decisions should be derivable without requiring much information from the system, probabilistic routing is one example coming to mind. In this paper, we propose a new routing strategy based on the idea of shift-invariant protocol sequences. We study this load balancing approach in the context of a queuing model of multi-server system. Our model and strategy can be applied to many practical systems, including wireless networks. Numerical studies were carried out to compare our strategy with other routing strategies such as probabilistic routing and random sequences routing. The results show that the proposed algorithm has better performance than these strategies.

In this paper, we propose a low cost diagnostic system for dental therapy. This system is capable of carrying out real time observation inside the narrow root canals of oral cavity with high precision and without blind areas using a single graded-index image fiber (GRIN image fiber). The system uses the GRIN image fiber to irradiate the sample to be observed and return the sample image back to the CMOS image sensor of the microscope through the same image fiber, which is different from conventional system that uses separate fibers for illumination and observation. The results suggest that this device can illuminate and observe sample patterns of up to 10μm line and space widths with approximately the same quality as those illuminated by external light source. This precision is sufficient to detect cracks and collaterals of the root canals.

An aspheric single-element lens made of birefringent material is proposed in this paper. This lens is capable of suppressing aliasing artifacts in a captured image, which is caused by inability of the image sensor to detect fine patterns with higher spatial frequency than the Nyquist frequency of the sensor array. At the same time, this lens possesses converging capability like a conventional convex lens and tolerable optical aberrations by employing aspheric surfaces. Compared to conventional imaging systems that use multi-lens structure to tackle optical aberrations and realize aliasing suppression by stacking up more than two optical low pass filters (OLPFs), an imaging system using our lens can greatly reduce the lens stack height and manufacturing cost. Image capturing simulation using the proposed lens indicates that aliasing artifacts are effectively reduced. This lens is expected to be used in miniaturized imaging system such as smartphone cameras and laptop cameras.

Foreground-background segmentation is an important problem in computer vision, and it has many applications. We propose a technique of Automatic foreground-background segmentation based on depth from coded aperture. This method first calculates a coarse depth map using technique of coded aperture depth extraction, then estimate the general area of foreground. At last, in order to get the foreground, we use the GrabCut algorithm to segment the image. The entire progress is fully automatic, without any manual intervention. Experiments have proved its effectiveness. And we also did some simple examples for application.

In this research, an ultra-small imaging system using birefringent lenses was presented. This system is designed for the next generation mobile phone camera with an image sensor of smaller pixel size. The system thickness is reduced to approximately 1.7mm (from the first lens' front surface to the image sensor) by employing one aspheric lens and one spherical lens made of birefringent uniaxial crystal, which also act as two optical low pass filters (OLPFs). In conventional cameras, however, the OLPFs are stacked up and assembled separately with optical lenses, which makes it difficult to reduce system thickness. Quantitative and visual results of ordinary and extra-ordinary ray separation on the image plane demonstrated that the birefringent lenses system is able to cut off all higher spatial frequencies than the Nyquist frequency of a 0.5µ×0.5µ image sensor.

TCP does not perform well in networks with stochastic channels, with links that randomly drop packets or have long outages. Diversity routing has been proposed to improve TCP's performance in these networks. In diversity routing, a sublayer between TCP and the network replicates each transmitted packet and sends the multiple copies along parallel paths. As long as at least one of the copies reaches the receiver, TCP considers the transmission successful and maintains high throughput. Previous investigations of diversity routing have analyzed TCP's performance when there is a single flow and no congestion. In this paper, we analyze the performance of multiple flows in a congested network. The contribution of this work is the discovery of three adverse effects that occur when TCP is combined with diversity routing. The effects are: link capacity overflow, rate unfairness, and lock-out of late-arriving flows. Simulation is used to analyze the root cause of these effects. Our conclusion is that naive diversity routing fundamentally breaks the TCP congestion control mechanism and cannot be used for TCP performance improvement in networks with stochastic channels, such as many wireless and satellite networks. We propose an improvement to diversity routing that may overcome these problems, enabling use of unmodified TCP.

In this research, a method is proposed to restore the radially blurred image which is produced by a spherical double-convex single lens imaging system designed for cell phone cameras. This is a new study because the pieces of lens for cell phone camera has reduced to one compared to the widely accepted compound lens system that uses several pieces of lens nowadays. However, single lens imaging system introduces stronger aberrations than compound lens system, especially the radially increased blurring effect. To deal with it, a novel method is studied that restore the blurred image by deconvolving polar blurred image and polar Point Spread Functions (PSFs) converted from Cartesian coordinate system. Simulation results suggest the proposed method is applicable to both computer-created gray scale test image and natural color photographs.

The increasing trends in the occurrence of chronic diseases, such as diabetes and hypertension, and an aging population present serious near-term problems in providing suitable healthcare within the existing medical structure. Further, ill and elderly patients experiencing mobility and transportation issues are at risk in obtaining both regular and emergency medical treatment. Mobile health monitoring systems can provide 24/7 health-care services and address these anticipated problems. However, the large number of patients and their related data cannot be efficiently processed with current systems and an efficient processing approach is needed for real-time response requirements for critical health situations. This paper proposes an efficient real-time Knowledge Base architecture for agent-based web Health Care Center (HCC) patient monitoring system for chronic disease management. The proposed system uses Case Based Reasoning (CBR) to improve diagnostic knowledge and is implemented in a diabetes monitoring and management system. An evaluation of the system’s performance is presented for evaluation and comparison to current technologies.

This paper proposes a u-Healthcare system that can perceive emergency situations in chronic hypertension patients and initiate emergency action. The proposed system considers patient mobility and integrates a medical recommendations knowledge base and communication protocols between system agents in an RFID and Cell phone architecture. A comparison and analysis of the proposed system and the operational characteristics of an implemented system is provided. The proposed system can supply information to assist patients in normal health management as well as emergency situations.