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This article aims to solve the problem of the hyperspectral imagery (HSI) demosaicing under a novel subsampling hyperspectral sensing strategy. The existing method utilizes the periodic structure of subsampling to estimate a fixed subspace in matrix form from the measurement result, which reduces the representation ability of the subspace in iterations and destroys the intrinsic structure of the tensor. To overcome these drawbacks, we propose a tensor-based HSI demosaicing (T-Hy-demosaicing) model with tensor subspace representation, which takes the low-tubal-rankness and the nonlocal self-similarity into account. In particular, we suggest a tensor singular value decomposition based on orthogonal transformation (Tran-based t-SVD) to learn the tensor subspace that possesses a more powerful representation ability. In addition, we develop an effective algorithm to solve the proposed nonconvex model under the framework of the proximal alternating minimization algorithm. Experiments conducted on simulated datasets illustrate that the proposed method outperforms other comparative methods in both visual and quantitative terms.

Epilepsy is an acute and recurrent neurological disease. Its main clinical symptoms are paroxysmal convulsions, sensory disorders, consciousness and mental disorders, which seriously affect the daily life and work of patients. Seizure detection has always been a challenging work. With the increase of the incidence of epilepsy, high-performance epilepsy automatic detection algorithm can reduce the workload of medical workers in clinic, and has important significance in clinical medical research. This paper combines the characteristics of frequency domain and time domain with nonlinear analysis characteristics to extract features of EEG signals. Firstly, the average Periodic diagram method is used to calculate the power spectral density features, and then the wavelet transform is used to obtain the time domain analysis features. Next, the permutation entropy of five different rhythms are obtained by permutation entropy and information entropy algorithms. Finally, the three features with different dimensions are input into a linear classifier as a single feature to identify seizure and non-seizure signals. The performance of the proposed detection algorithm is evaluated by experiments on the epileptic EEG data-set of the University of Bonn. Experimental results show that the proposed method has a high classification accuracy, which can reach 95. 8%.

Leaf angle distribution (LAD) is a key canopy structural parameter, playing an important role in light transfer. LAD can be estimated from fixed point of view photography, however this is time consuming and spatially limited. Recently, Terrestrial LiDAR Scanning (TLS) has been used to estimate LAD through 3D canopy space. The downside of TLS it is more costly than the cameras used in the photographic method. We propose a cost effective method to estimate LAD from drone based photogrammetry. We compare LAD estimates in different water treatment plots. Results show that LAD can be obtained from photogrammetric point clouds. Leaf angles were enhanced in stressed plots, presumably due to wilting. Further, the leaf azimuth distribution was not random but concentrated around 0 and 180 degrees. In summary, drone based photogrammetry can be used to estimate remote sensing parameters such as LAD paving the way for cost effective trait estimation.

Information and communication technology (ICT) for health has shown great potential to improve healthcare efficiency, especially artificial intelligence (AI). To better understand the influence of ICT technology on health, a framework of the digital health industry has been proposed in this paper. Factors from the health industry and the ICT part are extracted to study the interaction between two groups of component factors. Health factors include service and management; and ICT factors include sensors, networks, data resources, platforms, applications and solutions. The interaction between ICT and health can be traced through the development history, from the stage of institutional informationization to regional informationization, and finally to service intelligentization. Following such a developmental roadmap, AI was chosen as one of the most powerful technologies to study the penetration effect and key development trends from the perspectives of data, computing power and algorithms. The health industry will be much improved or redefined in the coming AI era. To better understand the strengths, weaknesses and limitations of AI for health, exogenous factors are discussed at the end of the paper; preparations on collaboration mechanism; standardization and regulation have been proposed for the sustainable development of digital health in the AI era.

A differential intensity modulation refractometer based on single-mode-no-core-single-mode (SNS) structure cascaded with two fiber Bragg gratings (FBGs) is proposed. The SNS structure acts as a band pass fiber (BPF), and the Bragg wavelengths of the two FBGs located at each edge of the BPF. Because the interference spectrum of the SNS is sensitive to refractive index (RI) but FBG is not, the reflective powers of the FBGs are modulated by the surrounding refractive index (SRI) and have contrary changing trends. The reflective intensity difference of the FBGs is used to describe the change of the SRI, which not only increases the RI sensitivity but diminishes the influence of the input light fluctuation as well. In addition, as the SNS and the FBGs have approximate temperature sensitivities, this sensor can accomplish temperature self-compensation. The experiments show that the RI sensitivity of this sensor is about -199.6 dB/RIU in the range of 1.3326-1.3702 and -355.5 dB/RIU in the range of 1.3702-1.4066, which is greater than the traditional SNS-FBG structure. However, its temperature sensitivity is only 0.0148 dB/°C. When the input light varies 7 mW, the shift of the intensity difference is only 0.28 dB, which is much smaller than the intensity shift of the single FBG.

The data driven speech enhancement approaches using regression-based deep neural network usually result in enormous number of model parameters, which increase the computational load and the difficulty of model training. In order to improve the model efficiency, we propose a densely connected network with time-frequency (T-F) dilated convolution for speech enhancement. The T-F dilated convolution block is designed to enlarge the receptive field and capture the contextual information in both temporal and frequency domains. Considering the computational efficiency, the 1-D convolution with the bottleneck structure is exploited in the T-F convolution block. Each T-F convolution block is then densely connected to ensure maximum information flow between layers and alleviate the vanishing gradient problem of the network. The experimental results reveal that the proposed scheme not only improves the computational efficiency significantly but also produces satisfactory enhancement performance comparing the competing methods.

In order to achieve safer and more dexterous performance, collision avoidance is a key element in manipulator trajectory planning. The obstacle detection and avoidance for traditional rigid-link manipulators have already been studied thoroughly, but it is still a challenging problem addressing soft and continuum manipulators with arbitrary arm shapes. In this paper, a real-time collision-free trajectory planning is proposed for manipulators with arbitrary shapes in point-to-point tasks. To deal with collisions on the whole body of the manipulator, a novel obstacle avoidance algorithm is proposed using Signed Distance Function (SDF). An articulated version of SDF is adopted in our scenario for easy implementation by taking advantage of the articulated manipulator structure. The articulated SDF offers critical points in an accurate and efficient way and can be used to obtain the evasive velocity conveniently according to gradient. Lastly, simulation results in both rigid-link manipulator and a 4-DoF deformable manipulator verified the effectiveness of our method.

Although the incremental Nystrom method has been used in kernel approximation, it is not suitable for online kernel learning due to the cubic time complexity and the lack of theoretical guarantees. In this paper, we propose a novel incremental Nystrom method, which is in a linear time complexity with respect to the sampling size at each round, and enjoys a sublinear regret bound for online kernel learning. We construct the intersection matrix using the ridge leverage score estimator, compute the rank-k approximation of the intersection matrix incrementally via the incremental singular value decomposition, and recalculate the generalized inverse matrix periodically. When applying the proposed incremental Nystrom method to online kernel learning, we approximate the kernel matrix using the updated generalized inverse matrix at each round, and formulate the explicit feature mapping by the singular value decomposition of the approximated kernel matrix, yielding the linear classifier for online kernel learning at each round. Theoretically, we prove that our incremental Nystrom method has a $(1+\epsilon)$ relative-error bound for kernel matrix approximation, enjoys a sublinear regret bound using online gradient descent method for online kernel learning, and reduces the time complexity of generalized inverse computation from $O(m^{3})$ to $O(mk)$ at each round, where $m$ is the sampling size and $k$ is the truncated rank. Experimental results show that the proposed incremental Nystrom method is accurate and efficient in kernel matrix approximation and is suitable for online kernel learning.

Satellite signal transmission is not only affected by weather condition at a particular time or region but may also be perturbed by multipath effects and shadowing. To be able to mitigate the effects of the aforementioned phenomena on the transmitted satellite signals, the communication channel condition needs to be estimated so as to take appropriate corrective measures. The study focuses on taking the advantage of the low complexity Least Square (LS) method and combining it with decision feedback to develop an improved equalization technique, Least Square Decision Feedback Equalization (LS-DFE), suitable for mobile satellite communication system. The LS-DFE algorithm involves first estimating the channel response; then the estimated channel statistics is used by the decision feedback equalization to obtain the channel response for the next frame. From the simulation results obtained, it was observed that the transmitter sequence is not well detected by both the conventional LS and Linear Minimum Mean Square Error (LMMSE) methods unlike the LS-DFE method. The ability of the LS-DFE method to detect the signal much better is due to the presence of decision feedback; which contributes significantly to the reduction of the fading effects on the received signals. Further results also reveal that the LS-DFE technique outperforms both the conventional LS and LMMSE in terms of BER performance and time of convergence.

Here we present a novel deformable manipulator composed of active rigid joints and deformable links. The deformable link consists of several passive spherical joints articulated with each other with preload force between socket-ball surfaces. Therefore the manipulator can reach more parts of the task space compared with rigid-link manipulators by bending deformable links according to different tasks. However, frequent changes in the links' shape lead to unknown kinematic parameters, which bring difficulties to the planning and control of the manipulator. In this paper, a real-time shape recognition algorithm is proposed for the deformable link by using Self-Organizing Map (SOM). To avoid topological error and local convergence problem, Least Square Method (LSQ) is utilized for initialization according to the link's current position and shape. The reinitialization process is added to improve the robustness when facing noise and occlusion. To meet the demand of real-time tracking, the GPU parallel computation is applied for acceleration. Moreover, an error metric based on Signed Distance Function (SDF) is presented for evaluation. In this paper, our algorithm is implemented on a deformable link with 11 components. Experimental results validate the feasibility and effectiveness of this method. The processing time descends from 700 ms/frame to 40 ms/frame by using GPU, and the overall average of tracking errors is below 4 mm.

This paper introduces a new method which using synchronized acquisition of four-channel heart sounds to acquire a heart sound signal source. First of all, we introduces the generation mechanism of heart sounds in detail, and puts forward that the heart sound signals have the characteristics of "time-sharing". Then, according to the basic characteristics of heart sound transmission in the thoracic cavity, we model the mathematical process of heart sound transmission in the thoracic, and propose a mathematical model about heart sound transmission. Finally, according to the mathematical characteristics of the model and the main feature of time-sharing in the heart sound signal, we propose a source separation method for the four-channel heart sound signal in order to obtain the source of heart sounds. The purpose of this algorithm is to eliminate the interaction between simultaneous acquisition of four-channel heart sound signal, which makes the acquired heart sound signal show the characteristics of time difference, energy concentration and low frequency of the spectrum so that we can obtain the source composition of heart sound signal. The experimental results show that the algorithm used in this paper can better obtain single heart sound source components, and single heart sound source components include four major components of the first heart sound (S1), the A and P components of the second heart sound (S2), which have a positive meaning in analyzing the original composition and production mechanism of the heart sound.

When the carrier does angular motion, the different installation position between master strapdown inertial navigation system (M-SINS) and the wing-mounted slave strapdown inertial navigation system (S-SINS) will lead to lever arm effect, which seriously affects the accuracy of transfer alignment of S-SINS. In practical flight, wing's elastic deformation will change the length of lever arm and lead to dynamic lever arm effect. This paper presents a real-time dynamic lever arm estimation method. The proposed method estimates the length of the dynamic lever arm in real time by adding the length into state variables of transfer alignment. Firstly, the relationships of the length of lever arm with lever-arm velocity error and lever-arm acceleration error are established, and both of them are added into the error equations of S-SINS system as parameters to establish the complete error equations of S-SINS system with lever arm effect error. Then, the filter model of S-SINS system is designed. On the one hand, the state matrix is derived according to the error equations of S-SINS, which contains the length of lever arm as state variable. On the other hand, the measurement equation is inferred from the relation between the output of M-SINS and S-SINS with velocity plus angular velocity matching method. Finally, the length of dynamic lever arm is estimated by unscented Kalman Filter (UKF) as one of state variables. The estimated length of dynamic lever arm can be used to calculate lever-arm velocity error and lever-arm acceleration error. To validate the proposed method, a simulation experiment is conducted in MATLAB. The simulation experiment uses second-order Markov process to simulate the elastic deformation of wing. The simulation results indicate that the proposed method is able to estimate the change of the dynamic lever arm caused by the elastic deformation of wing in real-time, and achieves the high accuracy estimation of misalignment angle after compensating the lever arm effect error.

This paper provided a detailed analysis of the side effect of bit sign transitions caused by NH codes, and proposed a BeiDou signal acquisition method based on new double block zero-padding (NDBZP) which could overcome bit sign transition problem, extend coherent integration time, remove NH code and improve estimate precision of Doppler shift simultaneously. The proposed method could achieve higher acquisition sensitivity comparing with traditional methods under same SNR environment. To validate the proposed method, this paper designed simulation experiments to compare NDBZP method with traditional methods in terms of computation complexity and detection probability under different SNR environments. The simulation results demonstrated that the proposed method could overcome bit sign transition problem caused by NH codes, and had excellent acquisition performance.

China plans to implement the navigation services of BeiDou Satellite Navigation System on a global scale in 2020. To ensure the acquisition in BeiDou-challenged environments, this paper proposed a two-step acquisition method to eliminate influence of bit sign transitions. To reduce the Doppler estimation time, the INS information is integrated to detect the receiver's speed. As satellite ephemeris information is downloaded by the assisted receiver, with the aid of INS, the relative speed of receiver and satellite can be obtained, and the Doppler shift searching range can be reduced within ±300 Hz from ±5 KHz.

GPS signal acquisition is the prerequisite for signal tracking and navigation message demodulation. In a high dynamic environment, the high-speed movement between GPS satellite and receiver will generate a large Doppler frequency shift, which makes it difficult to acquire GPS signal. This paper proposed a new INS-aided parallel code phase acquisition algorithm for high dynamic applications. For frequency search, the algorithm utilizes INS auxiliary information and GPS almanac data to estimate Doppler frequency shift and narrow down the search range of frequency. For code phase search, the algorithm adopts the parallel code phase searching method to reduce the time of code phase search and improve the overall acquisition speed. The proposed algorithm was validated by a semi-physical simulation experiment and the result shown that the proposed algorithm could improve the acquisition speed and the performance of GPS receiver under high dynamic environment remarkably.

Cardiovascular Disease (CVD) is a highly significant contributor to loss of quality and quantity of life all over the world. Early detection and risk prediction is very important for patients' treatment and doctors' diagnose. This paper focus on establishing a more accurate and practical risk prediction system based on data mining techniques to provide auxiliary medical service. In order to be practically used for collecting and analyzing patients' data in healthcare industries, the system consists of four parts: data interface, data preparation, feature selection and classification. Data interface response to obtain hospitals' raw data from hospital; data preprocessing is needed for data integration, data cleaning and rating mapping etc. Key features were then selected by CFS Subset Evaluation combined with Best-First-Search method to reduce dimensionality. Random forest was inducted as basic classifier to identify risk level, which is a prior trial in CVD risk prediction field. Cleveland Heart-Disease Database (CHDD) and Cardiology inpatient dataset of PKU People's Hospital were both tested to confirm accuracy as well as practicality. In CHDD test, our system has a significantly higher accuracy of 91.6% than other methods. In People's Hospital dataset test, it achieves an accuracy of 97%, which is better than most of other classifiers except SVM (98.9%), however random forest only take half of time than SVM. Comprehensively considering the risk prediction system shows great significance in accuracy and practical use for patients' treatment and doctors' diagnose.

Home service robot works in unstructured environments with various tasks, where a low-cost, dexterous, and intrinsically safe manipulator is important. Traditionally, a redundant manipulator with high degrees of freedom (DOFs) is employed. However, high DOFs bring problems like high cost, complex physical designing, and heavy weight. Here a novel low-cost deformable manipulator for home service robot, which is composed of rigid joints and deformable links, is introduced. The deformable manipulator can obtain relatively dexterous and extended workspace with fewer joints by bending its deformable links. Considering the typical hybrid behaviors caused by bending operation, a hybrid model and a practical kinematic-free control framework with no prior kinematic information are proposed to describe and control the deformable manipulator. The model and controller have been implemented on a 4-DOF deformable manipulator with two deformable links in simulation. The simulation results validate the proposed deformable manipulator and kinematic-free control.

A novel deformable manipulator with intrinsic safety for the home service robot, which is composed of rigid joints and deformable links, is studied in this paper. As the deformable links can be bent according to different tasks, the deformable manipulator obtains relatively dexterous end-effector and extended workspace to deal with multiple tasks. However frequent changes in the links' shapes bring difficulties to the kinematic model and control of the deformable manipulator. Thus a practical kinematic-free controller with no prior kinematic information is proposed in this paper. It is based on the hypothesis that the Jacobian matrix of the manipulator is almost constant in a local region. It can be estimated by actuating each joint independently an incremental amount and observing their effects on the robot's end-effector, instead of identifying the kinematic parameters. The controller is implemented to control a 4-DOF deformable manipulator in simulation. It is able to control the robot's end-effector to reach a desired position, as well as to track continuously a desired path. Besides, it can cope with the drastic changes in kinematic parameters, which is very difficult for the conventional model-based controller.

Cardiovascular disease (CVD) is a highly significant contributor to loss of quality and quantity of life all over the world. Early detection and prediction is very important for patients' treatment and doctors' diagnose which can help to reduce mortality. In this paper, we focus on practical problem of Chinese hospital dealing with cardiovascular patients' data to make an early detection and risk prediction. To better understand the prescription and advice in Chinese, basic natural language processing method was used to synonym recognition and attribute extraction in Ultrasonic echocardiography. After data preprocessing, over 50 data mining techniques was tested for real patents dataset. Totake full advantage of multi-methods and reduce bias, top 6sub classifiers was selected to form an ensemble system, adjusted voting mechanism was used to make a final result, which consists of risk prediction and confidence. System has a high precision of 79.3% for 2628 cases of real patents in experiment. Therisk prediction confidence and algorithm accuracy shows great significance in practical use for doctors' diagnosing.

Case study is a very common and important research method. It has been widely used in public administration. However, it tends to be questioned, misunderstood and misused in Chinese public administration. The scientific standard of case study method is far beyond establishment in China. In this paper, we try to explain what a case study is and how to use it scientifically, including the definition, characteristics, category, operational steps and quality criteria. We also analyze some common controversy and misunderstanding about case study in Chinese public administration, and point out that, Chinese public administrative case researchers should improve their case research skills, follow scientific procedures and requirements of case study and report case research results scientifically, as they using case study method.

Personalized Recommenders can help to find potential items and then recommend them for particular users. Conventional recommender methods always work on a rating schema that items are rated from 1 to 5. However, there are several rating Schemas (ways that items are rated) in reality, which are overlooked by conventional methods. By transforming rating Schemas into fuzzy user profiles to record users' preferences, our proposed method can deal with different system rating Schemas, and improve the scalability of recommender systems. Additionally, we incorporate user-based method with item-based collaborative methods by clustering users, which can help us to gain insight into the relationship between users. The aim of this research is to provide a new method for personalized recommendation. Our proposed method is the first to normalize the user vectors using fuzzy set theory before the k-medians clustering method is adjusted, and then to apply item-based collaborative algorithm with item vectors. To evaluate the effectiveness of our approach, the proposed algorithm is compared with two conventional collaborative filtering methods, based on MovieLens data set. As expected, our proposed method outperforms the conventional collaborative filtering methods as it can improve system scalability while maintaining accuracy.

A challenge in the design of rate-compatible (RC) low-density parity-check (LDPC) codes is how to maximize the range of code rates. In this paper, we propose a class of RC LDPC codes with a very wide range of code rates. To ensure linear encoding, dual-diagonal form for the parity part of the mother parity-check matrix is used. Constructed from shifted identity matrices, the proposed codes are particularly well-suited for the high-speed implementation of parallel encoders and parallel decoders. To widen the range of code rates, we have proposed an optimal transmission scheme, which keeps the optimal degree distribution unchanged for the mother code and all daughter codes. Thus, the proposed technique pushes the upper bound of code rates to 0.96, which is the highest rate in RC LDPC codes in the world, based on our best knowledge. The implementation results into field programmable gate array (FPGA) devices indicate that a parallel encoder (decoder) for the proposed RC LDPC codes is capable of reaching a throughput of 7.2 (1.8) Gigabits per second using a clock frequency of 150 MHz.

Fully homomorphic encryption scheme (FHE) has numerous applications, especially in the cloud computation. Recently, FHE has been the focus of extensive study, but mostly were researching into the construction and efficiency of the scheme, a fat lot focus on the application of the scheme. In this paper, we present a new system for searching on encrypted data which combined ABE (Attribute based Encryption) and FHE. Our system enables anyone even without private-key of the encrypted data to search the data. In the end, we discuss the application of FHE on outsourcing of computation and present two different systems matched the different requirement.

This paper describes the development status of electric power communication network and Electric Power Communication System Network. According to the features and networking mode, the paper analysis and establish electric power communication network based on soft switch feasibility research, combined with the features of electric power communication network.

The existed gob-areas such as roadway, goaf, headway and stope are of various sizes and shapes. These gob-areas are one of the main hazard which threatens mine production security. Metal gob-areas characteristics: gob-areas are not filled fully, mostly filled with water, or partly drop down, rock formation mostly have large dips. We present a converted shear waves method to detect the gob-areas in metal mines. The experimental results in Qidashan open pit iron mine show proposed method is suitable for positioning cavity without affecting excavation and transportation. Accurate rate of research results achieves 90%. This research plays a leading role in preventing geological hazards, provides reliable and detailed geological basis for mining process so as to guarantee the high production rate and high efficiency for metal mines.

This paper proposes a new method to locate and reform weak links for distribution network. Firstly, based on the total supply capability (TSC) theory under the N-1 security guideline, this paper recognizes weak links by analyzing branches, channels and channel groups. Then, two models of the reformation are established and solved through the sensitivity analysis. Finally, the paper discusses the weak links in an IEEE case. In summary, this paper proposes a new method for the distribution network reformation. The results can bring the links into full play in load transfer and bring a greater supply capacity for a whole region within limited links reformation.

This paper through explaining the theory of solar radiation energy, plant spectroscopy, ecology, and vegetation remotely sensed, these transdisciplinaries, to identify the vegetation cooling effect mechanism, is aimed to eliminate disagreement in this field. In this paper we propose vegetation stress monitoring, through investigating in urban greening's health state management, improving vegetation ecology service function should be a new research point. Urban vegetation has numerous ecology services function, especially important for mitigating urban heat island effect in summertime. Recently a lot of research study on the correlation between vegetation cover and urban heat island, based on extracting information from remote sensing data, however most of the study ignored about the real mechanism of vegetation cooling temperature.

With the improvement of the intelligence degree of video surveillance system, the people-counting in surveillance area becomes a studying focus. This paper proposes a fast and effective people-counting method. In this method, we divide the monitoring area into blocks at first, and then we recognize people in each block and combine both the static detection and the dynamic detection. In the static detection part, we use covariance algorithm to avoid factitious adjustment of threshold. As a result, it will be more persuasive by comparing the entire difference distributions. What is more, some interference of non-human objects can be excluded effectively by using the dynamic detection. Therefore we can get a more accurate final result.

Through the Internet we can make the virtual experiments platform published on the web, sharing resources and avoid duplication of equipment purchased and waste of resources. A lot of data acquisition and wireless communication framework was developed based on the graphic programming platform of Lab VIEW. The system used the big and powerful Lab VIEW handling data and showing function to carry out a real-time processing, analysis and display. Two communication styles were given such as TCP/IP and Data Socket. When two computers run Lab VIEW at the same time, real-time date send and receive between computers by the interface of Virtual instrument, which can realize multi-machine wireless data transmission and reading, in order to complete remote data sharing and controlling.

The effects of dispersion states of carbon nanotubes on thermal conductivity and Micro-hardness of Multi-walled carbon nanotube (MWCNT) reinforced copper nanocomposites were investigated. The nanocomposites were fabricated in a novel method. It involves the synthesis of MWCNT-implanted copper composite spheres and the preparation of the MWCNT/copper bulk materials using vacuum hot pressing and hot rolling. The thermal conductivity of the composites with different concentration of MWCNTs were measured. Although the coefficient of thermal conductivity decreases with the increase of the MWCNT content, it is still high enough to be used as electronic packaging materials even the concentration of MWCNTS in the composite is up to 5 wt%. Furthermore, the microhardness of the nanocomposites are much higher than that of pure copper, which is ascribed to the good dispersion of the MWCNTs in matrix.

Waste iron chippings and fly ash can be used to remove Cr(VI) in groundwater, lab expriments show that, without changing the pH of groundwater, the compoud mediator of waste-iron chippings + fly ash has better effects on wiping off the Cr(VI) in water than pure waste-iron chippings or fly ash. The disposal efficiency of the compound mediator can reach 97 percent. And the repetitive results of the experiments can be achieved. Based on the analysis, we concludes: 1.Iron chippings and fly ash can compose many primary cells, which can deoxidize Cr(VI) to Cr(III) and form Cr(OH)3 deposits under the condition of alkalescency; 2. Cr(VI) in water can be reduce to Cr(III) by Fe2+; 3. Fe2+ and Fe3+ generated by electrode reaction are good flocculation agents and can react into multiform alkali salts which capture weeny suspending granules, and finally form sedimentation and subside.

The influence of climate change on water consumption under changing economic background has always been kept a important issue by global research institute. Taking Dongguan city as a study object, the influence factors of water requirement are divided into climate factor economic factor and social factor. Water demand forecasting models are established on different influence factors to analyze the response relationship between various influence factors and water demand. Optimum influence factors are chosen on this basis, and water demand forecasting models are established through applying various methods. Least squares (LS) combination forecasting technology is adopted to integrate the results of different forecasting models. The situation and reasons of water demand changes of Dongguan city are proclaimed, the variation tendency of water demand is revealed, and a theoretical foundation for Dongguan City water resources management is provided.

Ultrasonic micro-knife is a new minimally invasive surgical instrument. It was made by using MEMS technology. It works in an ultrasonic vibration mode to realize the precise incision with its sharp tip at one end. We chose the PZT-4 plates whose resonant frequency is close to the natural frequency of the Microknife and used them to excite the micro-knife into vibration. As a micro-driver, its vibration detection is very important. So we studied the optical detection and electrical detection techniques. The entire micro-knives' vibration was observed and measured by using the high-power digital microscope and CCD equipment in the precision optical platform. We also studied micro-knives vibration signal acquisition system based on DSP and PLL. The hardware circuit and software of the system were designed. The vibration voltage signal result was detected. And we can lock the vibration voltage frequency. These two methods can complement each other and provide an effective method to solve the detecting micro-knife problem.

High technology industry, a strategic leading industry in the national economy, has become commanding heights of today's world competition in comprehensive national strength. With the deepening extent of China's reform and opening up, the control of foreign investment on China's high technology industry has been also increasing gradually. It is used related statistical data of high technology industry (foreign-funded enterprises and the whole industry) from 1998 to 2008 in this paper to analyze the actual situation of foreign control. According to this article, it reaches the conclusion that the degree of foreign capital control on manufacture of computers and office equipments is the highest among the five subdivision industries, while the degree of that on manufacture of aircrafts and spacecrafts is the lowest and on manufacture of manufacture of medicines and manufacture of medical equipments and measuring instrument are relatively low.

This paper establishes the finite element model of continuous welded rail with the minor radius curve and infers the corresponding value formula. Using this finite element model, this paper analyzes the influence of dynamic and static load on the stability of continuous welded rail with the minor radius curve, which indicates that the model can be used to simulate more accurately the actual situation of the track lateral displacement due to the ambient temperature changing and dynamic load (unbalanced centrifugal force and vertical vibration force caused by track irregularity) effect.

Future progress in neuroscience hinges on reconstruction of neuronal circuits to the level of individual synapses. Because of the specifics of neuronal architecture, imaging must be done with very high resolution and throughput. While Electron Microscopy (EM) achieves the required resolution in the transverse directions, its depth resolution is a severe limitation. Computed tomography (CT) may be used in conjunction with electron microscopy to improve the depth resolution, but this severely limits the throughput since several tens or hundreds of EM images need to be acquired. Here, we exploit recent advances in signal processing to obtain high depth resolution EM images computationally. First, we show that the brain tissue can be represented as sparse linear combination of local basis functions that are thin membrane-like structures oriented in various directions. We then develop reconstruction techniques inspired by compressive sensing that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal connections across layers and, hence, high throughput reconstruction of neural circuits to the level of individual synapses.

As the characteristics of SEMG are weak and vulnerable to external noise, it must to establish the appropriate acquisition and amplification system. INA128 chip is used to be as the core IC of the SEMG amplifier. The IC UAF42 is used to be the notch filter to keep out of frequency of 50Hz. Multisim2001 is used to simulate and test the SEMG amplifier is feasible. The amplifier can be used to help amputation acquire EMG in effect.