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Ecological environment protection is an important issue in modern times, because the carbon cycle is an important factor in the ecological environment. The purpose of this paper is to explore the influence off ungalde composition rate, to analyze how fungal populations interact inastable and rapidly fluctuating environment, and on this basis to analyze the importance of fungal population diversity to nature. So the models are established.

Information system data transmission delays, packet loss, and bit errors will cause the information collected by the distribution network control center to deviate from the true value, and as the information failure problem intensifies, the original optimized control strategy cannot meet actual needs. A predictive control method considering the information failure of the distribution network under the cyber-physical fusion system is proposed. Aiming at the information failure problem in CPS, the information elements, routing model and link model are established. Then, combining with the real-time dispatching process of distribution network, a coordination optimization model of active distribution network considering information failure is proposed to minimize the cost. Predictive control model is used to adjust various distributed energy sources. Finally, through the simulation analysis of the improved IEEE33-node distribution network, the feasibility and effectiveness of the proposed method are verified, and the difference of different information failure cases is analyzed.

The conventional gating approach creates a dilemma for systems with large field-of-view (FOV) such as the uExplorer system. The motion effect is localized with variable motion amplitude across the whole FOV. A conventional gating approach would increase the noise for regions without motion, or with small motion amplitude. Existing local gating approach that allows optimized locally adaptive gating numbers requires a 4D reconstruction, which is time-consuming for generating of a 3D image. Image-domain motion amplitude is first estimated using a population-based or image-based approach. In the population-based approach, the motion amplitude is determined empirically based on the anatomy, and in the image-based approach, image registration is used. The motion amplitude for each line-of-response (LOR) was estimated using the forward projection approach. For time-of-flight (TOF) data, a regular forward projection was applied and for non-TOF data, a maximum intensity projection was applied. The useful percentage of counts for each LOR or TOF-LOR was determined by dividing the maximum allowed motion blurring amplitude by the motion amplitude for each LOR. The locally-gated sinogram data was generated by gating each LOR with the window determined by the useful percentage of counts. Locally gated sinograms were generated using the proposed gating method. More useful events can be preserved using the proposed approach when compared with conventional gating. Among the reconstructed images, the image reconstructed with locally gated sinograms was able to reduce motion blurring without an unnecessary increase in noise in regions unaffected by motion. We have proposed a novel local gating approach that can achieve locally-optimized resolution and noise tradeoff. The method can be directly incorporated into existing reconstruction methods with minimal modifications and extra computation.

A dedicated reconstruction algorithm, usually 4D image reconstruction, is usually required for respiratory motion compensation. The use of a dedicated reconstruction method may make it harder for implementation and can be time-consuming especially with the use of 4D reconstruction. In this study, we present a motion compensation approach that directly applied to Listmode or sinogram data. Using the proposed approach, a regular reconstruction method with minimal or no modifications can be applied to achieve motion compensation. The first step of the proposed approach is to generate image-based motion vector fields (MVF). With the use of the motion signal, the MVF for every time point is generated. The MVF along the time-of-flight (TOF) line-of-responses (LOR) was calculated using rigid approximations, and the event was transformed with a new set of detector pairs and TOF coordinates. The same principle was used when applying sinogram-based transformations. The sensitivity map was generated using the same transformations and used in the reconstruction. For easier implementations, only the translational motion was applied to the TOF LOR at the moment, and the rotational motion was ignored at the current stage. Motion compensation using the conventional approach (Gated reconstructions with matched attenuation map, and averaged after transformation) was also used to compare the effectiveness of the proposed approach. Reconstructed images from the patient data show that the proposed approach was able to reduce the motion blurring effects. However, it is less effective compared with the conventional method, which is likely caused by the approximations used in the current implementation. We have shown that with the proposed compensation method, respiratory motion compensation can be directly applied to TOF sinograms before image reconstruction. A regular reconstruction method can be used directly to achieve motion compensation.

For a dedicated brain scan, the carotid artery is the best location for acquiring an image-based input function. With improvements in PET spatial resolution, accurate quantitation may be achieved with PET data alone. With the ability to cover both the carotid artery and the thorax at high spatial resolution, the uEXPLORER datasets provide a unique opportunity to develop and validate input functions in multiple regions such as the carotid artery. The regions containing the carotid arteries were first manually identified using reconstructed images consisting of the first 60 seconds of data post-injection. The image-based point spread function (PSF) was measured using off-center phantom scans to approximate the locations of the carotid artery. The same reconstruction approach was used for both the phantom scans and the volunteer scans. The structure of the carotid artery at each slice was generated using a deconvolution approach. An additional constraint of a uniform activity distribution within the carotid artery was added in the deconvolution approach. The acquired carotid artery structure was then applied to the dynamic frames (1-hour data) from volunteer scans for partial volume correction to acquire the input function (CA-IF). The input function from the descending aorta (DA-IF) was also extracted as a gold standard. The area-under-curve (AUC) ratio between the two input functions was used to evaluate the accuracy of the method. Without correction, there was a significant visual difference between CA-IF and the DA-IF, which was reduced dramatically after correction. The quantitation difference was dramatically reduced with the proposed correction method. The AUC ratio between the two input functions was 0.78+-0.04 (original), and was 1.00+-0.03 after correction, suggesting much improved quantitative accuracy. The results demonstrated that with improved image resolution and sensitivity, it is possible to accurately acquire the input function from carotid arteries without reliance on extra anatomical imaging approaches such as MRI.

Dynamic positron emission tomography (PET) imaging has a limited temporal resolution, and always suffers from poor signal to noise ratio (SNR) due to low count statistics from short time frames and the ill-posed nature of expectation-maximization (EM) based reconstruction algorithm. Increasing acquisition time for each frame improves image SNR, but may introduce motion artefacts to the region of heart, lung and head. So, a trade-off has to be made between temporal resolution and image quality. Previous studies have demonstrated that aided by prior information from long time frames and/or anatomical images, the kernel method can effectively improve the SNR of the dynamic PET images from very low-count data. In this study, we have investigated the performance of kernel method in long range and simulated short range scanners. We implemented a spatial-temporal kernel method (ST-KEM) and applied it to the data collected from the 194-cm PET/CT scanner (uEXPLORER). Two short range PET scanners were simulated by using partial of the total eight units of uEXPLORER. Additionally, we exploited the way to improve prior images of short range scanners through deep learning. The results showed that ST-KEM outperformed the kernelized Expectation-Maximization (KEM) and conventional Ordered Subsets Expectation Maximization (OSEM) for frames as short as 0.1 second in uEXPLORER, from which noiseless cardiac motion signal could be extracted. In simulated short range scanners, similar image quality could be achieved when the frame duration extends to 1 to 2-second.

Adopting the amplify-and-forward (AF) relay protocol, this paper focuses on the design of a multiuser multiple-input multiple-output (MIMO) multi-relay system with direct source-destination links taken into consideration. In order to improve the quality of signal detection at the receiver, we make use of the minimal mean-squared error (MMSE)-decision feedback equalization (DFE) technique. With the constraints of the signal transmission power at both source and relay nodes, the minimization of the sum mean-squared error (MSE) for the signal waveform estimation of all users' data streams is employed as our design criterion. Through the block coordinate descent (BCD) method of Gauss-Seidel type, we develop an iterative algorithm with guaranteed convergence to conduct the joint optimization of all the source precoding, relay amplifying, feed-forward and decision feedback matrices, where each step we take is to solve a convex problem. It is shown from simulation results that, in comparison to the linear MMSE receiver-based algorithm, the proposed nonlinear one achieves better performance in terms of both MSE and bit-error-rate (BER). Moreover, the system reliability can be significantly improved when the signal-to-noise ratios (SNRs) of direct links become relatively good.

Session-based recommendation, which aims at predicting user’s action based on anonymous sessions, is a challenging problem due to the uncertainty of user’s behaviors and limited clicked information. Existing methods model users’ interests to relieve the uncertainty of user’s behavior prediction. However, most methods mainly focus on the current session, ignoring collaborative information (i.e., collaborative interest) in neighborhood sessions with similar interests. We argue that relying on limited implicit feedbacks within a session is insufficient to precisely infer user’s interest, especially in the absence of user’s profiles and historical behaviors. This paper proposes a novel model called Time-Sensitive Collaborative Interest Aware (TSCIA) to tackle this problem. It explicitly aggregates similar interests from neighborhood sessions to model the general collaborative interest, and simultaneously takes users’ interest drifts into account. Finally, both current session and collaborative information are used for next-item prediction. Extensive experiments on public datasets demonstrate the effectiveness of our model.

The thermal inertia of district heating system (DHS) has been considered in the combined heat and power (CHP) system operation to reduce wind curtailments. While DHS usually operates at the quality-regulation mode, under which the potential of public buildings to regulate heat demand is not fully exploited. In this paper, according to the different heat demand characteristics of public buildings (PB) and residential buildings (RB), a CHP system operation model adopting a centralized control with flow varied by steps in DHS operation regulation is proposed. Case studies show that the proposed model has better performance in economic benefits and wind power integration.

Human resources have gradually become the strategic resources and core competitiveness of regional development, making methods to improve the ability of urban talent gathering a research highlight. The first step of quantitative research on the urban talent gathering ability is to build an evaluation index system. This paper first analyzed the influencing factors of urban talent gathering, including several first-level indicators, such as economic development, urban construction and urban environment, education and medical service, living conditions and career platform, as well as 27 secondary indicators used to assemble Shenzhen talents. Then it aggregated the ability evaluation index system. The principal component analysis method was used to empirically evaluate the talent gathering ability of Shenzhen. The results showed that Shenzhen's indicators, such as per capita GDP, public library collection, number of foreign-funded enterprises, annual average wage level and patent authorization, have a positive impact on the city's talent pooling ability. Finally, specific measures were proposed to enhance its talent pooling. of ability. This research can provide theoretical support and a decision-making basis for the enhancement of the talent pooling ability of Shenzhen.

Time-Of-Flight (TOF) information gives an accurate estimation of the location of annihilation, which can provide improved contrast-versus-noise and bias-versus-variance trade-offs, and faster convergence in iterative reconstruction. On the other hand, manufacturing cost can be greatly reduced with the use of sparse detector configuration. The improved timing resolution may compensate image quality degradation caused by sensitivity loss of sparse detectors. So, it is possible to build a high performance long axial field of view (AFOV) PET scanner by improved timing resolution and sparsified detectors, without increasing the number of detectors as for the short AFOV PET scanner. In this work, we investigate the effect of improved timing resolution on TOF-PET reconstruction for the PET systems with dense and sparse detectors. The images are reconstructed by 3-D OP-OSEM within DIRECT data framework which allows very efficient reconstruction for high timing resolution list mode data. Our experiments indicate that sparse detectors with a high timing resolution can yield image quality comparable to that obtained from dense detectors with a low timing resolution.

In this paper, the model of inverted pendulum on a cart is established, and a high-gain observer is designed to observe the angular velocity of inverted pendulum and the vehicle speed when the angle of inverted pendulum and position of the cart are known. Based on the high-gain observer, a sliding mode controller for the inverted pendulum is designed. The simulation shows the performance of high-gain observer and sliding mode controller. Specifically, in terms of high-gain observer, the simulation results show that the design of high-gain observer is effective, and show the advantages of high-gain observer in comparison with the simulation of global state observer. The design of sliding mode controller enables the system states to complete tracking commands. However, because the switching function is used in the sliding mode controller, the chattering phenomenon can be seen in the simulation. Here we consider using the saturation function to suppress the chattering phenomenon. The simulation proves that the design idea proposed in this paper is effective.

In computer vision literature, it is really a challenging issue about removing the images blur resulted from camera shake. As the existing image deblurring methods do not apply to the image degraded by partial motion blur, and the existing partial blur detection approaches only used low -level blur features to measure the blurry degree of an image, the blur regions extracted via these methods usually have misclassification. To solve the imaging deblurring problem, we propose an image deblurring method based on Generative Adversarial Network (GAN) architecture using dual path connection. In comparison with the traditional image deblurring algorithm, this model can avoid the dependence on apriori-knowledge of blurred image. Experimental results show that the proposed method significantly outperforms other state-of-art algorithms on image deblurring.

In this paper, a new approach based on Neural Network is proposed to identify the unmodeled dynamics for a quadrotor, and a nonlinear controller is adopted for the proposed model. First, the integrated model for a quadrotor is constructed with the dominant model and the compensatory model. The dominant model ref ects the quadrotor mechanical dynamics, while the compensatory model denotes the unmodeled dynamics. Then a nonlinear controller combining the PID approach and Backstepping approach is proposed for the integrated quadrotor model. The controllers of position, altitude and heading are designed to track the desired trajectory, while the attitude controller is used to track the desired angles produced by position controller. Finally, the effectiveness of the proposed model and the designed controller is demonstrated by a maze scenario based on the Airsim platform.

Based on the two-meter-long total-body PET scanner, the multi-resolution reconstruction algorithm aims to reconstruct multiple ROI images with different parameters (voxel size, iteration number and post-filter strength, etc.) simultaneously. The proposed algorithm is an extension of the conventional LOR-OSEM algorithm. Instead of representing the whole image with only one image matrix, the proposed method uses a set of intermediate matrixes to store the whole image. Each matrix is for one part of the whole image. One LOR may pass through multiple matrixes, and the contributions of voxels in each matrix are calculated individually during the forward and backward projection process. Furthermore, parallel ray-tracing algorithm is developed for efficient GPU implementation. The proposed algorithm was validated using a 2D digit phantom and real data collected from a commercial PET/CT scanner with the axial field of view 300mm.

360 virtual reality (VR) video is one of the popular VR applications. Because VR video transmission needs giant bandwidth resources to guarantee users' quality of experience, plenty of schemes have been proposed to optimize the transmission of VR video. Most of these schemes aimed to reduce the data size of VR video source, thereby reducing the bandwidth requirement of VR video transmission. In our proposed scheme, expanding window fountain (EWF) codes is introduced to protect the Field of View (FOV) of VR video by sacrificing the quality of non-FOV. Under the same bandwidth conditions, this UEP method can improve the video quality of FOVs where users actually watching. By adjusting the window selection probability of EWF codes, we discuss the optimal degree of protection for the FOV. Moreover, to better evaluate the quality of VR video, we define a new metric called VP-PSNR to measure the quality of VR video. According to the simulation results, under the packet loss network transmission condition, our UEP scheme can effectively improve the quality of VR video compared with traditional equal error protection (EEP) scheme.

We propose and experimentally demonstrate stochastic computing (SC) with a single magnetic tunnel junction (MJT), exploiting the physical properties and behaviors of the device. Pulse amplitude, bias field, bias current, and pulse width are used as inputs; the output is the switching probability. A single MJT can implement the operations of addition and multiplication. The scheme benefits from the high energy efficiency of an MTJ operated by spintransfer torque (STT), or other future switching mechanisms. Stochastic operations naturally provide high error tolerance, low complexity and low area cost.

Attenuation correction for fixed MR components like MR table and coils in a simultaneous positron emission tomography and magnetic resonance (PET/MR) system is mandatory for accurate quantification of activity distribution. Conventional methods to derive the attenuation maps of MR table and coils include CT scan and transmission scan. Both methods have their advantages and disadvantages. CT scan method is straightforward, however, the images obtained from CT scan have severe metal artifacts due to wires and circuits, and the attenuation coefficients transformation from X-ray energy to Gamma ray energy needs to be remodeled. Transmission scan method avoids the transformation between different energy levels but needs an extra setup of a rotating rod source (usually 68Ge) and its resolution is poor. In this work, we aim to combine the merits of these two methods. Instead of complex rotating rod source device, a 68Ge cylinder source was placed at the center of field of view (FOV) as a stationary emission source. By doing so, the full scan reconstruction problem turned to be a limited angle reconstruction problem. Modified maximum likelihood expectation-maximization (MLEM) algorithm incorporated with Split Bregman algorithm was employed to reconstruct the attenuation map directly from the emission data. An additional CT scan provided the structure information of the MR table and coils, which was used as prior knowledge to confine the region of attenuation coefficients. The proposed method was validated by simulations and real data acquired with a simultaneous PET/MR system.

The low and medium frequency signal generator with high precision and high stability is widely used in experiment courses, mechanical, engineering and other fields. In order to meet the special needs of these fields, this paper puts forward a design scheme of signal generator based on DDS. Combined with the advantage of virtual instrument technology, the user can set the signal parameters on PC control panel made by LabVIEW. And the lower computer can generate kinds of signals with specific frequency, phase and amplitude efficiently. The experiment results show that the design scheme introduced in this paper can accurately produce sine wave, square wave, triangle wave within 0.1 Hz to 1 M Hz frequency and this design has been successfully applied to the experiment courses in colleges and universities.

In classical TOPSIS method, the criteria values and criteria weights are known precisely. However, the values of some criteria are often vague in real-life situation, and it expressed only by exact numerical values is inadequate, the application of linguistic variables or fuzzy terms in decision making is deemed to be relevant. Therefore, the aim of this paper is to develop a fuzzy TOPSIS method to deal with above multiple criteria decision making(MCDM) problems. Here, the linguistic variables are represented by membership degrees applying the axiomatic fuzzy set (AFS) theory, then the application of TOPSIS approach is extended. In addition, the semantic interpretations are provided for the decision results by AFS theory. The proposed method is used to assess thermal-energy storage in concentrated solar power (CSP) systems.

Based on a special angle of voltage or current of power system, controlled switching technology of vacuum circuit breaker is presented to decrease overvoltage and inrush current due to the power equipment operating such as transmission line closing and no-load transformer closing. The optimum switching instant and synchronous switching control strategies are given by synthetically considering closing action time dispersion and pre-strike characteristic of vacuum circuit breaker. The optimal target points of the controlled switching technology are deduced and calculated by Matlab. The switching over voltages are simulated by statistical in ATPdraw. Simulation results show that the closing voltage of transmission can be reduced to 2.35 p.u. and switching voltage of transformer can be reduced to 1.85 p.u. So the theoretical analysis and synchronous switching control strategies in the article are verified correctly.

The performance of SAA (Sense and Avoid) for UAV (Unmanned Aerial Vehicle) is the key function to ensure the UAV's autonomous flight safety. Due to the limitation of payload and size, the micro UAV can't equip large sensors such as radar, etc. Toward the challenging operation condition in low altitude flight environment, we proposed a complete stereo vision based SAA solution suitable for MUAV (Micro Unmanned Aerial Vehicle). By processing the stereo vision data onboard the micro UAV, the proposed method is able to efficiently detect obstacle, evaluate threat level and perform collision avoid maneuver. Experimental results show that the SAA method based on stereo vision can achieve accurate obstacles sensing and effective avoidance maneuver in low altitude airspace, The proposed SAA method can be a practical resolution to ensure low altitude SUAV's safety.

From the requirement of Made in China 2025, it is an important task for the construction of Chinese National Manufacturing Innovation Centers based on the Strategic Alliance on Industrial Technology Innovation (SAITI). Based on the characteristics and function analysis for SAITIs, the paper points out that the SAITIs become an important measure to promoting the industry-academia-research cooperation. Further it gives the intensive study for the Manufacturing innovation center in the United States. On the basis of experience reference, it put forward the development ideas for the promoting of SAITI and policy recommendations for the manufacturing innovation centers.

In this work, we have experimentally demonstrated for the first time, an Analog-to-Digital Converter (ADC) based on the unique voltage-dependent switching probability of a Magnetic Tunnel Junction (MTJ). The switching probability was calculated by applying repetitive voltage pulses and measuring the resolved MTJ states in each sampling time window. Temperature sensitivity and MgO breakdown issues were minimized by optimizing the voltage pulse width. Circuit level techniques were utilized to improve the ADC linearity and increase the input voltage range. The proposed ADC achieves an 8-bit resolution with excellent linearity at 30 and 85°C.

This work experimentally demonstrates for the first time a True Random Number Generator (TRNG) based on the random switching probability of Magnetic Tunnel Junctions (MTJs). A conditional perturb and real-time output probability tracking scheme is proposed to enhance the reliability, speed, and power consumption while maintaining a 100% bit efficiency.

Shading cast by tall trees in urban areas can significantly limit the access of direct solar irradiance to building facades. In this paper, we present a method to model tree crowns and quantify tree shadow effect on solar irradiance received by buildings. Three-dimension (3D) models for trees were constructed using Poisson surface reconstruction method from Light Detection and Ranging (LiDAR) point clouds and QuickBird image data. Tree crown shadows were modeled by applying the improved Inclusion-exclusion Principle. Direct normal irradiance was derived from remote sensing data first and then employed to the calculation of solar energy radiation received by building rooftops and facades. The proposed method was applied to a case study in Nanjing City. Results indicated that tree shadow played an important role in determining the solar radiation energy received by a building. The improved solar potential result provided fundamental data for solar-related equipment selection and installment, as well as building energy consumption estimation.

The electric field induced magnetic anisotropy (EMA) has been observed through ferromagnetic resonance (FMR) in magnetic tunnel junctions (MTJs) by varying the DC bias voltage [1,2]. In this work, however, we show that by changing the magnetization and field setup, the effect of EMA in FMR can be observed and distinguished without an application of DC bias voltage.