Post-processing Free Quantum Random Number Generator Based on Avalanche Photodiode Array

Quantum random number generators adopting single negligible dead time of avalanche photodiodes （APDs） photon detection have been restricted due to the non- We propose a new approach based on an APD array to improve the generation rate of random numbers significantly. This method compares the detectors＇ responses to consecutive optical pulses and generates the random sequence. We implement a demonstration experiment to show its simplicity, compactness and scalability. The generated numbers are proved to be unbiased, post-processing free, ready to use, and their randomness is verified by using the national institute of standard technology statistical test suite. The random bit generation efficiency is as high as 32.8% and the potential generation rate adopting the 32× 32 APD array is up to tens of Gbits/s.

Development of a Post-Processing Automation Procedure for the GPS-Based Travel Time Data Collection Technique

The travel time data collection method is used to assist the congestion management. The use of traditional sensors (e.g. inductive loops, AVI sensors) or more recent Bluetooth sensors installed on major roads for collecting data is not sufficient because of their limited coverage and expensive costs for installation and maintenance. Application of the Global Positioning Systems (GPS) in travel time and delay data collections is proven to be efficient in terms of accuracy, level of details for the data and required data collection of man-power. While data collection automation is improved by the GPS technique, human errors can easily find their way through the post-processing phase, and therefore data post-processing remains a challenge especially in case of big projects with high amount of data. This paper introduces a stand-alone post-processing tool called GPS Calculator, which provides an easy-to-use environment to carry out data post-processing. This is a Visual Basic application that processes the data files obtained in the field and integrates them into Geographic Information Systems (GIS) for analysis and representation. The results show that this tool obtains similar results to the currently used data post-processing method, reduces the post-processing effort, and also eliminates the need for the second person during the data collection.

A study on fast post-processing massive data of casting numerical simulation on personal computers

When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of rendering.Based on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal computers.Based on this method,a new postprocessor is developed.It is capable to illustrate filling and solidification processes of casting,as well as thermal stess.The new post-processor also provides fast interaction to simulation results.Theoretical analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on surface.Meanwhile,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.

A method of line spectrum extraction based on target radiated spectrum feature and its post-processing

To improve the ability of detecting underwater targets under strong wideband interference environment,an efficient method of line spectrum extraction is proposed,which fully utilizes the feature of the target spectrum that the high intense and stable line spectrum is superimposed on the wide continuous spectrum.This method modifies the traditional beam forming algorithm by calculating and fusing the beam forming results at multi-frequency band and multi-azimuth interval,showing an excellent way to extract the line spectrum when the interference and the target are not in the same azimuth interval simultaneously.Statistical efficiency of the estimated azimuth variance and corresponding power of the line spectrum band depends on the line spectra ratio(LSR)of the line spectrum.The change laws of the output signal to noise ratio(SNR)with the LSR,the input SNR,the integration time and the filtering bandwidth of different algorithms bring the selection principle of the critical LSR.As the basis,the detection gain of wideband energy integration and the narrowband line spectrum algorithm are theoretically analyzed.The simulation detection gain demonstrates a good match with the theoretical model.The application conditions of all methods are verified by the receiver operating characteristic(ROC)curve and experimental data from Qiandao Lake.In fact,combining the two methods for target detection reduces the missed detection rate.The proposed post-processing method in2-dimension with the Kalman filter in the time dimension and the background equalization algorithm in the azimuth dimension makes use of the strong correlation between adjacent frames,could further remove background fluctuation and improve the display effect.

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.

Hybrid Post-Processing Procedure for Displacement-Based Plane Elements

In the analysis of high-rise building, traditional displacement-based plane elements are often used to get the in-plane internal forces of the shear walls by stress integration. Limited by the singular problem produced by wall holes and the loss of precision induced by using differential method to derive strains, the displacement-based elements cannot always present accuracy enough for design. In this paper, the hybrid post-processing procedure based on the Hellinger-Reissner variational principle is used for improving the stress precision of two quadrilateral plane elements. In order to find the best stress field, three different forms are assumed for the displacement-based plane elements and with drilling DOF. Numerical results show that by using the proposed method, the accuracy of stress solutions of these two displacement-based plane elements can be improved.

Post processing of additive manufactured Mg alloys:Current status,challenges,and opportunities

Magnesium(Mg)and its alloys are emerging as a structural material for the aerospace,automobile,and electronics industries,driven by the imperative of weight reduction.They are also drawing notable attention in the medical industries owing to their biodegradability and a lower elastic modulus comparable to bone.The ability to manufacture near-net shape products featuring intricate geometries has sparked huge interest in additive manufacturing(AM)of Mg alloys,reflecting a transformation in the manufacturing sectors.However,AM of Mg alloys presents more formidable challenges due to inherent properties,particularly susceptibility to oxidation,gas trapping,high thermal expansion coefficient,and low solidification temperature.This leads to defects such as porosity,lack of fusion,cracking,delamination,residual stresses,and inhomogeneity,ultimately influencing the mechanical,corrosion,and surface properties of AM Mg alloys.To address these issues,post-processing of AM Mg alloys are often needed to make them suitable for application.The present article reviews all post-processing techniques adapted for AM Mg alloys to date,including heat treatment,hot isostatic pressing,friction stir processing,and surface peening.The utilization of these methods within the hybrid AM process,employing interlayer post-processing,is also discussed.Optimal post-processing conditions are reported,and their influence on the microstructure,mechanical,and corrosion properties are detailed.Additionally,future prospects and research directions are proposed.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

后处理加工工艺对饲用凝结芽孢杆菌活菌收率的影响

【目的】探明提高饲用凝结芽孢杆菌发酵后处理加工贮存过程中的活菌收率,为降低规模化生产成本提供科学依据。【方法】以饲用凝结芽孢杆菌(Bacilluscoagulans)W-02菌株为研究对象,分别检测凝结芽孢杆菌发酵后处理加工贮存过程中离心浓缩工艺、干燥方式、粉碎时间和低温保存时间前后活菌数,计算各环节活菌收率,最终计算产品活菌总收率。【结果】经4000 r/min、15 min离心浓缩工艺,凝结芽孢杆菌菌泥平均活菌收率为91.57%;经-58℃、100 mT真空冷冻干燥工艺,干燥产品活菌收率为92.98%;经转速24000 r/min、粉碎15 s,粉碎产品活菌收率为83.37%;经4℃低温保存365 d,凝结芽孢杆菌活菌收率为71.21%。【结论】饲用凝结芽孢杆菌发酵液经离心浓缩、吸附干燥、高速粉碎最优条件处理后,低温保存365 d时活菌总收率(各环节活菌收率之积)为50.55%。

Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

广东省垦造水田项目农田水利优化设计及应用——以南雄市珠玑镇古田村项目为例

为贯彻落实“占优补优、占水田补水田”耕地占补平衡要求,保障粮食安全,努力实现“藏粮于田”,2016年初广东省政府正式谋划垦造水田工作,南雄市珠玑镇古田村是全省垦造水田试点项目和市级示范项目。该文以古田村项目为例,对垦造水田工作的选址、设计、施工、验收及入库形成水田指标几个阶段进行了分析。该项目通过对符合条件的旱地、荒地水源条件及灌排系统进行改造和设计,结合土壤改良等其他技术措施,最终将旱地、荒地垦造为水田,水利设施的优化设计起到关键作用,研究成果可为垦造水田工作提供技术支持。

Image Representations of Numerical Simulations for Training Neural Networks

A large amount of data can partly assure good fitting quality for the trained neural networks.When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality data.Once the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation tools.Correspondingly,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and programmers.In this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by images.The temporal and spatial information is kept and the data are greatly compressed.In addition,the results are readable for not only computers but also human resources.Some examples are given,indicating the effectiveness of the proposed strategy.

黑玉米罐头的研制

研究了经浸泡、去皮、预煮、排气、杀菌等过程 。

经尿道等离子体双极电切术治疗膀胱肿瘤27例

目的探讨经尿道等离子体双极电切治疗膀胱肿瘤的安全性与有效性.方法用经尿道等离子体双极电切膀胱肿瘤27例,肿瘤分级I级7例,Ⅱ级14例,Ⅲ级6例.结果27例膀胱肿瘤1次切除,手术时间15～65min,平均30min.1例发生膀胱穿孔.结论经尿道等离子体双极电切治疗膀胱肿瘤安全有效.

山西古代谷子(粟)栽培技术

根据古文献记载 ,分析了山西省古代谷子栽培技术发展的历史。认为山西谷子栽培技术经过历代的发展 ,特别是汉代以后推广先进技术 ,积累了丰富经验 ,到魏清时期 ,贾思勰、祁隽藻加以总结 ,达到了科学化、系统化 ,形成一整套比较完整的栽培技术 ,现在仍具有重要的现实意义。

经翼点入路显微外科手术治疗大型鞍旁脑膜瘤34例临床经验

目的总结分析经翼点入路治疗大型鞍旁脑膜瘤的显微外科手术方法及其疗效。方法回顾性分析2006年7月至2016年8月间上海中医药大学附属第七人民医院收治的接受翼点或扩大翼点入路显微外科手术的34例大型鞍旁脑膜瘤患者的临床资料,其中男性13例、女性21例;年龄21~72岁,平均(49±13)岁;病程1~55个月,平均(13±10)个月;瘤体最大直径为3.3~5.2 cm,平均(4.3±0.6)cm。结果本组无手术死亡患者。术后头颅影像学检查示,34例患者中肿瘤全切者24例、次全切者5例、部分切除者5例。术后随访时间为24~48个月,平均(36±13)个月。预后优良者28例(Karnofsky评分为80~100分),预后一般者6例(Karnofsky评分为60~79分)。5例患者术后发生动眼神经麻痹,给予营养神经处理后症状好转。结论翼点或扩大翼点入路显微外科手术可以提高肿瘤切除率。对于无法实施全切的肿瘤,可行大部手术切除后辅以放射治疗以提高疗效。

激光散斑引起的象强度中心漂移的理论计算

在传感技术的许多应用中，经常需要用探测器在象面上接收一个成象斑点，以便从中提取空间深度和位置信息。由于激光的高亮度和极好的方向性，使之成为非常理想的投影光源。但是由于激光散斑噪声的存在，使得象斑强度中心与象斑几何中心不再重合，导致对物体空间深度和位置的估计错误。本文从散斑统计光学理论出发，针对四种不同的光瞳函数，提出在相干照明条件下象斑强度中心计算方法。

锦橙的营养特性与施肥技术

通过多年的田间与盆栽试验，探讨了锦橙营养特性与平衡施肥的关系，植株在年周期中的营养变化规律及３个营养关键期；对种植于不同土壤母质上的３种砧木锦橙进行了缺素症状观察及诱因分析；探讨了紫色土锦橙的矿质营养；提出了适宜的施肥量与营养元素比例。

一种基于压扩变换降低OFDM系统峰均比的方法

对一种用于减小OFDM信号峰均比的压缩扩展技术进行了分析,该技术能够以相对较小的计算复杂度大大降低OFDM信号的PAPR。作者采用μ率压扩算法,其中的压扩拐点值取为均值而不是传统方法中的峰值。计算机仿真表明这种方法能大大降低OFDM信号的PAPR,并且与PTS技术相比这种方法更有效,计算复杂度也比PTS技术低。