A Fingerprinting Method Based on Module Extraction Used to Protect Intellectual Property

Intellectual Property （IP） reuse methodology has been widely used in Integrate Circuit （IC） design. Meanwhile, the corresponding security problems caused by illegal IP distribution have aroused lots of attentions. Unlike using IP watermark to identify IP＇s ownership, IP fingerprinting can be used to trace illegal distributor. In this paper, IP buyer＇s fingerprint is mapped into different derived instances of extracted modules, and then is embedded into IP to identify distributor in case of illegal distribution. Comparing with other fingerprinting method, the proposed method has some good characteristics such as low design effort, small storage demand, high security and few physical overheads.

Research of Image pre-processing Algorithm in Automation Fingerprint Identification

In the paper, a valid method of fingerprint Image pre- processing is introduced. Experiment results show that this kind of algorithm can availably wipe off yawp imported by the incom- plete leave fingerprint - marking of sensor surface when finger- print sensor record fingerprint. Meanwhile, it can extract the ef- fective and uneffective zone of fingerprint effectively, and also further enhance ridge line and vale line of fingerprint so that make the lines of fingerprint clear, continuum, lubricity and has better contrast, at the same time, has quite quick speed, this fingerprint Image pre- processing time can be shorten greatly.

Fingerprint Study of Polygonati Rhizoma with Steaming and Exposing to the Sun Alternatively for Different Times

[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

Gender Classification from Fingerprint Using Hybrid CNN-SVM

Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business profiling.Although biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable qualities.This research employs fingerprints to classify gender,with the intention of being relevant for future studies.Several methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based classifiers.Although these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running time.In this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main components.The main goal of this study is to use CNN to extract fingerprint information.These features are then sent to an SVM classifier to determine gender.The hybrid model’s performance measures are examined and compared to those of the conventional CNN model.Using a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.

痛风颗粒成型工艺研究及其物理指纹图谱建立

目的:优选痛风颗粒的成型工艺并建立其物理指纹图谱。方法:在单因素试验的基础上,以稠膏与辅料的比例、可溶性淀粉与微晶纤维素(MCC)的比例、稠膏与胶态二氧化硅的比例为考察因素,以成型率、休止角、吸湿率及软材性状的综合评分为评价指标,利用响应面法优选最佳成型工艺。采用物理指纹图谱对痛风颗粒的均一性、流动性、稳定性等物理属性指标进行综合表征,评价不同批次颗粒的质量一致性。结果:最佳成型工艺为稠膏与辅料比例为1∶0.99,可溶性淀粉与微晶纤维素(MCC)的比例为7∶2,稠膏与胶态二氧化硅的比例为1∶0.09,12批颗粒物理指纹图谱的相似度均高于0.9。结论:该工艺稳定可行,生产出的颗粒性质稳定,符合痛风颗粒实际大生产要求。

Modeling of Mass Transfer in Cavity Limited by a Semi Permeable Membrane （Simulation of Spiral Wound Module）

The reverse osmosis process has been applied in large industrial fields （water treatment, food industry, biotechnology, and ect.）. Despite, this progress more investigation are required to optimize the reverse osmosis process. The present paper deals the modeling of mass transfer in a cavity limited by a semi-permeable membrane. Mass conservation and momentum balances are developed, dimensionless and control volume method has been applied. The velocity and concentration profiles versus the Reynolds number and Sherwood are studied. The results show that the permeability of the membrane decreases as function of the transversal （radial） component of the velocity. The axial （tangential） component of the velocity presents a good stability along the thickness of the cavity; this phenomenon can be attributed to the zero gradient of the tangential velocity. These preliminary results show that the phenomenon of the concentration polarization affects the mass transfer coefficient in a channel. Current study has considered the cavity without a promoter of the turbulence; whereas, the design of the spacer has an important role on mass transfer coefficient in the reverse osmosis module. Our next interest is the integration of the spacer in the cavity, and the study of the effect of its design on the concentration and velocity profiles and the mass transfer coefficient through the reverse osmosis membrane.

School Debit Transaction Using Fingerprint Recognition System

This paper proposed a fingerprint based school debit transaction system using minutiae matching biometric technology.This biometric cashless transaction system intensely shortens the luncheon line traffic and labour force compared to conventional cash payment system.Furthermore,contrast with card cashless transaction system,fingerprint cashless transaction system with benefit that user need not carry additional identification object and remember lengthy password.The implementation of this cashless transaction system provides a more organize,reliable and efficient way to operate the school debit transaction system.

Adaptive and Intelligent Digital Signal Processing for Improved Optical Interconnection

In recent years, explosively increasing data traffic has been boosting the con?tinuous demand of high speed optical interconnection inside or among data centers, high performance computers and even consumer electronics. To pursue the improved intercon?nection performance of capacity, energy efficiency and simplicity, effective approaches are demonstrated including particularly advanced digital signal processing (DSP) meth?ods. In this paper, we present a review about the enabling adaptive DSP methods for opti?cal interconnection applications, and a detailed summary of our recent and ongoing works in this field. In brief, our works focus on dealing with the specific issues for short-reach interconnection scenarios with adaptive operation, including signal-to-noise-ratio (SNR) limitation, level nonlinearity distortion, energy efficiency consideration and the de?cision precision.

Accurate Fingerprint Enhancement and Identification Using Minutiae Extraction

Fingerprints are an extraordinary source for recognizable proof of people. Unique finger impression acknowledgment is one of the most seasoned types of biometric identification. However, getting a decent unique finger impression picture isn’t that simple. So we must process unique finger impression picture before coordinating. A crucial advance in measurements of fingerprint minutiae is to obtain minutiae from the finger impression pictures dependably. However, fingerprint images are occasionally of perfect quality. They might be debased and defiled because of varieties in skin and impression conditions. Along these lines, image enhancement strategies utilize other details extraction to acquire a more reliable estimation of minutiae areas. The primary objective of this research work is to introduce a superior and improved unique fingerprint image. We studied the elements identifying with getting elite component focuses detection algorithm, for example, picture quality, segmentation, picture upgrade and highlight recognition. Usually utilized features for enhancing unique finger impression picture quality are Fourier spectrum energy, Sobel filter energy, and local orientation. Precise segmentation of unique finger impression edges from a broad foundation is vital. For productive improvement and feature extraction algorithms, we zero the commotion in segmented features. As a pre-processing method, we need to perform comprising of field introduction, ridge frequency estimation, Sobel filtering, division. Then connect the resulting picture to a thinning algorithm and consequent minutiae extraction. After resultant extraction of these minutiae focuses, we will utilize the picture with focuses for coordinating or finding the offenders and also for other security issues. The procedure of image pre-processing and minutiae extraction is explored. The simulations are performed in the MATLAB environment to assess the execution of the implemented algorithm.

充电桩充电模块功率器件故障诊断研究综述

大功率直流充电桩是未来电动汽车充电设施的发展方向,充电模块是直流充电桩最重要以及故障率最高的部件,其中功率器件开路故障较为常见。为保证充电模块安全可靠运行,需要对充电模块功率器件开路故障进行准确识别和定位。文中首先对充电模块拓扑结构和故障类型进行分析,然后分别对基于解析模型、基于信号处理和基于知识的功率器件开路故障诊断方法进行总结,分别介绍了各类方法的基本思想、研究进展和优缺点,最后总结并展望充电模块功率器件开路故障诊断方法未来的研究和发展方向。

专业群“模块递进、项目贯穿”工作过程导向的实践教学体系改革与实践——以电气自动化技术专业群为例

高职院校专业群建设的实质,就是依据产业和人才培养逻辑对群内专业进行有效整合,实现资源共享,凸显群效应,服务产业对人才的需求。明确专业群服务产业的人才培养定位,理清专业群内各专业能力培养的自身特色以及服务产业的逻辑关联。以课程为本搭建实践能力递进模块,以工作过程为导向开发课程,项目贯穿设计专业群实践教学体系。建设工程化的实践环境,利用信息化管理手段,推动过程评价和增值评价,持续改进培养质量,融“三教”改革于项目化教学,实现专业群高水平服务学生成长成才,高质量服务区域经济发展,适应产业发展新形势和新业态的要求。

多轴线运输车的模块化设计方法研究

目前多轴线运输车存在设计周期长、生产制造困难等问题。为了对此类轴线车进行快速开发,在分析现有模块划分方法的基础上,引入复杂网络理论,开展针对多轴线运输车的模块化设计方法研究。首先从零部件间的几何、功能及设计变更三个角度分析评价多轴线运输车零部件之间的关联强度,然后采用基于七标度法则的区间层析分析法(IAHP)确定三个相关属性权重大小,并建立产品零部件综合复杂网络模型。在此基础上,采用Louvain算法进行该网络的社区发现,从而得到多轴线运输车模块划分方案,与GN算法、LPA算法、FCM算法相比,采用Louvain算法能更有效地对轴线车进行模块划分。基于模块化理论对多轴线运输车的设计进行研究,能够实现此轴线车的快速设计,为轴线车的优化升级奠定了基础。

基于STM32的智能航行系统设计

为解决航行器智能确定航向并航行的问题,本文采取模块化的思想设计了一款基于STM32的红外导引的智能航行控制系统。该智能航行系统利用红外接收模块进行红外导引信号的接收,采取线性加权法进行航向与导引信号之间的偏离角确定,利用比例-积分-微分(proportion-integration-differentiation,PID)控制算法对控制舵机转角的脉冲宽度调制(pulse width modulation,PWM)信号的占空比进行确定,从而对螺旋桨的转速与舵桨的转角进行可靠控制。根据水池测试结果,该智能航行控制系统能够对红外接收信号进行快速处理,在保持较高航速的条件下进行可靠的航行控制。

调频信号宽带采集与解调实验系统设计

针对射频实验系统成本昂贵问题,提出了一种基于共享数据采集硬件,利用互联网向实验终端分发数据的信号处理实验教学系统结构。基于此结构,以无线信号宽带采集和调频广播信号解调为目标,设计并实现了用于信号处理类课程的实验教学系统,同时给出了实验内容设计和扩展阅读材料。

难变形金属小模数齿形件电流辅助分形旋压成形工艺优化

目的针对30CrMnSiA合金结构钢小模数齿形件电流辅助分形旋压成形工艺,探究工艺参数对小模数齿形件成形质量的影响规律,分析工艺参数间的相关性,获得优化后的工艺参数组合。方法选取小模数齿形件齿廓偏差、齿距偏差、轮齿饱和度为成形质量响应指标,以电流密度、占空比以及旋轮进给速度为影响因素,设计响应面试验方案;根据有限元模拟结果建立成形质量回归预测模型,分析电流密度、占空比以及旋轮进给速度对小模数齿形件成形质量的影响规律,计算获得最优的参数组合并进行试验验证。结果电流密度、脉冲占空比、旋轮进给速度对小模数齿形件成形质量有显著影响,成形质量随着电流密度与占空比的升高而升高,随旋轮进给速度的升高而先升高后降低;且电流密度与旋轮进给速度、脉冲占空比与旋轮进给速度在对轮齿饱和度的影响上有一定的交互性;通过建立的回归模型预测得到的优化工艺参数如下:电流密度为17.5 A/mm2、脉冲占空比为40%、旋轮进给速度为0.6 mm/min。结论回归模型预测值与试验值的相对误差小于8%,说明所构建的成形质量回归预测模型是准确的,利用优化后的参数可制备出成形质量良好的小模数齿形件。

基于Python编程技术的专家数据库服务模块开发与实现

为解决专家库管理与维护中工作量大、人工操作繁琐、机械重复劳动且易出错等问题,开发一种基于Python编程技术的专家数据库服务模块,可以实现专家信息自动提取录入、专家成果智能挖掘、自动生成聘书和荣誉证书等功能。实践证明,执行该模块的编程代码后,241份专家申请表成功读取率可达92.95%,生成Excel表单的信息正确率高达100%。该模块的程序设计具有操作简单、可按需修改代码中目标参数的特点,不仅能大大提高编辑的工作效率,后续还可以尝试与出版流程进行整合,以增加模块的适用性与广泛性。

基于卷积调制与空间协作的水下图像增强

针对光线在水中的散射和吸收效应造成水下图像纹理和结构不清晰的问题,提出一种基于卷积调制(CM)与空间协作(SC)的水下图像增强算法。以编码器-解码器作为基础网络,使用RepVGG的浅层和深层网络分别提取水下图像的纹理和结构特征。首先,特征主导网络将RepVGG中提取到的水下图像特征转化成具有不同尺度的纹理和结构特征,使其与解码器中的特征图进行拼接融合。其次,在编码器中使用卷积调制模块,采用深度可分离卷积(DSConv)模拟自注意力机制的方式减少图像细节信息的丢失,提高编码器特征提取的能力。最后,在解码器中使用空间协作卷积(SCConv),在空间维度上处理水下特征保留更多的位置信息,以提高解码器对融合后特征的增强能力。实验结果表明,该算法在视觉感知与性能指标上优于对比算法,峰值信噪比(PSNR)和结构相似性(SSIM)指标最高达到23.4465 dB和0.8946,水下彩色图像质量评价(UCIQE)和水下图像质量测量(UIQM)指标最高达到0.5826和3.0689,进一步证明了该算法能够有效增强水下图像的纹理和结构特征,具有较好的视觉感知效果。

基于51单片机的指纹密码锁设计与制作

文章以单片机STC89C52作为指纹密码锁的控制核心,通过4×4矩阵键盘实现按键输入,实现普通密码解锁以及指纹解锁2种解锁方式。在开机初始界面,矩阵键盘输入数字密码则实现普通密码开锁功能,如果按压AS608指纹模块,则实现指纹解锁功能。LCD12864液晶显示屏实时显示密码锁的当前状态。矩阵键盘输入实现人性化按键功能:密码输入实时加密显示输入位数,如果输入出错,则可以实现退格或者返回初始界面功能。密码锁在管理员界面,可以实现录入指纹、删除指纹以及修改开锁密码功能。设计采用AT24C02存储芯片,实现开锁密码的稳定存储功能,利用AS608集成指纹处理模块,实现指纹模板的存储功能。

突破数据驱动限制:经典信号处理赋能基于深度学习的无线电调制识别

自动调制识别在无线通信中具有关键作用。随着深度学习技术的发展,研究人员将其应用于无线电信号的调制识别中,表现出优于传统方法的性能。然而,纯数据驱动的方法存在诸多限制,如对大规模数据的依赖、对信道环境变化的敏感性以及高计算复杂度。为了解决这些问题,将经典信号处理与深度学习相结合,通过经典信号处理方法来引导和优化深度学习模型,从而提升其在数据异构、小样本、低信噪比、多径信道、开集和轻量化等复杂场景下的识别能力。研究结果表明,经典信号处理赋能的深度学习方法在复杂场景下显著提高了调制识别的性能和可靠性。另外,讨论了未来研究面临的关键挑战,包括信号处理算法的选择、计算范式的创新以及深度学习模型的可解释性,这些挑战的解决将进一步促进经典信号处理与深度学习在无线电调制识别中的应用和发展,为未来的无线电信号智能处理指明新方向。