Measurement Sensors最新文献_第4页

Design and development of an EMG controlled transfemoral prosthesis 设计和开发 EMG 控制的经股假肢

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-29 DOI: 10.1016/j.measen.2024.101399

R. Dhanush Babu, S. Siva Adithya, M. Dhanalakshmi

Electromyography (EMG) signals are biomedical signals that measure electrical currents generated by the activity of muscles when they contract. EMG is essential for optimizing the control of various prosthetic devices, particularly for transfemoral amputees, where the complexity of muscle signal integration presents significant challenges. The proposed study aims to develop a prosthetic knee that actuates in real-time using the EMG signals from the amputee’s residual limb. Pre-processing techniques are employed to obtain EMG signals from the femoris and vastus muscle targets in the transfemoral region. Moving average filters and Butterworth bandpass filters are implemented to process the raw signals. Sliding windows of various widths were applied for feature extraction. The window size of 200 ms is determined for our study based on the outcomes of the t-SNE plots and the corresponding silhouette scores. After the extraction of the pertinent features, several supervised classifier algorithms are put into practice to classify the knee flexion and extension motion. The k-nearest Neighbor (KNN) algorithm, with an accuracy rating of 80 %, proved to be suitable for motor control. Real-time control is implemented using the Raspberry Pi board to power the prosthesis allowing above-the-knee amputees to voluntarily move the leg back and forth. The EMG signals are then extracted and used to drive the DC motor. The prosthesis would therefore be able to move more precisely since the EMG readings are being gathered in real-time. Thus, this work can enhance the patient’s comfort with the ease of carrying out knee movements.

肌电图（EMG）信号是一种生物医学信号，用于测量肌肉收缩时活动所产生的电流。EMG 对于优化各种假肢设备的控制至关重要，尤其是对于经股截肢者而言，肌肉信号整合的复杂性带来了巨大挑战。本研究旨在开发一种能利用截肢者残肢的肌电信号实时驱动的假肢膝关节。研究采用了预处理技术，以获取经股区域股肌和阔筋目标的肌电信号。采用移动平均滤波器和巴特沃斯带通滤波器来处理原始信号。不同宽度的滑动窗口用于特征提取。在我们的研究中，根据 t-SNE 图的结果和相应的剪影评分确定了 200 毫秒的窗口大小。提取相关特征后，几种有监督的分类器算法被用于对膝关节屈伸运动进行分类。k-nearest Neighbor (KNN) 算法的准确率为 80%，被证明适用于运动控制。使用 Raspberry Pi 板为假肢供电，实现实时控制，使膝上截肢者能够主动前后移动腿部。然后提取肌电信号并用于驱动直流电机。由于 EMG 读数是实时收集的，因此假肢能够更精确地移动。因此，这项工作可以提高病人的舒适度，使其更容易进行膝关节运动。

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引用次数: 0

Optimal planning of electric vehicle charging stations and distributed generators with network reconfiguration in smart distribution networks considering uncertainties 智能配电网中电动汽车充电站和分布式发电机的优化规划与网络重构（考虑不确定性因素

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-28 DOI: 10.1016/j.measen.2024.101400

Sravanthi Pagidipala, Vuddanti Sandeep

This paper proposes an optimal planning technique for placing the multiple renewable energy (RE) based distributed generators (DGs), Distribution Static Compensators (DSTATCOMs), and electric vehicle charging stations (EVCSs) in the radial distribution network (RDN) considering the related uncertainties. This approach gives optimal placement and sizes for DGs and DSTATCOMs as well as a number of electric vehicles (EVs) that can be charged at the EVCSs by considering the network reconfiguration (NR). The optimal allocation of EVCSs fulfills the power demand from EVs at various locations and minimizes the negative impact on the power network. The RE-based DGs considered for this work are solar photovoltaic (PV) and wind. The uncertainties related to RE-based DGs and EVCSs have been modeled by using the probabilistic-based two-point estimate method (2PEM). The best locations and sizes are identified by optimizing the individual objectives that is active power losses and voltage stability index (VSI) using the teaching learning based optimization (TLBO) algorithm. Then both objectives are optimized by using the non-dominated sorting-based TLBO algorithm. Furthermore, the optimal planning approach is implemented on IEEE 33 and 69 bus test systems to demonstrate the suitability, practicality, and efficiency of the proposed optimal planning strategy. The obtained results reveal that the proposed technique is beneficial for determining the optimal locations for DGs, DSTATCOMs, and EVCSs without affecting the grid stability. The proposed planning approach can search better network structure with reduced power losses and voltage deviation, enhanced voltage profile, and improved voltage stability.

考虑到相关的不确定性，本文提出了一种优化规划技术，用于在径向配电网（RDN）中布置多个基于可再生能源（RE）的分布式发电机（DGs）、配电静态补偿器（DSTATCOMs）和电动汽车充电站（EVCSs）。该方法给出了 DG 和 DSTATCOM 的最佳位置和大小，以及通过考虑网络重构 (NR) 可在 EVCS 充电的电动汽车 (EV) 数量。EVCS 的优化分配可满足不同地点电动汽车的电力需求，并最大限度地减少对电网的负面影响。本研究考虑的可再生能源发电设备是太阳能光伏（PV）和风能。使用基于概率的两点估算法（2PEM）对与可再生能源发电设备和 EVCS 相关的不确定性进行了建模。通过使用基于教学的优化算法（TLBO）优化单个目标，即有功功率损耗和电压稳定指数（VSI），确定了最佳位置和规模。然后使用基于非支配排序的 TLBO 算法对这两个目标进行优化。此外，还在 IEEE 33 和 69 总线测试系统上实施了优化规划方法，以证明所提优化规划策略的适用性、实用性和效率。结果表明，建议的技术有利于在不影响电网稳定性的前提下确定风电机组、DSTATCOM 和 EVCS 的最佳位置。所提出的规划方法可以找到更好的网络结构，减少功率损耗和电压偏差，改善电压曲线，提高电压稳定性。

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引用次数: 0

Detection, recognition and transmission of snoring signals by ESP32 通过 ESP32 检测、识别和传输打鼾信号

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-24 DOI: 10.1016/j.measen.2024.101397

Hernan Paz Penagos, Esteban Morales Mahecha, Adriana Melo Camargo, Edison Sanchez Jimenez, Diego Arturo Coy Sarmiento, Sara Valentina Hernández Salazar

This study focuses on the monitoring, transmission, recognition and detection of snoring signals and their relationship with obstructive sleep apnea. To achieve this purpose, the ESP32 microcontroller and a MEMS technology microphone were used to capture and measure characteristic parameters of snoring signals, such as their intensity, frequency and duration. In addition, the WiFi radio interface was used to send the signals to a server where the information was processed, the snoring was detected, linked to a chatbot in Nodred to show the user in a graphical interface his diagnosis of the snoring level. This comprehensive approach allows real-time, wireless monitoring of snoring, leading to a less invasive diagnosis of obstructive sleep apnea.

本研究的重点是监测、传输、识别和检测打鼾信号及其与阻塞性睡眠呼吸暂停的关系。为此，我们使用了 ESP32 微控制器和 MEMS 技术麦克风来捕捉和测量打鼾信号的特征参数，如强度、频率和持续时间。此外，还使用 WiFi 无线接口将信号发送到服务器，在服务器上进行信息处理，检测打鼾情况，并与 Nodred 聊天机器人连接，在图形界面上向用户显示对打鼾程度的诊断结果。这种综合方法可以对打鼾进行实时、无线监测，从而对阻塞性睡眠呼吸暂停进行侵入性较小的诊断。

引用次数: 0

Integrating sensor networks to facilitate efficient energy management for smart grids 整合传感器网络，促进智能电网的高效能源管理

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-24 DOI: 10.1016/j.measen.2024.101393

Arpita Nath Baruah , Malatesh S. Akkur , Jyoti Seth , Jaymeel Shah

Organizations are implementing Smart Energy Management Systems (SEMS) to regulate energy consumption patterns and respond to energy-saving instructions due to rising energy prices and demand. The study proposes an intelligent energy management solution that integrates an IoT middleware module and Energy Controller for effective demand side regulation. The study offers an intelligent energy management solution for smart settings that integrates an IoT middleware module and Energy Controller to regulate the demand side effectively. Every IoT device has an energy controller connected, incorporating sensors and actuators. The proposed solution optimizes air conditioner energy usage in Pakistan by analyzing exterior temperature conditions, building dynamics, and data from smart devices. It uses functional interfaces, robust algorithms, and continuous tracking for assessment. To evaluate the proposed methodology the research highlights the system's key benefits and reveals significant energy savings. The smart energy management system provides better air conditioning system control, real-time monitoring, cost savings, environmental advantages, and longer equipment life.

由于能源价格和需求不断上涨，各组织正在实施智能能源管理系统（SEMS），以调节能源消耗模式并响应节能指令。本研究提出了一种智能能源管理解决方案，该方案集成了物联网中间件模块和能源控制器，可实现有效的需求侧调节。该研究为智能环境提供了一种智能能源管理解决方案，它集成了物联网中间件模块和能源控制器，可有效调节需求侧。每个物联网设备都连接了一个能源控制器，其中包含传感器和执行器。所提出的解决方案通过分析外部温度条件、建筑动态和智能设备数据，优化了巴基斯坦空调的能源使用。它采用功能接口、稳健算法和持续跟踪进行评估。为了评估所提出的方法，研究强调了系统的主要优势，并揭示了显著的节能效果。智能能源管理系统可提供更好的空调系统控制、实时监控、成本节约、环境优势和更长的设备寿命。

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引用次数: 0

Ensem-DeepHAR: Identification of human activity in smart environments using ensemble of deep learning methods and motion sensor data Ensem-DeepHAR：利用深度学习方法集合和运动传感器数据识别智能环境中的人类活动

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-24 DOI: 10.1016/j.measen.2024.101398

S.M. Mohidul Islam, Kamrul Hasan Talukder

Recognizing human activity plays a crucial role in many applications such as medical care services in smart healthcare environments. Inertial or motion sensors can measure physiognomies such as acceleration and angular velocity of body movement while performing the activities and we can use them to learn the models capable of activity recognition. Over the past decades, many state-of-the-art activity recognition systems have been developed but there is still room to improve. In this paper, we have proposed a novel approach to identify human activity from motion sensor data by employing an enormous analysis of sensor data. Based on data analysis, we yielded quality data by preprocessing using a preprocessing chain for human activity recognition (PC-HAR) which also includes the Synthetic Minority Over-sampling Technique to balance the data of the dataset. As a recognition model, we proposed an ensemble of three different deep learning algorithms, namely, modified DeepConvLSTM, modified InceptionTime, and modified ResNet which is named ‘Ensem-DeepHAR’. The outcome of the proposed model is carried out by stacking predictions from each of the mentioned models and then a Random Forest as a meta-model uses those predictions to recognize the final activity. We evaluated our method on both person-dependent and person-independent cases and achieved 99.31 %, 99.08 %, and 97.52 % accuracies for the former case and 97.95 %, 98.11 %, and 99.51 % accuracies for the latter case using three common benchmark datasets: WISDM_ar_v1.1, PAMAP2, and UCI-HAR respectively. The various performance metrics and measures of experimental results establish the supremacy of the proposed model over the state-of-the-arts.

在智能医疗环境中的医疗服务等许多应用中，识别人类活动起着至关重要的作用。惯性或运动传感器可以测量人体活动时的加速度和角速度等生理特征，我们可以利用它们来学习能够进行活动识别的模型。在过去的几十年里，已经开发出了许多最先进的活动识别系统，但仍有改进的余地。在本文中，我们提出了一种新方法，通过对传感器数据进行大量分析，从运动传感器数据中识别人类活动。在数据分析的基础上，我们使用人类活动识别预处理链（PC-HAR）对数据进行预处理，从而获得高质量的数据。作为识别模型，我们提出了三种不同深度学习算法的集合，即改进的 DeepConvLSTM、改进的 InceptionTime 和改进的 ResNet，并将其命名为 "Ensem-DeepHAR"。拟议模型的结果是通过堆叠来自上述每个模型的预测，然后由随机森林作为元模型使用这些预测来识别最终的活动。我们使用三个常见的基准数据集，在与人相关和与人无关的情况下对我们的方法进行了评估，前者的准确率分别为 99.31 %、99.08 % 和 97.52 %，后者的准确率分别为 97.95 %、98.11 % 和 99.51 %：分别为 WISDM_ar_v1.1、PAMAP2 和 UCI-HAR。实验结果的各种性能指标和衡量标准证明了所提出的模型优于同行。

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引用次数: 0

African vulture optimized RNN algorithm maximum power point tracking (MPPT) controller for photovoltaic (PV) system 用于光伏（PV）系统的非洲秃鹫优化 RNN 算法最大功率点跟踪（MPPT）控制器

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-24 DOI: 10.1016/j.measen.2024.101392

Chundi Jiang

Normally, the solar photovoltaic system, the stand-alone or grid-connected system delivers power; it has a photovoltaic panel, a DC-DC converter, and a load consist. A fast and efficient MPPT method is required to track maximum power from photovoltaic panels and DC-DC converters under varying temperatures and irradiance. This study presents a smart controller-established MPPT procedure for a separate photovoltaic structure to trace the maximum power. A meta-heuristic African vulture optimized recurrent neural network (AVO-RNN) is proposed to remove the extreme power as of presented solar vitality for a 3-phase shunt Active Power Filter (APF) grid-linked PV structure. To enhance MPP tracking in photovoltaic arrays a hybrid technique is proposed. It addresses the limitations of traditional methods under varying irradiation by incorporating both current and voltage from the photovoltaic array with the duty cycle of the DC-DC Boost converter as the output constraint. The suggested method reduced as AVOA-RNN MPPT controller established on the African vulture optimization (AVO) algorithm that is beneficial to train the established RNN and to change the joining weights and preferences to get the optimum ideals of duty-cycle converter conforming to the maximum power point of a photovoltaic array. To address grid requirements a 3-phase shunt active power filter (SAPF) is utilized. The proposed MPPT algorithm is validated with MATLAB. The proposed hybrid AVOA-RNN technique achieves an overall accuracy of 99.81 % than existing hybrid PSO-RNN, conventional INC, SSA-GWO, and FO-INC techniques of 93.11 %, 94.42 %, 96.75 % and 98.12 % respectively.

通常情况下，太阳能光伏系统，无论是独立系统还是并网系统，都由光伏板、DC-DC 转换器和负载组成。需要一种快速高效的 MPPT 方法来跟踪光伏电池板和 DC-DC 转换器在不同温度和辐照度下的最大功率。本研究提出了一种智能控制器建立的 MPPT 程序，用于跟踪独立光伏结构的最大功率。该研究提出了一种元启发式非洲秃鹫优化递归神经网络（AVO-RNN），用于消除三相并联有源电力滤波器（APF）并网光伏结构的极端功率。为增强光伏阵列的 MPP 跟踪，提出了一种混合技术。它通过将光伏阵列的电流和电压与 DC-DC 升压转换器的占空比作为输出约束，解决了传统方法在不同辐照度下的局限性。所建议的方法还原为 AVOA-RNN MPPT 控制器，该控制器建立在非洲秃鹫优化（AVO）算法上，有利于训练已建立的 RNN，并改变加入权重和偏好，以获得符合光伏阵列最大功率点的占空比转换器的最佳理想值。为满足电网要求，采用了三相并联有源电力滤波器（SAPF）。MATLAB 验证了所提出的 MPPT 算法。与现有的混合 PSO-RNN、传统 INC、SSA-GWO 和 FO-INC 技术（分别为 93.11%、94.42%、96.75% 和 98.12%）相比，拟议的混合 AVOA-RNN 技术的总体精度达到了 99.81%。

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引用次数: 0

Addressing output ripples in low-power CMOS-based multistage DC-DC boost converters for self-powered electrochemical sensors applications 解决基于 CMOS 的低功耗多级 DC-DC 升压转换器的输出纹波问题，用于自供电电化学传感器应用

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-24 DOI: 10.1016/j.measen.2024.101396

Ahmat Abdel Wahid, Sameh O. Abdellatif

In modern electronic systems, the DC-DC converter plays a pivotal role by facilitating the conversion of direct current (DC) from one voltage level to another. Given the diverse voltage requirements of contemporary chips, a single chip may encompass multiple circuitry groupings, each necessitating specific voltage levels for optimal functionality. To address this need, voltage converters are essential, whether it involves increasing the voltage (Boost converter), decreasing the voltage level (Buck converter), or performing both functions (Buck-boost converter). This paper presents a novel multistage DC-DC converter designed to minimize voltage ripples. The proposed three-stage DC-DC converter is supplied with a 1.5-V input and achieves a four-boost ratio. The cascaded architecture integrates switching capacitors and a gyrator, complemented by a terminating low-pass filter, resulting in a minimal voltage ripple of 0.0003 % relative to the maximum output voltage. Notably, integrating the gyrator enables an inductorless CMOS-based architecture, significantly reducing layout area to 30 × 140 μm². Furthermore, the converter's transient response was simulated, yielding a response time of 90 ms.

在现代电子系统中，直流-直流转换器通过促进直流电（DC）从一个电压电平到另一个电压电平的转换，发挥着举足轻重的作用。鉴于现代芯片对电压的要求多种多样，单个芯片可能包含多个电路组，每个电路组都需要特定的电压水平才能实现最佳功能。为了满足这一需求，电压转换器是必不可少的，无论是提高电压（升压转换器）、降低电压水平（降压转换器），还是同时实现这两种功能（降压-升压转换器）。本文介绍了一种新型多级直流-直流转换器，旨在最大限度地减少电压纹波。所提出的三级 DC-DC 转换器采用 1.5 V 输入，实现了四升压比。级联结构集成了开关电容器和回旋器，并辅以终端低通滤波器，从而将电压纹波降至相对于最大输出电压的 0.0003%。值得注意的是，通过集成回旋器，实现了基于 CMOS 的无电感器架构，将布局面积大幅缩小至 30 × 140 μm2。此外，还模拟了转换器的瞬态响应，结果显示响应时间为 90 毫秒。

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引用次数: 0

Evaluating the performance of triple and double metal gate charge plasma transistors for applications in biological sensors at a dual cavity location 评估三重和双重金属栅电荷等离子晶体管在双腔位置生物传感器中的应用性能

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-24 DOI: 10.1016/j.measen.2024.101394

Akanksha Singh , Rajendra Kumar

Background

Biosensors have become essential tools in biotechnology, environmental monitoring, and healthcare industries due to their ability to detect and analyze biological signals. However, conventional Tunnel Field-Effect Transistors (TFETs) used in biosensors face challenges like reduced ON-state current, random dopant fluctuations, and complex manufacturing processes, which limit their effectiveness.

Aim

The study aims to investigate the effectiveness of Charge Plasma-based Tunnel Field-Effect Transistors (CP-TFETs) with dual and triple metal gate-dual cavity locations for improving the sensitivity and performance of biosensors.

Methodology

The study compares dual and triple metal gate CP-TFET configurations for signal amplification and detection in biosensors. The CP-TFETs use high-k gate dielectric materials to enhance ON-state current and reduce OFF-state current, while the impact of neutralized and charged substances in the cavities on surface energy, electric field, and energy bands is analyzed.

Results

The triple metal gate configuration demonstrated superior sensitivity in detecting biomolecules compared to the dual metal gate. By utilizing high-k materials and optimizing the gate work function, the triple metal gate approach achieved higher drain current and reduced OFF-state current, leading to improved overall performance.

Conclusion

The triple metal gate CP-TFET outperforms its dual metal counterpart in biosensor applications, offering higher sensitivity, increased ON-state current, and improved detection capabilities, making it a promising approach for enhancing biosensor effectiveness.

背景生物传感器由于能够检测和分析生物信号，已成为生物技术、环境监测和医疗保健行业的重要工具。然而，用于生物传感器的传统隧道场效应晶体管（TFET）面临着导通电流降低、随机掺杂波动和复杂制造工艺等挑战，限制了其有效性。该研究旨在调查基于等离子体的电荷隧道场效应晶体管（CP-TFET）的双金属栅和三金属栅双腔位置在提高生物传感器灵敏度和性能方面的有效性。CP-TFET 使用高 k 栅极电介质材料来增强导通态电流和降低关断态电流，同时分析了空腔中的中和带电物质对表面能、电场和能带的影响。结果与双金属栅极相比，三金属栅极配置在检测生物分子方面表现出更高的灵敏度。结论三金属栅 CP-TFET 在生物传感器应用中的性能优于双金属栅，它具有更高的灵敏度、更大的导通电流和更强的检测能力，是增强生物传感器功效的一种有前途的方法。

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引用次数: 0

Optimization of big data analysis resources supported by XGBoost algorithm: Comprehensive analysis of industry 5.0 and ESG performance 在 XGBoost 算法支持下优化大数据分析资源：全面分析工业 5.0 和 ESG 性能

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-22 DOI: 10.1016/j.measen.2024.101310

Qing Su , Lifeng Chen , Limin Qian

To enable state-owned enterprises in Industry 5.0 to better carry out M&A activities, it is important and necessary to provide early warning of M&A risks, which directly affects the interests of both parties and even affects the effectiveness of state-owned enterprise reform. The author proposes the optimization of big data analysis resources supported by the XGBoost algorithm: a comprehensive analysis of Industry 5.0 and ESG performance. Design a comprehensive evaluation system to measure the M&A risk of state-owned listed companies. Using Python programming language to achieve data crawling and processing. Build an early warning model using the XGBoost algorithm. To further evaluate the effectiveness of the early warning model, comparative experiments were conducted. Using multiple linear regression models to study the significant factors of merger and acquisition risk. The experimental results show that the prediction accuracy based on the XGBoost algorithm is 80 %, which performs the best among all models and has stronger reliability and applicability.

Conclusion

The return on investment capital, operating profit margin, and net profit from paid consideration are more important and effective in predicting merger and acquisition risks; The total asset turnover rate, return on investment capital, equity balance, and audit quality are more conducive to suppressing merger and acquisition risks.

为了使工业 5.0 中的国有企业更好地开展并购活动，对并购风险进行预警是非常重要和必要的，这直接影响到并购双方的利益，甚至影响到国有企业改革的成效。笔者提出了在XGBoost算法支持下的大数据分析资源优化：工业5.0与ESG绩效综合分析。设计衡量国有上市公司并购风险的综合评价体系。使用 Python 编程语言实现数据抓取和处理。使用 XGBoost 算法建立预警模型。为进一步评估预警模型的有效性，进行了对比实验。使用多元线性回归模型研究并购风险的重要因素。实验结果表明，基于 XGBoost 算法的预测准确率为 80%，在所有模型中表现最好，具有更强的可靠性和适用性。结论投资资本回报率、营业利润率、有偿对价净利润对预测并购风险更重要、更有效；总资产周转率、投资资本回报率、股权制衡度、审计质量更有利于抑制并购风险。

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引用次数: 0

Image sensor fusion for multimodal biometric recognition in mobile devices 图像传感器融合用于移动设备中的多模态生物识别

Q4 Engineering

Measurement Sensors

Pub Date : 2024-10-03 DOI: 10.1016/j.measen.2024.101309

J. Bhuvana , Amit Barve , Shah Pradeep Kumar , Sukanya Dikshit

Aim

Multimodal biometric recognition authenticates or identifies someone using various biometric features or modalities. Multimodal biometric systems use different biometric qualities to improve the recognition process's accuracy, security, and reliability rather than depending solely on one biometric property, such as fingerprint or facial recognition. Sensor fusion techniques can merge data from several biometric sensors, including fingerprint scanners and facial recognition cameras, to boost the accuracy and dependability of biometric authentication in the context of multimodal biometric recognition in mobile devices. The process of combining data from numerous image sensors to enhance a system's overall performance is known as image sensor fusion.

Challenges

Challenges includeintegration difficulties in achieving accurate and secure multimodal biometric recognition on mobile devices. The study goal was Image sensor fusion for multimodal biometric recognition in mobile devices.

Methodology

The study uses a specially made database of 450 face photos roughly 450 fingerprints.Weiner filter (WF) is used for preparing data. Battle Royale optimized with deep convolutional neural network (BRO-DCNN) is proposed to improve the overall performance of fingerprint and facial information.

Findings

Accuracy, sensitivity, specificity, and precision are used to measure the effectiveness of the proposed BRO-DCNN system. Study results show that BRO-MLANN solves the many characteristics of smart cities and is an efficient approach compared to other current methods in recognition rate, 98 % accuracy, 99 % precision, 96 % specificity, and 94 % sensitivity.

多模态生物识别利用各种生物识别特征或模式来验证或识别某人。多模态生物识别系统使用不同的生物特征来提高识别过程的准确性、安全性和可靠性，而不是仅仅依赖一种生物特征，如指纹或面部识别。在移动设备的多模式生物识别中，传感器融合技术可以合并来自多个生物识别传感器（包括指纹扫描仪和面部识别相机）的数据，从而提高生物识别认证的准确性和可靠性。挑战挑战包括在移动设备上实现准确、安全的多模态生物识别的集成困难。研究目标是在移动设备上实现多模态生物识别的图像传感器融合。研究方法该研究使用了一个专门制作的数据库，其中包含 450 张人脸照片和 450 个指纹。研究结果用准确度、灵敏度、特异性和精确度来衡量所建议的 BRO-DCNN 系统的有效性。研究结果表明，BRO-MLANN 解决了智慧城市的诸多特点，与其他现有方法相比，在识别率、准确率、精确率、特异性和灵敏度方面，BRO-MLANN 是一种高效的方法，准确率为 98%，精确率为 99%，特异性为 96%，灵敏度为 94%。

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引用次数: 0