ECS Sensors Plus最新文献

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Challenges and Opportunities for Developing Electrochemical Biosensors with Commercialization Potential in the Point-of-Care Diagnostics Market 开发具有商业化潜力的医疗点诊断市场电化学生物传感器的挑战与机遇

ECS Sensors Plus

Pub Date : 2024-03-05 DOI: 10.1149/2754-2726/ad304a

Amir Ali Akhlaghi, Harmanjit Kaur, Bal-Ram Adhikari, L. Soleymani

There is a plethora of electrochemical biosensors developed for ultrasensitive detection of clinically-relevant biomarkers. However, many of these systems lose their performance in heterogeneous clinical samples and are too complex to be operated by end users at the point-of-care (POC), prohibiting their commercial success. Integration of biosensors with sample processing technology addresses both of these challenges; however, it adds to the manufacturing complexity and the overall cost of these systems. Herein, we review the different components of a biosensor and avenues for creating fully-integrated systems. In the context of integration, we focus on discussing the trade-offs between sensing performance, cost, and scalable manufacturing to guide the readers toward designing new electrochemical biosensors with commercialization potential.

目前已开发出大量电化学生物传感器，用于超灵敏检测临床相关的生物标记物。然而，其中许多系统在异质临床样本中失去了性能，而且过于复杂，最终用户无法在床旁 (POC) 进行操作，从而阻碍了它们在商业上的成功。将生物传感器与样本处理技术相结合可以解决这两个难题，但却增加了这些系统的制造复杂性和总体成本。在此，我们回顾了生物传感器的不同组成部分以及创建完全集成系统的途径。在集成方面，我们重点讨论了传感性能、成本和可扩展制造之间的权衡，以指导读者设计出具有商业化潜力的新型电化学生物传感器。

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HGSSA-bi LSTM: A Secure Multimodal Biometric Sensing Using Optimized Bi-Directional Long Short-Term Memory with Self-Attention HGSSA-bi LSTM：使用具有自注意功能的优化双向长短时记忆的安全多模态生物识别传感技术

ECS Sensors Plus

Pub Date : 2024-01-04 DOI: 10.1149/2754-2726/ad1b3a

Juhi Priyani, Pankaj Nanglia, Paramjit Singh, Vikrant Shokeen, Anshu Sharma

Biometric sensing technology has become a frequent element of everyday life as a result of the global demand for information security and safety legislation. In recent years, multimodal biometrics technology has become increasingly popular due to its ability to overcome the shortcomings of unimodal biometric systems. A hunger game search self-attention based Bi-LSTM model (HGSSA-Bi LSTM, Bi-directional long short-term memory) modal is presented in this paper for multimodal biometric identification. For removal of noise (unwanted) the pre-processing stage is used in the initial stage. An extended cascaded filter (ECF) is used with a combination of median and wiener filter in the pre-processing stage. Then, using the CNN model, feature extraction is utilized to extract features from the processed images. After feature extraction, fusing of feature is used with the aid of discriminant correlation analysis (DCA). Finally, the recognition process is performed by using the novel optimized HGSSA-Bi LSTM. The obtained outcome for the developed model is finally compared with other previous approaches such as CNN, RNN, DNN, and autoencoder models and the calculated performance based on accuracy 98.5%, precision 98%, F1-score 97.5%, sensitivity 98.5%, and specificity 99% accordingly.

由于全球对信息安全和安全立法的需求，生物识别传感技术已成为日常生活中的一种常见元素。近年来，多模态生物识别技术因其能够克服单模态生物识别系统的缺陷而越来越受欢迎。本文提出了一种基于饥饿游戏搜索自我注意的 Bi-LSTM 模型（HGSSA-Bi LSTM，双向长短期记忆），用于多模态生物识别。为了去除噪音（不需要的），在初始阶段使用了预处理阶段。在预处理阶段，使用了结合中值滤波器和维纳滤波器的扩展级联滤波器（ECF）。然后，利用 CNN 模型进行特征提取，从处理过的图像中提取特征。提取特征后，借助判别相关分析（DCA）对特征进行融合。最后，使用新型优化 HGSSA-Bi LSTM 执行识别过程。最后，将所开发模型的结果与之前的其他方法（如 CNN、RNN、DNN 和自动编码器模型）进行了比较，并计算出相应的准确率 98.5%、精确率 98%、F1 分数 97.5%、灵敏度 98.5%、特异性 99%。

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