AI and Neural Network Algorithms

AI and Neural Network Algorithms: Comparison

Please note this is a comparison between Version 2 by 熙魏 and Version 1 by 熙魏.

This paper focuses on the use of Al增加了微机电系统生物传感器的潜力，并为自动化，消费电子，工业制造，国防，医疗设备等开辟了新的机会。微机电系统微悬臂生物传感器目前正在进入我们的日常生活，并在社会技术的进步中发挥重要作用。与传统的生物传感器相比，具有微悬臂结构的微机电系统生物传感器具有许多优点，包括小尺寸，高灵敏度，批量生产，简单的阵列，集成等。I in various MEMS (Micro-Electro-Mechanical System) biosensor types. Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into our daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a num- ber of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active development of artificial intelligence (AI) and neural networks. As a result, this paper examines the fundamentals of the neural algorithm and goes into great detail on the fundamentals and uses of the principal component analysis approach. A neural algorithm application in Micro-Electro-Mechanical System microcantilever biosensors is anticipated through the associated application of the principal com-ponent analysis approach. Our investigation has more scientific study value, because there are currently no favorable reports on the market regarding the use of AI with Micro-Electro-Mechanical System microcantilever sensors. Focusing on AI and neural networks, this paper introduces Micro-Electro-Mechanical System biosensors using artificial intelligence, which greatly promotes the development of next-generation intelligent sensing systems, and the potential applications and prospects of neural networks in the field of microcantilever biosensors.

microcantilever
AI
neural network
biosensors
微悬臂
神经网络
生物传感器

1. 引言

1.Introduction

由于科学技术的进步和信息技术的普及，传感器技术正在不断发展。到目前为止，传感器的发展已经经历了三代。结构传感器构成了第一代传感器。通过利用相关的传感器系统变量，这种传感器可以转换信号并检测信号的变化。固态传感器是第二种传感器，由半导体，电解质和磁性元件组成。在20世纪70年代，这种传感器开始发展。这一代传感器目前约占传感器行业的75%，通常具有低成本、高精度、性能突出等特点。本文主要介绍的智能传感器是第三代传感器。由于互联网的增长和人类需求的提高，小型化和智能化是目前传感器系统的两大要求。微机电系统（MEMS）集成在纳米级和微米级。因此，MEMS传感器与传统传感器的不同之处在于它们小型化，与微电子集成，并以极高的精度并行制造。MEMS和纳米技术在传感领域的潜在用途通常被认为是因为它们的尺寸更小，更容易集成到系统中，更好的便携性和移动性。

Sensor technology is evolving as a result of advances in science technology and the popularization of information technology. Sensor development has gone through three generations thus far. Structural sensors make up the initial generation of sensors. By utilizing the pertinent sensor system variables, this kind of sensor can convert the signal and detect changes in the signal. A solid-state sensor is the second kind of sensor and is made of semiconductors, electrolytes, and magnetic components. In the 1970s, this kind of sensor started to evolve. This generation of sensors, which currently account for about 75% of the sensor industry, are often characterized by low cost, high accuracy, and outstanding performance. The smart sensor that this paper primarily introduces is the third-generation sensor. Miniaturization and intelligence are currently the two major requirements for sensor systems due to the growth of the Internet and the improvement in human demands. Microelectromechanical systems (MEMS) are integrated at the nanoscale and microscale. As a result, MEMS sensors differ from conventional sensors in that they are miniaturized, integrated with microelectronics, and manufactured in parallel with great precision. The potential use of MEMS and nanotechnology in the sensing sector is typically thought of due to their lower size, simpler integration into systems, better portability, and mobility.

自1986年原子力显微镜[1]问世以来，其无标签、高灵敏度、便携、低成本、响应快等优点吸引了大量研究人员的探索和研究。从那时起，基于MEMS的生物传感器不断发展;与此同时，出现了许多类型的MEMS生物传感器，如光学声学[2，3，4，5]。高效、快速、灵敏，能快速获取和处理信息，与人体的感觉器官一样;它可以感知外部环境并感知重要的物理信息，例如声音，光线，压力和温度。由于这些优势，MEMS生物传感器广泛应用于自动化、航空航天、消费电子、国防、工业制造、医疗设备、生命科学和电信[6，7，8]。

人工智能越来越多地用于与人类和计算机连接和互动。用户可以通过此交互式系统获得更加身临其境的体验，该系统包含未来传感器的功能。它可用于各种应用场景，包括娱乐、医疗康复、运动训练模拟等（如图 1 所示）[9]。

图 1.未来生物传感器的发展趋势。

第2章 AI在生物感测器中的应用研究现状

2. Research State of AI Applications in Biosensors

人工智能发展迅速，目前是一个受欢迎的技术研究领域。它使用密集连接的网络以模仿人脑处理信息的方式处理信息。它能够自学习，并行处理和强大的信息存储。随着人工智能领域的理论和技术的成熟，人工智能的应用领域正在增长。人们已经能够看到人工智能和MEMS传感器如何协同工作。

Artificial intelligence has advanced quickly and is currently a popular field of study in technology. It uses densely connected networks to process information in a manner that mimics how the human brain processes information. It is capable of self-learning, parallel processing, and powerful information storage. The fields of application for artificial intelligence are growing as the field’s theory and technology mature. People are already able to see how artificial intelligence and MEMS sensors work together.Include Gas Sensing Fiel, Sound Detection Field, Body Sensor Field and Wearable Sensing Field etc.

气体感测领域

3. Research Status of MEMS Microcantilever Biosensors

气体监测系统用于各种环境，包括商业和住宅环境，特别是在危险气体的检测中。半导体型电阻式气体传感器的微小尺寸和高灵敏度使其具有吸引力。它们也便宜且易于创建。这些优势意味着基于半导体的气体传感器是物联网应用的绝佳选择。

Micro-Electro-Mechanical Systems, sometimes known as MEMS, are electronic mechanical systems. It is a microelectromechanical system that incorporates interface circuits, signal processing and control circuits, microsensors, and microactuators. The microcantilever biosensor operates on the theory that, when the material being tested adheres to or remains on its surface, the mass of the microcantilever changes, which causes the microresonant cantilever’s frequency to change. The quality of the test object can be determined by measuring the size of the frequency offset.

Suh发布了用于物联网应用的完全集成的便携式多气体传感器模块[10]。对于物联网（IoT）应用，如多气体传感器读取和数据分析、模拟/数字信号处理、加热器控制和无线通信，Suh 提供便携式气体传感模块和超紧凑型 MEMS 气体传感器设备。微处理器算法的简化原理图如图2所示。（ESP32）.当系统最初上电时，AFE电路元件会经历多次初始化，并产生斜坡电压以使加热器功率达到其目标水平（VDAC）。微处理器可以通过修改加热器控制算法的VDAC来计算功率并保持所需的功率，方法是跟踪通过加热器（Iheat）的电流。Rf应该自动选择，以便实时读取电压。不要让 TIA 输出电压饱和（Vout）。当电压几乎为零伏时，选择较低的RF。选择 2.1 V 作为 RF 更改的上限。

The benefits of the miniaturization, integration, intelligence, low cost, and mass production of MEMS microcantilever biosensors have made them popular in a variety of industries, including wireless communication 32, biomedicine 33, military defense 34, consumer electronics 35, and many more. It may be claimed that MEMS microcantilever biosensors, to a certain extent, represent the future development of sensor technology, because these advantages are consistent with the path of future sensor development.

图 2.在 ESP32 微处理器上编程的算法示意图。第一个过程产生斜坡电压以初始化AFE中的电路组件，并将每个加热功率设置为目标值（VDAC）。通过监控 Iheat 并随后更换 VDAC，加热器控制将加热功率保持在所需的水平。为了防止TIA输出饱和，Rsens读出控制会自动选择合适的Rf。

4. The Application Prospect of Neural Network in MEMS Microcantilever Biosensor

无需中间硬件，因为该系统完全集成了RF，AFE，数字信号处理和传感器，使其现在适用于物联网应用。该技术便携且功能强大，可用于监测家庭，汽车和制造设施等场所的空气质量，如图3所示。

A type of sensor with significant current development potential is the MEMS micro- cantilever biosensor. Due to its benefits in miniaturization, integration, intelligence, low cost, and mass production, it has been extensively employed in wireless communication, biomedicine, military defense, consumer electronics, and other disciplines. It is now pos- sible to use high tech in microcantilever biosensors and spur their development in the present world, where computer science and technology are advancing one after the other.

图 3.通过Wi-Fi从多个传感器传输原始数据进行数据传输。（a）通过连接到互联网的Wi-Fi接入点传输数据。在这种情况下，传感器数据记录在Google电子表格文件中，并显示为图表。（b）通过BLE将数据传输到实时创建传感器数据图形的Android应用程序。

Neural networks have advanced significantly in models, learning techniques, and ap plications in recent years. Due to its traits of self-adaptation, generalization, nonlin- ear mapping, and extremely parallel processing, it has been widely used in the field of smart sensors. The application increases the sensor’s intelligence and raises its level of intelligence.

2.2. 声音检测场

5. Application of Principal Component Analysis in Biosensors

语音识别是人与智能设备之间双向通信的最人性化界面;然而，基于人工智能（AI）和物联网（IoT）的语音用户界面（VUI）和生物识别系统也引起了很多关注。在声音检测方面，Han等人提出了一个利用机器学习辅助方法进行说话人识别的平台，如图4所示。测试数据由40个人组成，使用的机器学习说话人识别算法基于高斯混合模型（GMM）。其中有 2800 个训练数据。他们断言，使用来自多通道输出的最敏感和第二敏感的数据，机器学习中的高斯混合模型（GMM）与参考MEMS麦克风相比，扬声器识别率为97.5%，误差降低了75%[11]。

The principal component analysis (PCA) has been employed for a long time as a fault neural network. It uses a vast network of extensive connections of a significant number of detection technique to extract pertinent data from multivariate sensors. The PCA has been neurons for information processing and simulates the information processing function of used to analyze multivariate data for a number of multivariate data analysis technolo-the human brain. It offers powerful parallel processing, information storage, and self-gies, including sensor process monitoring, quality control, and problem diagnosis.

图 4.使用机器学习辅助方法识别说话人平台。（a）利用通用的TIDIGITS数据集和GMM算法进行发言者测试和培训（20名男子和20名女发言者，每个发言者77次发言，总共3080份语音数据）。90% 的 TIDIGITS 数据集用于训练目的，10% 用于测试目的。（b）来自40个人的2800个训练数据的训练STFT特征显示在t-SNE图中。t-SNE图形将相似对象的高维数据集成到与概率分布相关的低维空间中。（c）如果发现第12位发言者，则使用多数投票方法在整个框架中测试说话人识别机制。

6. Conclusions

使用GMM算法对TIDIGITS语音数据（40人，2800个语音）进行基于机器学习的训练，该算法已被修改为多信号处理。将随机选择的人的语音与用于说话人识别的训练语音数据集进行比较。

此外，如图5所示，Jung等人报告了一种用于语音处理的柔性压电声学传感器和机器学习。柔性压电声学传感器响应扬声器的声音振动以存储电脉冲，为预处理提供信息。若要训练数据并从语音中提取语言信息，请使用基于机器学习的模型。这种技术将协助电子系统从触摸到语音操作的过渡。他们断言，利用人工智能（AI）服务的新用户界面将由基于尖端声音传感器和优化的机器学习算法构建的语音识别系统创建[12]。

图 5.语音用户界面平台应用前景示意图。通过响应扬声器声音的振动，灵活的压电声学传感器将话语转换为电多信号，从而可以提供用于预处理的数字化数据。一旦使用基于机器学习的模型训练数据，就会从语音中收集语言信息。此过程将有助于将触摸操作设备过渡到声音操作设备。

由于其简单性和双向通信，语音用户界面（VUI）是物联网（IoT）和人工智能（AI）的基础技术，引起了人们的极大兴趣。智能声学传感器可应用于多种行业，包括生物识别、智能家居产品和说话人识别。语音识别软件将音频数据转换为机器学习算法的二进制数字格式。深度学习的最新发展大大提高了语音处理任务的性能，优于传统的机器学习方法。但是，由于敏感的硬件问题和缺乏音频数据，这些系统的识别率仍然很差。未来的语音技术应侧重于智能声学传感器与AI算法之间的协同作用，以克服语音识别的根本弱点[13，14，15]。

穿戴式感测领域

随着物联网（IoT）和人工智能（AI）的兴起，智能可穿戴设备和基于物联网的服装已经进入了我们的视野，并且由于无与伦比的协调性和便利性以及时尚带来的享受，它们对我们变得越来越重要。生活有着深远的影响。如今，通过纺织品和电子产品的快速结合，传感器与纺织品的无缝和广泛集成成为可能。智能服装的互联网时代已经到来，智能面料可以与智能手机通信，以处理心率，温度，呼吸，压力，运动，加速甚至激素水平等生理数据。

根据Tiago M.的说法，服装必须包含图6中描述的基本子系统，才能成为未来智能服装互联网的一部分。无线技术或导电织物都可以用于子系统通信[16]。第一种选择通常在技术方面花费更多，并且使用更多的能源，但它消除了制造导电纱线并将其掺入智能服装的需要。新型导电织物和印花电子产品将有可能将传感器大规模无缝集成到纺织品中[17，18]。

Figure 6. General architecture of IoT smart clothing system.

The architecture consists of the following main components:

Communication gateway, exchanging information with smart clothing in order to send information to cloud server or blockchain via internet or intranet [19].

Cloud servers that collect and store data and provide certain remote services for smart clothing and remote users.

A blockchain. It is not essential to the basic functions of the smart clothing system.

Additionally, the Internet of Things (IoT) and wearable technology, in conjunction with device-to-device communication (D2D) [20], virtual/augmented reality (VR/AR) [21,22], cyber-physical systems (CPS) [23], artificial intelligence (AI) [24], and smart textiles [25], as well as other developments in 5G communication networks, can enhance human-to-human and human-to-machine connections and interactions.

2.4. Body Sensor Field

Body sensors have drawn a lot of interest from scientists lately because of their useful uses in the area of smart medicine. Body sensors are now widely used in a variety of real-world settings, including entertainment, security, health, and healthcare. The ability of body sensors to protect and enhance people’s healthy lifestyles is a significant benefit of employing them to monitor individuals. Body sensor-based human motion detection yields insightful information about a person’s functioning and way of life. Figure 7 illustrates Uddin’s proposed body sensor-based behavior identification system, in which a person wears various body sensors on various body areas, including the wrist, ankle, and chest. Sensor data is acquired via wireless media and saved to a computer. The basic flow of the system is shown in Figure 8, which has two basic steps: training and testing [26].

Figure 7. A schematic device of a human activity prediction system based on human sensors.

图 8.所提出的基于人体传感器的身体活动识别系统的基本流程。

作者还采用了一种有前途的深度学习方法，称为深度递归神经网络（RNN），该方法基于序列数据。对用于评估时间序列应用中节律事件的递归神经网络（RNN）的研究兴趣正在增加。与目前使用的其他深度学习技术相比，它可以提供更强的辨别力。因此，使用基于人类传感器的RNN训练和识别各种活动[27，28，29，30，31]。下表用于显示应用于不同类型生物传感器的AI的比较，如表1所示。

表 1.AI的比较适用于不同类型的生物传感器。

类型	应用	优势
气体传感器	多传感器数据读取和分析，模拟/数字信号处理，无线通信	体积小，功能强大
声传感器	语音识别算法改进多信号处理	方便的双向广泛
可穿戴传感器	人机交互远程业务无线通信	改善连接和交互大商业价值健康检查方便
人体传感器	识别活动身体检测信号传输	健康检查身体防护方便存放

由于信号复杂，无法手动测量每种化学品的含量信息，这对于MEMS微悬臂生物传感器同时更准确地检测许多物质是必要的。MEMS微悬臂生物传感器的问题可以通过AI有效解决，这也提高了检测精度。

Future sensors and microelectromechanical systems (MEMS) will play an increasingly significant part in our daily lives as we enter the new era of intelligence and experience the rapid growth of technology. As a result, MEMS sensor systems integrate neural networks and artificial intelligence (AI), and the following iteration of sensors will have a distinct development trajectory. This essay explores the possibility of fusing MEMS microcantilever biosensors with neural algorithms, as well as the specific case of fusing AI with MEMS biosensors. As a sensor type with several benefits, including portability, affordability, and high sensitivity, its integration with the Internet should go beyond the use of neural networks and instead help people interact with other objects in a variety of scenario applications.