领先同行RENESAS VCXO OSCILLATOR编码曝光

领先同行RENESAS VCXO OSCILLATOR编码曝光，在最近几年，随着传感器和MCU成本的下降和出货量的飙升，越来越多的组织试图通过将传感器驱动的嵌入式AI添加到他们的产品中来加以利用。

汽车正在推动这一趋势——目前平均每辆非自动驾驶汽车有100个传感器，向30-50个微控制器发送信息，这些微控制器运行大约100万行代码，每天每辆汽车产生1TB的数据。汽车中的微控制器需要石英晶体振荡器产品的结合，才可造就豪华汽车的数量可能是这个数字的两倍，而自动驾驶汽车增加传感器检查的幅度要大得多。

然而，这不仅仅是汽车行业的趋势。随着旋转、往复和其他类型设备的创造者争相增加状态监控和预测支持的有用性，以及大量新的消费产品(从牙刷到真空吸尘器，再到健身监控器)增加仪器和“智能”，工业设备正变得越来越“聪明”。

每个月都会推出越来越多的智能设备。我们现在正处于一个点上，人工智能和机器学习在其异常重要的结构中发现了进入嵌入式设备核心的方法。例如，智能家居照明系统会根据房间内是否有人而自动开关。从各方面来看，这个系统看起来并不时尚。然而，当你考虑所有的事情时，你会明白这个系统实际上是独自决定选择的。鉴于传感器与压控晶振的贡献，微控制器/片上系统(SoC)决定是否开灯。

要同时做到这一切，在边缘、重要的限制范围内，击败多样性，实现实时、麻烦的检测，一点也不简单。在任何情况下，利用当前的工具，整合信号机器学习的新选项(如Reality AI)变得越来越简单。

它们可以定期实现逃避传统工程模型的检测。他们通过显著提高数据的生产率和利用率来克服变化，从而做到这一点。传统的工程方法通常建立在物理模型上，利用数据来评估参数，而机器学习方法可以自动适应这些模型。他们想出了如何从原始信息中直接识别签名，并利用机器学习(数学)的机制将目标从非目标中分离出来，而不依赖于物理科学。

预测是许多ML/AI对话的中心，因为组织希望使用神经网络和深度学习来推测时间序列数据。价值在于吸收信息并迅速了解信息如何改变长期前景的能力。此外，很大一部分情况依赖于全球供应链，这使得改进变得更加难以精确预测。

也许生产线上最不安全的位置现在是由机器处理的。由于嵌入式电子和工业自动化的进步，我们有了突破性的微控制器来运行装配厂的整个机械生产系统。然而，这些机器中的大多数并不完全是自动的，仍然需要一种人工干预。无论如何，机器学习的引入将有助于工程师制造真正智能的机器，这些机器可以在没有人类干预的情况下工作。领先同行RENESAS VCXO OSCILLATOR编码曝光.

Throughout the most recent years, as sensor and MCU costs dove and shipped volumes have gone through the roof, an ever-increasing number of organizations have attempted to exploit by adding sensor-driven embedded AI to their products.

Automotive is driving the trend– the average non-autonomous vehicle presently has 100 sensors, sending information to 30-50 microcontrollers that run about 1m lines of code and create 1TB of data per vehicle every day. Extravagance vehicles may have twice the same number of, and autonomous vehicles increase the sensor check significantly more drastically.

Yet, it's not simply an automotive trend. Industrial equipment is turning out to be progressively "brilliant" as creators of rotating, reciprocating and other types of equipment rush to add usefulness for condition monitoring and predictive support, and a huge number of new consumer products from toothbrushes, to vacuum cleaners, to fitness monitors add instrumentation and "smarts".

An ever-increasing number of smart devices are being introduced each month. We are now at a point where artificial intelligence and machine learning in its exceptionally essential structure has discovered its way into the core of embedded devices. For example, smart home lighting systems that automatically turn on and off depend on whether anybody is available in the room. By all accounts, the system doesn't look excessively stylish. Yet, when you consider everything, you understand that the system is really settling on choices all alone. In view of the contribution from the sensor, the microcontroller/system-on-chip (SoC) concludes if to turn on the light or not.

To do all of this simultaneously, defeating variety to achieve troublesome detections in real-time, at the edge, inside the vital limitations isn't at all simple. In any case, with current tools, integrating new options for machine learning for signals (like Reality AI) it is getting simpler.

They can regularly achieve detections that escape traditional engineering models. They do this by making significantly more productive and compelling utilization of data to conquer variation. Where traditional engineering approaches will ordinarily be founded on a physical model, utilizing data to appraise parameters, machine learning approaches can adapt autonomously of those models. They figure out how to recognize signatures straightforwardly from the raw information and utilize the mechanics of machine learning (mathematics) to isolate targets from non-targets without depending on physical science.

There are a lot of different regions where the convergence of machine learning and embedded systems will prompt great opportunities. Healthcare, for example, is now receiving the rewards of putting resources into AI technology. The Internet of Things or IoT will likewise profit enormously from the introduction of artificial intelligence. We will have smart automation solutions that will prompt energy savings, cost proficiency as well as the end of human blunder.

Forecasting is at the center of so many ML/AI conversations as organizations hope to use neural networks and deep learning to conjecture time series data. The worth is the capacity to ingest information and quickly acknowledge insight into how it changes the long-term outlook. Further, a large part of the circumstance relies upon the global supply chain, which makes improvements significantly harder to precisely project.

Probably the most unsafe positions in production lines are as of now being dealt by machines. Because of the advancement in embedded electronics and industrial automation, we have ground-breaking microcontrollers running the whole mechanical production systems in assembling plants. However, the majority of these machines are not exactly completely automatic and still require a type of human intercession. In any case, the time will come when the introduction of machine learning will help engineers concoct truly intelligent machines that can work with zero human mediation.