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領先全球Renesas quartz crystal oscillator專用智能家居

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領先全球Renesas quartz crystal oscillator專用智能家居

掃一掃!

瀏覽：- 發(fā)布日期：2023-09-23 09:09:23【大中小】

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領先全球Renesas quartz crystal oscillator專用智能家居，隨著傳感器和MCU成本的下降和出貨量的飆升，越來越多的組織試圖通過將傳感器驅(qū)動的嵌入式AI添加到他們的產(chǎn)品中來加以利用。

汽車正在推動這一趨勢——目前平均每輛非自動駕駛汽車有100個傳感器，向30-50個微控制器發(fā)送信息，這些微控制器運行大約100萬行代碼，每天每輛汽車產(chǎn)生1TB的數(shù)據(jù)。豪華汽車的數(shù)量可能是這個數(shù)字的兩倍，而自動駕駛汽車增加傳感器檢查的幅度要大得多。

然而，這不僅僅是汽車行業(yè)的趨勢。隨著旋轉(zhuǎn)、往復和其他類型設備的創(chuàng)造者爭相增加狀態(tài)監(jiān)控和預測支持的有用性，以及大量新的消費產(chǎn)品(從牙刷到真空吸塵器，再到健身監(jiān)控器)增加儀器和“智能”，工業(yè)設備正變得越來越“聰明”。因此，需要更加優(yōu)質(zhì)的有源晶體振蕩器元器件加以搭配使用。

每個月都會推出越來越多的智能設備。我們現(xiàn)在正處于一個點上，人工智能和機器學習在其異常重要的結構中發(fā)現(xiàn)了進入嵌入式設備核心的方法。例如，智能家居照明系統(tǒng)會根據(jù)房間內(nèi)是否有人而自動開關。從各方面來看，這個系統(tǒng)看起來并不時尚。然而，當你考慮所有的事情時，你會明白這個系統(tǒng)實際上是獨自決定選擇的。鑒于傳感器的貢獻，微控制器/片上系統(tǒng)(SoC)決定是否開燈。

要同時做到這一切，在邊緣、重要的限制范圍內(nèi)，擊敗多樣性，實現(xiàn)實時、麻煩的檢測，一點也不簡單。在任何情況下，利用當前的工具，整合信號機器學習的新選項(如Reality AI)變得越來越簡單。

Mfr Part #	Mfr	Description	Series	Frequency	Output	Voltage - Supply	Frequency Stability	Absolute Pull Range (APR)
XLH738080.000000X	Renesas晶振	XTAL OSC XO 80.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	80 MHz	HCMOS	3.3V	±20ppm	-
XLH738051.840000X	Renesas晶振	XTAL OSC XO 51.8400MHZ HCMOS SMD	XPRESSO FXO-HC73	51.84 MHz	HCMOS	3.3V	±20ppm	-
XLH738050.000000X	Renesas晶振	XTAL OSC XO 50.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	50 MHz	HCMOS	3.3V	±20ppm	-
XLH738048.000000X	Renesas晶振	XTAL OSC XO 48.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	48 MHz	HCMOS	3.3V	±20ppm	-
XLH738040.960000X	Renesas晶振	XTAL OSC XO 40.9600MHZ HCMOS SMD	XPRESSO FXO-HC73	40.96 MHz	HCMOS	3.3V	±20ppm	-
XLH738040.000000X	Renesas晶振	XTAL OSC XO 40.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	40 MHz	HCMOS	3.3V	±20ppm	-
XLH738033.000000X	Renesas晶振	XTAL OSC XO 33.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	33 MHz	HCMOS	3.3V	±20ppm	-
XLH738027.000000X	Renesas晶振	XTAL OSC XO 27.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	27 MHz	HCMOS	3.3V	±20ppm	-
XLH738025.000000X	Renesas晶振	XTAL OSC XO 25.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	25 MHz	HCMOS	3.3V	±20ppm	-
XLH738020.000000X	Renesas晶振	XTAL OSC XO 20.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	20 MHz	HCMOS	3.3V	±20ppm	-
XLH738019.440000X	Renesas晶振	XTAL OSC XO 19.4400MHZ HCMOS SMD	XPRESSO FXO-HC73	19.44 MHz	HCMOS	3.3V	±20ppm	-
XLH738019.200000X	Renesas晶振	XTAL OSC XO 19.2000MHZ HCMOS SMD	XPRESSO FXO-HC73	19.2 MHz	HCMOS	3.3V	±20ppm	-
XLH738018.432000X	Renesas晶振	XTAL OSC XO 18.4320MHZ HCMOS SMD	XPRESSO FXO-HC73	18.432 MHz	HCMOS	3.3V	±20ppm	-
XLH738017.000000X	Renesas晶振	XTAL OSC XO 17.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	17 MHz	HCMOS	3.3V	±20ppm	-
XLH738016.393000X	Renesas晶振	XTAL OSC XO 16.3930MHZ HCMOS SMD	XPRESSO FXO-HC73	16.393 MHz	HCMOS	3.3V	±20ppm	-
XLH738016.000000X	Renesas晶振	XTAL OSC XO 16.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	16 MHz	HCMOS	3.3V	±20ppm	-
XLH738015.360000X	Renesas晶振	XTAL OSC XO 15.3600MHZ HCMOS SMD	XPRESSO FXO-HC73	15.36 MHz	HCMOS	3.3V	±20ppm	-
XLH738012.288000X	Renesas晶振	XTAL OSC XO 12.2880MHZ HCMOS SMD	XPRESSO FXO-HC73	12.288 MHz	HCMOS	3.3V	±20ppm	-
XLH335156.250000I	Renesas晶振	XTAL OSC XO 156.2500MHZ HCMOS	XPRESSO FXO-HC33	156.25 MHz	HCMOS	3.3V	±50ppm	-
XLH335150.000000I	Renesas晶振	XTAL OSC XO 150.0000MHZ HCMOS	XPRESSO FXO-HC33	150 MHz	HCMOS	3.3V	±50ppm	-
XLH536036.571428I	Renesas晶振	OSC 36.571428 MHZ 3.3V SMD	*	-	-	-	-	-
XLH536026.666000I	Renesas晶振	XTAL OSC XO 26.6660MHZ HCMOS SMD晶振	XPRESSO FXO-HC53	26.666 MHz	HCMOS	3.3V	±25ppm	-
XLH726125.000000I	Renesas晶振	XTAL OSC XO 125.0000MHZ HCMOS	XPRESSO FXO-HC72	125 MHz	HCMOS	2.5V	±25ppm	-
XLH736033.333330I	Renesas晶振	XTAL OSC XO 33.33333MHZ HCMOS	XPRESSO FXO-HC73	33.33333 MHz	HCMOS	3.3V	±25ppm	-
XLH736070.000000I	Renesas晶振	XTAL OSC XO 70.0000MHZ HCMOS SMD	XPRESSO FXO-HC73	70 MHz	HCMOS	3.3V	±25ppm	-
XLH736074.250000I	Renesas晶振	XTAL OSC XO 74.2500MHZ HCMOS SMD	XPRESSO FXO-HC73	74.25 MHz	HCMOS	3.3V	±25ppm	-
XLH736065.536000I	Renesas晶振	XTAL OSC XO 65.5360MHZ HCMOS SMD	XPRESSO FXO-HC73	65.536 MHz	HCMOS	3.3V	±25ppm	-
XLH736007.372800I	Renesas晶振	XTAL OSC XO 7.3728MHZ HCMOS SMD	XPRESSO FXO-HC73	7.3728 MHz	HCMOS	3.3V	±25ppm	-
XLH538028.891426X	Renesas晶振	XTAL OSC XO 28.891426MHZ HCMOS	XPRESSO FXO-HC53	28.891426 MHz	HCMOS	3.3V	±20ppm	-
XLH738018.874368X	Renesas晶振	XTAL OSC XO 18.874368MHZ HCMOS	XPRESSO FXO-HC73	18.874368 MHz	HCMOS	3.3V	±20ppm	-
XLH726033.000000I	Renesas晶振	XTAL OSC XO 33.0000MHZ HCMOS SMD	XL	33 MHz	HCMOS	2.5V	±25ppm	-
XLH726060.000000I	Renesas晶振	XTAL OSC XO 60.0000MHZ HCMOS SMD	XL	60 MHz	HCMOS	2.5V	±25ppm	-
XLH336022.118400I	Renesas晶振	XTAL OSC XO 22.1184MHZ HCMOS SMD	XL	22.1184 MHz	HCMOS	3.3V	±25ppm	-
XLH726026.214400I	Renesas晶振	XTAL OSC XO 26.2144MHZ HCMOS SMD	XL	26.2144 MHz	HCMOS	2.5V	±25ppm	-
XLH736038.000000I	Renesas晶振	XTAL OSC XO 38.0000MHZ HCMOS SMD	XL	38 MHz	HCMOS	3.3V	±25ppm	-
XLH536148.511000X	Renesas晶振	XTAL OSC XO 148.5110MHZ HCMOS	XL	148.511 MHz	HCMOS	3.3V	±25ppm	-
XLH736026.214400I	Renesas晶振	XTAL OSC XO 26.2144MHZ HCMOS SMD	XL	26.2144 MHz	HCMOS	3.3V	±25ppm	-
XLH736028.000000I	Renesas晶振	XTAL OSC XO 28.0000MHZ HCMOS SMD	XL	28 MHz	HCMOS	3.3V	±25ppm	-
XLH736033.554400I	Renesas晶振	XTAL OSC XO 33.5544MHZ HCMOS SMD	XL	33.5544 MHz	HCMOS	3.3V	±25ppm	-
XUH518027.120000X	Renesas晶振	CLCC 5.00X3.20X1.10 MM, 2.54MM P	XU	27.12 MHz	HCMOS	1.8V	±20ppm	-
XLH326002.000000I	Renesas晶振	XTAL OSC XO 2.0000MHZ HCMOS SMD	XL	2 MHz	HCMOS	2.5V	±25ppm	-
XLH536121.500000I	Renesas晶振	XTAL OSC XO 121.5000MHZ HCMOS	XL	121.5 MHz	HCMOS	3.3V	±25ppm	-
XLH726026.563828I	Renesas晶振	XTAL OSC XO 26.563828MHZ HCMOS	XL	26.563828 MHz	HCMOS	2.5V	±25ppm	-
XLH736034.368000I	Renesas晶振	XTAL OSC XO 34.3680MHZ HCMOS SMD	XL	34.368 MHz	HCMOS	3.3V	±25ppm	-
XLH536110.000000I	Renesas晶振	XTAL OSC XO 110.0000MHZ HCMOS	XL	110 MHz	HCMOS	3.3V	±25ppm	-
XUH538024.576000X	Renesas晶振	CLCC 5.00X3.20X1.10 MM, 2.54MM P	XU	24.576 MHz	HCMOS	3.3V	±20ppm	-
XLH736039.950000I	Renesas晶振	XTAL OSC XO 39.9500MHZ HCMOS SMD	XL	39.95 MHz	HCMOS	3.3V	±25ppm	-
XLH330148.500000I	Renesas晶振	XTAL OSC XO 148.5000MHZ HCMOS	XL	148.5 MHz	HCMOS	3.3V	±100ppm	-
XLH736027.540000I	Renesas晶振	XTAL OSC XO 27.5400MHZ HCMOS SMD	XL	27.54 MHz	HCMOS	3.3V	±25ppm	-
XLH726026.561172I	Renesas晶振	XTAL OSC XO 26.561172MHZ HCMOS	XL	26.561172 MHz	HCMOS	2.5V	±25ppm	-
XLH736028.450560I	Renesas晶振	XTAL OSC XO 28.45056MHZ HCMOS	XL	28.45056 MHz	HCMOS	3.3V	±25ppm	-
XLH336024.545455I	Renesas晶振	XTAL OSC XO 24.545455MHZ HCMOS	XL	24.545455 MHz	HCMOS	3.3V	±25ppm	-
XLH738092.500000X	Renesas晶振	XTAL OSC XO 92.5000MHZ HCMOS SMD	XL	92.5 MHz	HCMOS	3.3V	±20ppm	-
XLH736048.839200I	Renesas晶振	XTAL OSC XO 48.8392MHZ HCMOS SMD	XL	48.8392 MHz	HCMOS	3.3V	±25ppm	-
XLH736090.136800I	Renesas晶振	XTAL OSC XO 90.1368MHZ HCMOS SMD	XL	90.1368 MHz	HCMOS	3.3V	±25ppm	-
XLH736028.375000I	Renesas晶振	XTAL OSC XO 28.3750MHZ HCMOS SMD	XL	28.375 MHz	HCMOS	3.3V	±25ppm	-
XUH738016.125000X	Renesas晶振	CLCC 7.00X5.00X1.30 MM, 2.54MM P	XU	16.125 MHz	HCMOS	3.3V	±20ppm	-
XLH328019.200000X	Renesas晶振	XTAL OSC XO 19.2000MHZ HCMOS SMD	XL	19.2 MHz	HCMOS	2.5V	±20ppm	-
XLH528040.000000X	Renesas晶振	XTAL OSC XO 40.0000MHZ HCMOS SMD	XL	40 MHz	HCMOS	2.5V	±20ppm	-
XUH738044.736000X	Renesas晶振	CLCC 7.00X5.00X1.30 MM, 2.54MM P	XU	44.736 MHz	HCMOS	3.3V	±20ppm	-
XUH518033.333333X	Renesas晶振	CLCC 5.00X3.20X1.10 MM, 2.54MM P	XU	33.333333 MHz	HCMOS	1.8V	±20ppm	-
XUH738025.000000X	Renesas晶振	CLCC 7.00X5.00X1.30 MM, 2.54MM P	XU	25 MHz	HCMOS	3.3V	±20ppm	-
XLH526080.000000I	Renesas晶振	XTAL OSC XO 80.0000MHZ HCMOS SMD	XL	80 MHz	HCMOS	2.5V	±25ppm	-
XLH738078.643200X	Renesas晶振	XTAL OSC XO 78.6432MHZ HCMOS SMD	XL	78.6432 MHz	HCMOS	3.3V	±20ppm	-
XLH726096.000000I	Renesas晶振	XTAL OSC XO 96.0000MHZ HCMOS SMD	XL	96 MHz	HCMOS	2.5V	±25ppm	-
XLH728080.000000X	Renesas晶振	XTAL OSC XO 80.0000MHZ HCMOS SMD	XL	80 MHz	HCMOS	2.5V	±20ppm	-
XLH738000.921600X	Renesas晶振	XTAL OSC XO 921.6000KHZ HCMOS	XL	921.6 kHz	HCMOS	3.3V	±20ppm	-
XLH336029.500000I	Renesas晶振	XTAL OSC XO 29.5000MHZ HCMOS SMD	XL	29.5 MHz	HCMOS	3.3V	±25ppm	-
XLH735015.360000I	Renesas晶振	XTAL OSC XO 15.3600MHZ HCMOS SMD	XL	15.36 MHz	HCMOS	3.3V	±50ppm	-
XLH736045.000000X	Renesas晶振	XTAL OSC XO 45.0000MHZ HCMOS SMD	XL	45 MHz	HCMOS	3.3V	±25ppm	-
XAH526033.300000I	Renesas晶振	XTAL OSC XO 33.3000MHZ LVCMOS	XA	33.3 MHz	LVCMOS	2.5V	±25ppm	-
XLH336013.330000I	Renesas晶振	XTAL OSC XO 13.3300MHZ LVCMOS	XL	13.33 MHz	LVCMOS	3.3V	±25ppm	-
XLH336037.125000I	瑞薩差分晶振	XTAL OSC XO 37.1250MHZ LVCMOS	XL	37.125 MHz	LVCMOS	3.3V	±25ppm	-
XLH336002.048000I	Renesas晶振	XTAL OSC XO 2.0480MHZ LVCMOS SMD	XL	2.048 MHz	LVCMOS	3.3V	±25ppm	-
XLH336032.051655I	Renesas晶振	XTAL OSC XO 32.051655MHZ LVCMOS	XL	32.051655 MHz	LVCMOS	3.3V	±25ppm	-
XLH328033.333000X	Renesas晶振	XTAL OSC XO 33.3330MHZ LVCMOS	XL	33.333 MHz	LVCMOS	2.5V	±20ppm	-
XLH336033.330000I	Renesas晶振	XTAL OSC XO 33.3300MHZ LVCMOS	XL	33.33 MHz	LVCMOS	3.3V	±25ppm	-
XLH336031.948512I	Renesas晶振	XTAL OSC XO 31.948512MHZ LVCMOS	XL	31.948512 MHz	LVCMOS	3.3V	±25ppm	-
XLH336019.000000I	Renesas晶振	XTAL OSC XO 19.0000MHZ LVCMOS	XL	19 MHz	LVCMOS	3.3V	±25ppm	-
XLH730030.875520I	Renesas晶振	XTAL OSC XO 30.87552MHZ LVCMOS	XL	30.87552 MHz	LVCMOS	3.3V	±100ppm	-
XLH535003.686000I	Renesas晶振	XTAL OSC XO 3.6860MHZ LVCMOS SMD	XL	3.686 MHz	LVCMOS	3.3V	±50ppm	-
XLH736078.125000X	Renesas晶振	XTAL OSC XO 78.1250MHZ LVCMOS	XL	78.125 MHz	LVCMOS	3.3V	±25ppm	-
XLH736025.750000X	Renesas晶振	XTAL OSC XO 25.7500MHZ LVCMOS	XL	25.75 MHz	LVCMOS	3.3V	±25ppm	-
XLH535013.330000I	Renesas晶振	XTAL OSC XO 13.3300MHZ LVCMOS	XL	13.33 MHz	LVCMOS	3.3V	±50ppm	-
XLH338040.953873X	Renesas晶振	XTAL OSC XO 40.953873MHZ LVCMOS	XL	40.953873 MHz	LVCMOS	3.3V	±20ppm	-

它們可以定期實現(xiàn)逃避傳統(tǒng)工程模型的檢測。他們通過顯著提高數(shù)據(jù)的生產(chǎn)率和利用率來戰(zhàn)勝變化，從而做到這一點。傳統(tǒng)的工程方法通常建立在物理模型上，利用數(shù)據(jù)來評估參數(shù)，而機器學習方法可以自動適應這些模型。他們想出了如何從原始信息中直接識別簽名，并利用機器學習(數(shù)學)的機制將目標從非目標中分離出來，而不依賴于物理科學。

在許多不同的領域，機器學習和嵌入式系統(tǒng)的融合將帶來巨大的機遇。例如，醫(yī)療保健現(xiàn)在正在獲得將資源投入人工智能技術的回報。物聯(lián)網(wǎng)也將從人工智能的引入中獲得巨大收益。我們將擁有智能自動化有源晶振解決方案，促進能源節(jié)約、成本效率以及人類失誤的終結。

預測是許多ML/AI對話的中心，因為組織希望使用神經(jīng)網(wǎng)絡和深度學習來推測時間序列數(shù)據(jù)。價值在于吸收信息并迅速了解信息如何改變長期前景的能力。此外，很大一部分情況依賴于全球供應鏈，這使得改進變得更加難以精確預測。領先全球Renesas quartz crystal oscillator專用智能家居.

也許生產(chǎn)線上最不安全的位置現(xiàn)在是由機器處理的。由于嵌入式電子和工業(yè)自動化的進步，我們的OSC晶振有了突破性的微控制器來運行裝配廠的整個機械生產(chǎn)系統(tǒng)。然而，這些機器中的大多數(shù)并不完全是自動的，仍然需要一種人工干預。無論如何，機器學習的引入將有助于工程師制造真正智能的機器，這些機器可以在沒有人類干預的情況下工作。

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.

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