标签: error

这个问题是因为在编译OpenCV CUDA时，CMake无法识别Visual Studio生成器。你可以尝试以下方法解决这个问题： 1. 确保你已经安装了Visual Studio和相应的C++工作负载。你可以在Visual Studio安装程序中选择“使用C++的桌面开发”工作负载来安装。 2. 在运行CMake时，指定正确的生成器。例如，如果你使用的是Visual Studio 2019，你可以使用以下命令： ```bash cmake -G "Visual Studio 16 2019" -A x64 .. ``` 这里的`-G "Visual Studio 16 2019"`表示使用Visual Studio 2019作为生成器，`-A x64`表示生成64位的可执行文件。根据你的实际需求进行修改。 3. 如果问题仍然存在，尝试更新你的CMake版本。确保你的CMake版本与OpenCV CUDA兼容。你可以在CMake官网下载最新版本的CMake。

All data streams experience jitter, some of which is statistically random, so that means we must characterize it with statistics. In jitter analysis, random jitter (RJ) is often described as having a Gaussian distribution.   RJ is typically quantified as an rms value equal to σ of the Gaussian formula. Since Gaussian distributions are unbounded, a peak-to-peak value of RJ requires a measure of probability of bit errors, or BER (bit error ratio). For a typical specification of BER = 10 -12 , (1 error for every 10 12 bits) the peak-to-peak value of RJ is closely approximated by 14σ, where σ is one standard deviation.   Figure 1 shows a plot of the familiar Gaussian distribution. We are often concerned with probability in any given area of this bell curve. Table 1 shows the probability of events occurring in the peak and tail regions for different spans of σ.   Figure 1. Gaussian distribution.   Table 1. Probability of peak and tail areas vs. number of σ.   At ±6σ, we see that the probability of the tails is about two in a billion. For ±7σ, the probability of the tails is about 2.6 in a trillion, which is roughly how we came to use 14σ to bound RJ at BER = 10 -12 . Even though we frequently talk about probabilities of less than one in a trillion, few people actually have an intuitive grasp of such large/small numbers. I came across a very interesting example to illustrate Gaussian distributions using the distribution of height in American men.   The US has approximately 300 million people, out of whom approximately 100 million are non-acromegalic adult men (i.e., no abnormal pituitary problems affecting their height). The mean height of men in the US is about 5'9" with a standard deviation of 3". Knowing how Gaussian distributions work, we can estimate how many men are in each height bracket, shown below in Table 2.   Table 2. The probability of the height of men.   We see that about 68 million men are within ±1σ of the mean, which is between 5'6" and 6' tall. We refer to this range as average height. We think of men between 6' and 6'3" as being relatively tall, but there is still a large population of them, about 13.6 million or 13.6 percent. The numbers drop off very quickly for increasing heights, as expected.   What is interesting is that Gaussian statistics tell us that there should be no men in the US who are above 7'3" tall. But, many sports fans can immediately point out that Yao Ming is 7'6". Someone of Yao Ming's height is statistically unlikely in the normal height distribution of American men.   So, why is Yao Ming Chinese? Because the US does not have enough adult men to realize someone who is greater than 6σ in height! Instead, Yao Ming was born in China, where the current population of adult men is about 500 million, which is five times more likely to grow a 6σ person. Furthermore, the mean height of men in China is approximately 5'7", which makes Yao Ming's stature even more statistically rare!   Yao Ming, basketball player for the Houston Rockets (retired).   Many readers will quickly point out that India also has a population comparable to China's. Is there at least one Indian man who is 6σ above the mean in height? The answer is a resounding yes! Sunil Chaudhary currently claims to be the tallest living man in India at a height of 7’6", a number that neatly fits into our understanding of 6σ in Gaussian distributions.   So, in jitter analysis, when we are looking for that less-than-one-in-a-trillion event that determines our BER compliance, or when we test for violations with a PRBS31 (2 31 −1) pattern -- 2.4 billion bits long -- I like to tell people I'm searching for the "Yao Ming of jitter."   Daniel Chow

All data streams experience jitter, some of which is statistically random, meaning that we must characterize it with statistics. In jitter analysis, random jitter (RJ) is often described as having a Gaussian distribution.   RJ is typically quantified as an rms value equal to σ of the Gaussian formula. Since Gaussian distributions are unbounded, a peak-to-peak value of RJ requires a measure of probability of bit errors, or BER (bit error ratio). For a typical specification of BER = 10 -12 , (1 error for every 10 12 bits) the peak-to-peak value of RJ is closely approximated by 14σ, where σ is one standard deviation.   Daniel Chow   Figure 1 shows a plot of the familiar Gaussian distribution. We are often concerned with probability in any given area of this bell curve. Table 1 shows the probability of events occurring in the peak and tail regions for different spans of σ.   Figure 1. Gaussian distribution.   Table 1. Probability of peak and tail areas vs. number of σ.   At ±6σ, we see that the probability of the tails is about two in a billion. For ±7σ, the probability of the tails is about 2.6 in a trillion, which is roughly how we came to use 14σ to bound RJ at BER = 10 -12 . Even though we frequently talk about probabilities of less than one in a trillion, few people actually have an intuitive grasp of such large/small numbers. I came across a very interesting example to illustrate Gaussian distributions using the distribution of height in American men.   The US has approximately 300 million people, out of whom approximately 100 million are non-acromegalic adult men (i.e., no abnormal pituitary problems affecting their height). The mean height of men in the US is about 5'9" with a standard deviation of 3". Knowing how Gaussian distributions work, we can estimate how many men are in each height bracket, shown below in Table 2.   Table 2. The probability of the height of men.   We see that about 68 million men are within ±1σ of the mean, which is between 5'6" and 6' tall. We refer to this range as average height. We think of men between 6' and 6'3" as being relatively tall, but there is still a large population of them, about 13.6 million or 13.6 percent. The numbers drop off very quickly for increasing heights, as expected.   What is interesting is that Gaussian statistics tell us that there should be no men in the US who are above 7'3" tall. But, many sports fans can immediately point out that Yao Ming is 7'6". Someone of Yao Ming's height is statistically unlikely in the normal height distribution of American men.   So, why is Yao Ming Chinese? Because the US does not have enough adult men to realize someone who is greater than 6σ in height! Instead, Yao Ming was born in China, where the current population of adult men is about 500 million, which is five times more likely to grow a 6σ person. Furthermore, the mean height of men in China is approximately 5'7", which makes Yao Ming's stature even more statistically rare!   Yao Ming, basketball player for the Houston Rockets (retired).   Many readers will quickly point out that India also has a population comparable to China's. Is there at least one Indian man who is 6σ above the mean in height? The answer is a resounding yes! Sunil Chaudhary currently claims to be the tallest living man in India at a height of 7’6", a number that neatly fits into our understanding of 6σ in Gaussian distributions.   So, in jitter analysis, when we are looking for that less-than-one-in-a-trillion event that determines our BER compliance, or when we test for violations with a PRBS31 (2 31 −1) pattern -- 2.4 billion bits long -- I like to tell people I'm searching for the "Yao Ming of jitter."

Several days ago, when I started my truck after a night when the temperatures had dived to well below freezing, I noticed a warning light glaring at me balefully from the console.   Of course this couldn’t say something useful like "Check Engine." Instead, the message was presented in the form of an inscrutable icon, thereby forcing me to root through the 200+ manual skulking at the bottom of the glove compartment.   When I finally tracked these little rascals down (first the manual, then the icon), I discovered that the icon's raison d'être was to cryptically inform me that I needed to "Check Gauges." Who writes these manuals anyway? I mean, we're not talking about a vehicle that originated in some obscure part of the world -- this is a Dodge Ram truck, for goodness sake. I'm assuming it was built in the USA, so I have no idea why we appear to have commissioned a non-English speaker to write our documentation for us, but we digress...   I'm sure it goes without saying that I treated this warning of impending doom coolly and calmly as is my wont, with only a teensy-weensy whimper wending its way through my lips.     I remember hearing that helicopter pilots in the Korean War would resolve this sort of problem by throwing the errant warning light out of the window. Now, this may be a viable solution when you are scooting around thousands of feet up in the air with no one else around you, but the traffic here in Huntsville has gotten pretty hectic recently. This is only exacerbated by the fact that no one else around here seems to have the faintest clue what the indicators on their vehicles are for, but -- once again -- we digress...   I decided not to do anything hasty, so I continued on to work. When I started my truck again that evening, the warning icon was no longer illuminated. "O frabjous day!" I thought to myself, but my joy was short-lived because the little scamp returned to taunt me on the way into my office yesterday.   Thus it was that I stopped off at the Express Oil Change Service Center, which is about 1 mile or so away from my office. When the young lad approached my truck and asked me what was wrong, I pointed accusingly at the misbehaving warning light and he said "Ah, the Check Engine light, eh?" (I felt it would be churlish of me to correct him by informing him in haughty tones that this was, in fact, officially known as the "Check Gauges" indicator.)   So I left my truck with the sterling chaps at EOCSC and one of their merry band ran me into work. If the truth be told, I quickly forgot all about this, so I would have had something of a surprise when I exited the building in the evening and discovered an empty space where my truck should be. Fortunately, the guys at EOCSC called me in the late afternoon to tell me what they had discovered, which could be summed up in a nutshell as "nothing useful."   Apparently, they started off by connecting my truck to a diagnostic unit, which reported that one of my cylinders had been misfiring. Two obvious suspects were the spark plugs or the coil (or distributor or whatever -- I no longer recall exactly what they said). My understanding is that they swapped the leads to two of the cylinders (I didn’t know they could do that) and then drove the truck around for a while. This was supposed to inform them whether they should exchange the spark plugs or replace the coil.   Sad to relate, however, the warning light never came back on. When I returned to pick up my truck, I took out my wallet in dread anticipation of horrors to come. Instead, the guy behind the counter presented me with my keys and said "There's no charge." Somewhat foolishly, I responded "But I thought there was a diagnostic fee." He replied "But we didn’t diagnose anything." What could I say? You can’t argue with logic like that!   I do have to say that I find this attitude to be somewhat heartwarming in an age where a plumber will happily charge you a $50 service fee just to wipe his muddy feet on your front doormat. The upshot was that we both agreed that transient faults are the worse ones to have; that if I am really lucky the indicator will never return to haunt me; but that the next best thing would be for the indicator to go hard on, thereby allowing them to actually diagnose -- and correct -- the problem.   I know that when I'm diagnosing an issue in an embedded system, I much prefer a hard fault to a transient one. In the case of a hard error, it's almost like solving a mystery by using your intuitive skills to track the issue down to the hardware or the software, and to then keep on burrowing down until you stalk the little scallywag to its lair. When it comes to transient errors, however, it can be extremely frustrating to see them flicker in and out of existence. It's especially irritating when the act of applying your diagnostic tools causes the fault to evaporate, and for the little scoundrel to subsequently reappear as soon as you disengage your diagnostic devices.   I could tell you some stories... but I'd rather hear yours. Have you ever run across a truly tortuous transient error that caused you to gnash your teeth, rend your garb, and pull your hair out in frustration? If so, please share it in the comments below.

  问题： 平台为 windows 7 32bit ultimate 。安装包为 镜像，使用 UltraISO 做虚拟光驱。 安装 VS 2005 和 VS 2010 均出现无法继续安装。 解决： 使用 Daemon Tool，即可解决无法安装的问题。但是安装 VS 2005 提示需要预装 windows XP SP2。所以最后装的 VS 2010。 总结： 实验室电脑使用XP SP3，所以可以装 VS 2005。而 win7 下装 VS 2010 较稳妥。