Another benefit of biased representation, is that it makes error values extremely noticeable.
It is common in automotive situations to reserve part of the representable range to indicate error or SNA (signal not available) conditions. If you wanted to reserve the largest positive numbers for error codes, then their hex representation would look something like 0x7FFF, which can be hard to spot in a data stream. If you used the largest negative numbers, then they would look something like 0x8000, which is also not very intuitive.
By using biased representation, you can use values like 0xFFFF to indicate error values.
Of course, you could use something like bit-flags to represent errors, but that wastes a whole bit of bandwidth. Much more efficient to amortize the enumerated value over the whole signal.
Most RTCs do use the same crystals that are in wristwatches, which is actually the source of their error.
The tuning fork crystal design used in wrist watches has a parabolic temperature coefficient, which means that the clock is only really accurate at room temperature. This isn't a problem for wristwatches, because, presumably your wrist is at approximately room temperature, but it does become a problem for electronics that operate with large temperature swings. (like the inside of a phone or computer, for instance).
> Embedded systems, IMO, must be deterministic, reliable and consistent.
This is the definition of a hard real-time system. In most of the literature, 'embedded system' is a broader term that just means there is some compute embedded in a device that performs a larger task.
Actually this article is talking about mild hybrid technology: Which is about upping the voltage for traditional non hybrid vehicles from 12V to 48V, giving them some hybrid like features on the cheap.
If you try to estimate power spectral density using the intuitive unbiased estimator (the DFT), you are going to have a bad time. A vanilla periodogram has very high sideband leakage, which means that the energy of the signal at a single frequency will look as if it "smeared out" across neighboring frequencies. The standard solution to this is to use the so-called 'modified periodogram' where the implicit rectangular window is replaced by a different windowing function with lower sideband leakage. In general, there is a direct tradeoff between sideband power and center frequency power, and in this application, you would do well to use a different window, such as the blackman-harris, or hamming window. See [1] for more details.
In addition even the modified periodogram discussed above has asymptotically nonzero variance [2], which means no matter how many samples you take, you will still have 'noise' in your PSD estimate. If you use biased estimators of the periodogram, such as the welch-bartlett method or the blackman-tukey algorithm, you will get much better results.
Claiming that the FT is not necessary for digial audio is like claiming that the you don't need the rocket equation in order to build a missile. Sure, its technically possible, and yes, there were probably early pioneers that forged ahead before the mathematical theory was fully sketched out, but our understanding of the fourier transform has drastically increased our ability to design acoustical systems.
Those analog anti-aliasing and anti-imaging filters are designed using LTI systems theory, that fundamentally rely on the Fourier tranform to reason about their transfer functions. The Nyquist-Shannon sampling theorem was proven using the fourier transform. Without the fourier transform, you need to rely entirely upon time domain representations of signals, and perform your analysis using tedious convolutions. You can't use a spectrum analyzer to examine the signal to noise ratio of your CD player. While it's true that digital music could technically exist without the fourier, there is no way in hell it would be as pervasive as it is today.
Yes. Reformulate the systems transfer function into a state space representation, and then solve the algebraic ricatti equation to find an optimal gain matrix. This is know and the LQR problem.
In addition, there is no way in hell that the control algorithms it was using could have been developed without the use of computers. State-space control theory was specifically developed to take advantage of discrete-time control systems.
OSEK is a an industry standard rtos that is used by almost all automotive players. It is specifically designed for use in the automotive environment. Toyota actually claimed to use an OSEK compliant rtos, but it later surfaced in this lawsuit that they had written their own implementation that was never certified by an outside organization. OSEK is in the process of being superseded by AUTOSAR, which defines much more than just the os, and included a large hal that allows for plug and play middleware libraries. Unfortunately it isn't economical for every ecu to make use of AUTOSAR: it has heavy resource requirements (> 2Mbyte RAM) and so many applications don't use it.
Also on the horizon is ISO26262, which mandates quality assurance for automotive embedded code in the form of paper trails. Unfortunately due to the huge amount of work required by the standard, some automakers are choosing to ignore it and hope it doesn't become mandatory.
Modern EPA requirements mean that it is impossible to design a compliant off highway commercial vehicle without relying on advanced electronic process controls. Your grandfather's tractor might be repairable with baling wire, but it also releases a ton more diesel particulates.
There's no incentive for companies to publish information on their vehicle bus interfaces, since it would basically give away trade secrets to competitors.
Adding a new sensor to monitor the fuses would not be cheap:first off, you have to have signal conditioners for the a/ds that can handle esd transients, and >60 v load shed events. In addition, the existing design probably just has fuse holders physically mounted to the holder packaging, which means now your paying for microcontroller, can transceiver, and pcb. This doesn't include NRE costs including development and testing.
Suddenly your "inexpensive" adc had become a $200 ecu that merely tells the driver wheat part of the car isn't functioning, right before they check the most common cause of the problem.
Signal conditioning is still an important part of EE design. Not all signals are TTL level and unipolar.
For example, this appnote[0] uses a Delta-Sigma converter to digitize a load cell, with the ability to "detect one sheet of copier paper dropped on a stack of 20 reams". In order to do this, it relies on an intrumentation amplifier and custom correlated-double sampling to get a measurement accurate to 200,000 counts.
And even if your input signal can be directly input to your ADC, you'll still need an analog anti-aliasing filter.
Signal conditioning is an important part of signal processing that has to happen in the analog domain.
Generally the system will fall back to a 'limp home mode' running on a simpler algorithm on a watchdog microcontroller. See for example, Freescale's reference design for an airbag controller.
Most of the magic doesn't come from the robots, it comes from the soldermask. Surface tension from molten solder will move misaligned parts by a surprising amount:
Bonus: Once you have a couple of data points from the tape method, you can extrapolate the emissivity of the toast itself and calculate a correction factor for your IR gun.
Make a circuit "digital" does not automagically make it more accurate, and you cannot "add bits" to a datatype to make your measurement more accurate.
Before a digital circuit can process a signal, it must first be quantized, an inherently analog process. Your digital data is only as good as your analog signal, plus inherent quantization noise[1]. In addition, any mixed-signal IC represents a design compromise between digital and analog constraints.
Any signal processing system is inherently limited by the SNR of its source signal. There is no point in building a more accurate converter than the noisiest component of your input data. In fact, when metrologists are trying achieve the maximum accuracy in their measurements, it is not uncommon to leave a test circuit on for days at a time, in order to reach a thermal equilibrium to minimize the seebeck effect[2].
And when engineers to need to make the most accurate of measurements, what do they turn to? Analog circuits. The world's most accurate voltage reference, the Kelvin-Varley Divider, dates back to the late 19th century[3]. For a modern example of a precision reference, see Jim William's excellent white paper, "Quantifying Silence"[4].
It is common in automotive situations to reserve part of the representable range to indicate error or SNA (signal not available) conditions. If you wanted to reserve the largest positive numbers for error codes, then their hex representation would look something like 0x7FFF, which can be hard to spot in a data stream. If you used the largest negative numbers, then they would look something like 0x8000, which is also not very intuitive.
By using biased representation, you can use values like 0xFFFF to indicate error values.
Of course, you could use something like bit-flags to represent errors, but that wastes a whole bit of bandwidth. Much more efficient to amortize the enumerated value over the whole signal.
You can see a real-world example of these types of signals as defined by SAE for heavy duty trucks here: https://www.scania.com/content/dam/scanianoe/market/au/produ...