I have a project that consisted of transmitting data wirelessly from 15 tractors to a station, the maximum distance between tractor and station is 13 miles. I used a raspberry pi 3 to collect data from tractors. with some research I found that there is no wifi or GSM coverage so the only solution is to use RF communication using VHF. so is that possible with raspberry pi or I must add a modem? if yes, what is the criterion for choosing a modem? and please if you have any other information tell me?
and thank you for your time.
I had a similar issue but possibly a little more complex. I needed to cover a maximum distance of 22 kilometres and I wanted to monitor over 100 resources ranging from breeding stock to fences and gates etc. I too had no GSM access plus no direct line of sight access as the area is hilly and the breeders like the deep valleys. The solution I used was to make my own radio network using cheap radio repeaters. Everything was battery operated and was driven by the receivers powering up the transmitters. This means that the units consume only 40 micro amps on standby and when the transmitters transmit, in my case they consume around 100 to 200 milliamps.
In the house I have a little program that transmits a poll to the receivers every so often and waits for the units to reply. This gives me a big advantage because I can, via the repeater trail (as each repeater, the signal goes through, adds its code to the returning message) actually determine were my stock are.
Now for the big issue, how long do the batteries last? Well each unit has a 18650 battery. For the fence and gate controls this is charged by a small 5 volt solar panel and after 2 years running time I have not changed any of them. For the cattle units the length of time between charges depends solely on how often you poll the units (note each unit has its own code) with one exception (a bull who wants to roam and is a real escape artist) I only poll them once or twice a day and I swap the battery every two weeks.
The frequency I use is 433Mhz and the radio transmitters and receivers are very cheap ( less then 10 cents a pair if you by them in Australia) with a very small Attiny (I think) arduino per unit (around 30 cents each) and a length on wire (34.6cm long as an aerial) for the cattle and 69.2cm for the repeaters. Note these calculations are based on the frequency used i.e. 433Mhz.
As I had to install lots of the repeaters I contacted an organisation in China (sorry they no longer exist) and they created a tiny waterproof and rugged capsule that contained everything, while also improving on the design (range wise while reducing power) at a cost of $220 for 100 units not including batterys. I bought one lot as a test and now between myself and my neighbours we bought another 2000 units for only $2750.
In my case this was paid for in less then three months when during calving season I knew exactly were they were calving and was on site to assist. The first time I used it we saved a mother who was having a real issue.
To end this long message I am not an expert but I had an idea and hired people who were and the repeater approach certainly works over long distances and large areas (42 square kilometres).
Following on from the comments above, I'm not sure where you are located but spectrum around the 400mhz range is licensed in many countries so it would be worth checking exactly what you can use.
If this is your target then this is UHF rather than VHF so if you search for 'Raspberry PI UHF shield' or 'Raspberry PI UHF module' you will find some examples of cheap hardware you can add to your raspberry pi to support communication over these frequencies. Most of the results should include some software examples also.
There are also articles on using the pins on the PI to transmit directly by modulating the voltage them - this is almost certainly going to interfere with other communications so I doubt it would meet your needs.
Related
I run an infectious disease spread model similar to "VIRUS" model in the model library changing the "infectiousness".
I did 20 runs each for infectiousness values 98% , 95% , 93% and the Maximum infected count was 74.05 , 73 ,78.9 respectively. (peak was at tick 38 for all 3 infectiousness values)
[I took the average of the infected count for each tick and took the maximum of these averages as the "maximum infected".]
I was expecting the maximum infected count to decrease when the infectiousness is reduced, but it didn't. As per what I understood this happens, because I considered the average values of each simulation run. (It is like I am considering a new simulation run with average infected count for each tick ).
I want to say that, I am considering all 20 simulation runs. Is there a way to do that other than the way I used the average?
In the Models Library Virus model with default parameter settings at other values, and those high infectiousness values, what I see when I run the model is a periodic variation in the numbers three classes of person. Look at the plot in the lower left corner, and you'll see this. What is happening, I believe, is this:
When there are many healthy, non-immune people, that means that there are many people who can get infected, so the number of infected people goes up, and the number of healthy people goes down.
Soon after that, the number of sick, infectious people goes down, because they either die or become immune.
Since there are now more immune people, and fewer infectious people, the number of non-immune healthy grows; they are reproducing. (See "How it works" in the Info tab.) But now we have returned to the situation in step 1, ... so the cycle continues.
If your model is sufficiently similar to the Models Library Virus model, I'd bet that this is part of what's happening. If you don't have a plot window like the Virus model, I recommend adding it.
Also, you didn't say how many ticks you are running the model for. If you run it for a short number of ticks, you won't notice the periodic behavior, but that doesn't mean it hasn't begun.
What this all means that increasing infectiousness wouldn't necessarily increase the maximum number infected: a faster rate of infection means that the number of individuals who can infected drops faster. I'm not sure that the maximum number infected over the whole run is an interesting number, with this model and a high infectiousness value. It depends what you are trying to understand.
One of the great things about NetLogo and some other ABM systems is that you can watch the system evolve over time, using various tools such as plots, monitors, etc. as well as just looking at the agents move around or change states over time. This can help you understand what is going on in a way that a single number like an average won't. Then you can use this insight to figure out a more informative way of measuring what is happening.
Another model where you can see a similar kind of periodic pattern is Wolf-Sheep Predation. I recommend looking at that. It may be easier to understand the pattern. (If you are interested in mathematical models of this kind of phenomenon, look up Lotka-Volterra models.)
(Real virus transmission can be more complicated, because a person (or other animal) is a kind of big "island" where viruses can reproduce quickly. If they reproduce too quickly, this can kill the host, and prevent further transmission of the virus. Sometimes a virus that reproduces more slowly can harm more people, because there is time for them to infect others. This blog post by Elliott Sober gives a relatively simple mathematical introduction to some of the issues involved, but his simple mathematical models don't take into account all of the complications involved in real virus transmission.)
EDIT: You added a comment Lawan, saying that you are interested in modeling COVID-19 transmission. This paper, Variation and multilevel selection of SARS‐CoV‐2 by Blackstone, Blackstone, and Berg, suggests that some of the dynamics that I mentioned in the preceding remarks might be characteristic of COVID-19 transmission. That paper is about six months old now, and it offered some speculations based on limited information. There's probably more known now, but this might suggest avenues for further investigation.
If you're interested, you might also consider asking general questions about virus transmission on the Biology Stackexchange site.
I want to set up a point-to-point communication link between two Raspberry Pi using LoRa.
I know for lorawan there is (at least in Europe, where I live) a duty cycle limitation so the nodes can transmit only for an average of 30 seconds uplink time on air, per day, per device.
Is this valid also for point-to-point lora communications? Because my sender keeps on sending.
I am using the code provided here.
Yes, this is also valid for your LoRa application, since it is emitting radio waves. You can look up limits for europe for specific frequency bands in the ERC Recommendation 70-03 (page 7). In the ERC Recommendation 70-03 on page 42 you can then look up which of the frequecny bands are allowed for each country.
Example
Let's say you live in Germany and you want to use frequency 869,400 MHz to 869,650 MHz (this frequency band is called h1.6):
A quick lookup in the ERC Recommendation 70-03 page 39 shows that this band is allowed to be used in Germany:
Further this specific band allows you to use 10% time-on-air (duty-cycle) for your transmitter. This basically means you are allowed to transmit 1 second and are obligated to pause 9 seconds after that.
Currently I'm building my monitoring services for my e-commerce Server, which mostly focus on CPU/RAM usage. It's likely Anomaly Detection on Timeseries data.
My approach is building LSTM Neural Network to predict next CPU/RAM value on chart trending and compare with STD (standard deviation) value multiply with some number (currently is 10)
But in real life conditions, it depends on many differents conditions, such as:
1- Maintainance Time (in this time "anomaly" is not "anomaly")
2- Sales time in day-off events, holidays, etc., RAM/CPU usages increase is normal, of courses
3- If percentages of CPU/RAM decrement are the same over 3 observations: 5 mins, 10 mins & 15 mins -> Anomaly. But if 5 mins decreased 50%, but 10 mins it didn't decrease too much (-5% ~ +5%) -> Not an "anomaly".
Currently I detect anomaly on formular likes this:
isAlert = (Diff5m >= 10 && Diff10m >= 15 && Diff30m >= 40)
where Diff is Different Percentage in Absolute value.
Unfortunately I don't save my "pure" data for building neural network, for example, when it detects anomaly, I modified that it is not an anomaly anymore.
I would like to add some attributes to my input for model, such as isMaintenance, isPromotion, isHoliday, etc. but sometimes it leads to overfitting.
I also want to my NN can adjust baseline over the time, for example, when my Service is more popular, etc.
There are any hints on these aims?
Thanks
I would say that an anomaly is an unusual outcome, i.e. a outcome that's not expected given the inputs. As you've figured out, there are a few variables that are expected to influence CPU and RAM usage. So why not feed those to the network? That's the whole point of Machine Learning. Your network will make a prediction of CPU usage, taking into account the sales volume, whether there is (or was) a maintenance window, etc.
Note that you probably don't need an isPromotion input if you include actual sales volumes. The former is a discrete input, and only captures a fraction of the information present in the totalSales input
Machine Learning definitely needs data. If you threw that away, you'll have to restart capturing it. As for adjusting the baseline, you can achieve that by overweighting recent input data.
Hi,
In the CDMA cellular networks when MS (Mobile Station) need to change a BS(Base Station), exactly necessary for hand-off, i know that is soft hand-off (make a connection with a target BS before leaving current BS-s). But i want to know, because connection of MS remaining within a time with more than one BS, MS use the same code in CDMA to communicate with all BS-s or different code for different BS-s ?
Thanks in advance
For the benefit of everyone, i have touched upon few points before coming to the main point.
Soft Handoff is also termed as "make-before-break" handoff. This technique falls under the category of MAHO (Mobile Assisted Handover). The key theme behind this is having the MS to maintain a simultaneous communication link with two or more BS for ensuring a un-interrupted call.
In DL direction, it is achieved using different transmission codes(transmit same bit stream) on different physical channels in the same frequency by two or more BTS wherein the CDMA phone simultaneously receives the signals from these two or more BTS. In the active set, there can be more than one pilot as there could be three carriers involved in soft hand off. Also, there shall also be a rake receiver that shall do maximal combining of received signals.
In UL direction, MS shall operate on a candidate set where there could be more than 1 pilot that have sufficient signal strength for usage as reported by MS. The BTS shall tag each of the user's data with Frame reliability indicator that can provide details about the transmission quality to BSC. So, even though the signals(MS code channel) are received by both base stations, it is achieved by routing the signals to the BSC along with information of quality of received signals, which shall examine the quality based on the Frame reliability indicator and choose the best quality stream or the best candidate.
Yamaha InfoSound and ShopKick application use technologies that allow to transfer data using ultrasound. That is playing an inaudible signal (>18kHz) that can be picked up by modern mobile phones (iOS, Android).
What is the approach used in such technologies? What kind of modulation they use?
I see several problems with this approach. First, 18kHz is not inaudible. Many people cannot hear it, especially as they age, but I know I certainly can (I do regular hearing tests, work-related). Also, most phones have different low-pass filters on their A/D converters, and many devices, especially older Android ones (I've personally seen that happen), filter everything below 16 kHz or so. Your app therefore is not guaranteed to work on any hardware. The iPhone should probably be able to do it.
In terms of modulation, it could be anything really, but I would definitely rule out AM. Sound has next to zero robustness when it comes to volume. If I were to implement something like that, I would go with FSK. I would think that PSK would fail due to acoustic reflections and such. The difficulty is that you're working with non-robust energy transfer within a very narrow bandwidth. I certainly do not doubt that it can be achieved, but I don't see something like this proving reliable. Just IMHO, that is.
Update: Now that i think about it, a plain on-off would work with a single tone if you're not transferring any data, just some short signals.
Can't say for Yamaha InfoSound and ShopKick, but what we used in our project was a variation of frequency modulation: the frequency of the carrier is modulated by a digital binary signal, where 0 and 1 correspond to 17 kHz and 18 kHz respectively. As for demodulator, we tried heterodyne. More details you could find here: http://rnd.azoft.com/mobile-app-transering-data-using-ultrasound/
There's nothing special in being ultrasound, the principle is the same as data transmission through a modem, so any digital modulation is -in principle- feasible. You only have a specific frequency band (above 18khz) and some practical requisites (the medium is very unreliable, I guess) that suggest to use a simple-robust scheme with low-bit rate.
I don't know how they do it but this is how I do it:
If it is a string then make sure it's not a long one (the longer the higher is the error probability ). Lets assume we're working with the vital part of the ASCII code, namely up to character number 127, then all you need is 7 bits per character. Transform this character into bits and modulate those bits using QFSK (there are several modulations to choose from, frequency shift based ones have turned out to be the most robust I've tried from the conventional ones... I've created my own modulation scheme for this use case). Select the carrier frequencies as 18.5,19,19.5, and 20 kHz (if you want to be mathematically strict in your design, select frequency values that assure you both orthogonality and phase continuity at symbol transitions, if you can't, a good workaround to avoid abrupt symbols transitions is to multiply your symbols by a window of the same size, eg. a Gaussian or Bartlet ). In my experience you can move this values in the range from 17.5 to 20.5 kHz (if you go lower it will start to bother people using your app, if you go higher the average type microphone frequency response will attenuate your transmission and induce unwanted errors).
On the receiver side implement a correlation or matched filter receiver (an FFT receiver works as well, specially a zero padded one but it might be a little bit slower, I wouldn't recommend Goertzel because frequency shift due to Doppler effect or speaker-microphone non-linearities could affect your reception). Once you have received the bit stream make characters with them and you will recover your message
If you face too many broadcasting errors, try selecting a higher amount of samples per symbol or band-pass filtering each frequency value before giving them to the demodulator, using an error correction code such as BCH or Reed Solomon is sometimes the only way to assure an error free communication.
One topic everybody always forgets to talk about is synchronization (to know on the receiver side when the transmission has begun), you have to be creative here and make a lot a tests with a lot of phones before you can derive an actual detection threshold that works on all, notice that this might also be distance dependent
If you are unfamiliar with these subjects I would recommend a couple of great books:
Digital Modulation Techniques from Fuqin Xiong
DIGITAL COMMUNICATIONS Fundamentals and Applications from BERNARD SKLAR
Digital Communications from John G. Proakis
You might have luck with a library I created for sound-based modems, libquiet. It gives you a handful of profiles to work from, including a slow "Ultrasonic whisper" profile with spectral content above 19kHz. The library is written in C but would require some work to interface with iOS.