sqlite query for range search having text for ios app - ios
I have a table with columns as LowerFrequency, HigherFrequency and ID. The frequencies entered have a suffix of khz or mhz. I want to search a specific frequency by checking the range it falls in i.e between the lower and higher frequencies and fetch the respective ID.
The query I implemented was as below but it returns a wrong output:
select tablename.ID where "100 khz" between tablename.LowerFrequency and tablename.HigherFrequency;
I know the reason is because of the khz that follows the integer. But I need some suggestions to handle this as I am not in a situation of changing the whole DB file because it is time consuming.
I will be integrating this DB with my iPhone app. So any solutions in Objective C would also be appreciated. I mean some kind of conversion.
Related
Sound Wave Peak Detection (Cricket Chirping)
I am looking for a way to code or find a program that can record the chirps of crickets either live or through a prerecorded audio file (large ~24 hours) for a lab experiment. I'm not too sure how to approach this as I'm a web developer, but I have experience with JS and python, along with libraries. My initial idea was to use Matplotlib to produce an audio visualizer, and then count each time a certain range of db is reached which matches the db of a cricket chirp, but I have no idea how to approach it. I have successfully visualized the chirps on a online spectrum analyzer (Spectrum Visualizer of Audio Chirps), and can see it clearly, however I don't know how I can use code to count each "chirp" and record it along with the date and time for each chirp in a table of values / dataset of some sort. Any guidance or help would be greatly appreciated!
Really naive solution: For every unit of time (column of pixels in the spectrogram), you can calculate the sum of all the values in a column. For example, if all the pixels in a column are black, the sum for that column will be 0, if some of them are colored, the sum will be >0. Then loop over the sums: if at a certain step you go from 0 to > 0, and then after a while you go back to 0, you've hit a peak. If the output is too "noisy", you can use a small threshold value instead of 0. Tweak it until results seem OK. This works well when the input background noise is pretty much the same all the way through, but if it's skewed up and down during the whole recording, you need something more complicated (for example, the threshold constantly changes with the average of the last n sums) Or you can Google "peak detection algorithm" and implement one of them. Or you can Google "peak detection library" in your favourite language and use one of them.
Lowest value from 2 payloads in Node-Red
I have a IoT system in home and two temperature sensors.
One of the sensors could work in some hours in direct sun.
The real temperature is always the lowest value, so sometimes temp1, sometimes temp2.
What I want to achieve is:
read the temperature from sensors1 (via MQTT)
read the temperature from sensors2 (via MQTT)
compare values
find the lowest one and send in via MQTT
go back to reading in loop
For this example I can simulate readings with injection nodes
How to do that? I am new in Node-Red, have tried but without success.
Here is my flow:
[{"id":"fa6372cc.47f92","type":"tab","label":"Flow 8","disabled":false,"info":""},{"id":"5ac90e03.22da3","type":"join","z":"fa6372cc.47f92","name":"","mode":"custom","build":"object","property":"payload","propertyType":"msg","key":"topic","joiner":"","joinerType":"str","accumulate":true,"timeout":"","count":"2","reduceRight":false,"reduceExp":"","reduceInit":"","reduceInitType":"","reduceFixup":"","x":990,"y":340,"wires":[["f09774bf.3c8428","a197b84d.6a7338"]]},{"id":"f09774bf.3c8428","type":"debug","z":"fa6372cc.47f92","name":"","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"true","x":1130,"y":340,"wires":[]},{"id":"43900e79.98cd8","type":"change","z":"fa6372cc.47f92","name":"set payload value","rules":[{"t":"set","p":"payload","pt":"msg","to":"req.params.value","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":790,"y":340,"wires":[["5ac90e03.22da3"]]},{"id":"b71d9143.c03bd","type":"change","z":"fa6372cc.47f92","name":"set topic temp1","rules":[{"t":"set","p":"topic","pt":"msg","to":"temp1","tot":"str"}],"action":"","property":"","from":"","to":"","reg":false,"x":560,"y":320,"wires":[["43900e79.98cd8"]]},{"id":"e87114aa.6cd1","type":"change","z":"fa6372cc.47f92","name":"set topic temp2","rules":[{"t":"set","p":"topic","pt":"msg","to":"temp2","tot":"str"}],"action":"","property":"","from":"","to":"","reg":false,"x":560,"y":360,"wires":[["43900e79.98cd8"]]},{"id":"783c47fd.8dd58","type":"inject","z":"fa6372cc.47f92","name":"temp source 2","topic":"","payload":"12","payloadType":"num","repeat":"3","crontab":"","once":false,"onceDelay":"1.5","x":380,"y":360,"wires":[["e87114aa.6cd1"]]},{"id":"271dedab.aaa7b2","type":"inject","z":"fa6372cc.47f92","name":"temp source 1","topic":"","payload":"10","payloadType":"num","repeat":"2","crontab":"","once":false,"onceDelay":"1","x":380,"y":320,"wires":[["b71d9143.c03bd"]]},{"id":"a197b84d.6a7338","type":"mqtt out","z":"fa6372cc.47f92","name":"temperature","topic":"domoticz/in","qos":"","retain":"","broker":"7e3561ec.acad","x":1150,"y":280,"wires":[]},{"id":"7e3561ec.acad","type":"mqtt-broker","z":"","name":"Domoticz","broker":"192.168.6.11","port":"8084","clientid":"","usetls":false,"compatmode":true,"keepalive":"60","cleansession":true,"birthTopic":"","birthQos":"0","birthRetain":"false","birthPayload":"","closeTopic":"","closeRetain":"false","closePayload":"","willTopic":"","willQos":"0","willRetain":"false","willPayload":""}]
One way to do it would be like this:
This is storing the two temps in flow variables - the first flow initially sets them to a high number so the "min" in "choose lower value" will later work. In this case I've used a change node setting the payload to the JSONata of
$min([$flowContext("temp1"), $flowContext("temp2")])
but there's a few ways you could choose to do it.
Here is the code to try:
[{"id":"6bc2755e.9feb9c","type":"debug","z":"f454a93f.0e89d8","name":"","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"true","x":990,"y":340,"wires":[]},{"id":"38bd03eb.f7d06c","type":"change","z":"f454a93f.0e89d8","name":"choose lower value","rules":[{"t":"set","p":"payload","pt":"msg","to":"$min([$flowContext(\"temp1\"), $flowContext(\"temp2\")])\t","tot":"jsonata"}],"action":"","property":"","from":"","to":"","reg":false,"x":790,"y":340,"wires":[["6bc2755e.9feb9c"]]},{"id":"9066677f.eb0358","type":"change","z":"f454a93f.0e89d8","name":"store temp1","rules":[{"t":"set","p":"temp1","pt":"flow","to":"payload","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":550,"y":320,"wires":[["38bd03eb.f7d06c"]]},{"id":"a70c9b2a.e7db58","type":"change","z":"f454a93f.0e89d8","name":"store temp2","rules":[{"t":"set","p":"temp2","pt":"flow","to":"payload","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":550,"y":360,"wires":[["38bd03eb.f7d06c"]]},{"id":"4bd27616.d022c8","type":"inject","z":"f454a93f.0e89d8","name":"temp source 2","topic":"","payload":"12","payloadType":"num","repeat":"","crontab":"","once":false,"onceDelay":"1.5","x":370,"y":360,"wires":[["a70c9b2a.e7db58"]]},{"id":"7378dd4f.3825b4","type":"inject","z":"f454a93f.0e89d8","name":"temp source 1","topic":"","payload":"10","payloadType":"num","repeat":"","crontab":"","once":false,"onceDelay":"1","x":370,"y":320,"wires":[["9066677f.eb0358"]]},{"id":"314eb0ec.85211","type":"inject","z":"f454a93f.0e89d8","name":"","topic":"","payload":"","payloadType":"date","repeat":"","crontab":"","once":true,"onceDelay":0.1,"x":370,"y":260,"wires":[["688646b.138a6b8"]]},{"id":"688646b.138a6b8","type":"change","z":"f454a93f.0e89d8","name":"set to high","rules":[{"t":"set","p":"temp1","pt":"flow","to":"999","tot":"num"},{"t":"set","p":"temp2","pt":"flow","to":"999","tot":"num"}],"action":"","property":"","from":"","to":"","reg":false,"x":550,"y":260,"wires":[[]]}]
Measure (frequency-weighted) sound levels with AudioKit
I am trying to implement an SLM app for iOS using AudioKit. Therefore I need to determine different loudness values to a) display the current loudness (averaged over a second) and b) do further calculations (e.g. to calculate the "Equivalent Continuous Sound Level" over a longer time span). The app should be able to track frequency-weighted decibel values like dB(A) and dB(C). I do understand that some of the issues im facing are related to my general lack of understanding in the field of signal and audio processing. My question is how one would approach this task with AudioKit. I will describe my current process and would like to get some input: Create an instance of AKMicrophone and a AKFrequencyTracker on this microphone Create a Timer instance with some interval (currently 1/48_000.0) Inside the timer: retrieve the amplitude and frequency. Calculate a decibel value from the amplitude with 20 * log10(amplitude) + calibrationOffset (calibration offset will be determined per device model with the help of a professional SLM). Calculate offsets for the retrieved frequency according to frequency-weighting (A and C) and apply these to the initial dB value. Store dB, dB(A) and dB(C) values in an array. Calculate the average for arrays over the give timeframe (1 second). I read somewhere else that using a Timer this is not the best approach. What else is there that I could use for the "sampling"? What exactly is the frequency of AKFrequencyTracker? Will this frequency be sufficient to determine dB(A) and dB(C) values or will I need an AKFFTTap for this? How are values retrieved from the AKFrequencyTracker averaged, i.e. what time frame is used for the RMS? Possibly related questions: Get dB(a) level from AudioKit in swift, AudioKit FFT conversion to dB?
What should i do to maintain performance of a mobile app which is using database?
I'm building an app using database. I have a words table and everytime user types something, this app will record and update word the database. And the frequency field will be auto increase after user enter one matched word. But the trouble is user type day by day and i afraid the search performance will be reduce after times and also the Int field will reach to the limit (max limit Int) someday. So, i limit the database to around less than 50.000 records. I delete less-used records after a certain time. But i don't know how to deal with frequency Int field of each word? How to know exactly frequency usage of each word without increasing the field forever?
I recommend that you use a logarithmic scale for the frequency values. That's what is often done in situations like this. See Wikipedia to learn about logarithmic scales. For example, if you have a word MAN that has a frequency of 15, the value you store in the database would be log(15) ~= 1.17609125906. If you then find 4 new occurrences of MAN, then you want to add 4 to the field. You cannot add the log values directly because log(x)+log(y)=log(x*y). (See the Logarithm Rules section of this article for more information on log rules.) Instead -- assuming you use a base 10 logarithm, you would use this formula: SET frequency = log(10^frequency+4)
Depending on the length of your words, the few bytes for the frequency don't matter. With an unsigned four bytes integer, you can count up to more than two billion, which is way above the number of words what the user can type in in their whole lifespan. So may want to go for two or three bytes, but the savings may be negligible. Anyway, there are the following approaches for preventing overflow: You can detect it, and then undo the operations, scale everything down by some factor of two, and then redo. You can periodically check all your numbers and do the scaling when approaching the limit. You can do a probabilistic update like below. Probabilistic update Instead of simply incrementing the frequency every time by one, you do it only with a probability which gets lower and lower as the counter grows. For example, you can do the increment with a probability of 1.0 / (oldValue + 1) or 2 ** -oldValue. The latter leads to a logarithmic growth, but, unlike the idea in the other answer, it works. There are obviously some disadvantages due to the randomness and precision loss, but when all you care about is the relative frequency, it should be good enough.
National Weather Service (NOAA) REST API returns nil for parameters of forecast
I am using the NWS REST API as my weather service for an app I am making. I was initially reluctant to use NWS because of its bad documentation, but I couldn't resist as it is offered completely free. Now that I am trying to use it, I am running into some difficulty. When making a request for multiple days, the minimum temperature appears nil for several days. (EDIT: As I have been testing the API more I have found that it is not always the minimum temperatures that are nil. It can be a max temp or a precipitation, it seems completely random. If you would like to make test calls using their web interface, you can do so here: http://graphical.weather.gov/xml/sample_products/browser_interface/ndfdBrowserByDay.htm and here: http://graphical.weather.gov/xml/sample_products/browser_interface/ndfdXML.htm) Here is an example of a request the minimum temperatures are empty: http://graphical.weather.gov/xml/sample_products/browser_interface/ndfdBrowserClientByDay.php?listLatLon=40.863235,-73.714780&format=24%20hourly&numDays=7 Surprisingly, on their website, the minimum temperatures are available: http://forecast.weather.gov/MapClick.php?textField1=40.83&textField2=-73.70 You'll see under the Minimum temperatures that it is filled with about 5 (sometimes less, it is inconsistent) blank fields that say <value xsi:nil="true"/> If anybody can help me it would be greatly appreciated, using the NWS API can be a little overwhelming at times. Thanks,
The nil values, from what I can understand of the documentation, here and here, simply indicate that the data is unavailable. Without making assumptions about NOAA's data architecture, it's conceivable that the information available via the API may differ from what their website displays. Missing values are represented by an empty element and xsi:nil=”true” (R2.2.1). Nil values being returned seems to involve the time period. Notice the difference between the time-layout keys (see section 5.3.2) in 1 in these requests: k-p24h-n7-1 k-p24h-n6-1 The data times are different. <layout-key> element The key is derived using the following convention: “k” stands for key. “p24h” implies a data period length of 24 hours. “n7” means that the number of data times is 7. “1” is a sequential number used to keep the layout keys unique. Here, startDate is the factor. Leaving it off includes more time and might account for some requested data not yet being available. Per documentation: The beginning day for which you want NDFD data. If the string is empty, the start date is assumed to be the earliest available day in the database. This input is only needed if one wants to shorten the time window data is to be retrieved for (less than entire 7 days worth), e.g. if user wants data for days 2-5. I'm not experiencing the randomness you mention. The folks on NOAA's Yahoo! Groups forum might be able to tell you more.