How do you set the ESP32 to maximum BLE Power? - esp32

How do you increase the ESP32 BLE to maximum power, for maximum distance? It appears the default is set to index 5, which is only +3dbm. (ESP_PWR_LVL_P3 = index 5, Corresponding to +3dbm) More Details

There used to be a bug, it appears its fixed now. As indicated above, ESP_PWR_LVL_P9 = index 7, which indicates +9dbm.
After "BLEDevice::init("ESP32");", add this to get the maximum output:
esp_ble_tx_power_set(ESP_BLE_PWR_TYPE_DEFAULT, ESP_PWR_LVL_P9);
esp_ble_tx_power_set(ESP_BLE_PWR_TYPE_ADV, ESP_PWR_LVL_P9);
esp_ble_tx_power_set(ESP_BLE_PWR_TYPE_SCAN ,ESP_PWR_LVL_P9);
Check via this:
int pwrAdv = esp_ble_tx_power_get(ESP_BLE_PWR_TYPE_ADV);
int pwrScan = esp_ble_tx_power_get(ESP_BLE_PWR_TYPE_SCAN);
int pwrDef = esp_ble_tx_power_get(ESP_BLE_PWR_TYPE_DEFAULT);
Serial.println("Power Settings: (ADV,SCAN,DEFAULT)"); //all should show index7, aka +9dbm
Serial.println(pwrAdv);
Serial.println(pwrScan);
Serial.println(pwrDef);
Can also confirm via an Android phone app like nRF Connect, that the Tx Power Level is now 9dBm.

Related

Why does my running total resets after few weeks?

I have managed to write a code which counts running total on Citect Scada using Cicode-language. Now I have a problem because it seems that my loop has somehow reset my counter. I'm pretty new to this.
At this point, I don't know what causes this. This morning total was around 1480 before reset happened and I'm using INT as datatype. Counter has been active for about few weeks now. I'm using Citect Scada 6.1V.
FUNCTION Laskuri()
INT iState14 =0
INT iState15
WHILE 1 DO
Sleep(3600)
iState15 = ReadVar(5,"Ar",59)
iState14 = iState14 + iState15;
SetVar(5,"Ar", 58, iState14);
END
END

Set GPIO bank number in device tree

Do you know how to set the GPIO bank number in the device tree for an i2c gpio controller?
I tried with gpio-base (described in the gpio driver doc) but it was not very conclusive.
I have a device tree:
...
&i2c2 {
pca9502: pca9502#4C {
compatible = "nxp,pca9502";
reg = <0x4C>;
gpio-controller;
/* HERE */
gpio-base = <1>;
}
}
...
and the driver fetch the DT to find the gpio-base:
of_get_property(dev.of_node, "gpio-base", NULL);
In this case, a gpiochip is created with the number 2^32 giving: gpiochip16777216. And I can't access my GPIO using echo XX > export.
When I don't add gpio-base = <1>;, it works but the gpiochip number is 504 (with -1 as a default bank number) which I don't understand.
I don't know how to define the bank number into the device tree.
Well, my issue was not linked with the device tree structure but with the endianness of the processor.

Pyaudio : how to check volume

I'm currently developping a VOIP tool in python working as a client-server. My problem is that i'm currently sending the Pyaudio input stream as follows even when there is no sound (well, when nobody talks or there is no noise, data is sent as well) :
CHUNK = 1024
p = pyaudio.PyAudio()
stream = p.open(format = pyaudio.paInt16,
channels = 1,
rate = 44100,
input = True,
frames_per_buffer = CHUNK)
while 1:
self.conn.sendVoice(stream.read(CHUNK))
I would like to check volume to get something like this :
data = stream.read(CHUNK)
if data.volume > 20%:
self.conn.sendVoice(data)
This way I could avoid sending useless data and spare connection/ increase performance. (Also, I'm looking for some kind of compression but I think I will have to ask it in another topic).
Its can be done using root mean square (RMS).
One way to build your own rms function using python is:
def rms( data ):
count = len(data)/2
format = "%dh"%(count)
shorts = struct.unpack( format, data )
sum_squares = 0.0
for sample in shorts:
n = sample * (1.0/32768)
sum_squares += n*n
return math.sqrt( sum_squares / count )
Another choice is use audioop to find rms:
data = stream.read(CHUNK)
rms = audioop.rms(data,2)
Now if do you want you can convert rms to decibel scale decibel = 20 * log10(rms)

Aparapi add sample

I'm studing Aparapi (https://code.google.com/p/aparapi/) and have a strange behaviour of one of the sample included.
The sample is the first, "add". Building and executing it, is ok. I also put the following code for testing if the GPU is really used
if(!kernel.getExecutionMode().equals(Kernel.EXECUTION_MODE.GPU)){
System.out.println("Kernel did not execute on the GPU!");
}
and it works fine.
But, if I try to change the size of the array from 512 to a number greater than 999 (for example 1000), I have the following output:
!!!!!!! clEnqueueNDRangeKernel() failed invalid work group size
after clEnqueueNDRangeKernel, globalSize[0] = 1000, localSize[0] = 128
Apr 18, 2013 1:31:01 PM com.amd.aparapi.KernelRunner executeOpenCL
WARNING: ### CL exec seems to have failed. Trying to revert to Java ###
JTP
Kernel did not execute on the GPU!
Here's my code:
final int size = 1000;
final float[] a = new float[size];
final float[] b = new float[size];
for (int i = 0; i < size; i++) {
a[i] = (float)(Math.random()*100);
b[i] = (float)(Math.random()*100);
}
final float[] sum = new float[size];
Kernel kernel = new Kernel(){
#Override public void run() {
int gid = getGlobalId();
sum[gid] = a[gid] + b[gid];
}
};
Range range = Range.create(size);
kernel.execute(range);
System.out.println(kernel.getExecutionMode());
if (!kernel.getExecutionMode().equals(Kernel.EXECUTION_MODE.GPU)){
System.out.println("Kernel did not execute on the GPU!");
}
kernel.dispose();
}
I tried specifying the size using
Range range = Range.create(size, 128);
as suggested in a Google group, but nothing changed.
I'm currently running on Mac OS X 10.8 with Java 1.6.0_43. Aparapi version is the latest (2012-01-23).
Am I missing something? Any ideas?
Thanks in advance
Aparapi inherits a 'Grid Style' of implementation from OpenCL. When you specify a range of execution (say 1024), OpenCL will break this 'range' into groups of equal size. Possibly 4 groups of 256, or 8 groups of 128.
The group size must be a factor of range (so assert(range%groupSize==0)).
By default Aparapi internally selects the group size.
But you are choosing to fully specify the range and group size to using
Range r= Range.range(n,128)
You are responsible for ensuring that n%128==0.
From the error, it looks like you chose Range.range(1000,128).
Sadly 1000 % 128 != 0 so this range will fail.
If you specifiy
Range r = Range.range(n)
Aparapi will choose a valid group size, by finding the highest common factor of n.
Try dropping the 128 as the the second arg.
Gary

Calculate time remaining with different length of variables

I will have to admit the title of this question sucks... I couldn't get the best description out. Let me see if I can give an example.
I have about 2700 customers with my software at one time was installed on their server. 1500 or so still do. Basically what I have going on is an Auto Diagnostics to help weed out people who have uninstalled or who have problems with the software for us to assist with. Currently we have a cURL fetching their website for our software and looking for a header return.
We have 8 different statuses that are returned
GREEN - Everything works (usually pretty quick 0.5 - 2 seconds)
RED - Software not found (usually the longest from 5 - 15 seconds)
BLUE - Software found but not activated (usually from 3 - 9 seconds)
YELLOW - Server IP mismatch (usually from 1 - 3 seconds)
ORANGE - Server IP mismatch and wrong software type (usually 5 - 10 seconds)
PURPLE - Activation key incorrect (usually within 2 seconds)
BLACK - Domain returns 404 - No longer exists (usually within a second)
UNK - Connection failed (usually due to our load balancer -- VERY rare) (never countered this yet)
Now basically what happens is a cronJob will start the process by pulling the domain and product type. It will then cURL the domain and start cycling through the status colors above.
While this is happening we have an ajax page that is returning the results so we can keep an eye on the status. The major problem is the Time Remaining is so volatile that it does not do a good estimate. Here is the current math:
# Number of accounts between NOW and when started
$completedAccounts = floor($parseData[2]*($parseData[1]/100));
# Number of seconds between NOW and when started
$completedTime = strtotime("now") - strtotime("$hour:$minute:$second");
# Avg number of seconds per account
$avgPerCompleted = $completedTime / $completedAccounts;
# Total number of remaining accounts to be scanned
$remainingAccounts = $parseData[2] - $completedAccounts;
# The total of seconds remaining for all of the remaining accounts
$remainingSeconds = $remainingAccounts * $avgPerCompleted;
$remainingTime = format_time($remainingSeconds, ":");
I could create a count on all of the green, red, blue, etc... and do an average of how long each color does, then use that for the average time, although I don't believe that would give much better results.
With the difference in times that are so varied, any suggestions would be grateful?
Thanks,
Jeff
OK, I believe I have figured it out. I had to create a class so I could calculate a single regression over a period of time.
function calc() {
$n = count($this->mDatas);
$vSumXX = $vSumXY = $vSumX = $vSumY = 0;
//var_dump($this->mDatas);
$vCnt = 0; // for time-series, start at t=0<br />
foreach ($this->mDatas AS $vOne) {
if (is_array($vOne)) { // x,y pair<br />
list($x,$y) = $vOne;
} else { // time-series<br />
$x = $vCnt; $y = $vOne;
} // fi</p>
$vSumXY += $x*$y;
$vSumXX += $x*$x;
$vSumX += $x;
$vSumY += $y;
$vCnt++;
} // rof
$vTop = ($n*$vSumXY – $vSumX*$vSumY);
$vBottom = ($n*$vSumXX – $vSumX*$vSumX);
$a = $vBottom!=0?$vTop/$vBottom:0;
$b = ($vSumY – $a*$vSumX)/$n;
//var_dump($a,$b);
return array($a,$b);
}
I take each account and start building an array, for the amount of time it takes for each one. The array then runs through this calculation so it will build a x and y time sets. Finally I then run the array through the predict function.
/** given x, return the prediction y */
function calcpredict($x) {
list($a,$b) = $this->calc();
$y = $a*$x+$b;
return $y;
}
I put static values in so you could see the results:
$eachTime = array(7,1,.5,12,11,6,3,.24,.12,.28,2,1,14,8,4,1,.15,1,12,3,8,4,5,8,.3,.2,.4,.6,4,5);
$forecastProcess = new Linear($eachTime);
$forecastTime = $forecastProcess->calcpredict(5);
This overall system gives me about a .003 difference in 10 accounts and about 2.6 difference in 2700 accounts. Next will be to calculate the Accuracy.
Thanks for trying guys and gals

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