How to load a dataset (.rs) which contains nodes and their RSSI in cooja simulator - contiki
I have a dataset with extension (.rs) this data set contain : creation of nodes, and variation of links characteristics over time, this is a small example of my dataset:
motetype;Client
motetype;Server;1.0
39000;baserssi;20.0;-97
41000;baserssi;1.0;-93
48000;baserssi;8.0;-94
51000;baserssi;7.0;-94
56000;baserssi;10.0;-94
63000;baserssi;9.0;-98
65000;baserssi;6.0;-97
67000;baserssi;3.0;-96
74000;baserssi;5.0;-96
0;addnode;20.0
0;addnode;1.0
108000;setedge;20.0;1.0;75,000000;-87;91
0;addnode;3.0
108000;setedge;20.0;3.0;62,500000;-41;104
108000;baserssi;20.0;-97
112000;setedge;1.0;20.0;50,000000;-91;87
0;addnode;8.0
112000;setedge;1.0;8.0;87,500000;-77;106
112000;setedge;1.0;3.0;62,500000;-89;101
112000;baserssi;1.0;-93
116000;setedge;8.0;1.0;87,500000;-77;107
0;addnode;6.0
116000;setedge;8.0;6.0;62,500000;-86;103
0;addnode;9.0
I want to create a simulation in cooja using this network topology ( which is in my dataset) with DGRM (Directed Graph Radio Medium) configuration.
I would to ask how can I load it in cooja simulator?
You need to add the RealSim plugin to the Cooja. Please get the source code and read the instructions here: https://github.com/cmorty/realsim/tree/master/cooja-plugin
Related
How to make your own custom image dataset?
As I am working on my project that is to detect FOD (Foreign Object Debirs) that is found on the runway. FOD include anything like nuts, bolts, screws, locking wires, plastic debris, stones etc. that has the potential to cause damage to the aircraft. Now I have searched on the Internet to find any image dataset but no dataset is available related to FOD. Now my question is kindly guide me that how can I make my own dataset of images that can then be used for training purpose. Kindly guide me in making image dataset for both classification and detection purposes. And also the data pre-processing that will be required. Thanks and waiting for the reply!
Although the question is a bit vague regarding your requirements and the specs of your machine, I'll try to answer it. You'll need object detection to do your task. There are many models available which you can use like Yolo, SSD, etc.. To create your own dataset, you can follow these steps: Take lots of images of your objects of interest in various conditions, viewpoints and backgrounds. (Around 2000 per class should be good enough). Now annotate (or mark) where your object is in the image. If you're using Yolo, make use of Yolo-mark for annotating. There should be other similar tools for SSD and other models. Now you can begin training. These steps should get you started or at least point you in the right direction.
You can build your own dataset with this code. I wrote it, and it works correctly.
You need to import the libraries and add your DATADIR.
if __name__ == "__main__":
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path,img))
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
for features, label in training_data:
x_train.append(features)
y_train.append(label)
#create pikle
pickle_out = open("x_train.pickle", "wb")
pickle.dump(x_train, pickle_out)
pickle_out.close()
pickle_out = open("y_train.pickle", "wb")
pickle.dump(y_train, pickle_out)
pickle_out.close()
In case if you're starting completely from scratch, you can use "Dataset Directory", available on Play store. The App helps you in creating custom datasets using your mobile. You'll have to sign in to your Google drive such that your dataset is stored in Drive rather on your mobile. Additionally, It also contains Labelling the entity for classification and Regression predictive models. Currently, the App supports Binary Image Classification and Image Regression. Hope this Helped! Download Link : https://play.google.com/store/apps/details?id=com.applaud.datasetdirectory
Writing BLE to Cycling Control Point - Adding Resistance
I have bee working with BLE for a while now, but primarily for reading and notifying characteristics.
The devices specifically are Virtual cycle trainers that support GATTS Cycling Power Service - 0x1818 link
I know that it's possible to increase resistance on this trainer, but I have read the documentation on Cycling Power Control Point - 0x2A66 [link][2] which is the only one with Mandatory write functions, but non of the documentation seem to be make sense.
Trainer: Cycleops Magnus
Reading and writing characteristic
// Reads all characteristics
var characteristics = service.characteristics;
for(BluetoothCharacteristic c in characteristics) {
List<int> value = await device.readCharacteristic(c);
print(value);
}
// Writes to a characteristic
await device.writeCharacteristic(c, [0x12, 0x34])
Reading and writing descriptors
// Reads all descriptors
var descriptors = characteristic.descriptors;
for(BluetoothDescriptor d in descriptors) {
List<int> value = await device.readDescriptor(d);
print(value);
}
// Writes to a descriptor
await device.writeDescriptor(d, [0x12, 0x34])
The closest I can see is setting the crank length, or chain weight but at this stage I am only guessing and am looking for some guidance.
The questions is this..
What characteristic or descriptor should I use to adjust Virtual Power
trainer resistance and what is the best way to do this?
Any coding Language is fine, I can transpose it.
Screenshot of services available for device
[2]: https://www.bluetooth.com/specifications/gatt/viewer?attributeXmlFile=org.bluetooth.characteristic.cycling_power_control_point.xml
I think you're using the wrong Bluetooth service for this. The Cycling Power Service is for collecting data from cycling power meters like this one: https://www.cyclist.co.uk/reviews/6705/long-term-review-fsa-powerbox-carbon-power-cranks For your requirements, I believe you should be using the Fitness Machine Service (0x1826) which includes the Indoor Bike Data characteristic (0x2AD2) and most importantly for you, the Fitness Machine Control Point characteristic. Take a look at section 4.16.1 of the Fitness Machine Service specification and you'll see details of operations which the control point supports, including a reference to 4.16.2.5 Set Target Resistance Level Procedure. I think this is what you need.
You cannot use cycling control point(CPP) for adding resistance. CPP can only be used to copy data like wheel Revolution from old peripheral to new one or if you want to reset data on peripheral you can use cpp. If you want to add resistance you need to check for fitness machine i am using elite and elite have Fitness Machine Control point you can write resistance and other things like inclination, elevation etc using FTCP. Few of the vendor support fitness machine and other have given their api or source code you can use that to add resistance and other stuff like that.
Indoor trainers have a few services: Cycling Power Service (ANT+ or BT also have) ANT+FEC (ANT only) BTLE Fitness control (FTMS) TACX ANT+ FEC. over Bluetooth (https://blog.lazerwalker.com/2019/02/15/bike-game-part-2.html) Wahoo's Extension to the Cycling Power Service (to be able to set Target power for instance) To Add Resistance to the trainer to #1, you need to check if it also has the #5 service as well. (this is the UUID used - A026E005-0A7D-4AB3-97FA-F1500F9FEB8B) #4 is actually a protocol which Tacx came up w/ before FTMS was a standard and some trainers still use this.
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":[[]]}]
Is MQTT support in Cumulocity?
it's possible to receive MQTT messages from Cumuloyity API? How can I get with Java Clients the values from following Measurements: Analog Measurement Motion Measurement Thanks
Querying measurements is described here: http://cumulocity.com/guides/java/developing/, Section "Accessing events and measurements". There are currently no pre-defined Java classes for analog measurements and motion measurements, however, you can still retrieve them as generic properties. Check the example on the web page and instead of
measurementFilter.byFragmentType(SignalStrength.class);
try
measurementFilter.byFragmentType("c8y_MotionMeasurement");
and instead of
measurement.get(SignalStrength.class);
try
measurement.getProperty("c8y_MotionMeasurement");
You can also create the Java classes representing the measurements on your own by "stealing" and modifying one of the existing classes:
https://bitbucket.org/m2m/cumulocity-clients-java/src/53216dc587e24476e0578b788672416e8566f92b/device-capability-model/src/main/java/c8y/?at=default
Text clustering within a log file
I am working on a problem of finding similar content in a log file. Let's say I have a log file which looks like this:
show version
Operating System (OS) Software
Software
BIOS: version 1.0.10
loader: version N/A
kickstart: version 4.2(7b)
system: version 4.2(7b)
BIOS compile time: 01/08/09
kickstart image file is: bootflash:/m9500-sf2ek9-kickstart-mz.4.2.7b.bin
kickstart compile time: 8/16/2010 13:00:00 [09/29/2010 23:10:48]
system image file is: bootflash:/m9500-sf2ek9-mz.4.2.7b.bin
system compile time: 8/16/2010 13:00:00 [09/30/2010 00:46:36]`
Hardware
xxxx MDS 9509 (9 Slot) Chassis ("xxxxxxx/xxxxx-2")
xxxxxxx, xxxx with 1033100 kB of memory.
Processor Board ID xxxx
Device name: xxx-xxx-1
bootflash: 1000440 kB
slot0: 0 kB (expansion flash)
For a human eye, it can easily be understood that "Software" and the data below is a section and "Hardware" and the data below is another section. Is there a way I can model using machine learning or some other technique to cluster similar sections based on a pattern? Also, I have shown 2 similar kinds of pattern but the patterns between sections might vary and hence should identify as different section. I have tried to find similarity using cosine similarity but it doesn't help much because the words aren't similar but the pattern is.
I see actually two separate machine learning problems: 1) If I understood you correctly the first problem you want to solve is the problem to split each log into distinct section, so one for Hardware, one for Software etc. In order to achieve this one approach could be try to extract heading which mark the beginning of a new section. In order to do so you could manually label a set of different logs and label each row as heading=true, heading= false No you could try to train a classifier which takes your labeled data as an input and the result could be a model. 2) Now that you have this different sections, you can split each log into those section and treat each section as a separate document. Now I would first try a straigt-forward document clustering using a standard nlp pipeline: Tokenize your document to get the tokens Normalize them (maybe stemming is not the best idea for logs) Create for each document a tf-idf vector Start with a simple clustering algorithm like k-means to try to cluster the different section After the clustering you should have the section similar to each other in the same cluster I hope this helped, I think especially the first task is quit hard and maybe hand-tailored patterns will perform better.