<1 mA certainly sounds like a breakthrough for this kind of device. I hope you are able to report back  with some real world performance information about your project @jcturn3 . Sounds very promising. Will the device run directly off the optional solar cell or will you include a capacitor since you cannot recharge the lithium thionyl chloride cell. I had trouble obtaining the Tadarian TL-5920 cells in Australia (they would send me old SL-2770s though) so I took a gamble on a couple of brands of Chinese cells (EVE and FANSO) which seemed to perform the same job without a hitch. Maybe in the USA you can get Israeli cells more easily than Chinese ones? 

Message me if you think some feeding sounds, snoring, grooming and heart sounds of koalas would be any use for your model training.

Really interesting project. Interesting chip set you found. With up to around 2mb sram that’s quite a high memory for a  ultra low power soc I think.

It might also be interesting while doing your research thinking about if there are any other requirements people could have for such a platform with a view towards more mass usage later. Thanks for sharing.

I am sure Simon can chime in with exact specifications. I do not have it with me now. The centre distance between the attachment holes at each in end is 20mm wich will fit the standard holes in a collar from Vectronics Aerospace. 



Simon posted images of the logger attached to a collar on a spotted Hyeana here: https://twitter.com/chamaillejammes/status/1441657479612542990

To-do now done in South Africa:

☑️ Being ridiculously proud of achieving data transfer over local GSM and LoRa networks for tracker

☑️ Being ridiculously proud of the way our wehear logger fits on lions and hyaenas. Looking forward to listening to their life. pic.twitter.com/CvgvovVjOp

— Simon Chamaillé (@chamaillejammes) September 25, 2021

We are studying muskoxen which are exposed to a polar night of between 3-4 month where the sun does not come over the horizon. On the other hand it also means that it will be continously OVER the horizon during the 3-4 summer months.

We are keeping an eye out for kinetic energy harvesting and there has been some interesting progress recently:

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285930

Read "A novel kinetic energy harvesting system for lifetime deployments of wildlife trackers | PLOS One" on

A novel kinetic energy harvesting system for lifetime deployments of wildlife trackers | PLOS One

Wildlife tracking devices are key in obtaining detailed insights on movement, animal migration, natal dispersal, home-ranges, resource use and group dynamics of free-roaming animals. Despite a wide use of such devices, tracking for entire lifetimes is still a considerable challenge for most animals, mainly due to technological limitations. Deploying battery powered wildlife tags on smaller animals is limited by the mass of the devices. Micro-sized devices with solar panels sometimes solve this challenge, however, nocturnal species or animals living under low light conditions render solar cells all but useless. For larger animals, where battery weight can be higher, battery longevity becomes the main challenge. Several studies have proposed solutions to these limitations, including harvesting thermal and kinetic energy on animals. However, these concepts are limited by size and weight. In this study, we used a small, lightweight kinetic energy harvesting unit as the power source for a custom wildlife tracking device to investigate its suitability for lifetime animal tracking. We integrated a Kinetron MSG32 microgenerator and a state-of-the-art lithium-ion capacitor (LIC) into a custom GPS-enabled tracking device that is capable of remotely transmitting data via the Sigfox &lsquo;Internet of Things&rsquo; network. Prototypes were tested on domestic dog (n = 4), wild-roaming Exmoor pony (n = 1) and wisent (n = 1). One of the domestic dogs generated up to 10.04 joules of energy in a day, while the Exmoor pony and wisent generated on average 0.69 joules and 2.38 joules per day, respectively. Our results show a significant difference in energy generation between animal species and mounting method, but also highlight the potential for this technology to be a meaningful advancement in ecological research requiring lifetime tracking of animals. The design of the Kinefox is provided open source.

@M_Stanton , you provide a nice list of uses of animal borne audio. We could add environmental sounds both abiotic and biotic.

We would LOVE to use audio more for ground truthing behavioural states and we would LOVE if the audio recordings could be GPS time synced...

@jared , it sounds interesting with the mentioned increased capabilities of the Iridium system. Can you link to any references for that?

Cheers, 

Lars

I heard it through industry chatter but can't find a news release on it yet.

I have a question about Arbimon. I'm working on a project looking for bird use of wet meadow (and associated matrices of habitat). We have two bird lists we've created for BirdNet, a "Master List" of all species (to get an understanding of community data as per input from Indigenous partners) and a "Focal Species List" as per the land managers in put. I will have volunteers doing manual verification + passive listening to attempt to catch false positives and species BirdNet has missed. I recently learned about Arbimon from the Soundscapes to Landscapes project and I'm curious about the audio detector function. Is it detecting spectrograms/sonograms from a provided classifier or does it function similar to BirdNet where we can tell it which species to look for? 

Hi Teresa, 

Thanks for your interest in Arbimon! The platform has a couple different analysis tools that range from unsupervised (like audio event detection & clustering, or AED-C) to semi-automated (pattern matching, random forest). We've got lots more info about each in our support docs here. 

The AED-C is an unsupervised machine learning model, so you aren't providing any labels (though the validation page allows you to assign events or clusters as particular species after the fact). The pattern matching is a cross-correlation template matching function where you provide 1 template (example of the species-specific call you're looking for) and the algorithm looks for matches similar to that template. Random forest is a decision-tree-based machine learning model where you provide training clips (presence & absence clips for a species) which the model uses to learn how to classify that species' call. We have developed a number of CNNs (like BirdNet is) but they have more of a regional focus (e.g., one for Kenya, one for western Sumatra, etc. etc.). Right now we run these on the backend, but we are currently working on a public-facing CNN page that we hope to phase in this year.   

Hope that helps, but feel free to reach out if you have more questions! You're welcome to also email me directly at carly@rfcx.org .

All the best,

Carly 

"Seychelles and South Africa did not allow Starlink to operate"

This is not good, has implications for anti-poaching measures.

Power usage for microcontrollers with solar is  much more manageable. For Raspberry Pi's and higher it gets expensive and big.

I'm quite impressed by the specs from the Goal Zero Yeti devices. This can have high capacity and be charged with Solar. Not small though. And the price is not in proportion to the Pi's.

So this 200x model for example, would be close to 16 days running the audio recorder. Let's say 10. without solar. Add solar? Depends on the size of the panels I guess. Power usage for mobile networking? Depends on how much you transmit.

Probably some well documented experiments would be really nice for people here. Sounds like something nice for the next set of grants :)

Real nice video. I'll have another look in the weekend in detail.

Hi Dilip,

I do not have data about signal distortion in a forest area and with the signal you are intended to use.

However, in a savannah environment, when I put a tour on the highest point of the park, Lora signal (avg 900MHz) is less distorted than WiFi signal (2.4GHz). This is normal as a physics law: the frequency determines the wave length, and the less the length (obviously the less the frequency), the less obstructed the signal.

So, without interfering with your design, I would say that in a forest configuration, WiFi will need more access points deployed and may be more costly, and in your context, even when using LoRa, you will need more gateways than I have in a savannah.

To design the approximate number of gateways, you may need to use terrain Visibility analysis.

To design the cameras deployment, you will need to comply with the sampling methods defined in your research. However, if it is on for surveillance reasons, you may need to rely on terrain visibility analysis also.

Best regards.