wilzy123
Founding Member
To answer part of your questions... aggregate data is available here --> https://asic.gov.au/regulatory-resources/markets/short-selling/short-position-reports-table/
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
db1969oz
Regular
Thanks Wilzy.
Izzzzzzzzzzy
Regular
Make a few more for by the name of the WANCAs in the place we do not nameIn the meantime.................
View attachment 11051
Changed handle to "To be advised" but still shows up under original.
@Quatrojos came up with WANCA
Techinvestor17
Regular
There’s a website called shorthand.co.au that have the number of shares short. As for when they have to be closed out that’s up to the person holding the position. I got the 10 day average from yahoo finance
Hi Rskiff & Rise,
I just watched it & screen recorded it for you both, it was the best interview ever.
I have asked JK200SX to upload it to youtube for me so I can then provide you all with a link.
Thanks for the edit feature Z, now I can add youtube link right here, thanks JK200SX for uploading for me, you are the best
It's great to be a Shareholder
So good thankyou all. I just love Jerome's articulation and the way he so easily bridge's the gap between such an exicitng, all invasive tech framework, with the complexity of the tech itself.
I really enjoyed this video. It's one thing to have the technology, it's another to have the team of visionaries with the business and market expertise to maximise its potential.. It really does feel we are ticking all of the right boxes.
I loved Jerome's simple explanation of the ecosystem and the differentiation of the inner years and the outer years of our too market strategy.
Highly recommended viewing.
Thanks to all who brought it to our attention and @TechGirl for providing the direct vid
Like all, I'm desperately sad that @Fact Finder may not be among us for some time.. Any chance Blind Freddie is still keen to stick around?
So appreciative of you FF. Cheers, Al
Perhaps
Regular
Always couple of days behind, but gives a good overview:
Townyj
Ermahgerd
wilzy123
Founding Member
The larger that number gets... the funnier it is for me as a sturdy long holder.
For two reasons:
1. It presents me with more buying (bargains imo) opportunities
2. They (the short folk) are imo only increasing their risk of some serious financial punishment... not something I will lose sleep over lol.
Perhaps
Regular
That's up to the lately invested funds cause of ASX200. Some of them like the several iShares funds are well known for offering their shares to short activities.
Dougie54
Regular
Reuben
Founding Member
Putting up this post to help anyone learn different levels of driver automation as we talk a lot about LIDAR, ADAS, Valeo etc (just educational if it helps). Source - https://www.porscheengineering.com/peg/en/about/magazine/
Stable Genius
Regular
I came across this at Renesas; it’s 1/2 hr old.
I’m not convinced it’s Akida as there’s no mention of NN etc but it’s something we would be capable of and best suited.
I noticed the images were left out of the original pasting to here so I’ve added them at the start:
Karol Saja
Staff Engineer
Endpoint AI is a new frontier in the space of artificial intelligence that takes the processing power of AI to the edge. It is a revolutionary way of managing information, accumulating relevant data, and making decisions locally on a device. Endpoint AI employs intelligent functionality at the edge of the network, in other words, it transforms the IoT devices that are used to compute data into smarter tools embedded with AI features. This in turn improves real-time decision-making capabilities and functionalities. The goal is to bring machine-learning based intelligent decision-making physically closer to the source of the data. In this context, Embedded vision shifts to the Endpoint. Embedded vision incorporates more than breaking down images or videos into pixels - it is the means to understand pixels, make sense of what is inside and support making a smart decision based on specific events that transpire. There have been massive endeavours at the research and industry levels to develop and improve AI technologies and algorithms.
Embedded vision is one of leading technologies with embedded AI utilised in smart endpoint applications in a wide range of consumer and industrial applications. There are a number value-added use cases examples; such as counting/analysing quality of products on a factory line, keeping a tally of people in a crowd, identifying objects, analysing contents of a specific area in the environment to name a few.
Whilst considering the processing of embedded vision applications at the endpoint, the performance of such operation may face some challenges. The data flow from the vision sensing device to the cloud for the purposes of analysing and processing could be very large, and may exceed the network available bandwidth. For instance, a camera with 1920 ×1080 and operating with 30 FPS (Frame Per Second) may generate about 190 MB/S of data. In addition to privacy concerns, this substantial amount of data contributes to latency during the round trip of data from the edge to the cloud, then back again to the endpoint. These limitations could negatively impact employment of embedded vision technologies in real-time applications.
IoT Security is also a concern in the adoption and growth of embedded vision applications across any segment. In general, all IoT devices must be secured. A critical issue and concern in the use of smart visions devices is the possible misuse of sensitive images and videos. The unauthorised access to smart cameras form example is not only a breach of privacy, but it could pave a way for more harmful outcome.
Image
In general, Embedded vision can serve many purposes, and these functionalities can be used after customization and the needed training on different types of datasets from many areas. Functionalities include monitoring physical area, recognition of intrusion, detecting crowd density, counting humans or objects, or animals. They also include identifying people or finding cars based on car number, detection of motion, and human behaviour analysis in different cases.
The process that is used in this case study can be applied to any other detection. There are three main steps for using deep learning in computer/machine vision:
Image
Step one is performed on normal computers in the lab, whereas step 2 is deployed on a microcontroller at the endpoint, which can be on the farm. Results in step 3 are displayed on the screen at the user side. The following diagram shows the process in general.
Image
I’m not convinced it’s Akida as there’s no mention of NN etc but it’s something we would be capable of and best suited.
I noticed the images were left out of the original pasting to here so I’ve added them at the start:
Revolution of Endpoint AI in Embedded Vision Applications
Image
Karol Saja
Staff Engineer
Endpoint AI is a new frontier in the space of artificial intelligence that takes the processing power of AI to the edge. It is a revolutionary way of managing information, accumulating relevant data, and making decisions locally on a device. Endpoint AI employs intelligent functionality at the edge of the network, in other words, it transforms the IoT devices that are used to compute data into smarter tools embedded with AI features. This in turn improves real-time decision-making capabilities and functionalities. The goal is to bring machine-learning based intelligent decision-making physically closer to the source of the data. In this context, Embedded vision shifts to the Endpoint. Embedded vision incorporates more than breaking down images or videos into pixels - it is the means to understand pixels, make sense of what is inside and support making a smart decision based on specific events that transpire. There have been massive endeavours at the research and industry levels to develop and improve AI technologies and algorithms.
What is Embedded Vision?
Embedded computer vision is a technology that imparts machines with the ability to see i.e., the sense of sight, which enables them to explore the environment with the support of machine-learning and deep-learning algorithms. There are numerous applications across several industries whose functionality relies on computer vision, thus becoming an integral part of technological procedures. In precise terms, computer vision is one of the Artificial Intelligent (AI) fields that enables machines to extract meaningful information from digital multimedia sources to take actions or make recommendations based on the information that has been obtained. Computer vision is, to some extent, akin to the human sense of sight. However, the two differ on several grounds. Human sight has the exceptional ability to understand many and varied things from what it sees. On the other hand, computer vision recognizes only what it has been trained on and what it is designed to do exactly, and that too with an error rate. AI in embedded vision processes trains the machines to perform supposed functions with the least processing time and has an upper edge over human sight in analysing hundreds of thousands of images in a lesser timeframe.Embedded vision is one of leading technologies with embedded AI utilised in smart endpoint applications in a wide range of consumer and industrial applications. There are a number value-added use cases examples; such as counting/analysing quality of products on a factory line, keeping a tally of people in a crowd, identifying objects, analysing contents of a specific area in the environment to name a few.
Whilst considering the processing of embedded vision applications at the endpoint, the performance of such operation may face some challenges. The data flow from the vision sensing device to the cloud for the purposes of analysing and processing could be very large, and may exceed the network available bandwidth. For instance, a camera with 1920 ×1080 and operating with 30 FPS (Frame Per Second) may generate about 190 MB/S of data. In addition to privacy concerns, this substantial amount of data contributes to latency during the round trip of data from the edge to the cloud, then back again to the endpoint. These limitations could negatively impact employment of embedded vision technologies in real-time applications.
IoT Security is also a concern in the adoption and growth of embedded vision applications across any segment. In general, all IoT devices must be secured. A critical issue and concern in the use of smart visions devices is the possible misuse of sensitive images and videos. The unauthorised access to smart cameras form example is not only a breach of privacy, but it could pave a way for more harmful outcome.
Vision AI at the Endpoint
- Endpoint AI can enable image processing to infer a complex insight from a huge number of captured images.
- AI uses machine-learning and deep-learning capabilities within the smart imaging devices to check a huge amount of previously well-known use cases.
- For optimum performance, Embedded vision requires AI algorithms to run on the endpoint devices and not transmit data to the cloud. The data here is captured by the imaging recognition device, then processed and analysed in the same device.
Deployment of AI Vision Applications
There are unlimited use cases for the deployment of AI in vision applications in the real world. Here are some of the examples where Renesas can provide comprehensive MCU and MPU based solutions inc. all the necessary SW and tools to enable quick development.Smart Access Control:
Security access control systems are becoming more valuable with the addition of voice and facial recognition features. Real-time recognition requires embedded systems with very high computational capabilities and on-chip hardware acceleration. To meet this challenge, Renesas provides a choice of MCU or MPU that offer very high computational power that also integrates many key features that are critical to high performance facial and voice recognition systems such as built-in H.265 hardware decoding, 2D/3D graphic acceleration, and ECC on internal and external memory to eliminate soft-errors and allow for high-speed video processing.Industrial Control:
Embedded vision has a huge impact as it utilizes many applications, including product safety, automation, sorting of products, and many more. AI techniques can perform multiple operations in the production process such as packaging and distribution, which can ensure quality and safety during production in all stages. Safety is needed in areas such as critical infrastructure, warehouses, production plants and buildings which require a high level of human resources.Transportation:
Computer vision presents a large scale of ways to improve transportation services. In self-driving cars as an example, computer vision is used to detect and classify objects on the road. It is also used to create 3D maps and estimate movement around. By using computer vision, self-driving cars gather information from the environment using cameras and sensors, which then interpret and analyse the data to make the most suitable response by using vision techniques such as pattern recognition, feature extraction, and object tracking.Image
In general, Embedded vision can serve many purposes, and these functionalities can be used after customization and the needed training on different types of datasets from many areas. Functionalities include monitoring physical area, recognition of intrusion, detecting crowd density, counting humans or objects, or animals. They also include identifying people or finding cars based on car number, detection of motion, and human behaviour analysis in different cases.
Case study: Agricultural Plant Disease Detection
Vision AI & deep learning maybe employed to detect various anomalies - plant disease detection is one example of this type of system. Deep learning algorithms -one of the AI techniques- are used widely for this purpose. According to research, computer vision gives better, accurate, fast, and low-cost results, as compared to the costly and slow labour-intensive results of previous methods.The process that is used in this case study can be applied to any other detection. There are three main steps for using deep learning in computer/machine vision:
Image
Step one is performed on normal computers in the lab, whereas step 2 is deployed on a microcontroller at the endpoint, which can be on the farm. Results in step 3 are displayed on the screen at the user side. The following diagram shows the process in general.
Image
Conclusion:
We are experiencing a revolution in the high-performance smart vision applications across a number of segments. The trend is well supported by growing computational power of microcontrollers and Microprocessors at the endpoints, opening-up great opportunities for exciting new vision applications. Renesas Vision AI solutions can help you to enhance overall system capability by delivering embedded AI technology with intelligent data processing at the endpoint. Our advanced image processing solutions at the edge are provided through a unique combination of low power, multimodal, multi-feature AI inference capabilities. Take the chance now and start developing your vision AI application with Renesas Electronics.
Bravo
If ARM was an arm, BRN would be its biceps💪!
OK, who wants to call the United Kingdom's Police Department to let them know about AKIDA?
This problem would be solved if all of their police cars were able to travel 1000km on one charge just like the Mercedes EQXX (thanks to BrainChip, of course).
PS: Please don't be gone for too long FF!
This problem would be solved if all of their police cars were able to travel 1000km on one charge just like the Mercedes EQXX (thanks to BrainChip, of course).
PS: Please don't be gone for too long FF!
VictorG
Member
It's only trading lower because I bought another parcel this morning at .88c, it will recover by the end of trade today.
wilzy123
Founding Member
Nice one!
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