Proga
Regular
We already know the EQXX was using Akida IP. If Jesse is our Chapman, he should know. Why is he asking?
BRN Discussion Ongoing
- Thread starter TechGirl
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Xhosa12345
Regular
Cody rhodes..
Show him fighting seth rollins with the bruised pec.. hopefully the shorters soon... relentless
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uiux
Regular
To get some insight from Mercedes I guess......
Same reason he asked the Ford chief scientist something and got a reply.
@chapman89 is a wizard
Teach22
Regular
Hi @Zeebot - is it possible to have a thread dedicated to BRN short selling?
Its hard enough to navigate on the BRN Discussion thread as it is due to multiple people posting the same information, not to mention off-topic banter.
According to shortman.com the outstanding shorts on Jun 24 was 4.5%, on Aug 3 it was 4.51%, so nothing to see here ATM.
I am wondering why this fixation continues on the BRN threads.
There will always be people that don’t agree with our own views, that’s just how it is, but it is nothing new. The numbers are fairly steady. Of course, if that was to change, it would be newsworthy.
I have been accused of liking shorters. That is bs. I wish it was illegal but it isn’t.
No doubt I have offended some, that is not my intention.
Anyway, just a request - I have no problem if it is denied.
Its hard enough to navigate on the BRN Discussion thread as it is due to multiple people posting the same information, not to mention off-topic banter.
According to shortman.com the outstanding shorts on Jun 24 was 4.5%, on Aug 3 it was 4.51%, so nothing to see here ATM.
I am wondering why this fixation continues on the BRN threads.
There will always be people that don’t agree with our own views, that’s just how it is, but it is nothing new. The numbers are fairly steady. Of course, if that was to change, it would be newsworthy.
I have been accused of liking shorters. That is bs. I wish it was illegal but it isn’t.
No doubt I have offended some, that is not my intention.
Anyway, just a request - I have no problem if it is denied.
Reuben
Founding Member
I am sure he is well aware and it's always better to hear again from Mercedes that they are using akida.
this post was put up as an FYI.
uiux
Regular
If you build it they will come
Too right. Well done Jesse!
Proga
Regular
I can see the neuromorphic computing market size being $8,582.85 million by 2030 just for ADAS.
AusEire
Founding Member. It's ok to say No to Dot Joining
I presume that your talking about tracking the numbers rather than wanting to short sell?
I don't see an issue with it and I'm sure most wouldn't but we do have a TA thread so maybe it could fall into that thread?
Fwiw though, shorts don't worry me too much anymore as those c**** are going to have their ass burned real bad in the not too distant future and boy will that be a beautiful sight
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OMG those numbers are mind boggling. Even tiny percentages of these figures would create huge revenue. To think that Brainchip are positioned at the forefront of this new era. Dare to imagine the actual market share they will achieve with AKIDA.
It'll be even greater being a shareholder
GStocks123
Regular
Sorry if already posted in the past (Page5)
chapman89
Founding Member
I sure do know it has akida in it.
But it’s my follow up question/questions that makes the impact, just like I have with Ford, Prophesee CEO, NVISO Tata and a couple others that I’ve got replies from
If we just mention akida they won’t respond.
Learning
Learning to the Top 🕵♂️
Hi Proga,
As Jesse is a our resident Verification Engineer, he need to verify these sorts of thing!
It's great to be a shareholder.
KMuzza
Mad Scientist
HI Tech- we had a ” Monthly Newsletter “ that was posted to all registered shareholders- the 4c has come and gone but still no monthly newsletter- I am a happy as a BRN LTH until 2025 or maybe earlier if my plan eventuates- so not really worried -Me again !
I forgot to mention that our Research Institute here in Perth is "finally" going to be moving into the company's new offices this
month, that is, bigger premises and at a cheaper lease !...I was of the understanding that the move was taking place in late May
or early June, but never-the-less, I'm looking forward to visiting, taking some photos of Tony and the team, I think Peter is out of
the country in the US helping Anil and the team, my timing maybe off on that point, anyway I'm having an operation on my hand
in the next month, so once I can operate my EQXX properly, I'll or Akida will drive me into town to Brainchip HQ !!
I look forward to sharing some photos, with the company's permission of course !!....watch this space Brainchip loversTech x
(but heavily over invested in BRN non the less )- can you possibly mention that we as registered shareholders request another “monthly update “ between each 4c- as counting down the days before the next 4c -
AKIDA BALLISTA UBQTS
Proga
Regular
I seeI sure do know it has akida in it.
But it’s my follow up question/questions that makes the impact, just like I have with Ford, Prophesee CEO, NVISO Tata and a couple others that I’ve got replies from
If we just mention akida they won’t respond.
KMuzza
Mad Scientist
HI Tech- we had a ” Monthly Newsletter “ that was posted to all registered shareholders- the 4c has come and gone but still no monthly newsletter- I am a happy as a BRN LTH until 2025 or maybe earlier if my plan eventuates- so not really worried -
(but heavily over invested in BRN non the less )- can you possibly mention that we as registered shareholders request another “monthly update “ between each 4c- as counting down the days before the next 4c -
AKIDA BALLISTA UBQTS
| Jerome Nadel | Jun 7, 2022, 4:00 AM | ||
|
Learning
Learning to the Top 🕵♂️
Apology if this has been posted.
www.newelectronics.co.uk
It's great to be a shareholder.
Edge Impulse releases support for BrainChip Akida Neuromorphic IP - New Electronics
Edge Impulse, a platform that enables ML at the edge, and BrainChip, a provider of neuromorphic AI IP technology, have announced support for deploying Edge Impulse projects on the BrainChip MetaTF platform.
It's great to be a shareholder.
MDhere
Top 20
Fresh tweet
Sensor Fusion with Deep Learning
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Suad Jusuf
Senior Manager
Sensors are increasingly being used in our everyday lives to help collect meaningful data across a wide range of applications, such as building HVAC systems, industrial automation, healthcare, access control, and security systems, just to name a few. Sensor Fusion network assists in retrieving data from multiple sensors to provide a more holistic view of the environment around a smart endpoint device. In other words, Sensor Fusion provides techniques to combine multiple physical sensor data to generate accurate ground truth, even though each individual sensor might be unreliable on its own. This process helps to reduce the amount of uncertainty that may be involved in overall task performance.
To increase intelligence and reliability, the application of deep learning for sensor fusion is becoming progressively important across a wide range of industrial and consumer segments.
From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and sensor fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviour in industrial machinery, tools, and processes to anticipate critical events and damage, eventually preventing important economic losses and safety issues.
Renesas Electronics provides intelligent endpoint sensing devices as well as a wide range of analog rich Microcontrollers that can become the heart of smart sensors, which enable a more accurate sensor fusion solution across different applications. In this context combining sensor data in a typical sensor fusion network may be achieved as follows:
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- Redundant sensors: All sensors give the same information to the world.
- Complementary sensors: The sensors provide independent (disjointed) types of information about the world.
- Coordinated sensors: The sensors collect information about the world sequentially.
The communication in a sensor network is the backbone of the entire solution and could be in any of the schemes mentioned below:
The centralized scheme can be regarded as a special case of the distributed scheme where the sensors communicate every scan to each other. A pictorial representation of the fusion process is given in the figure below.
- Decentralized: No communication exists between the sensor nodes.
- Centralized: All sensors provide measurements to a central node.
- Distributed: The nodes interchange information at a given communication rate (e.g., every five scans, i.e., one-fifth communication rate).
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From Industry 4.0 perspective, feedback from one sensor is typically not enough, particularly for the implementation of control algorithms.
Deep Learning
Precisely calibrated and synchronized sensors are a precondition for effective sensor fusion. Renesas provides a range of solutions to enable informed decision-making by executing advanced sensor fusion at the endpoint on a centralized processing platform.
Performing late fusion allows for interoperable solutions, while early fusion gives AI rich data for predictions. Leveraging the complementary strengths of different strategies gives us the key advantage. The modern approach involves time and space synchronization of all onboard sensors before feeding synchronized data to the neural network for predictions. This data is then used for AI training or Software-In-the-Loop (SIL) testing of real-time algorithm that receives just a limited piece of information.
Deep learning involves the use of neural networks for the purpose of advanced machine learning techniques that leverage high-performance computational platforms such as Renesas RA MCU and RZ MPU for enhanced training and execution. These deep neural networks consist of many processing layers arranged to learn data representations with varying levels of abstraction from sensor fusion. The more layers in the deep neural network, the more abstract the learned representations become.
Deep learning offers a form of representation learning that aims to express complicated data representations by using other simpler representations. Deep learning techniques can understand features using a composite of several layers, each with unique mathematical transforms, to generate abstract representations that better distinguish high-level features in the data for enhanced separation and understanding of true form.
Multi-stream neural networks are useful in generating predictions from multi-modal data, where each data stream is important to the overall joint inference generated by the network. Multi-stream approaches have been shown successful for multi-modal data fusion, and deep neural networks have been applied successfully in multiple applications such as neural machine translation and time-series sensor data fusion.
This is a tremendous breakthrough that allows deep neural networks to train and deploy on MCU-based Endpoint applications, thereby helping to accelerate industrial adoption. Renesas RA MCU platform and associated Flexible SW Package combined with AI modeling tools offer the ability to apply many of the neural network layers as a multi-layer structure. Typically, more layers lead to more abstract features learned by the network. It has been proven that stacking multiple types of layers in a heterogeneous mixture can outperform a homogeneous mixture of layers. Renesas sensing solutions can be used to compensate for deficiencies in information by utilizing feedback from multiple sensors. The deficiencies associated with individual sensors to calculate types of information can be compensated for by combining the data from multiple sensors.
The flexible Renesas Advanced (RA) Microcontrollers (MCUs) are industry-leading 32-bit MCUs and are a great choice for building smart sensors. With a wide range of Renesas RA family MCUs, you can choose the best one as per your application needs. The Renesas RA MCU platform, combined with strong support & SW ecosystem, will help accelerate the development of Industry 4.0 applications with sensor fusion and deep learning modules.
As part of Renesas' extensive solution and design support, Renesas provides a reference design for a versatile Artificial Internet of Things (AIoT) sensor board solution. It targets applications in industrial predictive maintenance, smart home/IoT appliances with gesture recognition, wearables (activity tracking), and mobile for innovative human-machine interface, or HMI, (FingerSense) solutions. As part of this solution, Renesas can provide a complete range of devices, including an IoT-specified RA microcontroller, air quality sensor, light sensor, temperature and humidity sensor, a 6-axis inertial measurement unit as well as Cellular and Bluetooth communication support.
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With the increasing number of sensors in Industry 4.0 systems comes a growing demand for sensor fusion to make sense of the mountains of data that those sensors produce. Suppliers are responding with integrated sensor fusion devices. For example, an intelligent condition monitoring box is available designed for machine condition monitoring based on fusing data from vibration, sound, temperature, and magnetic field sensors. Additional sensor modalities for monitoring acceleration, rotational speeds, and shock and vibration can be included optionally.
The system implements sensor fusion through AI algorithms to classify abnormal operating conditions with better granularity resulting in high probability decision making. This edge AI architecture can simplify handling the big data produced by sensor fusion, ensuring that only the most relevant data is sent to the edge AI processor or to the cloud for further analysis and possible use in training ML algorithms.
The use of AI-based Deep Learning has several benefits:
Sensor fusion combined with AI deep learning produces a powerful tool to maximize the benefits when using a variety of sensor modalities. AI/ML-based enhanced sensor fusion can be employed at several levels in a system, including at the data level, the fusion level, and the decision level. Basic functions in sensor fusion implementations include smoothing and filtering sensor data and predicting sensor and system states.
- The AI algorithm can employ sensor fusion to utilize the data from one sensor to compensate for weaknesses in the data from other sensors.
- The AI algorithm can classify the relevance of each sensor to specific tasks and minimize or ignore data from sensors determined to be less important.
- Through continuous training at the edge or in the cloud, AI/ML algorithms can learn to identify changes in system behaviour that were previously unrecognized.
- The AI algorithm can predict possible sources of failures, enabling preventative maintenance and improving overall productivity.
At Renesas Electronics, we invite you to take advantage of our high-performance MCUs and A&P portfolio combined with a complete SW platform providing targeted deep learning models and tools to build next generation sensor fusion solutions.
Thankyou littleshort!
and there it is again Sensor Fusion!.And Brainchip have also used the same words in a couple positions- Customer Support Engineer and Solutions Architect roles -
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