BRN Discussion Ongoing

Fun fact
Back in the ’90s, Mercedes’ know-how was driven by European AI research, not BrainChip. But the irony is this: the exact problems Dickmanns was facing back then – computing power, energy consumption, real-time processing – are precisely the challenges BrainChip is tackling today. In a way, Mercedes formulated the “need” 30 years ago, and BrainChip is now delivering the technology to meet it.

So… COME ON MERCEDES BRAINCHIP TEAM!! BRING IT OOOON WE HAVE THE SOLUTION


Edit

Who was Dickmanns?

He was a professor at the University of the Bundeswehr Munich, where he led the Institute of Flight Mechanics and System Dynamics during the 1980s and 1990s. Under his leadership, the legendary VaMoRs project was created (short for Experimental Vehicle for Autonomous Mobility and Computer Vision) – a Mercedes van that, as early as 1987, was already driving autonomously using cameras and image processing. Later came the VaMP (a modified Mercedes 500 SEL) as part of the European research program EUREKA-PROMETHEUS.

This is my first time to experience such a low volume stock movement even the news flow is amazing… this is very suspicious IMO… something is brewing

7für7 said:

This is my first time to experience such a low volume stock movement even the news flow is amazing… this is very suspicious IMO… something is brewing

Click to expand...

Not such a mystery.

Labor Day holiday in America. Just the ASX doing nothing until the US markets open again.

Space Cybersecurity Company Evaluation Report 2025 | Thales, Leonardo, and Lockheed Martin Strengthen Leadership with Advanced Encryption, Mission-Critical Solutions, and Strategic Partnerships​

Research and Markets
Tue, September 2, 2025 at 1:07 AM GMT+10 6 min read

Company Logo
The Space Cybersecurity Companies Quadrant offers a crucial analysis of the global Space Cybersecurity market, spotlighting advancements, trends, and key players. This study identifies the Top 24 Space Cybersecurity leaders among over 100 evaluated firms, highlighting their contributions to military, governmental, and commercial sectors. The market's growth is driven by technological innovations and the increasing need for secure satellite communications. Companies like Thales, Leonardo S.p.A., and Lockheed Martin lead in providing solutions to protect vital space infrastructure from sophisticated cyber threats. As space technologies become integral across sectors, cybersecurity remains essential for safeguarding data transmission and ensuring operational continuity.

Dublin, Sept. 01, 2025 (GLOBE NEWSWIRE) -- The "Space Cybersecurity Company Evaluation" report has been added to ResearchAndMarkets.com's offering.

The Space Cybersecurity Companies Quadrant is a comprehensive industry analysis that provides valuable insights into the global market for Space Cybersecurity. This quadrant offers a detailed evaluation of key market players, technological advancements, product innovations, and emerging trends shaping the industry. The analyst's '360 Quadrants' evaluated over 100 companies, of which the Top 24 Space Cybersecurity Companies were categorized and recognized as quadrant leaders.

The space cybersecurity market is fueled by progress in cybersecurity technologies, the growing deployment and use of satellite constellations, and rising demand for encrypted and secure satellite communication networks. Enhanced cybersecurity capabilities allow satellites and ground-based infrastructure to effectively detect, prevent, and recover from sophisticated cyber threats, ensuring operational continuity across military, governmental, and commercial applications. Robust cybersecurity frameworks are essential for safeguarding vital space assets from cyberattacks and for ensuring secure, reliable data transmission and mission operations.

The space cybersecurity market encompasses all technologies and strategies aimed at protecting space-based assets - such as satellites, ground control stations, and other space infrastructure - from cyber threats. It involves the creation and implementation of solutions that safeguard data transmission, communication systems, and mission-critical operations. This market addresses the specific challenges of the space domain, including long distances, extreme environmental conditions, and the high strategic value of space-based systems.
These factors have made cybersecurity an essential aspect of military, government, and commercial space endeavors. As space technologies become increasingly vital to numerous sectors, the market continues to grow, covering a broad range of applications - from secure satellite communications to defending space infrastructure against advanced cyberattacks.

The 360 Quadrant maps the Space Cybersecurity companies based on criteria such as revenue, geographic presence, growth strategies, investments, and sales strategies for the market presence of the Space Cybersecurity quadrant. Key players in the Space Cybersecurity market are actively investing in research and development, forming strategic partnerships, and engaging in collaborative initiatives to drive innovation, expand their global footprint, and maintain a competitive edge in this rapidly evolving market.

Top 3 Companies

1. Thales

Thales is a prominent player in the space cybersecurity landscape. The company has a robust product portfolio focusing on cybersecurity solutions that secure space-based operations. Thales specializes in high-performance technologies such as advanced encryption and secure communication protocols to protect critical space infrastructures. The company's strategic acquisitions and collaborations have enhanced its market share and strengthened its company positioning within the industry.

2. Leonardo S.p.A.

Leonardo S.p.A. is another major entity within the market, known for its comprehensive cybersecurity solutions tailored to the needs of complex, multi-domain, and international programs. The company collaborates with various organizations and implements acquisitions as part of its growth strategy, enhancing its technological resources and competitive positioning.

3. Lockheed Martin Corporation

Lockheed Martin is a leader in the application of space cybersecurity solutions, leveraging its extensive experience in aerospace and defense. The company offers a wide array of services focusing on mission-critical operations. As a central player, Lockheed Martin emphasizes product development and strategic partnerships to maintain its standing as a key market player.

https://finance.yahoo.com/news/space-cybersecurity-company-evaluation-report-150700268.html

FJ-215 said:

Not such a mystery.

Labor Day holiday in America. Just the ASX doing nothing until the US markets open again.

Click to expand...

OTC in the U.S. is just a mirror … the real market for BRN is the ASX. If Wall Street takes a holiday, sure, U.S. investors aren’t active, but that doesn’t set the price in Sydney. The ASX is the primary listing, OTC just follows plus the 19–21 cent range has been locked in for weeks. That’s not about one quiet session, it’s structural. The market is waiting for a real catalyst, and until we see a licensing deal or confirmation from a big OEM, it doesn’t matter whether Wall Street is open or not … the chart won’t budge. IMO

Can't recall if this recent presentation paper at MWSCAS has been posted (probs) but thought put it up as couldn't be bothered doing a search.

From a group out at Dayton Uni and nice results for Akida against Loihi.

7für7 said:

OTC in the U.S. is just a mirror … the real market for BRN is the ASX. If Wall Street takes a holiday, sure, U.S. investors aren’t active, but that doesn’t set the price in Sydney. The ASX is the primary listing, OTC just follows plus the 19–21 cent range has been locked in for weeks. That’s not about one quiet session, it’s structural. The market is waiting for a real catalyst, and until we see a licensing deal or confirmation from a big OEM, it doesn’t matter whether Wall Street is open or not … the chart won’t budge. IMO

Click to expand...

To put it another way, traders in Australia won't place any bets on the ASX platform without knowing what direction the US markets are heading. It's not about BRN, volumes will be lower for the majority on the ASX today.

The old saying is, if Wall Street sneezes, the ASX catches a cold.

Safety first for traders is to react to what the US markets are doing, not guess what they might do.

FJ-215 said:

To put it another way, traders in Australia won't place any bets on the ASX platform without knowing what direction the US markets are heading. It's not about BRN, volumes will be lower for the majority on the ASX today.

The old saying is, if Wall Street sneezes, the ASX catches a cold.

Safety first for traders is to react to what the US markets are doing, not guess what they might do.

Click to expand...

It’s your opinion and that’s fine… but as I see it, the OTC market doesn’t even follow Nasdaq-level rules. It’s thin, it’s secondary, and for BRN it’s nothing more than a mirror. If we were talking about a real Nasdaq-listed BRN share, I’d agree with you 79%.

7für7 said:

It’s your opinion and that’s fine… but as I see it, the OTC market doesn’t even follow Nasdaq-level rules. It’s thin, it’s secondary, and for BRN it’s nothing more than a mirror. If we were talking about a real Nasdaq-listed BRN share, I’d agree with you 79%.

Click to expand...

I'm not talking OTC.

ASX trading volumes




My guess is equity trades for today (02/09/2025) will be lower again.

PS

I might go paint something just to watch it dry.

Jefwilto said:

The following Article is from 30th January 22 its worth revisiting ”Mercedes Benz delivers smarter operation” as the latest EV model is due to be released soon,here in Aust.it will be sunday evening,we may find out more.

https://www.allaboutcircuits.com/news/computing-hardware-underpinning-the-next-wave-of-sony-hyundai-mercedes-evs/

Click to expand...

I like going back to the comments on this post from Mercedes on Linkedin from about 7 months ago....."more to be announced at a later date....stay tuned "......could it be next week we hear more??









Happy as Larry

FJ-215 said:

I might go paint something just to watch it dry.

Click to expand...

Did you listen to this? I missed it ... It is 2 weeks old and quite interesting.

Good to see independent external projects starting to pop up.

AI-Enabled Safe Locker with BrainChip Project​

Build a smart locker system that opens only when both the user’s face and voice command match authorized patterns — all processed using low-power neuromorphic AI. This article provides brief information on an AI-enabled safe locker with BrainChip, features, etc.

AI-Enabled Safe Locker with BrainChip​

Features​

• Face recognition (Vision AI using SNN)
• Wake word detection (Audio SNN)
• Servo-controlled locking mechanism
• Local, low-latency inference (no cloud)
• On-chip learning (for adding new users)

Components Required​

Software Setup​

1. Install Dependencies​

• sudo apt update.
• sudo apt install python3-pip libatlas-base-dev.
• pip3 install akida speechrecognition opencv-python numpy pyserial.

Install Akida SDK:​

Download the BrainChip Akida SDK from the official site. Follow their instructions to install the Python SDK and runtime.

AI-Enabled Safe Locker Project Architecture​

AI-Enabled Safe Locker

Step-by-Step Implementation​

Step 1: Data Collection & Preprocessing​

a. Face Dataset (Images)

Collect 20–30 frontal face images per authorized person using OpenCV:
import cv2
cap = cv2.VideoCapture(0)
for i in range(30):
ret, frame = cap.read()
cv2.imwrite(f”user_face_{i}.jpg”, frame)
cap.release()

b.Voice Samples (Wake Word)​

Record your custom phrase (e.g., “Unlock Akida”) using PyAudio or Audacity.

Step 2 : Train SNN Models with Akida​

a. Convert Face Classifier to SNN​

Use MobileNet or a custom CNN for feature extraction and convert to SNN using akida.Model.
from akida import Model
model = Model(“cnn_model.h5”)
model.quantize()
model_to_akida = model.convert()
model_to_akida.save(“face_model.akd”)

b. Convert Wake Word Classifier​

• Use MFCC preprocessing → CNN → SNN
• Convert the audio classifier to an Akida model using the Akida tools.

Step 3: Load Models and Infer​

from akida import AkidaModel
face_model = AkidaModel(“face_model.akd”)
audio_model = AkidaModel(“wake_model.akd”)

Audio Inference (Wake Word)​

def is_wake_word(audio):
prediction = audio_model.predict(audio)
return prediction == “unlock_akida”

Face Inference (Real-Time Face Match)​

def is_authorized_face(frame):
face = detect_and_crop_face(frame)
prediction = face_model.predict(face)
return prediction == “authorized_user”

Control the Servo Lock​

import RPi.GPIO as GPIO
import time
servo_pin = 17
GPIO.setmode(GPIO.BCM)
GPIO.setup(servo_pin, GPIO.OUT)
servo = GPIO.PWM(servo_pin, 50)
servo.start(0)
def open_locker():
servo.ChangeDutyCycle(7.5) # Adjust as per lock
time.sleep(1)
servo.ChangeDutyCycle(0)
def close_locker():
servo.ChangeDutyCycle(2.5)
time.sleep(1)
servo.ChangeDutyCycle(0)

Step 5: Integration Logic​

import cv2
import speech_recognition as sr
cam = cv2.VideoCapture(0)
while True:
# Wake word check
audio = record_audio_sample()
if not is_wake_word(audio):
continue
# Face check
ret, frame = cam.read()
if is_authorized_face(frame):
open_locker()
print(“Locker opened!”)
else:
print(“Face not recognized.”)

Testing and Validation​

• Add a new user using Brainchip Akida’s on-chip learning API.
• Try unlocking with the wrong voice or face → the system should deny access.
• Log each attempt (success/failure) for analytics.

Try Implementing the above project and let us know your results..

miaeffect said:

Are you 7rangïpanï

Click to expand...

I can not talk about it… but no it’s not me

Fullmoonfever said:

Good to see independent external projects starting to pop up.

AI-Enabled Safe Locker with BrainChip Akida SDK

This Article Explains how to build a Smart AI-enabled Safe Locker with BrainChip Akida SDK, Features, Architecture, and Implementation.

www.elprocus.com

AI-Enabled Safe Locker with BrainChip Project​

Build a smart locker system that opens only when both the user’s face and voice command match authorized patterns — all processed using low-power neuromorphic AI. This article provides brief information on an AI-enabled safe locker with BrainChip, features, etc.

AI-Enabled Safe Locker with BrainChip​

Features​

• Face recognition (Vision AI using SNN)
• Wake word detection (Audio SNN)
• Servo-controlled locking mechanism
• Local, low-latency inference (no cloud)
• On-chip learning (for adding new users)

Components Required​

Component	Description
BrainChip Akida USB Dev Kit	Neuromorphic processor (main AI engine)
USB Microphone	Audio input (wake-word detection)
USB Camera	Visual input (face recognition)
Servo Motor (SG90/996R)	Physical lock control
Raspberry Pi 4 / Jetson Nano	Host controller with Linux (Ubuntu 20.04)
Breadboard + jumper wires	To connect the servo motor
Power Source	USB power bank or adapter

Software Setup​

1. Install Dependencies​

• sudo apt update.
• sudo apt install python3-pip libatlas-base-dev.
• pip3 install akida speechrecognition opencv-python numpy pyserial.

Install Akida SDK:​

Download the BrainChip Akida SDK from the official site. Follow their instructions to install the Python SDK and runtime.

AI-Enabled Safe Locker Project Architecture​

AI-Enabled Safe Locker

Step-by-Step Implementation​

Step 1: Data Collection & Preprocessing​

a. Face Dataset (Images)

Collect 20–30 frontal face images per authorized person using OpenCV:
import cv2
cap = cv2.VideoCapture(0)
for i in range(30):
ret, frame = cap.read()
cv2.imwrite(f”user_face_{i}.jpg”, frame)
cap.release()

b.Voice Samples (Wake Word)​

Record your custom phrase (e.g., “Unlock Akida”) using PyAudio or Audacity.

Step 2 : Train SNN Models with Akida​

a. Convert Face Classifier to SNN​

Use MobileNet or a custom CNN for feature extraction and convert to SNN using akida.Model.
from akida import Model
model = Model(“cnn_model.h5”)
model.quantize()
model_to_akida = model.convert()
model_to_akida.save(“face_model.akd”)

b. Convert Wake Word Classifier​

• Use MFCC preprocessing → CNN → SNN
• Convert the audio classifier to an Akida model using the Akida tools.

Step 3: Load Models and Infer​

from akida import AkidaModel
face_model = AkidaModel(“face_model.akd”)
audio_model = AkidaModel(“wake_model.akd”)

Audio Inference (Wake Word)​

def is_wake_word(audio):
prediction = audio_model.predict(audio)
return prediction == “unlock_akida”

Face Inference (Real-Time Face Match)​

def is_authorized_face(frame):
face = detect_and_crop_face(frame)
prediction = face_model.predict(face)
return prediction == “authorized_user”

Control the Servo Lock​

import RPi.GPIO as GPIO
import time
servo_pin = 17
GPIO.setmode(GPIO.BCM)
GPIO.setup(servo_pin, GPIO.OUT)
servo = GPIO.PWM(servo_pin, 50)
servo.start(0)
def open_locker():
servo.ChangeDutyCycle(7.5) # Adjust as per lock
time.sleep(1)
servo.ChangeDutyCycle(0)
def close_locker():
servo.ChangeDutyCycle(2.5)
time.sleep(1)
servo.ChangeDutyCycle(0)

Step 5: Integration Logic​

import cv2
import speech_recognition as sr
cam = cv2.VideoCapture(0)
while True:
# Wake word check
audio = record_audio_sample()
if not is_wake_word(audio):
continue
# Face check
ret, frame = cam.read()
if is_authorized_face(frame):
open_locker()
print(“Locker opened!”)
else:
print(“Face not recognized.”)

Testing and Validation​

• Add a new user using Brainchip Akida’s on-chip learning API.
• Try unlocking with the wrong voice or face → the system should deny access.
• Log each attempt (success/failure) for analytics.

Try Implementing the above project and let us know your results..

Click to expand...

Yes - many a mickle ...

I missed the Akida USB Development Kit:

The edge Box does have USB:

https://shop.brainchipinc.com/products/akida™-edge-ai-box

• USB 3.0 Port Type A Port
• USB 2.0 Port Micro B Port (Flashing and Debug port)

so in what format is the Akida chip packaged?


Well apparently you can get an M.2 USB adapter:

https://www.bing.com/search?qs=CT&p...DzSAQg5OTA5ajBqMagCALACAA&FORM=ANNTA1&PC=DCTS


Who knew?

Gaia launches AI phone that runs entirely on-device

Just a snippet from Sweden (my usual TLG walk)

"...
Saab develops new unmanned underwater vehicle for FMV
...
The aim of the FMV-led project is to develop a concept for a larger unmanned underwater vehicle, a so-called Large Uncrewed Undersea Vehicle (LUUV). The company is the main supplier for the LUUV and will be responsible for the design, construction and testing of the vehicle.

Part of the ship system will be Saab's autonomous control system "Autonomous Ocean Core", which gives ships autonomous capabilities on and under the water surface.
..."

Frangipani said:

Any Akidaholics meeting the below eligibility criteria interested in NASA’s Beyond the Algorithm Challenge that was launched today?

View attachment 79697

NASA Beyond the Algorithm Challenge

NASA Beyond the Algorithm Challenge: Novel Computing Architectures for Flood Analysis The NASA Earth Science Technology Office (ESTO) seeks solutions to complex Earth Science problems using transformative or unconventional computing technologies such as quantum computing, quantum machine...

www.nasa-beyond-challenge.org

View attachment 79698 View attachment 79699 View attachment 79706 View attachment 79701

Click to expand...

One of the nine finalist teams of NASA’s Beyond the Algorithm: Novel Computing Architectures for Flood Analysis Challenge, consisting of four Columbia University Computer Science students, submitted a solution they named NEO-FLOOD:

“This paper introduces NEO-FLOOD (Neuromorphic Onboard Flood-mapping), a satellite architecture that eliminates this latency by deploying autonomous AI directly in orbit. NEO-FLOOD integrates space-validated neuromorphic processors (Intel Loihi 2, BrainChip Akida) consuming just 2-5W with our novel Spike2Former-Flood algorithm-a spiking neural network optimized for real-time optical SAR fusion onboard small satellites.”