BRN Discussion Ongoing

[FJ-215]

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

Not such a mystery.

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

 

[Bravo]

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

 

[7für7]

Not such a mystery.

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

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

 

[Fullmoonfever]

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.

MWSCAS 2025

www.mwscas2025.org

13:30-13:45, Paper TueLecB04.1
Ultra-Efficient Network Intrusion Detection Implemented on Spiking Neural Network Hardware (I)
Islam, Rashedul	University of Dayton
Yakopcic, Chris	University of Dayton
Rahman, Nayim	University of Dayton
Alam, Shahanur	University of Dayton
Taha, Tarek	University of Dayton
Keywords: Neuromorphic System Algorithms and Applications, Machine Learning at the Edge, Other Neural and Neuromorphic Circuits and Systems Topics Abstract: Network intrusion detection is crucial for securing data transmission against cyber threats. Traditional anomaly detection systems use computationally intensive models, with CPUs and GPUs consuming excessive power during training and testing. Such systems are impractical for battery-operated devices and IoT sensors, which require low-power solutions. As energy efficiency becomes a key concern, analyzing network intrusion datasets on low-power hardware is vital. This paper implements a low-power anomaly detection system on Intel’s Loihi and Brainchip’s Akida neuromorphic processors. The model was trained on a CPU, with weights deployed on the processors. Three experiments—binary classification, attack class classification, and attack type classification—are conducted. We achieved approximately 98.1% accuracy on Akida and 94% on Loihi in all experiments while consuming just 3 to 6 microjoules per inference. Also, a comparative analysis with the Raspberry Pi 3 and Asus Tinker Board is performed. To the best of our knowledge, this is the first performance analysis of low power anomaly detection based on spiking neural network hardware.

 

[FJ-215]

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

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.

 

[7für7]

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.

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%.

 

[FJ-215]

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%.

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.

 

[TheDrooben]

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/

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??

Neuromorphic Computing | Mercedes-Benz AG | 21 comments

The intelligent vehicle functionalities of the future call for pioneering new algorithms and hardware. That’s why Mercedes-Benz is researching artificial neural networks that could radically increase the speed and energy efficiency of complex AI computations. This could revolutionise...

www.linkedin.com

Happy as Larry

 

[Pom down under]

I might go paint something just to watch it dry.

 

[CHIPS]

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

 

[Fullmoonfever]

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..

 

[7für7]

Are you 7rangïpanï

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

 

[IloveLamp]

https://buff.ly/fnXr914

How To: BeEmotion.ai builds AeroMind - Hunches to Evidence: Objective Emotional Analytics in Flight Training

BeEmotion.ai’s AeroMind tool, tested at Simnest Pilot Academy, delivered objective emotional analytics to enhance pilot training effectiveness.

www.halldale.com

 

[Diogenese]

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..

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?

 

[Getupthere]

Gaia launches AI phone that runs entirely on-device

Gaia launches AI phone that runs entirely on-device — Cointelegraph

Controlled initial release demonstrates breakthrough edge-first AI architecture on premium hardwareSAN FRANCISCO, CA, September 2, 2025 — Gaia today announced the launch of the Gaia AI Phone, the world's first smartphone designed for complete AI sovereignty.Built on Galaxy S25 Edge hardware and...

apple.news

 

[Tothemoon24]

Five Industries That Benefit from Spiking Neural Networks

We present five industries where spiking neural networks can make a difference.

www.iis.fraunhofer.de

 

[cosors]

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.
..."

Saab utvecklar ny obemannad undervattensfarkost åt FMV

Saab har tecknat kontrakt med Försvarets materielverk (FMV) för ett utvecklingsprojekt för en större undervattensfarkost. Ordervärdet uppgår till SEK 60 miljoner. Målet med projektet, som leds av FMV, är att ta fram ett koncept för en större obemannad undervattensfarkost, en så kallad Large...

www.industrinyheter.se

 

[Frangipani]

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

NASA-Beyond-Challenge.org

www.nasa-beyond-challenge.org

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.”

NASA-Beyond-Challenge.org

www.nasa-beyond-challenge.org

 

[MegaportX]

Air Force Research Lab, Raytheon and BrainChip to Bring AIoT to Radar

BrainChip is partnering with Raytheon to service a contract for $1.8M from the Air Force Research Laboratory on neuromorphic radar signaling processing.

www.iotevolutionworld.com

 

[MegaportX]

Air Force Research Lab, Raytheon and BrainChip to Bring AIoT to Radar

BrainChip is partnering with Raytheon to service a contract for $1.8M from the Air Force Research Laboratory on neuromorphic radar signaling processing.

www.iotevolutionworld.com

The Small Business Innovation Research (SBIR) contract is a development effort expansion after a successful demonstration of radar processing algorithms running on BrainChip’s Akida™ commercial off-the-shelf (COTS) neuromorphic hardware. The signal processing algorithms encompass the processing of RF signals to decode communication and radar waveforms from raw in-phase and quadrature data.(Source: BUSINESS WIRE)