D
Deleted member 3781
Guest
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
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
FJ-215
Regular
Not such a mystery.
Labor Day holiday in America. Just the ASX doing nothing until the US markets open again.
Bravo
Meow Meow 🐾
Space Cybersecurity Company Evaluation Report 2025 | Thales, Leonardo, and Lockheed Martin Strengthen Leadership with Advanced Encryption, Mission-Critical Solutions, and Strategic Partnerships
Research and MarketsTue, 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.
D
Deleted member 3781
Guest
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
Top 20
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.
www.mwscas2025.org
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
Regular
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.
D
Deleted member 3781
Guest
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
Regular
I'm not talking OTC.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%.
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
Pretty Pretty Pretty Pretty Good
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...
Happy as Larry
Pom down under
Top 20
Fullmoonfever
Top 20
Good to see independent external projects starting to pop up.
www.elprocus.com
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
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()
from akida import Model
model = Model(“cnn_model.h5”)
model.quantize()
model_to_akida = model.convert()
model_to_akida.save(“face_model.akd”)
face_model = AkidaModel(“face_model.akd”)
audio_model = AkidaModel(“wake_model.akd”)
prediction = audio_model.predict(audio)
return prediction == “unlock_akida”
face = detect_and_crop_face(frame)
prediction = face_model.predict(face)
return prediction == “authorized_user”
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)
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.”)
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
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 AkidaModelface_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 GPIOimport 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 cv2import 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.
D
Deleted member 3781
Guest
Last edited by a moderator:
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.
Yes - many a mickle ...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.
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
Try Implementing the above project and let us know your results..
- 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.
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
Regular
Gaia launches AI phone that runs entirely on-device
apple.news
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...
Tothemoon24
Top 20
Five Industries That Benefit from Spiking Neural Networks
We present five industries where spiking neural networks can make a difference.
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.
..."
www.industrinyheter.se
"...
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...
Frangipani
Top 20
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
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.
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