This guy gets it: Byron Callaghan
In the boundless expanse of technological galaxies, there exists a singular constellation that outshines all - the Akida 2.0 system. It is not just a beacon of brilliance; it's a veritable black hole, drawing in all realms of possibility and spewing out pure innovation. In the theatre of Edge AI, where countless players jostle for the limelight, Akida 2.0 doesn't just steal the show; it is the show.
Hi Facty,If you add the following known facts together in my opinion you get Microchip already working with Brainchip:
1. Brainchip partnered with SiFive with announced compatibility with the x280 Intelligence Series,
2. Brainchip partnered with NASA,
3. Brainchip partnered with GlobalFoundries, and
4. Brainchip taping out AKD1500 minus the ARM Cortex 4, plus
5. The following article:
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January 30, 2023
NASA Recruits Microchip, SiFive, and RISC-V to Develop 12-Core Processor SoC for Autonomous Space Missions
by Steven Leibson
NASA’s JPL (Jet Propulsion Lab) has selected Microchip to design and manufacture the multi-core High Performance Spaceflight Computer (HPSC) microprocessor SoC based on eight RISC-V X280 cores from SiFive with vector-processing instruction extensions organized into two clusters, with four additional RISC-V cores added for general-purpose computing. The project’s operational goal is to develop “flight computing technology that will provide at least 100 times the computational capacity compared to current spaceflight computers.” During a talk at the recent RISC-V Summit, Pete Fiacco, a member of the HPSC Leadership Team and JPL Consultant, explained the overall HPSC program goals.
Despite the name, the HPSC is not strictly a processor SoC for space. It’s designed to be a reliable computer for a variety of applications on the Earth – such as defense, commercial aviation, industrial robotics, and medical equipment – as well as being a good candidate for use in government and commercial spacecraft. Three characteristics that the HPSC needs beyond computing capability are fault tolerance, radiation tolerance, and overall platform security. The project will result in the development of the HPSC chip, boards, a software stack, and reference designs with initial availability in 2024 and space-qualified hardware available in 2025. Fiacco said that everything NASA JPL does in the future will be based on the HPSC.
NASA JPL set the goals for the HPSC based on its mission requirements to put autonomy into future spacecraft. Simply put, the tasks associated with autonomy are sensing, perceiving, deciding, and actuating. Sensing involves remote imaging using multi-spectral sensors and image processing. Perception instills meaning into the sensed data using additional image processing. Decision making includes mission planning that incorporates the vehicle’s current and future orientation. Actuation involves orbital and surface maneuvering and experiment activation and management.
Correlating these tasks with NASA’s overall objectives for its missions, Fiacco explained that the HPSC is designed to allow space-bound equipment to go, land, live, and explore extraterrestrial environments. Spacecraft also need to report back to earth, which is why Fiacco also included communications in all four major tasks. All of this will require a huge leap in computing power. Simulations suggest that the HPSC increases computing performance by 1000X compared to the processors currently flying in space, and Fiacco expects that number to improve with further optimization of the HPSC’s software stack.
![]()
It’s hard to describe how much of an upgrade the HPSC represents for NASA JPL’s computing platform without contrasting the new machine with computers currently operating off planet. For example, the essentially similar, nuclear-powered Curiosity and Perseverance rovers currently trundling around Mars with semi-autonomy are based on RAD750 microprocessors from BAE Systems. (See “Baby You Can Drive My Rover.”) The RAD750 employs the 32-bit PowerPC 750 architecture and is manufactured with a radiation-tolerant semiconductor process. This chip has a maximum clock rate of 200 MHz and represents the best of computer architecture circa 2001. Reportedly, more than 150 RAD750 processors have been launched into space. Remember, NASA likes to fly hardware that’s flown before. One of the latest space artifacts to carry a RAD750 into space is the James Webb Space Telescope (JWST), which is now imaging the universe in the infrared spectrum and is collecting massive amounts of new astronomical data while sitting in a Lagrange orbit one million miles from Earth. (That’s four times greater than the moon’s orbit.) The JWST’s RAD750 processor lopes along at 118 MHz.
Our other great space observatory, the solar-powered Hubble Space Telescope (HST), sports an even older processor. The HST payload computer is an 18-bit NASA Standard Spacecraft Computer-1 (NSSC-1) system built in the 1980s but designed even earlier. This payload computer controls and coordinates data streams from the HST’s various scientific instruments and monitors their condition. (See “Losing Hubble – Saving Hubble.”)
The original NSSC-1 computer was developed by the NASA Goddard Space Flight Center and Westinghouse Electric in the early 1970s. The design is so old that it’s not based on a microprocessor. The initial version of this computer incorporated 1700 DTL flat-pack ICs from Fairchild Semiconductor and used magnetic core memory. Long before the HST launched in 1990, the NSSC-1 processor design was “upgraded” to fit into some very early MSI TTL gate arrays, each incorporating approximately 130 gates of logic.
I’m not an expert in space-based computing, so I asked an expert for his opinion. The person I know who is most versed in space-based computing with microprocessors and FPGAs is my friend Adam Taylor, the founder and president of Adiuvo Engineering in the UK. I asked Taylor what he thought of the HPSC and he wrote:
“The HPSC is actually quite exciting for me. We do a lot in space and computation is a challenge. Many of the current computing platforms are based on older architectures like the SPARC (LEON series) or Power PC (RAD750 / RAD5545). Not only do these [processors] have less computing power, they also have ecosystems which are limited. Limited ecosystems mean longer development times (less reuse, more “fighting” with the tools as they are generally less polished) and they also limit attraction of new talent, people who want to work with modern frameworks, processors, and tools. This also limits the pool of experienced talent (which is an increasing issue like it is in many industries).
“The creation of a high-performance multicore processor based around RISC-V will open up a wide ecosystem of tools and frameworks while also providing attraction to new talent and widening the pool of experienced talent. The processors themselves look very interesting as they are designed with high performance in mind, so they have SIMD / Vector processing and AI (urgh such an overstated buzz word). It also appears they have considered power management well, which is critical for different applications, especially in space.
“It is interesting that as an FPGA design company (primarily), we have designed in several MicroChip SAM71 RT and RH [radiation tolerant and radiation hardened] microcontrollers recently, which really provide some great capabilities where processing demands are low. I see HPSC as being very complementary to this range of devices, leaving the ultrahigh performance / very hard real time applications to be implemented in FPGA. Ultimately HPSC gives engineers another tool to choose from, and it is designed to prevent the all-too-common, start-from-scratch approach, which engineers love. Sadly, that approach always increases costs and technical risk on these projects, and we have enough of that already.”
One final note: During my research for this article, I discovered that NASA’s HPSC has not always been based on the RISC-V architecture. A presentation made at the Radiation Hardened Electronics Technology (RHET) Conference in 2018 by Wesley Powell, Assistant Chief for Technology at NASA Goddard Space Flight Center’s Electrical Engineering Division, includes a block diagram of the HPSC, which shows an earlier conceptual design based on eight Arm Cortex-A53 microprocessor cores with NEON SIMD vector engines and floating-point units. Powell continues to be the Principal Technologist on the HPSC program. At some point in the HPSC’s evolution over the past four years, at least by late 2020 when NASA published a Small Business Innovation Research (SBIR) project Phase I solicitation for the HPSC, the Arm processor cores had been replaced by a requirement for RISC-V processor cores. That change was formally cast in stone last September with the announcement of the project awards to Microchip and SiFive. A sign of the times, perhaps?
My opinion only DYOR
FF
AKIDA BALLISTA
Hi Facty,I do not recall this being posted before and the date of release suggests not so guess which space program is using a COTS anomaly detection SNN on space missions:
Small Business Innovation Research/Small Business Tech Transfer
Neuromorphic Spacecraft Fault Monitor, Phase II
Completed Technology Project (2020 - 2022)
Project Introduction
The goal of this work is to develop a low power machine learning anomaly detector. The low power comes from the type of machine learning (Spiking Neural Network (SNN)) and the hardware the neuromorphic anomaly
detector runs on. The ability to detect and react to anomalies in sensor readings on board resource constrained spacecraft is essential, now more than ever, as enormous satellite constellations are launched and humans push out again beyond low Earth orbit to the Moon and beyond. Spacecraft are autonomous systems operating in dynamic environments. When monitored parameters exceed limits or watchdog timers are not reset, spacecraft can automatically enter a 'safe' mode where primary functionality is reduced or stopped completely. During safe mode the primary mission is put on hold while teams on the ground examine dozens to hundreds of parameters and compare them to archived historical data and the spacecraft design to determine the root cause and what corrective action to take. This is a difficult and time consuming task for humans, but can be accomplished faster, in real- time, by machine learning. As humans travel away from Earth, light travel time delays increase, lengthening the time it takes for ground crews to respond to a safe mode event. The few astronauts onboard will have a hard time replacing the brain power and experience of a team of experts on the ground. Therefore, a new approach is needed that augments existing capabilities to help the astronauts in key decision moments. We provide a new machine learning approach that recognizes nominal and faulty behavior, by learning during integration, test, and on-orbit checkout. This knowledge is stored and used for anomaly detection in a low power neuromorphic chip and continuously updated through regular operations. Anomalies are detected and context is provided in real-time, enabling both astronauts onboard, and ground crews on Earth, to take action and avoid potential faults or safe mode events.
Anticipated Benefits
The software developed in Phase II can potentially be used by NASA for anomaly detection onboard the ISS, the planned Lunar Gateway, and future missions to Mars. The NSFM software can also be used by ground crews to augment their ability to monitor spacecraft and astronaut health telemetry once it reaches the ground. The NSFM software can furthermore be used during integration and test to better inform test operators of the functionality of the system during tests in real time.
The software developed in Phase II can potentially be used for anomaly detection onboard any of the new large constellations planned by private companies. It can also be applied to crewed space missions, deep space probes, UUVs, UAVs, and many industrial applications on Earth. The NSFM software developed in Phase II can also be used during Integration and Test of any commercial satellite.
![]()
NASA TechPort
NASA's Technology Portfolio Management System (TechPort) is a single, comprehensive resource for locating detailed information about NASA-funded technologies. Those technologies cover a broad range of areas, such as propulsion, nanotechnology, robotics, and human health. You can find useful...techport.nasa.gov
My opinion only DYOR
FF
AKIDA BALLISTA
That's for sure! No doubt Doodle Labs would want to be part of that action too.Well Ok I guess.
Just make sure the Third Eye doesn't enter the Lunar Gateway!
Not that there's anything wrong with that!![]()
That's for sure! No doubt Doodle Labs would want to be part of that action too.![]()
Well Ok I guess.
Just make sure the Third Eye doesn't enter the Lunar Gateway!
Not that there's anything wrong with that!![]()
Balance of the paper on Wallet protection:Hi All
Sorry cannot provide a link but for those unlike Pom who should just read the Abstract and Conclusion the full paper is probably interesting to a little exciting to think what AKIDA with a little Edge Impulse can do. Regards Fact Finder:
Safeguarding Public Spaces: Unveiling Wallet
Snatching through Edge Impulse Technology
Ujjwal Reddy K S
School of Computer Science and Engineering
VIT-AP University
Andhra Pradesh, India
ujjwal.20bci7203@vitap.ac.in
* Kuppusamy P
School of Computer Science and Engineering
VIT-AP University
Andhra Pradesh, India
drpkscse@gmail.com
Abstract—In contemporary society, public space security and
safety are of utmost significance. The theft of wallets, a frequent
type of street crime, puts people’s personal items at risk and
may result in financial loss and psychological misery. By utilizing
Edge Impulse technology to identify and expose wallet-snatching
incidents in public areas, this article offers a fresh solution to
the problem. To develop a reliable and effective wallet-snatching
detection solution, the suggested system blends machine learning
techniques with the strength of the Edge Impulse platform. This
study used Spiking Neural Networks (SNNs) which are inspired
by the biological neural networks found in the brain. Edge
Impulse offers a thorough framework for gathering, prepro-
cessing, and examining data, enabling the creation of extremely
precise machine learning models. The system can accurately
discriminate between legitimate interactions with wallets and
suspicious snatching attempts by training these models on a
dataset that includes both normal and snatching events. The
efficiency of the suggested method is 95% demonstrated by exper-
imental findings, which show high accuracy and low false positive
rates in recognizing wallet snatching instances. Increasing public
safety, giving people a sense of security in public places, and
discouraging prospective wallet-snatching criminals are all goals
of this research.
Index Terms—wallet snatching, public spaces, Edge Impulse,
sensor devices, machine learning, real-time monitoring, security,
privacy
I. INTRODUCTION
Public places are critical for societal interactions and com-
munity participation. They are places of recreation, social-
ization, and public meetings. However, these areas are not
immune to criminal activity, and one typical threat is wallet
snatching. Wallet snatching is the act of forcibly removing
someone’s wallet, which frequently results in financial losses,
identity theft, and psychological suffering for the victims.
Safeguarding public places and combating wallet snatching
necessitate new measures that make use of developing technol-
ogy. In this context, this introduction investigates the potential
of Edge Impulse technology in uncovering and preventing
wallet-snatching events [1].
Wallet-snatching instances can occur in a variety of public
places, including parks, retail malls, congested roadways, and
public transit. These attacks are frequently characterized by
their speed and stealth, giving victims little time to react or
seek aid. Traditional surveillance systems, such as Closed Cir-
cuit Television (CCTV) cameras, have difficulties in efficiently
identifying and preventing wallet-snatching occurrences owing
to variables such as limited coverage, video quality, and human
error in monitoring [2]. As a result, more advanced technical
solutions that can proactively identify and respond to such
situations are required.
Edge Impulse is a new technology that integrates machine
learning algorithms, sensor data, and embedded systems to
generate smart and efficient solutions [3]. It allows machine
learning models to be deployed directly on edge devices such
as smartphones, wearable devices, or Internet of Things (IoT)
devices, reducing the requirement for ongoing access to a
distant server. Edge Impulse is an appropriate solution for
tackling the problem of wallet snatching in public places
because of its capabilities.
Fig. 1. Edge Impulse Architecture.
It is essential to look into the vast amount of research
and studies done in this specific subject in order to prop-
erly understand the powers of Edge Impulse technology in
revealing instances of wallet theft. Numerous studies have
been conducted to examine the use of computer vision and
machine learning approaches in detecting and preventing crim-
inal activity in public spaces. The topic of utilizing cutting-
edge technologies to improve public safety and security has
been explored in a number of academic studies. This research
has shown how machine learning algorithms may be used
to examine video footage and identify patterns of suspicious
2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) | 979-8-3503-0570-8/23/$31.00 ©2023 IEEE | DOI: 10.1109/RMKMATE59243.2023.10369744
behavior that could be related to wallet-snatching instances.
These cutting-edge technologies may recognize people who
display suspicious motions or participate in potentially illegal
behaviors by utilizing computer vision techniques, such as
object identification and tracking, enabling proactive interven-
tion. Edge Impulse technology integration has a lot of potential
in this area. It may be trained to recognize certain traits
and attributes linked to wallet snatching through its strong
machine learning skills, improving its capacity to precisely
detect such instances in real-time. Edge Impulse can analyze
trends, spot abnormalities, and notify authorities or security
people to take immediate action by utilizing the enormous
volumes of data gathered from several sources, including
surveillance cameras and sensor networks. The possibility of
predictive analytics to foresee wallet theft episodes based on
previous data and behavioral trends has also been investigated
in this field of research. Machine learning algorithms are
able to recognize high-risk locations and deploy resources
appropriately by examining elements like the time of day,
location, and population density. With the use of this proactive
strategy, law enforcement organizations may deploy people
efficiently and put out preventative measures, which serve to
dissuade prospective criminal activity.
Based on these findings, the use of Edge Impulse technology
in the context of wallet snatching can improve the efficiency of
crime prevention systems [4]. The reaction time may be greatly
decreased by implementing machine learning models directly
on edge devices, enabling real-time detection and fast inter-
vention. Furthermore, Edge Impulse technology can record
and analyze essential data for recognizing wallet-snatching
instances using numerous sensors included in smartphones
or wearable devices, such as accelerometers, gyroscopes, and
cameras.
For example, accelerometer data may be utilized to detect
abrupt movements or violent behaviors that are suggestive of
wallet-snatching attempts [5]. The gyroscope data can offer
information regarding the direction and speed of the grab,
assisting in the tracking of the culprit. Additionally, camera
footage may be analyzed using computer vision algorithms to
detect suspicious activity, identify possible thieves, or collect
photographs for later identification and proof.
The increasing availability of data can further benefit the use
of Edge Impulse technology in wallet snatching prevention.
With the growth of smartphones and wearable devices, there is
an abundance of sensor data that can be gathered and analyzed
in order to create strong machine learning models. This data
may be used to train algorithms to recognize certain patterns or
abnormalities related with wallet-snatching instances, boosting
the system’s accuracy and dependability.
Furthermore, integrating Edge Impulse technology with
current surveillance systems can improve their capabilities.
A complete and intelligent system may be constructed by
integrating the strengths of both technologies, such as the
extensive coverage of CCTV cameras and the real-time anal-
ysis of edge devices. This integrated strategy would allow for
proactive identification and rapid reaction to wallet-snatching
occurrences, minimizing the impact on victims and discour-
aging future perpetrators.
Finally, wallet snatching in public places is a serious danger
to public safety and individual well-being [6]. Innovative
techniques are necessary to overcome this difficulty, and Edge
Impulse technology has intriguing possibilities. Edge Impulse
provides real-time detection and fast action in wallet snatching
occurrences by employing machine learning models installed
directly on edge devices. It captures and analyses pertinent
information using multiple sensors and data sources accessible
on smartphones and wearable devices. Integrating Edge Im-
pulse technology with current monitoring systems can improve
the efficacy of crime prevention efforts. These developments
can help to protect public places and expose wallet snatching,
resulting in safer and more secure communities.
A. Motivation
This study aims to use the potential of Edge Impulse
technology to make public areas safer for citizens by efficiently
fighting wallet-snatching events. We hope that by finding a
solution, we can contribute to the wider objective of protecting
public places and improving the general quality of life in our
communities.
B. Contribution
• The study presents an innovative use of Edge Impulse
technology for improving public safety.
• This study proposed SNNs.
• The created machine learning model detects wallet-
snatching episodes in public places with high accuracy
and efficiency.
II. RELATED WORK
The study proposes a framework comprised of two major
components: a behavior model and a detection technique [7].
The behavior model captures the software’s valid behavior
by monitoring its execution and gathering information about
its interactions with the system and the user. The detection
method compares the observed behavior of a software instance
to the behavior model to discover any differences that signal
probable theft. The authors conducted trials with real-world
software applications to assess the efficacy of their technique.
They tested their system’s detection accuracy, false positive
rate, and false negative rate. The results indicated promising
performance in detecting software theft occurrences properly
while keeping false alarms to an acceptable level. The study
presents an overview of the many processes involved in
the identification of anomalous behavior, including human
detection, feature extraction, and classification [8]. It em-
phasizes the importance of Convolutional Neural Networks
(CNNs) in dealing with the complexities of visual input and
extracting important characteristics for behavioral research.
Furthermore, the authors explore several CNN architectures
used for anomalous behavior identification, such as AlexNet,
Visual Geometry Group Network (VGGNet), and Residual
Neural Network (ResNet) [9]–[11]. They also investigate the
use of various datasets and assessment criteria in evaluating
the performance of these models. The survey includes a wide
range of applications where aberrant behavior identification is
critical, such as crowd monitoring, public space surveillance,
and anomaly detection in industrial settings [8]. The authors
assess the merits and limits of existing approaches, as well as
new research avenues and opportunities for development.
The suggested technique consists of two major steps: feature
engineering-based preprocessing and energy theft detection
using gradient boosting [12]. Various characteristics from
the electricity usage data are extracted during the feature
engineering-based preprocessing stage. These traits are in-
tended to detect trends and behaviors that may suggest possible
energy theft. After preprocessing the data, the authors use
gradient boosting, a machine learning approach, to detect
energy theft. Gradient boosting is an ensemble learning ap-
proach that combines numerous weak predictive models to
build a strong predictive model. It constructs decision trees in
a sequential manner, with each succeeding tree learning from
the mistakes of the preceding ones. The suggested strategy
is evaluated by the authors using real-world power use data.
They compare their approach’s performance to that of other
current approaches for detecting energy theft, such as decision
trees, random forests, and support vector machines [13]–
[15]. Accuracy, precision, recall, and F1-score are among the
assessment criteria employed. The paper’s results show that
the suggested technique beats the other methods in terms
of energy theft detection accuracy. The authors credit this
enhanced performance to the preprocessing stage based on
feature engineering and the efficiency of gradient boosting in
identifying complicated connections in the data.
The study is primarily concerned with analyzing power
use trends and discovering abnormalities that might suggest
theft [16]. The system learns to discern between regular use
patterns and suspicious actions that signal theft by training the
decision tree and Support Vector Machine (SVM) models on
historical data. The attributes chosen are used to categorize
incidents as either theft or non-theft. The suggested technique
is tested using real-world smart grid data. The findings show
that the decision tree and SVM-based methods can identify
theft in smart grids with high accuracy and low false positive
rates. The study focuses on identifying instances of theft by
collecting temporal relationships in energy use data [17]. The
system learns to recognize regular consumption patterns and
detect variations that suggest theft by training the CNN-Long
Short-Term Memory (LSTM) model on historical data. The
suggested method is tested using real-world smart grid data,
and the findings show that it is successful at identifying power
theft [18]. The CNN-LSTM-based technique beats existing
approaches in terms of detection accuracy. Both papers address
the important issue of theft detection in smart grid systems,
but they employ different techniques [16], [17]. The first
paper utilizes decision trees and SVM for feature selection
and classification, while the second paper employs CNNs and
LSTM networks for feature extraction and anomaly detection.
These approaches contribute to the development of effective
methods for enhancing the security and reliability of smart
grid systems.
The study most likely proposes an algorithm or strategy
that employs computer vision and motion analysis techniques
to detect suspicious or illegal behavior in video footage [19].
The suggested approach most likely seeks to discriminate
between routine activities and probable criminal behaviors
by analyzing the motion patterns of humans or items in a
setting [20]. It is difficult to offer a full description of the
methodology, results, or conclusions of the study based on
the information supplied. However, it may be deduced that the
authors suggest a way for developing an automated criminal
detection system that combines motion analysis with intel-
ligent information-concealing strategies. The authors suggest
a chain-snatching detection safety system that detects and
prevents chain-snatching accidents by utilizing sophisticated
technologies [21]. However, without complete access to the
article, it is difficult to offer extensive information regarding
the system’s methodology, components, or methods used. To
detect rapid and strong movements associated with chain
snatching attempts, the system is likely to include various
sensors such as motion sensors or accelerometers. Image
processing methods may also be used to identify possible
chain snatchers or to collect photographs of the occurrence
for additional investigation or proof [22]. In addition, when a
chain-snatching incident is identified, the system might contain
an alarm or notification mechanism that warns surrounding
persons or authorities in real time. This quick reaction can
dissuade offenders while also providing urgent support to
victims. The report will most likely offer experimental findings
and assessments to assess the suggested system’s usefulness
in effectively identifying chain-snatching occurrences while
minimizing false alarms [21]. It may also address the system’s
weaknesses, prospective areas for development, and future
research directions in this subject.
The document most likely presents a proposed approach or
algorithm for detecting snatch stealing [23]. It may describe
the selection and extraction of low-level video data elements
such as motion analysis, object tracking, or other relevant
information that can be utilized to detect snatch-snatching
instances. The authors may have also investigated various
strategies for identifying and discriminating between regular
and snatch-stealing incidents. Given that the paper was deliv-
ered in 2010, it is crucial to highlight that the material provided
in it is based on research and technology breakthroughs
accessible at the time [23]. It’s probable that recent advances in
computer vision, machine learning, and surveillance systems
have pushed the area of snatch-steal detection even further.
The authors present an action attribute modeling technique
for automatically recognizing snatch-stealing incidents [24].
To identify possible snatch-steal instances, the technique en-
tails analyzing the activities and characteristics displayed by
persons in surveillance recordings. The idea is to create a
system that can send real-time alerts to security workers or
law enforcement organizations in order to assist avoid such
crimes or respond promptly when they occur. The document
most likely outlines the methods and algorithms used to
detect snatch-stealing occurrences, including the extraction of
key characteristics, training a model using labeled data, and
evaluating the suggested solution. It might also go through
the datasets used for training and testing, as well as the
performance measures used to assess the system’s efficacy.
Because the study was published in 2018, it is crucial to
highlight that advances in the area may have occurred since
then, and other methodologies or approaches may have been
created [24].
The study describes the integrated framework’s many com-
ponents, such as data collecting, preprocessing, feature extrac-
tion, and crime detection [25]. In addition, the authors give
experimental results based on real-world data to illustrate the
efficacy of their technique. The results show that the suggested
framework may detect tiny crimes in a fast and accurate
manner, allowing law enforcement authorities to respond more
efficiently. The research focuses on the use of deep learning
algorithms to detect trustworthy human suspicious conduct
in surveillance films [26]. By using the capabilities of deep
learning algorithms, scientists hope to increase the accuracy
and reliability of suspicious behavior detection. The study
provides a full description of the suggested technique, which
includes surveillance video preprocessing, feature extraction
with CNNs, and categorization of suspicious actions with
Recurrent Neural Networks (RNNs) [27], [28]. The authors
also explore the difficulties connected with detecting sus-
picious behavior and provide strategies to overcome them.
The research focuses on the cap-snatching mechanism used
by the yeast L-A double-stranded Ribonucleic Acid (RNA)
virus [29]. The cap-snatching mechanism is a technique used
by certain RNA viruses to hijack the host’s messenger RNA
(mRNA) cap structure for viral RNA production. The authors
study the particular cap-snatching method used by the yeast
L-A double-stranded RNA virus and give deep insights into
its molecular processes. They investigate the viral variables
involved in cap-snatching and their interplay with host factors.
The authors’ research contributes to the knowledge of RNA
virus viral replication techniques and sheds insight on the
complicated mechanisms involved in the reproduction of the
yeast LA double-stranded RNA virus [29]. The findings of
this study are useful for virology research and increase our
understanding of viral replication techniques.
Continued in next post......
Hi All
Sorry cannot provide a link but for those unlike Pom who should just read the Abstract and Conclusion the full paper is probably interesting to a little exciting to think what AKIDA with a little Edge Impulse can do. Regards Fact Finder:
Safeguarding Public Spaces: Unveiling Wallet
Snatching through Edge Impulse Technology
Ujjwal Reddy K S
School of Computer Science and Engineering
VIT-AP University
Andhra Pradesh, India
ujjwal.20bci7203@vitap.ac.in
* Kuppusamy P
School of Computer Science and Engineering
VIT-AP University
Andhra Pradesh, India
drpkscse@gmail.com
Abstract—In contemporary society, public space security and
safety are of utmost significance. The theft of wallets, a frequent
type of street crime, puts people’s personal items at risk and
may result in financial loss and psychological misery. By utilizing
Edge Impulse technology to identify and expose wallet-snatching
incidents in public areas, this article offers a fresh solution to
the problem. To develop a reliable and effective wallet-snatching
detection solution, the suggested system blends machine learning
techniques with the strength of the Edge Impulse platform. This
study used Spiking Neural Networks (SNNs) which are inspired
by the biological neural networks found in the brain. Edge
Impulse offers a thorough framework for gathering, prepro-
cessing, and examining data, enabling the creation of extremely
precise machine learning models. The system can accurately
discriminate between legitimate interactions with wallets and
suspicious snatching attempts by training these models on a
dataset that includes both normal and snatching events. The
efficiency of the suggested method is 95% demonstrated by exper-
imental findings, which show high accuracy and low false positive
rates in recognizing wallet snatching instances. Increasing public
safety, giving people a sense of security in public places, and
discouraging prospective wallet-snatching criminals are all goals
of this research.
Index Terms—wallet snatching, public spaces, Edge Impulse,
sensor devices, machine learning, real-time monitoring, security,
privacy
I. INTRODUCTION
Public places are critical for societal interactions and com-
munity participation. They are places of recreation, social-
ization, and public meetings. However, these areas are not
immune to criminal activity, and one typical threat is wallet
snatching. Wallet snatching is the act of forcibly removing
someone’s wallet, which frequently results in financial losses,
identity theft, and psychological suffering for the victims.
Safeguarding public places and combating wallet snatching
necessitate new measures that make use of developing technol-
ogy. In this context, this introduction investigates the potential
of Edge Impulse technology in uncovering and preventing
wallet-snatching events [1].
Wallet-snatching instances can occur in a variety of public
places, including parks, retail malls, congested roadways, and
public transit. These attacks are frequently characterized by
their speed and stealth, giving victims little time to react or
seek aid. Traditional surveillance systems, such as Closed Cir-
cuit Television (CCTV) cameras, have difficulties in efficiently
identifying and preventing wallet-snatching occurrences owing
to variables such as limited coverage, video quality, and human
error in monitoring [2]. As a result, more advanced technical
solutions that can proactively identify and respond to such
situations are required.
Edge Impulse is a new technology that integrates machine
learning algorithms, sensor data, and embedded systems to
generate smart and efficient solutions [3]. It allows machine
learning models to be deployed directly on edge devices such
as smartphones, wearable devices, or Internet of Things (IoT)
devices, reducing the requirement for ongoing access to a
distant server. Edge Impulse is an appropriate solution for
tackling the problem of wallet snatching in public places
because of its capabilities.
Fig. 1. Edge Impulse Architecture.
It is essential to look into the vast amount of research
and studies done in this specific subject in order to prop-
erly understand the powers of Edge Impulse technology in
revealing instances of wallet theft. Numerous studies have
been conducted to examine the use of computer vision and
machine learning approaches in detecting and preventing crim-
inal activity in public spaces. The topic of utilizing cutting-
edge technologies to improve public safety and security has
been explored in a number of academic studies. This research
has shown how machine learning algorithms may be used
to examine video footage and identify patterns of suspicious
2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) | 979-8-3503-0570-8/23/$31.00 ©2023 IEEE | DOI: 10.1109/RMKMATE59243.2023.10369744
behavior that could be related to wallet-snatching instances.
These cutting-edge technologies may recognize people who
display suspicious motions or participate in potentially illegal
behaviors by utilizing computer vision techniques, such as
object identification and tracking, enabling proactive interven-
tion. Edge Impulse technology integration has a lot of potential
in this area. It may be trained to recognize certain traits
and attributes linked to wallet snatching through its strong
machine learning skills, improving its capacity to precisely
detect such instances in real-time. Edge Impulse can analyze
trends, spot abnormalities, and notify authorities or security
people to take immediate action by utilizing the enormous
volumes of data gathered from several sources, including
surveillance cameras and sensor networks. The possibility of
predictive analytics to foresee wallet theft episodes based on
previous data and behavioral trends has also been investigated
in this field of research. Machine learning algorithms are
able to recognize high-risk locations and deploy resources
appropriately by examining elements like the time of day,
location, and population density. With the use of this proactive
strategy, law enforcement organizations may deploy people
efficiently and put out preventative measures, which serve to
dissuade prospective criminal activity.
Based on these findings, the use of Edge Impulse technology
in the context of wallet snatching can improve the efficiency of
crime prevention systems [4]. The reaction time may be greatly
decreased by implementing machine learning models directly
on edge devices, enabling real-time detection and fast inter-
vention. Furthermore, Edge Impulse technology can record
and analyze essential data for recognizing wallet-snatching
instances using numerous sensors included in smartphones
or wearable devices, such as accelerometers, gyroscopes, and
cameras.
For example, accelerometer data may be utilized to detect
abrupt movements or violent behaviors that are suggestive of
wallet-snatching attempts [5]. The gyroscope data can offer
information regarding the direction and speed of the grab,
assisting in the tracking of the culprit. Additionally, camera
footage may be analyzed using computer vision algorithms to
detect suspicious activity, identify possible thieves, or collect
photographs for later identification and proof.
The increasing availability of data can further benefit the use
of Edge Impulse technology in wallet snatching prevention.
With the growth of smartphones and wearable devices, there is
an abundance of sensor data that can be gathered and analyzed
in order to create strong machine learning models. This data
may be used to train algorithms to recognize certain patterns or
abnormalities related with wallet-snatching instances, boosting
the system’s accuracy and dependability.
Furthermore, integrating Edge Impulse technology with
current surveillance systems can improve their capabilities.
A complete and intelligent system may be constructed by
integrating the strengths of both technologies, such as the
extensive coverage of CCTV cameras and the real-time anal-
ysis of edge devices. This integrated strategy would allow for
proactive identification and rapid reaction to wallet-snatching
occurrences, minimizing the impact on victims and discour-
aging future perpetrators.
Finally, wallet snatching in public places is a serious danger
to public safety and individual well-being [6]. Innovative
techniques are necessary to overcome this difficulty, and Edge
Impulse technology has intriguing possibilities. Edge Impulse
provides real-time detection and fast action in wallet snatching
occurrences by employing machine learning models installed
directly on edge devices. It captures and analyses pertinent
information using multiple sensors and data sources accessible
on smartphones and wearable devices. Integrating Edge Im-
pulse technology with current monitoring systems can improve
the efficacy of crime prevention efforts. These developments
can help to protect public places and expose wallet snatching,
resulting in safer and more secure communities.
A. Motivation
This study aims to use the potential of Edge Impulse
technology to make public areas safer for citizens by efficiently
fighting wallet-snatching events. We hope that by finding a
solution, we can contribute to the wider objective of protecting
public places and improving the general quality of life in our
communities.
B. Contribution
• The study presents an innovative use of Edge Impulse
technology for improving public safety.
• This study proposed SNNs.
• The created machine learning model detects wallet-
snatching episodes in public places with high accuracy
and efficiency.
II. RELATED WORK
The study proposes a framework comprised of two major
components: a behavior model and a detection technique [7].
The behavior model captures the software’s valid behavior
by monitoring its execution and gathering information about
its interactions with the system and the user. The detection
method compares the observed behavior of a software instance
to the behavior model to discover any differences that signal
probable theft. The authors conducted trials with real-world
software applications to assess the efficacy of their technique.
They tested their system’s detection accuracy, false positive
rate, and false negative rate. The results indicated promising
performance in detecting software theft occurrences properly
while keeping false alarms to an acceptable level. The study
presents an overview of the many processes involved in
the identification of anomalous behavior, including human
detection, feature extraction, and classification [8]. It em-
phasizes the importance of Convolutional Neural Networks
(CNNs) in dealing with the complexities of visual input and
extracting important characteristics for behavioral research.
Furthermore, the authors explore several CNN architectures
used for anomalous behavior identification, such as AlexNet,
Visual Geometry Group Network (VGGNet), and Residual
Neural Network (ResNet) [9]–[11]. They also investigate the
use of various datasets and assessment criteria in evaluating
the performance of these models. The survey includes a wide
range of applications where aberrant behavior identification is
critical, such as crowd monitoring, public space surveillance,
and anomaly detection in industrial settings [8]. The authors
assess the merits and limits of existing approaches, as well as
new research avenues and opportunities for development.
The suggested technique consists of two major steps: feature
engineering-based preprocessing and energy theft detection
using gradient boosting [12]. Various characteristics from
the electricity usage data are extracted during the feature
engineering-based preprocessing stage. These traits are in-
tended to detect trends and behaviors that may suggest possible
energy theft. After preprocessing the data, the authors use
gradient boosting, a machine learning approach, to detect
energy theft. Gradient boosting is an ensemble learning ap-
proach that combines numerous weak predictive models to
build a strong predictive model. It constructs decision trees in
a sequential manner, with each succeeding tree learning from
the mistakes of the preceding ones. The suggested strategy
is evaluated by the authors using real-world power use data.
They compare their approach’s performance to that of other
current approaches for detecting energy theft, such as decision
trees, random forests, and support vector machines [13]–
[15]. Accuracy, precision, recall, and F1-score are among the
assessment criteria employed. The paper’s results show that
the suggested technique beats the other methods in terms
of energy theft detection accuracy. The authors credit this
enhanced performance to the preprocessing stage based on
feature engineering and the efficiency of gradient boosting in
identifying complicated connections in the data.
The study is primarily concerned with analyzing power
use trends and discovering abnormalities that might suggest
theft [16]. The system learns to discern between regular use
patterns and suspicious actions that signal theft by training the
decision tree and Support Vector Machine (SVM) models on
historical data. The attributes chosen are used to categorize
incidents as either theft or non-theft. The suggested technique
is tested using real-world smart grid data. The findings show
that the decision tree and SVM-based methods can identify
theft in smart grids with high accuracy and low false positive
rates. The study focuses on identifying instances of theft by
collecting temporal relationships in energy use data [17]. The
system learns to recognize regular consumption patterns and
detect variations that suggest theft by training the CNN-Long
Short-Term Memory (LSTM) model on historical data. The
suggested method is tested using real-world smart grid data,
and the findings show that it is successful at identifying power
theft [18]. The CNN-LSTM-based technique beats existing
approaches in terms of detection accuracy. Both papers address
the important issue of theft detection in smart grid systems,
but they employ different techniques [16], [17]. The first
paper utilizes decision trees and SVM for feature selection
and classification, while the second paper employs CNNs and
LSTM networks for feature extraction and anomaly detection.
These approaches contribute to the development of effective
methods for enhancing the security and reliability of smart
grid systems.
The study most likely proposes an algorithm or strategy
that employs computer vision and motion analysis techniques
to detect suspicious or illegal behavior in video footage [19].
The suggested approach most likely seeks to discriminate
between routine activities and probable criminal behaviors
by analyzing the motion patterns of humans or items in a
setting [20]. It is difficult to offer a full description of the
methodology, results, or conclusions of the study based on
the information supplied. However, it may be deduced that the
authors suggest a way for developing an automated criminal
detection system that combines motion analysis with intel-
ligent information-concealing strategies. The authors suggest
a chain-snatching detection safety system that detects and
prevents chain-snatching accidents by utilizing sophisticated
technologies [21]. However, without complete access to the
article, it is difficult to offer extensive information regarding
the system’s methodology, components, or methods used. To
detect rapid and strong movements associated with chain
snatching attempts, the system is likely to include various
sensors such as motion sensors or accelerometers. Image
processing methods may also be used to identify possible
chain snatchers or to collect photographs of the occurrence
for additional investigation or proof [22]. In addition, when a
chain-snatching incident is identified, the system might contain
an alarm or notification mechanism that warns surrounding
persons or authorities in real time. This quick reaction can
dissuade offenders while also providing urgent support to
victims. The report will most likely offer experimental findings
and assessments to assess the suggested system’s usefulness
in effectively identifying chain-snatching occurrences while
minimizing false alarms [21]. It may also address the system’s
weaknesses, prospective areas for development, and future
research directions in this subject.
The document most likely presents a proposed approach or
algorithm for detecting snatch stealing [23]. It may describe
the selection and extraction of low-level video data elements
such as motion analysis, object tracking, or other relevant
information that can be utilized to detect snatch-snatching
instances. The authors may have also investigated various
strategies for identifying and discriminating between regular
and snatch-stealing incidents. Given that the paper was deliv-
ered in 2010, it is crucial to highlight that the material provided
in it is based on research and technology breakthroughs
accessible at the time [23]. It’s probable that recent advances in
computer vision, machine learning, and surveillance systems
have pushed the area of snatch-steal detection even further.
The authors present an action attribute modeling technique
for automatically recognizing snatch-stealing incidents [24].
To identify possible snatch-steal instances, the technique en-
tails analyzing the activities and characteristics displayed by
persons in surveillance recordings. The idea is to create a
system that can send real-time alerts to security workers or
law enforcement organizations in order to assist avoid such
crimes or respond promptly when they occur. The document
most likely outlines the methods and algorithms used to
detect snatch-stealing occurrences, including the extraction of
key characteristics, training a model using labeled data, and
evaluating the suggested solution. It might also go through
the datasets used for training and testing, as well as the
performance measures used to assess the system’s efficacy.
Because the study was published in 2018, it is crucial to
highlight that advances in the area may have occurred since
then, and other methodologies or approaches may have been
created [24].
The study describes the integrated framework’s many com-
ponents, such as data collecting, preprocessing, feature extrac-
tion, and crime detection [25]. In addition, the authors give
experimental results based on real-world data to illustrate the
efficacy of their technique. The results show that the suggested
framework may detect tiny crimes in a fast and accurate
manner, allowing law enforcement authorities to respond more
efficiently. The research focuses on the use of deep learning
algorithms to detect trustworthy human suspicious conduct
in surveillance films [26]. By using the capabilities of deep
learning algorithms, scientists hope to increase the accuracy
and reliability of suspicious behavior detection. The study
provides a full description of the suggested technique, which
includes surveillance video preprocessing, feature extraction
with CNNs, and categorization of suspicious actions with
Recurrent Neural Networks (RNNs) [27], [28]. The authors
also explore the difficulties connected with detecting sus-
picious behavior and provide strategies to overcome them.
The research focuses on the cap-snatching mechanism used
by the yeast L-A double-stranded Ribonucleic Acid (RNA)
virus [29]. The cap-snatching mechanism is a technique used
by certain RNA viruses to hijack the host’s messenger RNA
(mRNA) cap structure for viral RNA production. The authors
study the particular cap-snatching method used by the yeast
L-A double-stranded RNA virus and give deep insights into
its molecular processes. They investigate the viral variables
involved in cap-snatching and their interplay with host factors.
The authors’ research contributes to the knowledge of RNA
virus viral replication techniques and sheds insight on the
complicated mechanisms involved in the reproduction of the
yeast LA double-stranded RNA virus [29]. The findings of
this study are useful for virology research and increase our
understanding of viral replication techniques.
Continued in next post......
Hopefull final episode.Balance of the paper on Wallet protection:
III. WALLET SNATCHING THROUGH EDGE IMPULSE
TECHNOLOGY
This study aimed to investigate practical countermeasures
to wallet-snatching incidents in public places. To achieve this,
a dataset was collected, annotated, and submitted to the Edge
Impulse platform. The model was trained to recognize wallet
theft instances, with an impressive 95% accuracy rate. Despite
challenges, such as limited data, the researchers used cutting-
edge methods and tactics to enhance the training process
and improve performance. They considered camera angles,
lighting conditions, and the pace of the grab to ensure accurate
prediction. The research has immense potential for improving
public safety and reducing theft incidences, paving the way
for future security protocols.
A. Edge Impulse Technology
Fig. 2. Flowchart of Edge Impulse.
Edge Impulse is a cutting-edge machine learning platform
for developing and deploying intelligent applications on edge
devices [1], [32]. It provides developers with an easy-to-use
interface and a complete range of tools for collecting, process-
ing, and analyzing data in order to create machine learning
models. Edge Impulse enables machine learning at the edge,
allowing devices to make real-time choices without requiring
ongoing access to the cloud [3]. The platform supports a
variety of edge devices, such as microcontrollers, development
boards, and sensors, allowing developers to harness the po-
tential of machine learning in resource-constrained contexts.
Developers may use Edge Impulse to train and deploy models
for a variety of applications such as predictive maintenance,
anomaly detection, motion identification, and more. The plat-
form also allows for the training and optimization of ma-
chine learning models utilizing common techniques such as
neural networks, decision trees, and support vector machines.
It provides an easy-to-use interface for configuring model
parameters, evaluating model performance, and optimizing
models for deployment on edge devices.
B. Spiking Neural Networks (SNNs)
SNNs are artificial neural networks that are inspired by bio-
logical neural networks found in the brain. SNNs function with
discrete-time, event-driven processing, as opposed to standard
artificial neural networks, which are based on continuous-
valued activations, and employ backpropagation for learning.
Fig. 3. Architecture of a Spiking Neural Network (SNN)
SNNs may provide various benefits over typical neural net-
works, particularly for jobs involving temporal information
processing, event-based data, and bio-inspired computing. Low
energy consumption, improved temporal precision, and possi-
ble appropriateness for neuromorphic hardware implementa-
tions are some of the potential benefits.
The key components of SNNs are as follows:
• Spiking Neurons: These are the network’s fundamental
building components. Based on activation criteria, they
integrate input spikes and create output spikes.
• Spike Trains: Instead of continuous activations like in typ-
ical neural networks, information in SNNs is represented
as discrete spike trains, which are time-varying sequences
of spikes.
• Synaptic Weight Updates: To increase performance,
SNNs may learn from data and modify their synaptic
weights. Learning in SNNs is often characterized by
Spike-Timing-Dependent Plasticity (STDP), in which the
weight updates are determined by the relative timing of
presynaptic and postsynaptic events.
• Spike-Based Learning Rules: Depending on the timing of
the pulses, different learning rules are utilized to adjust
synaptic strengths.
C. Akida FOMO (Field-Programmable Object)
The Akida FOMO (Faster Objects, More Objects) paradigm
is a neuromorphic hardware platform created by BrainChip
that is inspired by the structure and function of the human
brain. It provides real-time neural network inference on edge
devices while reducing latency, increasing energy efficiency,
and allowing for large-scale parallel computing operations.
SNNs are used in the model to effectively handle temporal
and spatial input, replicating the behavior of neurons. This en-
ables operations like object recognition, gesture detection, and
anomaly detection to be performed on edge devices, removing
the need for cloud-based processing. This method is especially
beneficial in low latency and data privacy scenarios when
continuous data transmission to remote servers is not required.
The neural network model is a deep learning architecture for
image processing tasks, consisting of 21 layers. It begins with
an input layer, representing images with (None, 96, 96, 3)
i.e., 96 pixels and 3 color channels. The model then includes
4 layers of Conv2D, 4 layers of BatchNormalization, and 5
layers of Rectified Linear Unit (ReLU) activation functions,
which process the input data and learn complex patterns.
The two layers of SeparableConv2D enhance feature extrac-
tion capabilities. The model’s convolutional nature makes
it suitable for image-related tasks like image classification
and object detection. Each layer contributes to the overall
complexity, capturing important features and patterns from
the input images. The neural network model demonstrates
high performance in image tasks using deep learning and
convolutional networks, versatile for various computer vision
applications.
Algorithm 1 Akida FOMO Model Inference
Require: Input data
Ensure: Inference result
1: Load Akida FOMO model parameters
2: Initialize input data
3: Preprocess input data
4: Convert input data to SNN format
5: Initialize SNN state
6: while Not end of input data do
7: for Each input spike do
8: Propagate spike through SNN
9: Update SNN state
10: end for
11: end while
12: Perform output decoding on SNN state
13: return Inference result
D. Data Collection
Fig. 4. Images of dataset.
This model is able to locate the dataset for our research
using the internet. The dataset includes videos of chain
snatching, wallet snatching, and other forms of snatching.
For creating a dataset, this study gathered all of the essential
videos. After creating the dataset, it began analyzing the
videos to identify common patterns and behaviors among the
snatchers. We found that most snatchers targeted vulnerable
individuals, such as the elderly or those walking alone at night.
Additionally, we noticed that snatchers tended to operate in
specific areas, such as busy marketplaces or near public trans-
portation hubs. With this information, we were able to develop
a more targeted approach to preventing these crimes from
occurring [31]. Edge Impulse streamlines the machine learning
workflow by offering a step-by-step method that comprises
data collection, data preprocessing, model training, and model
deployment. It provides a number of data intake methods,
including direct sensor integration, data import, and interaction
with third-party services. Edge Impulse’s capacity to undertake
automatic data preparation is one of its most prominent charac-
teristics. It provides a variety of signal-processing techniques
and feature extraction strategies for transforming raw data
into relevant features for model training. This streamlines the
data pre-treatment procedure and saves time for developers.
The platform also allows for the training and optimization of
machine learning models utilizing common techniques such as
neural networks, decision trees, and support vector machines.
It provides an easy-to-use interface for configuring model
parameters, evaluating model performance, and optimizing
models for deployment on edge devices.
E. Data Preprocessing
See next post
Balance of the paper on Wallet protection:
III. WALLET SNATCHING THROUGH EDGE IMPULSE
TECHNOLOGY
This study aimed to investigate practical countermeasures
to wallet-snatching incidents in public places. To achieve this,
a dataset was collected, annotated, and submitted to the Edge
Impulse platform. The model was trained to recognize wallet
theft instances, with an impressive 95% accuracy rate. Despite
challenges, such as limited data, the researchers used cutting-
edge methods and tactics to enhance the training process
and improve performance. They considered camera angles,
lighting conditions, and the pace of the grab to ensure accurate
prediction. The research has immense potential for improving
public safety and reducing theft incidences, paving the way
for future security protocols.
A. Edge Impulse Technology
Fig. 2. Flowchart of Edge Impulse.
Edge Impulse is a cutting-edge machine learning platform
for developing and deploying intelligent applications on edge
devices [1], [32]. It provides developers with an easy-to-use
interface and a complete range of tools for collecting, process-
ing, and analyzing data in order to create machine learning
models. Edge Impulse enables machine learning at the edge,
allowing devices to make real-time choices without requiring
ongoing access to the cloud [3]. The platform supports a
variety of edge devices, such as microcontrollers, development
boards, and sensors, allowing developers to harness the po-
tential of machine learning in resource-constrained contexts.
Developers may use Edge Impulse to train and deploy models
for a variety of applications such as predictive maintenance,
anomaly detection, motion identification, and more. The plat-
form also allows for the training and optimization of ma-
chine learning models utilizing common techniques such as
neural networks, decision trees, and support vector machines.
It provides an easy-to-use interface for configuring model
parameters, evaluating model performance, and optimizing
models for deployment on edge devices.
B. Spiking Neural Networks (SNNs)
SNNs are artificial neural networks that are inspired by bio-
logical neural networks found in the brain. SNNs function with
discrete-time, event-driven processing, as opposed to standard
artificial neural networks, which are based on continuous-
valued activations, and employ backpropagation for learning.
Fig. 3. Architecture of a Spiking Neural Network (SNN)
SNNs may provide various benefits over typical neural net-
works, particularly for jobs involving temporal information
processing, event-based data, and bio-inspired computing. Low
energy consumption, improved temporal precision, and possi-
ble appropriateness for neuromorphic hardware implementa-
tions are some of the potential benefits.
The key components of SNNs are as follows:
• Spiking Neurons: These are the network’s fundamental
building components. Based on activation criteria, they
integrate input spikes and create output spikes.
• Spike Trains: Instead of continuous activations like in typ-
ical neural networks, information in SNNs is represented
as discrete spike trains, which are time-varying sequences
of spikes.
• Synaptic Weight Updates: To increase performance,
SNNs may learn from data and modify their synaptic
weights. Learning in SNNs is often characterized by
Spike-Timing-Dependent Plasticity (STDP), in which the
weight updates are determined by the relative timing of
presynaptic and postsynaptic events.
• Spike-Based Learning Rules: Depending on the timing of
the pulses, different learning rules are utilized to adjust
synaptic strengths.
C. Akida FOMO (Field-Programmable Object)
The Akida FOMO (Faster Objects, More Objects) paradigm
is a neuromorphic hardware platform created by BrainChip
that is inspired by the structure and function of the human
brain. It provides real-time neural network inference on edge
devices while reducing latency, increasing energy efficiency,
and allowing for large-scale parallel computing operations.
SNNs are used in the model to effectively handle temporal
and spatial input, replicating the behavior of neurons. This en-
ables operations like object recognition, gesture detection, and
anomaly detection to be performed on edge devices, removing
the need for cloud-based processing. This method is especially
beneficial in low latency and data privacy scenarios when
continuous data transmission to remote servers is not required.
The neural network model is a deep learning architecture for
image processing tasks, consisting of 21 layers. It begins with
an input layer, representing images with (None, 96, 96, 3)
i.e., 96 pixels and 3 color channels. The model then includes
4 layers of Conv2D, 4 layers of BatchNormalization, and 5
layers of Rectified Linear Unit (ReLU) activation functions,
which process the input data and learn complex patterns.
The two layers of SeparableConv2D enhance feature extrac-
tion capabilities. The model’s convolutional nature makes
it suitable for image-related tasks like image classification
and object detection. Each layer contributes to the overall
complexity, capturing important features and patterns from
the input images. The neural network model demonstrates
high performance in image tasks using deep learning and
convolutional networks, versatile for various computer vision
applications.
Algorithm 1 Akida FOMO Model Inference
Require: Input data
Ensure: Inference result
1: Load Akida FOMO model parameters
2: Initialize input data
3: Preprocess input data
4: Convert input data to SNN format
5: Initialize SNN state
6: while Not end of input data do
7: for Each input spike do
8: Propagate spike through SNN
9: Update SNN state
10: end for
11: end while
12: Perform output decoding on SNN state
13: return Inference result
D. Data Collection
Fig. 4. Images of dataset.
This model is able to locate the dataset for our research
using the internet. The dataset includes videos of chain
snatching, wallet snatching, and other forms of snatching.
For creating a dataset, this study gathered all of the essential
videos. After creating the dataset, it began analyzing the
videos to identify common patterns and behaviors among the
snatchers. We found that most snatchers targeted vulnerable
individuals, such as the elderly or those walking alone at night.
Additionally, we noticed that snatchers tended to operate in
specific areas, such as busy marketplaces or near public trans-
portation hubs. With this information, we were able to develop
a more targeted approach to preventing these crimes from
occurring [31]. Edge Impulse streamlines the machine learning
workflow by offering a step-by-step method that comprises
data collection, data preprocessing, model training, and model
deployment. It provides a number of data intake methods,
including direct sensor integration, data import, and interaction
with third-party services. Edge Impulse’s capacity to undertake
automatic data preparation is one of its most prominent charac-
teristics. It provides a variety of signal-processing techniques
and feature extraction strategies for transforming raw data
into relevant features for model training. This streamlines the
data pre-treatment procedure and saves time for developers.
The platform also allows for the training and optimization of
machine learning models utilizing common techniques such as
neural networks, decision trees, and support vector machines.
It provides an easy-to-use interface for configuring model
parameters, evaluating model performance, and optimizing
models for deployment on edge devices.
E. Data Preprocessing
See next post
Hopefull final episode.
E. Data Preprocessing
Fig. 5. Annotation images of dataset.
In this method, we start by deleting duplicate images from
datasets and filtering out datasets that don’t fit our criteria. By
removing irrelevant information, we focus on the photographs
that are most important to our project. The next key step is
annotating these images, which is essential for getting the
model ready for training. Each image is given the proper
tags or categories during annotation, which helps to properly
organize the data. Additionally, the machine learning system
can recognize patterns in the photos thanks to this annotation
process, which enables it to produce precise predictions. Next,
the model set the parameter of images to 96 X 96 size. We may
go on to the following step, which entails training the model
after the photographs have been annotated. In this stage, the
model is taught how to recognize and understand numerous
patterns and characteristics inside the photos using the anno-
tated data. The model continually improves its comprehension
and grows better at generating precise predictions through
a series of iterations and modifications. Generally speaking,
the process starts with data filtering to get rid of unwanted
datasets and duplicates. After that, each image is given the
proper labels via the annotation process, which facilitates data
organization and makes it possible for the machine learning
system to recognize patterns. In the end, the model is trained
using these annotated images, honing its forecasting skills and
deepening its comprehension of the visual information.
F. Implementation
We delivered the dataset to the three modules for training
after Data Preprocessing. They are further discussed below:
1) Creating Impulse: We will start the essential procedures
at this point in order to train our model successfully. To begin
with, we performed a few configuration changes, including
translating the entire dataset into dimensions of 96X96. We
used a resize setting that allowed for the shortest axis of the
photos to ensure compatibility. Then, we used the BrainChip
Akida model to do feature extraction from the dataset’s
images. We were able to draw out important details and useful
information from each image using this procedure. It is crucial
to remember that we will preserve and use each and every
one of these extracted characteristics to train our model in the
future. By following these steps, we are setting up an efficient
training procedure that will allow our model to develop and
produce precise predictions based on the supplied information.
2) Image: The color depth parameters are converted into
the Red-Green-Blue (RGB) format in the next stage, enabling
additional analysis and modification. We then started the
feature extraction procedure, successfully identifying all the
crucial traits that were present in the dataset. We created
a graph that successfully demonstrates the properties of the
dataset to display this data visually. This graph is shown in
Fig. 6, which offers a clear visual depiction of the retrieved
characteristics.
Fig. 6. Feature explorer.
3) Object Detection: In this phase, we set up the parameters
that will be used to train our model. We kept the validation
set size at 20%, the learning rate at 0.001, and the number
of epochs at 100. In addition, we trained our dataset using
the Akida Fomo model. We started the model training process
after making these adjustments, and as a consequence, we got
a remarkable training accuracy of 95.0%. Fig. 7 shows the
precise measurements and results of this training procedure.
In addition, we created a quantized version of the model in
addition to the outcomes. This quantization ensures that the
model performs in real-time without any hiccups even on
devices with modest Random-Access Memory (RAM) and
storage capabilities. The quantized model maintains its ability
to carry out the required activities while greatly reducing
the memory and storage needs. This development makes it
possible to install and use the model effectively in contexts
with limited resources.
4) Classification: In this part, we evaluated the model using
photos from the dataset and were successful in obtaining a
testing accuracy of 90%, which we believe can improve in the
future with a larger training dataset set so that the model may
be fine-tuned and have additional features for training. Fig. 8
depicts the testing accuracy. Overall, we are satisfied with the
performance of the model and optimistic about its potential for
further improvement. It is clear that the team has put in a lot
of effort into developing and evaluating the model. The testing
accuracy of 90% is impressive, and the team’s optimism about
Fig. 7. Model Training Accuracy.
the model’s potential for further improvement is encouraging.
It will be interesting to see how the model performs with a
larger training dataset and additional features for training.
Fig. 8. Model Testing Accuracy.
Fig. 9. Model output.
IV. CONCLUSION
In conclusion, our study made use of a sizable dataset
made up of a variety of occurrences, such as chain stealing
and wallet stealing. The videos in the collection were first
broken down into individual frames, and then redundant and
duplicate pictures were eliminated. After that, we worked on
training our model and annotating the pictures. Our model,
known as Akida FOMO, showed excellent accuracy of 95.0%
throughout the training phase and testing accuracy of 90%
after the training phase was complete. Therefore, we can state
with confidence that the use of the Edge Impulse platform and
the BrainChip Akida FOMO model contributed significantly to
the production of insightful findings for our research. The high
levels of accuracy reached by our model demonstrate that the
incorporation of Akida FOMO into our study was a resounding
success. Although setting up the dataset and annotating the
photos took a lot of time and work, the outcomes were
unquestionably trustworthy and the effort was justified. We are
adamant that future research into computer vision and object
identification has a great deal of potential when the cutting-
edge technology of BrainChip is paired with the Edge Impulse
platform. The positive results of our research demonstrate the
strong combination’s prospective trajectory and demonstrate
its potential to open up new directions for future developments
in this area.
V. FUTURE DIRECTION
There are a number of fascinating future areas that may
be investigated in the field of computer vision and object
identification in order to build upon the accomplishments and
beneficial results of our study using the Edge Impulse platform
and the Akida FOMO model.
Improved Model Performance: Although our existing model
achieved outstanding accuracy rates, there is still potential
for improvement. The model parameters may be adjusted in
future study, along with enhanced training methods, more
datasets, and a wider variety of training examples. The model
can push the limits of accuracy even further by continu-
ously improving the model’s performance. Real-time Object
Recognition: The Edge Impulse platform opens up oppor-
tunities for real-time object recognition applications when
combined with Akida FOMO’s capabilities. The development
of a system that can recognize and detect chain-snatching
or wallet-snatching instances in real-time may result from
broadening our study, which might have a substantial impact
on public safety and crime prevention initiatives. Generali-
sation and Transfer Learning: Exploring the possibilities of
transfer learning strategies might be an attractive area for
future study. We may be able to attain greater accuracy rates
and speed up the training process by utilizing pre-trained
models on related object identification tasks and fine-tuning
them with our particular dataset. The model’s usefulness
and applicability can also be increased by investigating its
capacity to generalize across various scenarios and contexts.
Scalability and Deployment: As our study develops, the Akida
FOMO model’s scalability and deployment need to be taken
into account. The model’s deployment on resource-constrained
edge devices, such as security cameras or smartphones, may be
facilitated by optimizing the model’s architecture and training
procedure to decrease computing needs and memory footprint.
This would make it possible to use our research’s results in the
real world, broadening its influence beyond the boundaries of a
sterile laboratory. Expansion to Other Applications: Although
the majority of our study was devoted to chain-snatching and
wallet-snatching occurrences, the approaches and techniques
created may be applied to a number of other fields and appli-
cations. Exploring the model’s capability to identify and detect
various objects or events, such as pedestrian identification or
traffic sign recognition, can advance computer vision in more
general ways and improve safety protocols in a variety of
scenarios.
Finally, the effective integration of Akida FOMO into the
Edge Impulse platform opens the door for interesting new
research trajectories. We may improve the area of computer
vision and object identification by continually improving
the model, investigating real-time applications, using transfer
learning, assuring scalability, and extending to new domains,
eventually helping society with increased safety and efficiency.