7 research outputs found
Secure Video Streaming Using Dedicated Hardware
Purpose: The purpose of this article is to present a system that enhances the
security, efficiency, and reconfigurability of an Internet-of-Things (IoT)
system used for surveillance and monitoring. Methods: A Multi-Processor
System-On-Chip (MPSoC) composed of Central Processor Unit (CPU) and
Field-Programmable Gate Array (FPGA) is proposed for increasing the security
and the frame rate of a smart IoT edge device. The private encryption key is
safely embedded in the FPGA unit to avoid being exposed in the Random Access
Memory (RAM). This allows the edge device to securely store and authenticate
the key, protecting the data transmitted from the same Integrated Circuit (IC).
Additionally, the edge device can simultaneously publish and route a camera
stream using a lightweight communication protocol, achieving a frame rate of 14
frames per Second (fps). The performance of the MPSoC is compared to a NVIDIA
Jetson Nano (NJN) and a Raspberry Pi 4 (RPI4) and it is found that the RPI4 is
the most cost-effective solution but with lower frame rate, the NJN is the
fastest because it can achieve higher frame-rate but it is not secure, and the
MPSoC is the optimal solution because it offers a balanced frame rate and it is
secure because it never exposes the secure key into the memory. Results: The
proposed system successfully addresses the challenges of security, scalability,
and efficiency in an IoT system used for surveillance and monitoring. The
encryption key is securely stored and authenticated, and the edge device is
able to simultaneously publish and route a camera stream feed high-definition
images at 14 fps
Deep Learning approach for Classifying Trusses and Runners of Strawberries
The use of artificial intelligence in the agricultural sector has been
growing at a rapid rate to automate farming activities. Emergent farming
technologies focus on mapping and classification of plants, fruits, diseases,
and soil types. Although, assisted harvesting and pruning applications using
deep learning algorithms are in the early development stages, there is a demand
for solutions to automate such processes. This paper proposes the use of Deep
Learning for the classification of trusses and runners of strawberry plants
using semantic segmentation and dataset augmentation. The proposed approach is
based on the use of noises (i.e. Gaussian, Speckle, Poisson and
Salt-and-Pepper) to artificially augment the dataset and compensate the low
number of data samples and increase the overall classification performance. The
results are evaluated using mean average of precision, recall and F1 score. The
proposed approach achieved 91%, 95% and 92% on precision, recall and F1 score,
respectively, for truss detection using the ResNet101 with dataset augmentation
utilising Salt-and-Pepper noise; and 83%, 53% and 65% on precision, recall and
F1 score, respectively, for truss detection using the ResNet50 with dataset
augmentation utilising Poisson noise
Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference
Modern-day life is driven by electronic devices connected to the internet.
The emerging research field of the Internet-of-Things (IoT) has become popular,
just as there has been a steady increase in the number of connected devices.
Since many of these devices are utilised to perform CV tasks, it is essential
to understand their power consumption against performance. We report the power
consumption profile and analysis of the NVIDIA Jetson Nano board while
performing object classification. The authors present an extensive analysis
regarding power consumption per frame and the output in frames per second using
YOLOv5 models. The results show that the YOLOv5n outperforms other YOLOV5
variants in terms of throughput (i.e. 12.34 fps) and low power consumption
(i.e. 0.154 mWh/frame)
Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques
Deep learning constitutes a pivotal component within the realm of machine
learning, offering remarkable capabilities in tasks ranging from image
recognition to natural language processing. However, this very strength also
renders deep learning models susceptible to adversarial examples, a phenomenon
pervasive across a diverse array of applications. These adversarial examples
are characterized by subtle perturbations artfully injected into clean images
or videos, thereby causing deep learning algorithms to misclassify or produce
erroneous outputs. This susceptibility extends beyond the confines of digital
domains, as adversarial examples can also be strategically designed to target
human cognition, leading to the creation of deceptive media, such as deepfakes.
Deepfakes, in particular, have emerged as a potent tool to manipulate public
opinion and tarnish the reputations of public figures, underscoring the urgent
need to address the security and ethical implications associated with
adversarial examples. This article delves into the multifaceted world of
adversarial examples, elucidating the underlying principles behind their
capacity to deceive deep learning algorithms. We explore the various
manifestations of this phenomenon, from their insidious role in compromising
model reliability to their impact in shaping the contemporary landscape of
disinformation and misinformation. To illustrate progress in combating
adversarial examples, we showcase the development of a tailored Convolutional
Neural Network (CNN) designed explicitly to detect deepfakes, a pivotal step
towards enhancing model robustness in the face of adversarial threats.
Impressively, this custom CNN has achieved a precision rate of 76.2% on the
DFDC dataset
Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners
Fasteners play a critical role in securing various parts of machinery.
Deformations such as dents, cracks, and scratches on the surface of fasteners
are caused by material properties and incorrect handling of equipment during
production processes. As a result, quality control is required to ensure safe
and reliable operations. The existing defect inspection method relies on manual
examination, which consumes a significant amount of time, money, and other
resources; also, accuracy cannot be guaranteed due to human error. Automatic
defect detection systems have proven impactful over the manual inspection
technique for defect analysis. However, computational techniques such as
convolutional neural networks (CNN) and deep learning-based approaches are
evolutionary methods. By carefully selecting the design parameter values, the
full potential of CNN can be realised. Using Taguchi-based design of
experiments and analysis, an attempt has been made to develop a robust
automatic system in this study. The dataset used to train the system has been
created manually for M14 size nuts having two labeled classes: Defective and
Non-defective. There are a total of 264 images in the dataset. The proposed
sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss
at 0.001 learning rate.Comment: 13 pages, 6 figure
Dynamic Training of Liquid State Machines
Spiking Neural Networks (SNNs) emerged as a promising solution in the field
of Artificial Neural Networks (ANNs), attracting the attention of researchers
due to their ability to mimic the human brain and process complex information
with remarkable speed and accuracy. This research aimed to optimise the
training process of Liquid State Machines (LSMs), a recurrent architecture of
SNNs, by identifying the most effective weight range to be assigned in SNN to
achieve the least difference between desired and actual output. The
experimental results showed that by using spike metrics and a range of weights,
the desired output and the actual output of spiking neurons could be
effectively optimised, leading to improved performance of SNNs. The results
were tested and confirmed using three different weight initialisation
approaches, with the best results obtained using the Barabasi-Albert random
graph method
A dynamic segmentation based activity discovery through topic modelling
Recent developments in ubiquitous and pervasive technologies have made it easier to capture activities through sensors. The “bag-of-word” topic models have been applied to discover latent topics in corpus of words. In this paper, we propose the Probabilistic Latent Semantic Analysis to discover activity routines. The framework we propose set latent topics as corresponding class labels and use the Expectation Maximization (EM) algorithm for posterior inference. The experimental results we present are based on the Kasteren dataset which validates our framework and shows that it is comparable to existing activity discovery approaches