UDEEP: Edge-based Computer Vision for In-Situ Underwater Crayfish and Plastic Detection

Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for resources and leading to declines in native populations. Moreover, pollution exacerbates the vulnerability of White-clawed crayfish, with their populations declining by over 90% in certain English counties, making them highly susceptible to extinction. To safeguard aquatic ecosystems, it is imperative to address the challenges posed by invasive species and discarded plastics in the United Kingdom's river ecosystem's. The UDEEP platform can play a crucial role in environmental monitoring by performing on-the-fly classification of Signal crayfish and plastic debris while leveraging the efficacy of AI, IoT devices and the power of edge computing (i.e., NJN). By providing accurate data on the presence, spread and abundance of these species, the UDEEP platform can contribute to monitoring efforts and aid in mitigating the spread of invasive species.

Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques

Deep learning is a crucial aspect of machine learning, but it also makes these techniques vulnerable to adversarial examples, which can be seen in a variety of applications. These examples can even be targeted at humans, leading to the creation of false media, such as deepfakes, which are often used to shape public opinion and damage the reputation of public figures. This article will explore the concept of adversarial examples, which are comprised of perturbations added to clean images or videos, and their ability to deceive DL algorithms. The proposed approach achieved a precision value of accuracy of 76.2% on the DFDC dataset.

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.

Multi-Objective Optimization Approach Using Deep Reinforcement Learning for Energy Efficiency in Heterogeneous Computing System

The growing demand for optimal and low-power energy consumption paradigms for Internet of Things (IoT) devices has garnered significant attention due to their cost-effectiveness, simplicity, and intelligibility. We propose an Artificial Intelligence (AI) hardware energy-efficient framework to achieve optimal energy savings in heterogeneous computing through appropriate power consumption management. A deep reinforcement learning framework is employed, utilizing the Actor-Critic architecture to provide a simple and precise method for power saving. The results of the study demonstrate the proposed approach's suitability for different hardware configurations, achieving notable energy consumption control while adhering to strict performance requirements. The evaluation of the proposed power-saving framework shows that it is more stable, and has achieved more than 23% efficiency improvement, outperforming other methods by more than 5%.

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.

Benchmarking Edge Computing Devices for Grape Bunches and Trunks Detection using Accelerated Object Detection Single Shot MultiBox Deep Learning Models

Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous platforms for object detection in real-time. This research benchmarks three architectures: embedded GPU -- Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB, and NVIDIA Jetson TX2), TPU -- Tensor Processing Unit (such as Coral Dev Board TPU), and DPU -- Deep Learning Processor Unit (such as in AMD-Xilinx ZCU104 Development Board, and AMD-Xilinx Kria KV260 Starter Kit). Method: The authors used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset. After the trained model was converted and compiled for target-specific hardware formats to improve the execution efficiency. Conclusions and Results: The platforms were assessed in terms of performance of the evaluation metrics and efficiency (time of inference). Graphical Processing Units (GPUs) were the slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays (FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson TX2. TPU and GPU are the most power-efficient, consuming about 5W. The performance differences, in the evaluation metrics, across devices are irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of about 60 %.

Exploiting High Quality Tactile Sensors for Simplified Grasping

Robots are expected to grasp a wide range of objects varying in shape, weight or material type. Providing robots with tactile capabilities similar to humans is thus essential for applications involving human-to-robot or robot-to-robot interactions, particularly in those situations where a robot is expected to grasp and manipulate complex objects not previously encountered. A critical aspect for successful object grasp and manipulation is the use of high-quality fingertips equipped with multiple high-performance sensors, distributed appropriately across a specific contact surface. In this paper, we present a detailed analysis of the use of two different types of commercially available robotic fingertips (BioTac and WTS-FT), each of which is equipped with multiple sensors distributed across the fingertips' contact surface. We further demonstrate that, due to the high performance of the fingertips, a complex adaptive grasping algorithm is not required for grasping of everyday objects. We conclude that a simple algorithm based on a proportional controller will suffice for many grasping applications, provided the relevant fingertips exhibit high sensitivity. In a quantified assessment, we also demonstrate that, due in part to the sensor distribution, the BioTac-based fingertip performs better than the WTS-FT device, in enabling lifting of loads up to 850g, and that the simple proportional controller can adapt the grasp even when the object is exposed to significant external vibrational challenges.

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 - now over 50 billion. Since many of these devices are utilised to perform \gls*{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 (FPS) 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).

Real-Time Gesture Recognition with Virtual Glove Markers

Due to the universal non-verbal natural communication approach that allows for effective communication between humans, gesture recognition technology has been steadily developing over the previous few decades. Many different strategies have been presented in research articles based on gesture recognition to try to create an effective system to send non-verbal natural communication information to computers, using both physical sensors and computer vision. Hyper accurate real-time systems, on the other hand, have only recently began to occupy the study field, with each adopting a range of methodologies due to past limits such as usability, cost, speed, and accuracy. A real-time computer vision-based human-computer interaction tool for gesture recognition applications that acts as a natural user interface is proposed. Virtual glove markers on users hands will be created and used as input to a deep learning model for the real-time recognition of gestures. The results obtained show that the proposed system would be effective in real-time applications including social interaction through telepresence and rehabilitation.

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.