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Improving agricultural productivity is essential due to rapid population growth, making early detection of crop diseases crucial. Although deep learning shows promise in smart agriculture, practical applications for identifying wheat diseases in complex backgrounds are limited. In this paper, we propose CropNet, a hybrid method that utilizes Red, Green, and Blue (RGB) imaging and a transfer learning approach combined with shallow convolutional neural networks (CNN) for further feature refinement. To develop our customized model, we conducted an extensive search for the optimal deep learning architecture. Our approach involves freezing the pre-trained model for feature extraction and adding a custom trainable CNN layer. Unlike traditional transfer learning, which typically uses trainable dense layers, our method integrates a trainable CNN, deepening the architecture. We argue that pre-trained features in transfer learning are better suited for a custom shallow CNN followed by a fully connected layer, rather than being fed directly into fully connected layers. We tested various architectures for pre-trained models including EfficientNetB0 and B2, DenseNet, ResNet50, MobileNetV2, MobileNetV3-Small, and Inceptionv3. Our approach combines the strengths of pre-trained models with the flexibility of custom architecture design, offering efficiency, effective feature extraction, customization options, reduced overfitting, and differential learning rates. It distinguishes itself from classical transfer learning techniques, which typically fine-tune the entire pre-trained network. Our aim is to provide a lightweight model suitable for resource-constrained environments, capable of delivering outstanding results. CropNet achieved 99.80% accuracy in wheat disease detection with reduced training time and computational cost. This efficient performance makes CropNet promising for practical implementation in resource-constrained agricultural settings, benefiting farmers and enhancing production. [ABSTRACT FROM AUTHOR]

Edge devices generate a lot of data, and the data is received and utilized according to each service cycle of applications. It is difficult to handle the amount of data in existing cloud environments. MEC environments can reduce network latency and eliminate performance bottlenecks so that attempts have been made to run DL services on various heterogeneous devices. However, due to limited computing resources, inference may fail to work or may be time-consuming. This paper proposes a MEC-based AI computing partitioning model that enables distributed execution of AI applications on heterogeneous edge devices. The suggested model allows users to determine the number of divisions of AI network models, and has partitioned models with uniform parameters. According to the experimental results, as the compute partitioning increases, the edge device's overhead decreases on average by 25.8%, 14.3%, and 3.27% in terms of execution time, CPU usage, and memory usage, respectively, making it possible to provide seamless AI application services through distributed execution. [ABSTRACT FROM AUTHOR]

The modulation transfer function (MTF) is a fundamental tool for assessing the sharpness of digital breast tomosynthesis (DBT) systems and is primarily measured using edge devices. We compared the MTF of a Senographe Pristina DBT system using four-edge devices. These devices were composed of stainless steel with a thickness of 0.6, 0.8, and 1.0 mm, and 1.0 mm tungsten, based on different international guidelines. We evaluated spatial frequencies at MTFs of 0.5 (MTF50%) and 0.1 (MTF10%). The collimator-equipped and non-collimator configurations of the DBT were compared. We found no appreciable differences between scan and chest wall–nipple directions. Both MTF50% (2.90–2.99 cycles/mm) and MTF10% (6.69–6.94 cycles/mm) demonstrated minimal variation across the different edge devices. The collimator-equipped system exhibited an MTF50% that was approximately 5% higher than that of the non-collimator configuration. The choice of the edge device did not appreciably impact the MTF. [ABSTRACT FROM AUTHOR]

Improving agricultural productivity is essential due to rapid population growth, making early detection of crop diseases crucial. Although deep learning shows promise in smart agriculture, practical applications for identifying wheat diseases in complex backgrounds are limited. In this paper, we propose CropNet, a hybrid method that utilizes Red, Green, and Blue (RGB) imaging and a transfer learning approach combined with shallow convolutional neural networks (CNN) for further feature refinement. To develop our customized model, we conducted an extensive search for the optimal deep learning architecture. Our approach involves freezing the pre-trained model for feature extraction and adding a custom trainable CNN layer. Unlike traditional transfer learning, which typically uses trainable dense layers, our method integrates a trainable CNN, deepening the architecture. We argue that pre-trained features in transfer learning are better suited for a custom shallow CNN followed by a fully connected layer, rather than being fed directly into fully connected layers. We tested various architectures for pre-trained models including EfficientNetB0 and B2, DenseNet, ResNet50, MobileNetV2, MobileNetV3-Small, and Inceptionv3. Our approach combines the strengths of pre-trained models with the flexibility of custom architecture design, offering efficiency, effective feature extraction, customization options, reduced overfitting, and differential learning rates. It distinguishes itself from classical transfer learning techniques, which typically fine-tune the entire pre-trained network. Our aim is to provide a lightweight model suitable for resource-constrained environments, capable of delivering outstanding results. CropNet achieved 99.80% accuracy in wheat disease detection with reduced training time and computational cost. This efficient performance makes CropNet promising for practical implementation in resource-constrained agricultural settings, benefiting farmers and enhancing production.

Running deep neural networks on edge devices faces two challenges: model scaling and domain adaptation. Existing model scaling techniques and unsupervised online domain adaptation techniques suffer from coarse scaling granularity, limited scaling space, and long online domain adaptation time. To address these two challenges, this paper proposes a block-grained model scaling and domain adaptation training method called EdgeScaler, which consists of offline and online phases. For the model scaling challenge, in the offline phase, blocks are detected and extracted from various DNN and then are converted into multiple derived blocks. In the online phase, based on the combination of blocks and the connections between them, a large-scale scaling space is provided to solve the model scaling problem. For the domain adaptation challenge, a block-specific residual Adapter is designed, which is inserted into the blocks in the offline phase. In the online phase, when a new target domain arrives, all adapters are trained to solve the domain adaptation problem for all options in the block-grained scaling space. Test results on the real edge device, Jetson TX2, show that EdgeScaler can reduce the domain adaptation training time by an average of 85.14% and reduce the training energy consumption by an average of 84.1%, while providing a large-scale scaling option. [ABSTRACT FROM AUTHOR]

In the promising Artificial Intelligence of Things technology, deep learning algorithms are implemented on edge devices to process data locally. However, high-performance deep learning algorithms are accompanied by increased computation and parameter storage costs, leading to difficulties in implementing huge deep learning algorithms on memory and power constrained edge devices, such as smartphones and drones. Thus various compression methods are proposed, such as channel pruning. According to the analysis of low-level operations on edge devices, existing channel pruning methods have limited effect on latency optimization. Due to data processing operations, the pruned residual blocks still result in significant latency, which hinders real-time processing of CNNs on edge devices. Hence, we propose a generic deep learning architecture optimization method to achieve further acceleration on edge devices. The network is optimized in two stages, Global Constraint and Start-up Latency Reduction, and pruning of both channels and residual blocks is achieved. Optimized networks are evaluated on desktop CPU, FPGA, ARM CPU, and PULP platforms. The experimental results show that the latency is reduced by up to 70.40%, which is 13.63% higher than only applying channel pruning and achieving real-time processing in the edge device. [ABSTRACT FROM AUTHOR]

The state of Kerala in India has seen multiple instances of intense cyclones in recent years, resulting in heavy flooding. One of the biggest challenges faced by rescuers is the accessibility to flooded areas and buildings during rescue operations. In such scenarios, unmanned aerial vehicles (UAVs) can deliver reliable aerial visual data to aid planning and operations during rescue. Object detectors based on deep learning methods provide an effective solution to automate the process of detecting relevant information from image/video data. These models are complex and resource-hungry, leading to severe speed constraints during field operations. The pixel displacement algorithm (PDA), a portable and effective technique, is developed in this work to speed up object detection models on devices with limited resources, such as edge devices. This method can be integrated with all object detection models to speed up the inference time. The proposed method is combined with multiple object detection models in this work to show its effectiveness. The YOLOv4 model combined with the proposed method outperformed the AP50 performance of the YOLOv4-tiny model by 6 % while maintaining the same processing time. This approach gave almost 10 × speed improvement to Jetson Nano at an accuracy cost of 3 % when compared to YOLOv4. Further, a model to predict maximum pixel shift with respect to frame skip is proposed using parameters such as the altitude and velocity of the UAV and the tilt of the camera. Accurate prediction of pixel shift leads to a reduced search area, leading to reduced inference time. The effectiveness of the proposed model was tested against annotated locations, and it was found that the method was able to predict the search area for each test video segment with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Audio classification using deep learning models, which is essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle to optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model-compression techniques, notably magnitude pruning, Taylor pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on the Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model-compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the digital instrument detection algorithm has poor real-time performance and poor generalization in edge equipment, an instrument detection-you only look once (ID-YOLO) model for substation digital instrument detection and recognition was proposed. The proposed algorithm was based on YOLOv5 model. Firstly, a lightweight light weight-YOLO(LW-YOLO) backbone network was designed to extract image features, reduce network parameters, and improve real-time detection. Then, a bi-level routing attention module (BRAM) was designed to improve the precision of decimal point detection and the robustness and generalization of the network. Finally, the loss function α-IoU was introduced, the detection accuracy of the model was improved by setting different adjustable parameter α to obtain more accurate calculation of the intersection ratio between the real frame and the predicted frame. The results show that compared with other digital instrument detection and recognition methods based on deep learning, the proposed method has better accuracy and generalization on digital instrument recognition tasks with different display modes, and the detection speed of the model on the edge device can be improved from 6. 87 frames/ s to 8. 77 frames/ s while the detection accuracy is leading. Its real-time performance and detection accuracy can meet the engineering needs of intelligent data acquisition, detection and identification of actual substation. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method reveals the widespread real-world applicability of malware detection, particularly in securing IoT devices. Its faster inference speed and high fidelity score illustrate the practicability of on-device malware detection without incorporating desktop/server-class hardware via the internet for faster inference. Detecting malware is a crucial defense mechanism against potential cyber-attacks. However, current methods illustrate significant limitations in achieving high performance while maintaining faster inference on edge devices. This study proposes a novel deep network with dual-attention feature refinement on a two-branch deep network to learn real-time malware detection on edge platforms. The proposed method introduces lightweight spatial-asymmetric attention for refining the extracted features of its backbone and multi-head attention to correlate learned features from the network branches. The experimental results show that the proposed method can significantly outperform existing methods in quantitative evaluation. In addition, this study also illustrates the practicability of a lightweight deep network on edge devices by optimizing and deploying the model directly on the actual edge hardware. The proposed optimization strategy achieves a frame rate of over 545 per second on low-power edge devices. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As open-source RISC-V cores continue to be released, the development of low-power multicore processors utilizing these cores is invigorating the edge/IoT device market. Nevertheless, comprehensive research on developing low-power multicore processors with integrated security features using existing open RISC-V cores remains limited. This study addresses this gap by introducing AESware, a dedicated lightweight hardware designed for energy-efficient AES (Advanced Encryption Standard) task execution, contributing to the development of AES-specific low-power RISC-V multicore processors. AESware supports variable key lengths and ensures minimal power consumption with a compact design. This standalone IP (Intellectual Property) is compatible with various open RISC-V cores, offering scalability and convenience. And importantly, we propose the most energy-efficient architecture for multicore processors equipped with AESware. Instead of assigning dedicated AESware to each core, we introduce a shared AESware architecture to maximize energy efficiency. We develop an operational algorithm for task scheduling in AESware, achieving maximum utilization and minimal latency while maintaining its lightweight nature. To evaluate our solution, we developed 24 processors into three groups: AESware-equipped, baseline, and those with an external AES accelerator per core. After FPGA (Field-Programmable Gate Array) prototyping for functional verification and power consumption analysis via 45 nm process technology synthesis, our findings revealed significant energy savings. AESware-equipped processors achieved up to 76%, 47%, and 33% energy savings at dual-, quad-, and octa-core configurations compared to baseline, respectively, and were more energy-efficient in running AES applications than those with individual accelerators.

Traditional weed management often involves blanket herbicide spraying, resulting in substantial herbicide wastage, environmental concerns, and herbicide resistant issues. Smart spraying systems utilizing robotics and sensors technologies can minimize herbicide usage and provide a sustainable solution for site-specific weed management. A machine vision-based spraying system was designed and developed for weed identification and precise spray application onto the target weeds. The sprayer platform utilizes a deep learning YOLOv4 model to accurately recognize multiple weed species, facilitating targeted spray application. The platform is equipped with an FLIR RGB camera for real-time image acquisition and Nvidia Jetson AGX Orin edge device for deploying weed detection deep-learning model. The GPIO pins of Nvidia Jetson were utilized to activate relay, providing precise on/off control over the TeeJet solenoid valves for spot spraying. Both indoor and field experiments were conducted to evaluate and compare the performance of vision-based sprayer system for weed identification and precise spraying onto the target weeds. In the indoor experiment, the sprayer system showed the average effective spraying rate of 93.33 %, with the precision of 100 % and recall of 92.8 %. Conversely, the field experiment resulted in a slightly lower average effective spraying rate of 90.6 %, while maintaining a precision of 95.5 % and a recall of 89.47 %. The reduced accuracy of the spraying system in field experiment was due to varying outdoor conditions such as lighting, shadows, and wind velocity. Overall, the result of this study demonstrates the spraying system's potential for targeted herbicide application onto the grid cells containing weeds, effectively reducing herbicide usage and overall weed management costs.

Advancements in technology has accelerated the evolution of bone age assessment (BAA) methodologies, one of which is deep learning algorithms, which overcome the drawbacks of conventional approaches. In spite of excellent effectiveness of deep neural networks in detection of the correct class for bone age, they have a significant degree of complexity due to the numerous parameters they employ for each region of interest (ROI). In this paper, we propose a BAA method using a hybrid knowledge distillation (KD) paradigm in order to conquer this difficulty by mapping different ROIs into a single ROI. In this regard, the student receives knowledge from a teacher network that has been pre-trained on six ROIs including bones of five fingers and the wrist, transfers the knowledge of its final response layer and internal layers to the student. Then, six student models each of which is constructed based on just one of these ROIs, while receiving the information of the teacher model. Empirical results on digital hand atlas report that our student model trained on one ROI obtains 95% accuracy on 19 classes of bone age.

AbstractFacilitated by modern sensing and integrated circuit technology, Internet of Things (IoT) systems now have the ability to intelligently and automatically collect data, perform computational tasks on an edge, and efficiently make decisions locally or remotely. However, existing multivariate statistical process control methods commonly assume that sensors directly transmit the raw measurements to the central server, and thus the edge computing capability of IoT systems is not effectively harnessed. To fill this literature gap, we propose a monitoring scheme tailored for IoT systems by leveraging their edge computational power. In particular, inspired by the inherent hierarchical structure of the IoT system, we construct the monitoring statistics hierarchically. At each edge device, we first construct a nonparametric stream-level statistic for each data stream and aggregate them to derive the device-level statistics. To eliminate the inconsistency in device-level statistics across different edge devices, the device-level statistics are transformed into time-related statistics, which are then sent to the central server. At the central server, a system-level monitoring statistic is constructed, and the most informative edge devices that should transmit statistics are intelligently determined. Theoretical analyses are carried out to justify the effectiveness of the data transmission strategy. Both simulations and a case study, which is based on an IoT system we built, are conducted to evaluate the performance and validate the superiority of the proposed monitoring scheme. [ABSTRACT FROM AUTHOR]

In this work, we present a system that detects Freezing of Gait Detection (FOG) that uses of a single wearable inertial sensor to automatically evaluate a Parkinson's patient's gait instability and detect FOG in real-time. A wearable vibrator is our cueing system which is triggered by the FOG detection whenever a FOG episode takes place. The vibration impulses help the patient to prevent FOG by switching to voluntarily movement execution. Sensor data were collected from nine patients with Parkinson's disease performing Unified Parkinson's Disease Rating Scale (UPDRS) test under the supervision of a clinical expert. Along with data recording, a video was taken from patient's parkour. The data were labeled through the recorded video of the patient's tests and FOG and non-FOG data were assigned. A machine learning model using a deep Long-Short-Term-Memory (LSTM) employ the accelerometer data from the sensor and the inference leads to a FOG or non-FOG classification. The FOG detection model is pruned, quantized and used for real-time inference on Google Coral board worn on the patient' body. The model deployed on a Google coral board sends a trigger to the cuing device right after the FOG detection and the patient get alert for the happening FOG. The individualized model for one-second windows applied in this work performed an average of 91.5% of sensitivity and 86.5% specificity for models running on PC and 91.7% of sensitivity and 86.7% of specificity for the models tested on Google coral with the latency of 50 millisecond on real-time testing. [ABSTRACT FROM AUTHOR]

This study introduces a novel selective multi-branch network architecture designed to speed up object localization and classification on low-performance edge devices. The concept builds upon the You Only Look at Interested Cells (YOLIC) method, which was proposed by us earlier. In this approach, we categorize cells of interest (CoIs) into distinct regions of interest (RoIs) based on their locations and urgency. We then employ some expert branch networks for detailed object detection in each of the RoIs. To steer these branches effectively, a selective attention unit is added into the detection process. This unit can locate RoIs that are likely to contain objects under concern and trigger corresponding expert branch networks. The inference can be more efficient because only part of the feature map is used to make decisions. Through extensive experiments on various datasets, the proposed network demonstrates its ability to reduce the inference time while still maintaining competitive performance levels compared to the current detection algorithms. [ABSTRACT FROM AUTHOR]

In recent years, a shortage of farm workers in green onion production has become a serious problem. Therefore, there is a need to further mechanize preparation. Current machinery cannot remove all unwanted leaves at once, requiring subsequent manual removal. To reduce this secondary processing, it is effective to align the nozzle with the uppermost branch position. It is necessary to recognize the branch position of each green onion and feed the plant into the machine with the branch position aligned. Detecting the branch position by image recognition and automatic alignment of the nozzle for preparation could improve the accuracy of preparation. In this paper, we propose a method for detecting the branch position by extracting a particular oblique line at the branch. The method is designed for implementation on low-power edge devices and uses a by lightweight edge detection algorithm based on image processing. On a Raspberry Pi 3, it achieved a correct detection rate of 90.6% and a processing time of 455 ms. This result shows that our method is effective for detecting the branch position and may be applied to real-world use. [ABSTRACT FROM AUTHOR]

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability. [ABSTRACT FROM AUTHOR]

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Despite the widespread applicability of malware detection on the Internet of Things (IoT) and edge platforms, efficient malware detection on edge devices is significantly challenging due to hardware limitations. This study proposes a novel edge-friendly framework for learning malware detection by incorporating quantization awareness. The proposed framework incorporates a lightweight deep neural network leveraging long-distance pixel dependency, spatial-asymmetric attention, and normalization-free multi-head attention (MHA) block to refine salient features. Additionally, the proposed framework introduces an efficient quantization-aware training (QAT) strategy to optimize the proposed model for edge-friendly INT8 precision. The practicality of the proposed model has been validated through deployment on ARM-based hardware and extensive evaluations on standard x64 hardware. Experimental results show that the proposed method significantly outperforms existing malware detection methods in numerous precision modes on both platforms. The proposed method achieved 99.36% accuracy on the MalImg dataset and 98.41% on the IoT malware dataset with FP16 precision at 662 fps. With INT8 precision, it achieved 95.62% accuracy on the MalImg dataset and 97.36% on the IoT malware dataset, maintaining 1197 fps on a low-power edge device. To our knowledge, this is the first work in the open literature demonstrating QAT’s practicality for malware detection on edge platforms.

In this paper, we present a novel off-chip network architecture, the Tile Serial Peripheral Interface (TSPI), designed for low-power, scalable edge AI accelerators. Our approach modifies the conventional SPI to support a modular network structure that facilitates the scalable connection of multiple accelerators. The TSPI network employs a subset mapping algorithm for efficient routing and integrates the message passing interface (MPI) protocol to ensure rapid data distribution and aggregation. This modular architecture significantly reduces power consumption and improves processing speed. Experimental results demonstrate that our proposed TSPI network achieves a 54.7% reduction in power consumption and an 82.3% decrease in switching power compared to traditional SPI networks, along with a 23% increase in processing speed when utilizing 16 nodes. These advancements make the TSPI network an effective solution for enhancing AI performance in edge computing environments.

While computer vision and computing technology advances have facilitated advanced driver assistance applications, systems with multi-task design remain highly demanding to operate at high speed on resource-constrained devices. Our study addresses this challenge by proposing a real-time driver assistance solution specifically developed for a single Jetson AGX Xavier embedded device. It simultaneously performs lane detection, traffic object detection and recognition, and rule-based scene analysis. To achieve high throughput (up to 43 frames per second) without reliance on additional hardware or cloud server, the system exploits Jetson device’s specialized AI accelerator and employs various optimization techniques: multithreaded programming, the TensorRT framework, and post-training quantization. The modular design integrates state-of-the-art task-specific methods and ensures adaptation to diverse traffic scenarios across countries as well as future hardware and solutions. Experimental results using a Korean dashcam traffic dataset validated the system’s effectiveness and practicality.

Despite the achievements of deep neural-network-based object detection, detecting small objects in low-resolution images remains a challenging task due to limited information. A possible solution to alleviate the issue involves integrating super-resolution (SR) techniques into object detectors, particularly enhancing feature maps for small-sized objects. This paper explores the impact of high-resolution super-resolved feature maps generated by SR techniques, especially for a one-stage detector that demonstrates a good compromise between detection accuracy and computational efficiency. Firstly, this paper suggests the integration of an SR module named feature texture transfer (FTT) into the one-stage detector, YOLOv4. Feature maps from the backbone and the neck of vanilla YOLOv4 are combined to build a super-resolved feature map for small-sized object detection. Secondly, it proposes a novel SR module with more impressive performance and slightly lower computation demand than the FTT. The proposed SR module utilizes three input feature maps with different resolutions to generate a super-resolved feature map for small-sized object detection. Lastly, it introduces a simplified version of an SR module that maintains similar performance while using only half the computation of the FTT. This attentively simplified module can be effectively used for real-time embedded systems. Experimental results demonstrate that the proposed approach substantially enhances the detection performance of small-sized objects on two benchmark datasets, including a self-built surveillance dataset and the VisDrone2019 dataset. In addition, this paper employs the proposed approach on an embedded system with a Qualcomm QCS610 and demonstrates its feasibility for real-time operation on edge devices. [ABSTRACT FROM AUTHOR]

Existing CNN-based video anomaly detection methods improve the accuracy continuously, which are faced with issues such as complex architecture, large parameters and lengthy training. Therefore, the hardware computing power requirements of them are high, which makes it difficult to adapt to edge devices with limited computing resources like UAVs. To this end, this paper proposed a lightweight abnormal event detection method for edge devices. Firstly, the method extracted gradient cuboids and optical flow cuboids from video sequence as appearance and motion feature representation; Secondly, the method designed a modified PCANet network to obtain high-level block-wise histogram features of gradient cuboids; Then, the method calculated the appearance anomaly score of each block based on histogram feature distribution, and calculated the motion anomaly score based on the accumulation of optical flow amplitudes of internal pixels; Finally, the method fused the appearance and motion anomaly scores to identify anomalous blocks, achieving appearance and motion abnormal events detection and localization simultaneously. The frame-level AUC of proposed method reached 86.7% on UCSD Ped1 dataset and 94.9% on UCSD Ped2 dataset, which were superior to other methods and the parameters are much smaller. Experimental results show that the method achieves better anomaly detection performance under low computational power requirements, making the balance between detection precision and computing resources, which is suitable for low-power edge devices. [ABSTRACT FROM AUTHOR]

The 4th Edge Computing Workshop (doors 2024) held in Zhytomyr, Ukraine, aimed to bring together researchers to share advances and applications in edge computing, a critical technology for enabling the Internet of Things and analytics close to data sources. This report summarises the event, highlighting the peer-reviewed papers covering topics from edge hardware to AI and security. The nine accepted papers, selected from 19 submissions, propose innovative approaches to advance the field. Common themes include leveraging edge computing for sensing applications and addressing resource utilisation and management challenges. The workshop stimulated valuable discussion and collaboration around these important research directions.

In deep learning, acquiring sufficient data is crucial for making informed decisions. However, due to concerns regarding security and privacy, obtaining enough data for training models in the era of deep learning is challenging. There is a growing need for machine learning (ML) solutions that can derive accurate conclusions from small data while preserving privacy. Smartphones, which are widely used and generate large amounts of data, can serve as an excellent source for data generation. One suitable approach for regularly evaluating real-world data from edge devices is Tiny Machine Learning (TinyML). With the increasing number of edge devices involved in transmitting private data, it's vital to have a method that allows computations to be performed on edge devices and pushed to the edge rather than over the network. Considering these obstacles, the combination of TinyML edge devices and Federated Learning can be applied in the early treatment of Bronchus Cancer. Under the framework of federated learning, local edge devices are trained independently and then integrated into the server without exchanging edge device data. This approach enables the creation of secure models without sharing information, resulting in a highly efficient solution with enhanced data security and accessibility. This article provides a comprehensive discussion of the key challenges addressed in recent literature, accompanied by an extensive examination of relevant studies. Additionally, a novel model based on edge devices and federated learning is proposed.

In this paper, we proposed, designed, and implemented an improved fog removal SoC system based on Intel's Cyclone-IV, an FPGA (Field Programmable Gate Array) hardware accelerator, without software image processing. . In addition, the improvement effect resulting from application or non-application of the Adaptive Enhancement Filter Engine proposed to improve performance was measured using Terasic's board (DE2-115), FRIDA (Fog Road Image Database), and actual CCTV footage. It was proven through experiments. And the components of the proposed fog removal SoC system, which are the fog removal algorithm (DCP, Dark Channel Prior), UDP/IP network stack, memory (SDRAM) interface, and video interface, are IP (Intellectual Property) independent and scalable. It is expected to be implemented in the reusable Verilog HDL language to ensure that it can be reused as a component of an intelligent edge device in the MEC (Multi-access Edge Computing) Access Network in the future. do. [ABSTRACT FROM AUTHOR]

This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (ARKF) of ~99.9%. The most important frequency range of rs-EEG for PD-based diagnostics lies in the range of 0–4 Hz, and the most informative signals were mainly received from the right hemisphere of the head. It was also found that ARKF significantly decreased as the length of rs-EEG segments decreased from 1000 to 150 samples. Using a procedure for selecting the most informative features, it was possible to reduce the computational costs of classification by 11 times, while maintaining an ARKF ~99.9%. The proposed method can be used in the healthcare internet of things (H-IoT), where low-performance edge devices can implement ML sensors to enhance human resilience to PD. [ABSTRACT FROM AUTHOR]

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. The proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5x faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption. [ABSTRACT FROM AUTHOR]

User authentication has traditionally been performed using methods such as passwords or fingerprints. However, passwords have security vulnerabilities, and fingerprints may hinder user convenience. To address these issues, a novel user authentication method based on biosignals, specifically electromyogram (EMG) signals, is proposed. Using biosignals like EMG offers several advantages, including the ability to acquire data without user awareness, independence from the user's environment, rapid acquisition, and enhanced security. However, one challenge with using EMG signals for authentication has been their relatively low accuracy. In this paper, a neural network is implemented using a small number of parameters (fewer than 7000) to produce a wearable device using biosignals, and user authentication accuracy is secured using the maximal overlap discrete wavelet transform (MODWT) method and the Siamese network. The MODWT method is highly effective for the time and frequency analysis of time series data, and the Siamese network is a representative method for few-shot learning. The proposed neural network is verified using Chosun University's user authentication dataset, encompassing data from 100 individuals. Finally, this proposed network is implemented on an edge device such as field-programmable gate arrays (FPGAs) so that it can be applied to a wearable user authentication system. By implementing the Siamese network in FPGA-based edge devices, it was possible to secure user authentication performance at 94% accuracy and an authentication speed within 1.5 ms. In the case of accuracy, it is expected to be further improved by using the multimodal technique of biosignals. Also, the proposed system can be easily fabricated for digital integrated chips (ICs). [ABSTRACT FROM AUTHOR]