The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

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Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019.

In recent years, convolutional neural network (CNN)-based methods have been widely used for optical remote sensing object detection and have shown excellent performance. Some aerospace systems, such as satellites or aircrafts, need to adopt these methods to observe objects on the ground. Due to the limited budget of the logical resources and power consumption in these systems, an embedded device is a good choice to implement the CNN-based methods. However, it is still a challenge to strike a balance between performance and power consumption. In this paper, we propose an efficient hardware-implementation method for optical remote sensing object detection. Firstly, we optimize the CNN-based model for hardware implementation, which establishes a foundation for efficiently mapping the network on a field-programmable gate array (FPGA). In addition, we propose a hardware architecture for the CNN-based remote sensing object detection model. In this architecture, a general processing engine (PE) is proposed to implement multiple types of convolutions in the network using the uniform module. An efficient data storage and access scheme is also proposed, and it achieves low-latency calculations and a high memory bandwidth utilization rate. Finally, we deployed the improved YOLOv2 network on a Xilinx ZYNQ xc7z035 FPGA to evaluate the performance of our design. The experimental results show that the performance of our implementation on an FPGA is only 0.18% lower than that on a graphics processing unit (GPU) in mean average precision (mAP). Under a 200 MHz working frequency, our design achieves a throughput of 111.5 giga-operations per second (GOP/s) with a 5.96 W on-chip power consumption. Comparison with the related works demonstrates that the proposed design has obvious advantages in terms of energy efficiency and that it is suitable for deployment on embedded devices.

FPGA Implementation for CNN-Based Optical Remote Sensing Object Detection

The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current—average value, median value, variance value, RMS value, and distance of the maximum and minimum values—were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared.

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Series DC Arc Fault Detection Using Machine Learning Algorithms

Vision-assisted recognition of stereotype behaviors for early diagnosis of Autism Spectrum Disorders

To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Lightweight transfer nets and adversarial data augmentation for photovoltaic series arc fault detection with limited fault data

Autism spectrum disorder (ASD), similar to many other developmental or behavioral conditions, is difficult to be precisely diagnosed. This difficulty increases when the subjects are young children due to the huge overlap between the ASD symptoms and typical behaviors of young children. Therefore, it is important to develop reliable methods that could help distinguish ASD from normal behaviors of children. In this paper, we implemented a computer vision based automatic ASD prediction approach to detect autistic characteristics in a video dataset recorded from a mix of children with and without ASD. Our target dataset contains 555 videos, out of which 8349 episodes (each approximately 10 seconds) are derived. Each episode is labeled as an atypical or typical behavior by medical experts. We first estimate children pose in each video frame by re-training a state-of-the-art human pose estimator on our manually annotated children pose dataset. Particle filter interpolation is then applied on the output of the pose estimator to predict the locations of missing body keypoints. For each episode, we calculate the children motion pattern defined as the trajectory of their keypoints over time by temporally encoding the estimated pose maps. Finally, a binary classification network is trained on the pose motion representations to discriminate between typical and atypical behaviors. We were able to achieve a classification accuracy of 72.4% (precision =0.72 and recall =0.92) on our test dataset.

Recognition Of Atypical Behavior In Autism Diagnosis From Video Using Pose Estimation Over Time

In recent years, it is highly demanding to deploy Deep Neural Networks (DNNs) on edge devices, such as mobile phones, drones, robotics, and wearable devices, to process visual data collected by the cameras embedded in these systems. In addition to the model inference, training DNNs locally can benefit model customization and data privacy protection. Since many edge systems are powered by batteries or have limited energy budgets, Field-Programmable Gate Array (FPGA) is commonly used as the primary processing engine to satisfy both demands in performance and energy-efficiency. Although many recent research papers have been published on the topic of DNN inference with FPGAs, training a DNN with FPGAs has not been well exploited by the community. This paper summarizes the current status of adopting FPGA for DNN computation and identifies the main challenges in deploying DNN training on FPGAs. Moreover, a performance metric and evaluation workflow are proposed to compare the FPGA-based systems for DNN training in terms of (1) usage of on-chip resources, (2) training efficiency, (3) energy efficiency, and (4) model performance for specific computer vision tasks.

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Challenges in Energy-Efficient Deep Neural Network Training With FPGA

DC arc faults, especially series arc faults, are becoming more common in photovoltaic (PV) systems. Without timely detection and interruption, such dangerous events can cause catastrophic fires, posing severe threat to human safety and properties. This paper presents a review on DC series arc fault detection using machine learning (ML) in PV systems. Technical details of applied ML methods, including conventional ML and deep learning (DL), in recent published paper are summarized and discussed. In addition, several popular ML methods are evaluated and compared using the same experimental datasets collected in laboratory to examine their effectiveness in DC series arc fault detection. Finally, practical challenges are identified, potential solutions are provided, and future research directions are recommended.

DC Series Arc Fault Detection Using Machine Learning in Photovoltaic Systems: Recent Developments and Challenges

Purpose To design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT). Methods We developed a deep learning-based image segmentation network for automated segmentation of the RNFL in SD-OCT B-scans of mouse eyes. In total, 5500 SD-OCT B-scans (5200 B-scans were used as training data with the remaining 300 B-scans used as testing data) were used to develop this segmentation network. Postprocessing operations were then applied on the segmentation results to fill any discontinuities or remove any speckles in the RNFL. Subsequently, a three-dimensional retina thickness map was generated by z-stacking 100 segmentation processed thickness B-scan images together. Finally, the average absolute difference between algorithm predicted RNFL thickness compared to the ground truth manual human segmentation was calculated. Results The proposed method achieves an average dice similarity coefficient of 0.929 in the SD-OCT segmentation task and an average absolute difference of 0.0009 mm in thickness estimation task on the basis of the testing dataset. We also evaluated our segmentation algorithm on another biological dataset with SD-OCT volumes for RNFL thickness after the optic nerve crush injury. Results were shown to be comparable between the predicted and manually measured retina thickness values. Conclusions Experimental results demonstrate that our automated segmentation algorithm reliably predicts the RNFL thickness in SD-OCT volumes of mouse eyes compared to laborious and more subjective manual SD-OCT RNFL segmentation. Translational Relevance Automated segmentation using a deep learning-based algorithm for murine eye OCT effectively and rapidly produced nerve fiber layer thicknesses comparable to manual segmentation.

Rui Ma

Papers

Lightweight transfer nets and adversarial data augmentation for photovoltaic series arc fault detection with limited fault data

Recognition Of Atypical Behavior In Autism Diagnosis From Video Using Pose Estimation Over Time

Challenges in Energy-Efficient Deep Neural Network Training With FPGA

DC Series Arc Fault Detection Using Machine Learning in Photovoltaic Systems: Recent Developments and Challenges

Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes.