Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.
Exponential growth of multimedia data has been witnessed in recent years from various industries, such as e-commerce, health, transportation, and social networks, etc. Access to desired data in such gigantic datasets require sophisticated and efficient retrieval methods. In the last few years, neuronal activations generated by a pretrained convolutional neural network (CNN) have served as generic descriptors for various tasks including image classification, object detection and segmentation, and image retrieval. They perform incredibly well compared to hand-crafted features. However, these features are usually high dimensional, requiring a lot of memory and computations for indexing and retrieval. For very large datasets, utilization of these high dimensional features in raw form becomes infeasible. In this paper, a highly efficient method is proposed to transform high dimensional deep features into compact binary codes using bidirectional Fourier decomposition. This compact bit code saves memory and eases computations during retrieval. Further, these codes can also serve as hash codes, allowing very efficient access to images in large datasets using approximate nearest neighbor (ANN) search techniques. Our method does not require any training and achieves considerable retrieval accuracy with short length codes. It has been tested on features extracted from fully connected layers of a pretrained CNN. Experiments conducted with several large datasets reveal the effectiveness of our approach for a wide variety of datasets.
Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.
Aims: Radial scars (RS) and complex sclerosing lesions (CSL) may be entirely benign but can simulate invasive carcinoma radiologically and in 30% of cases associated malignancy is present.The aim was to identify the incidence of subsequent breast pathology after excision of RS/CSL.Methods: A prospective study of 163 patients (median age 52 years) with RS/CSL was performed.Results: A total of 128 women were identified by the NHS Breast Screening Programme.Thirty-five patients were identified through the symptomatic breast service.Of the 108 patients with adequate follow-up data (median 72, range 12-156 months) three groups were identified.Group 1. Seventy-six patients had a benign RS/CSL: eight developed further lesions (two invasive and two in situ cancers, one fibroadenoma, one CSL in the ipsilateral breast; one invasive cancer, one RS in the contralateral breast).Group 2. Twenty-four patients underwent wide local excision for associated malignancy: two developed a benign lesion in the contralateral breast.Group 3: Eight patients underwent mastectomy for associated malignancy: one developed a contralateral carcinoma.Conclusions: The incidence of subsequent malignancy of 0•44% per year (95% CI 0•12-1•18) after excision of an entirely benign RS/CSL compares with value of 0•25% per year for an age-matched control population (P = 0•08).Longer follow-up of a larger number of cases is required to determine if RS/CSL is a risk factor for subsequent malignancy.
For Unmanned Aerial Vehicles (UAVs) with Tiny Machine Learning (TML), there is mutual exclusivity between the energy consumption for flight and the energy consumption to support their computation and processing. IoUAVs integrated with TML systems often consume substantial amounts of energy during flights, particularly when engaged in extended coverage and surveillance missions. The energy consumption of a UAV with TML performing long, wide-area coverage patrols and monitoring missions in complex areas is significant for the flight itself, and the energy required for the TML to perform calculations and processing is not guaranteed. Therefore, to better support TML computations, this study optimizes flight paths to reduce the energy consumption of UAVs while ensuring coverage. Specifically, in this study, the use of concave point elimination algorithms, enhanced convex decomposition algorithms, and determination of flight direction significantly reduced the frequency of UAV turns. The computational cost of obtaining a complete path is reduced by merging the subconvex regions and the weighted minimum traversal of the graph. This novel bidirectional forwarding path coverage path-planning (BFP-CPP) algorithm maximizes the reduction in the number of turns, reduces energy consumption, and achieves global coverage. The simulation experimental results show that compared with the existing methods without concave point elimination, the BFP-CPP algorithm can effectively reduce the number of subregions, minimize the number of drone turns, and lower energy consumption.
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