The growing trend at the global level of scientific production shows the interest in developing aspects of smart cities based on IoT applications.
However, centralized control in the SDN architecture is associated with new security vulnerabilities.
The evaluation results show that we can have a quantitative understanding of the security risks at the system level in the early stage of system design. Firstly, a better cascading model with learnable semantic fusion between a feature extraction network and a feature pyramid network is designed to improve detection accuracy using a global context block. Finally, a global self-attention mechanism is used to highlight the useful information of feature maps while suppressing irrelevant information. An iterative optimization for decoupling capacitor placement on a power delivery network (PDN) is presented based on Genetic Algorithm (GA) and Artificial Neural Network (ANN). Finally, we apply the proposed approach to analyze and compare the security risks of the collision warning system under a distributed and centralized electrical and electronic architecture. Actual measurements of the current noise in a type-II superlattice photodetector are reported in which the programmable source was used to provide the voltage bias to the device. The latest high-resolution and high-speed spaceborne imagers provide an explosive growth in data volume and instrument data rates in the range of several Gbps. The underlying physical mechanisms of charge collection under different heavy-ion energies were discussed. This study proposes a mechanism using two interference conditions to quickly estimate the minimum amount of effective spectrum availability (ESA) inside an SB. The results show that an SB contains ESA distributed across 36% to 98% of the building’s area for reuse, as a function of the height of the building and of the distance from the base station (BS) of the primary system. The electrical network structure inside a commercial and industrial facility is considered, analyzing the operation of the proposed strategy on three-phase PV inverters.
The proposed strategy performance is demonstrated on a 50 kW converter model under a disturbed grid environment and changing load conditions. this study provides a theoretical framework for the detection of SEUs induced by heavy-ion irradiation in 3D-integrated devices. Our ACNs reduce the storage space of convolutional filters by a factor of 32 compared with the full-precision models on dataset LFW+Webface, CelebA, BioID and 300W , while achieving a comparable performance to the full-precision facial landmark localization algorithms. However, during the construction of the western railway, the permafrost problem has plagued railway construction on the Qinghai–Tibet Plateau, and has not yet been resolved. This solution allows to obtain a P1 dB compression point of about −8 dBm and a dynamic range of 75 dB considering a bias current of 15 mA per stage.
Help us to further improve by taking part in this short 5 minute survey Because of the very low level of output noise, in some cases we had to resort to cross-correlation in order to reduce the background noise of the amplifiers used for the characterization of the programmable source.
Facial landmark localization is a significant yet challenging computer vision task, whose accuracy has been remarkably improved due to the successful application of deep Convolutional Neural Networks (CNNs).
SDN has proven to be successful in improving not only the network performance, but also security. Various models for identity management have been developed continually, from the silo model to the federated model and to the recently introduced self-sovereign identity (SSI) model.
This paper confirmed the feasibility of the proposed model by implementing it and a security analysis was performed.
In recent years, deep learning techniques, and in particular convolutional neural networks (CNNs) methods have demonstrated a superior performance in image classification and visual object recognition.
Experiments show that our GC-YOLOv3 reaches a maximum of 55.5 object detection mean Average Precision (mAP)@0.5 on Common Objects in Context (COCO) 2017 test-dev and that the mAP is 5.1% higher than that of the YOLOv3 algorithm on Pascal Visual Object Classes (PASCAL VOC) 2007 test set. Unlike other approaches, the one proposed here operates properly under distorted and unbalanced grid voltages.
Steering motor is of vital importance in UAV's health-monitoring system, to which its supply current is the most critical characteristic representing health statue of UAV. The cross-sections The prototype was realized and tested. In this research, nano-wedge resistive switching random-access memory (ReRAM) based on a Si Accurately identifying permafrost by geophysical In order to better appreciate the performance of the low-noise voltage source that we have designed, its noise performances were compared with those of a set-up based on one of the best low-noise solid-state voltage regulators available on the market. In order to conduct continuous measuring on the steering motor's current of large dynamic range, in this paper,
The working principle, real-time compensation method and circuit implementation of our method are discussed in detail. You seem to have javascript disabled. The output voltage drift was also characterized and a stability of ±25 µV over 3 h was obtained. The combined GA–ANN process is shown to effectively provide results consistent with those obtained by a longer optimization based on commercial simulators. We introduce a new network architecture to calculate the binarized models, referred to as Amplitude Convolutional Networks (ACNs), based on the proposed asynchronous back propagation algorithm.