Remote sensing technology is becoming more sophisticated and is extensively used for object tracking, urban planning, military reconnaissance and other fields. Complex backgrounds and diverse object scales are two imp...
Remote sensing technology is becoming more sophisticated and is extensively used for object tracking, urban planning, military reconnaissance and other fields. Complex backgrounds and diverse object scales are two important factors that affect the object detection effect of remote sensing images. To address this problem, this paper proposes a remote sensing object detection model that incorporates channel enhancement and multi-scale contextual features. Firstly, the multi-scale contextual feature enhancement module is constructed, which performs multi-order spatial interaction by cascading recursive convolution to obtain contextual information of feature maps at different scales, and introduces attention to reinforce unique features of objects and suppress background interference. Then, the spatial pyramid channel enhancement module combining sub-pixel convolution and adaptive sampling factor is designed to mitigate the semantic weakening of the depth feature maps caused by channel downscaling, thus enhancing the sampling effect between feature maps of different scales and reducing information loss. Finally, the effectiveness of the model is verified on the large-scale remote sensing image object detection dataset DIOR.
In recent years, there has been a swift progression in employing novel methods in classrooms to enhance students’ academic achievements, especially in line with the growing digitization of education. Such methods oft...
In recent years, there has been a swift progression in employing novel methods in classrooms to enhance students’ academic achievements, especially in line with the growing digitization of education. Such methods often encompass systems like facial recognition to monitor various aspects of students, including attendance, emotional states, and attention. These tools are capable of evaluating students’ presence and engagement in class, offering quantifiable metrics regarding their concentration and emotions. However, a prominent challenge has been the translation of this data into an accessible form that enables educators to assess and enhance their teaching techniques swiftly. Our suggested solution tackles this issue by offering a real-time visual depiction of students’ classroom status through different visualization techniques. These visual aids allow teachers to promptly recognize trends in student focus, thus aiding in the strategic alteration of teaching styles. Furthermore, these visual representations can be tailored to display various metrics and applied to tasks beyond monitoring attention, like overseeing attendance or assessing student progress. By integrating these advanced visualizations into the educational process, both teaching efficacy and the learning experience for students and teachers alike can be substantially elevated.
A device-to-device (D2D) wireless ad hoc network architecture enables dynamic self-organizing communications among mobile users who can directly exchange information with their peers without a pre-determined network i...
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ISBN:
(数字)9781728109626
ISBN:
(纸本)9781728109633
A device-to-device (D2D) wireless ad hoc network architecture enables dynamic self-organizing communications among mobile users who can directly exchange information with their peers without a pre-determined network infrastructure. Moreover, finite blocklength coding (FBC) is the promising candidate technique to support time sensitive multimedia wireless networks services, where mobile users transmit short packets to upper-bound the transmission delay of video/audio traffic. The scaling law technique models the maximum D2D channel capacity as a function of the density of mobile users. Recent studies have integrated D2D wireless ad hoc networks with FBC theory to further improve the performance of 5G wireless ad hoc networks. However, how to model and analyze the capacity of D2D wireless ad hoc networks under the finite blocklength regime is not well understood and has not been thoroughly studied. To overcome these challenges, applying the scaling law technique, we derive upper-bounds on the coding rate of each D2D channel and the number of time slots needed to complete all D2D transmissions. Combining the D2D channel's coding rate with the number of time slots needed for all D2D transmissions, we derive the maximum aggregate throughput for wireless ad hoc networks with all mobile users using D2D communications while mitigating interference. We also develop a model where each D2D channel follows the Nakagami-m distribution, under which we derive the average aggregate throughput and its upper-bound. Finally, we evaluate our derived results in the D2D wireless ad hoc networks over finite blocklength regime through numerical analyses.
Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender ...
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Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the improvement of scalability and efficiency of collaborative filtering (CF) algorithms become increasingly important and difficult. In this paper, we developed and implemented a scaling-up item-based collaborative filtering algorithm on MapReduce, by splitting the three most costly computations in the proposed algorithm into four Map-Reduce phases, each of which can be independently executed on different nodes in parallel. We also proposed efficient partition strategies not only to enable the parallel computation in each Map-Reduce phase but also to maximize data locality to minimize the communication cost. Experimental results effectively showed the good performance in scalability and efficiency of the item-based CF algorithm on a Hadoop cluster.
Pervasive networking environment as social infrastructure has been going to become a reality, especially through the recent considerable progress of semiconductor technology. Considering the case where next generation...
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ISBN:
(纸本)1601320841
Pervasive networking environment as social infrastructure has been going to become a reality, especially through the recent considerable progress of semiconductor technology. Considering the case where next generation of pervasive networking is realized over ad hoc network suitable for emergency and some tentative event, some ofauthors study about an data- driven imprementation of Ad hoc and ubiquitous communication environment. It is very important to implement effective protocol handling and routing process in Ad hoc networking environment especially. This paper describes the effectiveness ofan implementation ofprotocol off-loader using networking-oriented data-driven processors CUE (Coordinating User's requirements and engineering constraints). In Ad hoc network, it is necessary to realize connection-less protocol such as UDP/IPfor flexible routing and realtime communication. Firstly, this paper discusses data- driven implementation ofprotocol off-loader on CUE networking board to minimize overheads in protocol handling. Then this paper describes architecture of CUE-v2/CUE-v3 which is available version in CUE processors. This paper finally discusses effectiveness ofprotocol handling off-loader using CUE processor system. It shows that data-driven protocol off-loader can keep minimum turn-around time without runtime overheads.
In the automotive field, software development methods and tools are used to cope with the high complexity of automotive software development. However, problems occur with the tracing of information, the assessment and...
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In response to the challenge of limited training samples for synthetic aperture radar (SAR) target detection, a SAR image expansion method based on improved cycle generative adversarial network (CycleGAN) is proposed....
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
In response to the challenge of limited training samples for synthetic aperture radar (SAR) target detection, a SAR image expansion method based on improved cycle generative adversarial network (CycleGAN) is proposed. First, the structure of the CycleGAN is refined to enhance training efficiency. Then, instance normalization (IN) is incorporated into the residual unit of the CycleGAN to improve the realism of generated SAR images. Finally, SAR target recognition experiments were conducted using the single shot multibox detection (SSD) detection network with VoVNet structure. The experimental results prove that the data expansion method based on CycleGAN can improve the recognition accuracy of SAR targets.
Multigranulation rough set is a new expansion of the classical rough set since the former uses a family of the binary relations instead of single one for the constructing of approximations. In this paper, the model of...
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The integration of millimeter wave (mmWave) and multiple-input and multiple-output (MIMO) techniques has been designed to provide reliable communications with large degrees of freedom while supporting the explosively ...
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ISBN:
(数字)9781728109626
ISBN:
(纸本)9781728109633
The integration of millimeter wave (mmWave) and multiple-input and multiple-output (MIMO) techniques has been designed to provide reliable communications with large degrees of freedom while supporting the explosively growing number of mobile users. Under stringent requirements in terms of latency and reliability, due to the infinite blocklength assumption of the Shannon's capacity result, researchers have investigated new methods to characterize wireless data transmissions considering the block error probability. The finite blocklength coding (FBC) technique has been developed to model the finite blocklength coding rate in the non-asymptotic regime while supporting short-packet communications over 5G wireless ad-hoc networks. However, because of the design complexity when characterizing the second-order coding rate over mmWave MIMO based wireless channels while being integrated with FBC, how to accurately derive the finite blocklength coding rate over mmWave MIMO wireless fading channels is still an open problem over 5G wireless ad-hoc networks. To tackle the above-mentioned challenges, we propose and develop a system model that can efficiently integrate mmWave-MIMO techniques with finite blocklength coding over 5G wireless ad-hoc networks. In particular, we derive system equations that characterize the foundational informationtheoretic relationship between the finite blocklength channel capacity and the coding rate over our proposed mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime. Also conducted is a MATLAB-based performance evaluation, which validates and analyzes our proposed schemes over mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime.
Interference alignment (IA) has received great recent attention for its breakthrough performance. Classical IA algorithms require infinite dimensional symbol extension. On the other hand, IA algorithms which draw on t...
Interference alignment (IA) has received great recent attention for its breakthrough performance. Classical IA algorithms require infinite dimensional symbol extension. On the other hand, IA algorithms which draw on the finite signal dimension provided by multiple antennas, become infeasible when the network scales. To construct practical IA algorithms that are applicable to large-scale MIMO interference networks, it is essential to introduce flexible IA algorithms which selectively cancels the strongest interference perceived by the receivers (Rxs). In this work, we aim at understanding the feasibility conditions of such IA algorithms, which cancel interference selectively according to the alignment set. By exploiting methodologies from algebraic geometry and graph theory, we characterize the feasibility regions of IA schemes with general alignment set and obtain insights into how network configurations and alignment set affects the feasibility of IA schemes.
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