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Journal articles on the topic 'Dense networks'

Author: Grafiati

Published: 25 January 2025

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1

SIPPER, MOSHE. "CLUSTER-DENSE NETWORKS." International Journal of Modern Physics C 19, no. 06 (June 2008): 939–46. http://dx.doi.org/10.1142/s0129183108012650.

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Small-world networks, exhibiting short nodal distances and high clustering, and scale-free networks, typified by a scale-free, power-law node-degree distribution, have been shown to be widespread both in natural and artificial systems. We propose a new type of network — cluster-dense network — characterized by multiple clusters that are highly intra-connected and sparsely inter-connected. Employing two graph-theoretic measures — local density and relative density — we demonstrate that such networks are prevalent in the world of networks.

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Athanasiadou, Georgia E., Panagiotis Fytampanis, Dimitra A. Zarbouti, George V. Tsoulos, Panagiotis K. Gkonis, and Dimitra I. Kaklamani. "Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures." Electronics 9, no. 2 (February 16, 2020): 339. http://dx.doi.org/10.3390/electronics9020339.

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The 5G radio networks have introduced major changes in terms of service requirements and bandwidth allocation compared to cellular networks to date and hence, they have made the fundamental radio planning problem even more complex. In this work, the focus is on providing a generic analysis for this problem with the help of a proper multi-objective optimization algorithm that considers the main constraints of coverage, capacity and cost for high-capacity scenarios that range from dense to ultra-dense mmWave 5G standalone small-cell network deployments. The results produced based on the above analysis demonstrate that the denser the small-cell deployment, the higher the area throughput, and that a sectored microcell configuration can double the throughput for ultra-dense networks compared to dense networks. Furthermore, dense 5G networks can actually have cell radii below 400 m and down to 120 m for the ultra-dense sectored network that also reached spectral efficiency 9.5 bps/Hz/Km2 with no MIMO or beamforming.

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Wang, Wei, Yutao Li, Ting Zou, Xin Wang, Jieyu You, and Yanhong Luo. "A Novel Image Classification Approach via Dense-MobileNet Models." Mobile Information Systems 2020 (January 6, 2020): 1–8. http://dx.doi.org/10.1155/2020/7602384.

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As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

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Campbell, Lowell. "Dense group networks." Discrete Applied Mathematics 37-38 (July 1992): 65–71. http://dx.doi.org/10.1016/0166-218x(92)90125-t.

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Zou, Kingsley Jun, and Kristo Wenjie Yang. "Network synchronization for dense small cell networks." IEEE Wireless Communications 22, no. 2 (April 2015): 108–17. http://dx.doi.org/10.1109/mwc.2015.7096293.

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6

Oyakhire, Omuwa, and Koichi Gyoda. "Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks." Future Internet 12, no. 3 (March 9, 2020): 47. http://dx.doi.org/10.3390/fi12030047.

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In mobile ad hoc networks, network nodes cooperate by packet forwarding from the source to the destination. As the networks become denser, more control packets are forwarded, thus consuming more bandwidth and may cause packet loss. Recently, game theory has been applied to address several problems in mobile ad hoc networks like energy efficiency. In this paper, we apply game theory to reduce the control packets in dense networks. We choose a proactive routing protocol, Optimized Link State Routing (OLSR) protocol. We consider two strategies in this method: willingness_always and willingness_never to reduce the multipoint relay (MPR) ratio in dense networks. Thus, nodes with less influence on other nodes are excluded from nomination as MPRs. Simulations were used to confirm the efficiency of using our improved method. The results show that the MPR ratio was significantly reduced, and packet delivery ratio was increased compared to the conventional protocol.

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Koucheryavy, Andrey, Alexander Paramonov, Mariya Makolkina, Ammar Muthanna, Anastasija Vybornova, Roman Dunaytscev, Maxim Zaharov, Lyubov Gorbacheva, D. Tran, and Anastasiya Marochkina. "3 Dimension Multilayer Heterogenous Ultra Dense Networks." Telecom IT 10, no. 3 (December 23, 2022): 1–12. http://dx.doi.org/10.31854/2307-1303-2022-10-3-1-12.

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The development of communication networks poses new challenges in the field of scientific research. At the same time, one of the main directions of development is the creation of highdensity and ultra-dense networks. Ultra-dense networks already belong to the technologies of communication networks of the sixth generation and the requirements for them are formed in the conditions of their deployment in three-dimensional space. Starting with the construction of fifth generation communication networks, communication networks are considered as heterogeneous, in which various technologies can be used together in the process of providing network services, for example, the Internet of Things, unmanned aerial vehicles, vehicular ad hoc networks, etc. This leads to the need to define and study three-dimensional multi-layer heterogeneous ultra-dense networks, which is the subject of this article.

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Ge, Xiaohu, Song Tu, Guoqiang Mao, Cheng-Xiang Wang, and Tao Han. "5G Ultra-Dense Cellular Networks." IEEE Wireless Communications 23, no. 1 (February 2016): 72–79. http://dx.doi.org/10.1109/mwc.2016.7422408.

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9

F., Aguiló, F., E. E., Simó, and M. M., Zaragozá. "On Dense Triple-Loop Networks." Electronic Notes in Discrete Mathematics 10 (November 2001): 261–64. http://dx.doi.org/10.1016/s1571-0653(04)00406-8.

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Roy, Saptarshi, Titas Chanda, Tamoghna Das, Aditi Sen(De), and Ujjwal Sen. "Deterministic quantum dense coding networks." Physics Letters A 382, no. 26 (July 2018): 1709–15. http://dx.doi.org/10.1016/j.physleta.2018.04.033.

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11

Kamel, Mahmoud, Walaa Hamouda, and Amr Youssef. "Ultra-Dense Networks: A Survey." IEEE Communications Surveys & Tutorials 18, no. 4 (2016): 2522–45. http://dx.doi.org/10.1109/comst.2016.2571730.

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12

Bhowmick, Sourav S. "How Connected Are Our Conference Review Boards?" ACM SIGMOD Record 51, no. 4 (January 9, 2023): 74–78. http://dx.doi.org/10.1145/3582302.3582324.

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Dense co-authorship network formed by the review board members of a conference may adversely impact the quality and integrity of the review process. In this report, we shed light on the topological characteristics of such networks for three major data management conference venues. Our results show all these venues give rise to dense networks with a large giant component. We advocate to rethink the traditional way review boards are formed to mitigate the emergence of dense networks.

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Huang, Zhongzhan, Senwei Liang, Mingfu Liang, and Haizhao Yang. "DIANet: Dense-and-Implicit Attention Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4206–14. http://dx.doi.org/10.1609/aaai.v34i04.5842.

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Attention networks have successfully boosted the performance in various vision problems. Previous works lay emphasis on designing a new attention module and individually plug them into the networks. Our paper proposes a novel-and-simple framework that shares an attention module throughout different network layers to encourage the integration of layer-wise information and this parameter-sharing module is referred to as Dense-and-Implicit-Attention (DIA) unit. Many choices of modules can be used in the DIA unit. Since Long Short Term Memory (LSTM) has a capacity of capturing long-distance dependency, we focus on the case when the DIA unit is the modified LSTM (called DIA-LSTM). Experiments on benchmark datasets show that the DIA-LSTM unit is capable of emphasizing layer-wise feature interrelation and leads to significant improvement of image classification accuracy. We further empirically show that the DIA-LSTM has a strong regularization ability on stabilizing the training of deep networks by the experiments with the removal of skip connections (He et al. 2016a) or Batch Normalization (Ioffe and Szegedy 2015) in the whole residual network.

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Marabissi, Dania, Romano Fantacci, and Linda Simoncini. "SDN-Based Routing for Backhauling in Ultra-Dense Networks." Journal of Sensor and Actuator Networks 8, no. 2 (April 23, 2019): 23. http://dx.doi.org/10.3390/jsan8020023.

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Ultra-Dense Network (UDN) deployment is considered a key element to achieve the requested capacity in future fifth-generation (5G) mobile networks. Backhaul networks in UDNs are formed by heterogeneous links with multi-hop connections and must handle massive traffic. Backhauling in future 5G networks may represent the capacity bottleneck. Therefore, there is the need for efficient and flexible routing schemes able to handle the dynamism of the traffic load in capacity-limited networks. Toward this goal, the emerging Software-Defined Network (SDN) paradigm provides an efficient solution, transferring the routing operation from the data plane switches to a central controller, thus achieving more flexibility, efficiency, and faster convergence time in comparison to conventional networks. This paper proposes and investigates an SDN-approach for an efficient routing in a capacity-limited backhaul network that carries data and control traffic of a heterogeneous UDN. The routing algorithm is centralized in the SDN controller and two different types of traffic flow are considered: data and control plane coordination traffic. The goal is to reduce or even to avoid the amount of traffic that the backhaul network is not able to support, distributing in a fair way the eventual lack of bandwidth among different access points. Simulation results show that with the considered approach the performance significantly improves, especially when there is an excess of traffic load in the network. Moreover, thanks to the SDN-based design, the network can reconfigure the traffic routing depending on the changing conditions.

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Kułacz, Łukasz, Adrian Kliks, Paweł Kryszkiewicz, and Bartosz Bossy. "Dynamic Transmit Profile Selection in Dense Wireless Networks." Sensors 21, no. 1 (December 28, 2020): 134. http://dx.doi.org/10.3390/s21010134.

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The development of wireless networks can be characterized by both the increased number of deployed network nodes as well as their greater heterogeneity. As a consequence, the distance between the neighboring nodes decreases significantly, the density of such a wireless network is very high, and it brings to the mind the analogy to the human brain and nervous system, where a highly simplified scheme of information delivery is applied. Motivated by this similarity, in this paper, we study the possibility of the application of various transmission profiles in order to optimize the overall energy consumption in such dense wireless networks. The transmission profile specifies the radio access and energy consumption of the wireless transceiver (network node), and is characterized by the tuple of parameters, e.g., the total transmit power or minimal required signal-to-noise ratio (SNR). In the considered multi-hop network, we assume that each node can be set to the most promising transmission profile to achieve some predefined goals, such as (sensor) network reliability or transmission energy efficiency. We have proposed the new graph-based routing algorithm in such a dense wireless network, where total power consumption of message delivery is minimized by multihop and multimode transmission. The theoretical definition of the prospective transmission schemes is supported by the analysis of the results of the simulation experiments.

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Stoynov, Viktor, Vladimir Poulkov, Zlatka Valkova-Jarvis, Georgi Iliev, and Pavlina Koleva. "Ultra-Dense Networks: Taxonomy and Key Performance Indicators." Symmetry 15, no. 1 (December 20, 2022): 2. http://dx.doi.org/10.3390/sym15010002.

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One major influence on the future deployment of cellular networks will be a continuous increase in traffic inside mobile broadband systems. Moreover, traditional macrocell-based mobile communication networks will struggle to keep up with the enormous expansion in the demand for communications services in the future. Densification of networks is required if we are to meet the comprehensive needs for end terminals for a wide range of applications. One of the leading concepts in this competitive environment is the Ultra-Dense Network (UDN) where the access nodes and/or the communication links per unit area are densified, with the aim of improving overall network performance. The location of the UDN nodes meets the criteria for symmetry with a high probability. Ultra-dense cell deployment aims to reduce the physical distance between the transmitter and receiver in order to boost system performance and generally optimize the values of a wide variety of key performance indicators (KPIs). This paper aims to provide a taxonomy of UDNs and specifically of UDN-related KPIs. Initially, we address the complex questions “What is the current understanding of what ultra-dense networks are and what they should be, and how can we measure their performance?” by shedding light on the fundamental characteristics of UDNs.

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17

Gowda, V. Dankan, Avinash Sharma, S. Kumaraswamy, Parismita Sarma, Naziya Hussain, Santosh Kumar Dixit, and Anand Kumar Gupta. "A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 519–30. http://dx.doi.org/10.47974/jim-1678.

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An major constraint in the realm of feature selection is that users choose the ideal number of characteristics that must be picked. In this article, an effort is made to automate the process of determining a suitable value for the appropriate the quantity of characteristics that must be chosen for better recognition tasks. To estimate the ideal amount of features that should be maintained for properly describing the data, we use the dense subgraph discovery approach for this goal. Notably, the existing methods uses a similar kind of approach called the dense subgraph finding. But the earlier approach obtains a single dense subgraph, while the task of dense subgraphs finding obtains a number of dense subgraphs that are important for learning the internal structure of any network. Thus, dense subgraphs finding is likely to be adopted as a natural choice for realizing the complex relations among the nodes of massive real-life networks, such as biological networks, social networks.

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Koudouridis, Georgios P., and Pablo Soldati. "Trading off Network Density with Frequency Spectrum for Resource Optimization in 5G Ultra-Dense Networks." Technologies 6, no. 4 (December 1, 2018): 114. http://dx.doi.org/10.3390/technologies6040114.

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To effectively increase the capacity in 5G wireless networks requires more spectrum and denser network deployments. However, due to the increasing network density, the coordination of network and spectrum management becomes a challenging task both within a single operator’s network and among multiple operators’ networks. In this article, we develop new radio resource management (RRM) algorithms for adapting the frequency spectrum and the density of active access nodes in 5G ultra-dense networks (UDNs) to the traffic load and the user density in different geographical areas of the network. To this end, we formulate a network optimization problem where the allocation of spectrum bandwidth and the density of active access nodes are optimized to minimize a joint cost function, and we exploit Lagrange duality techniques to develop provably optimal network-scheduling algorithms. In particular, we develop density algorithms for two application scenarios. The first scenario solves the resource management problem for an operator of an ultra-dense network with exclusive access to a pool of frequency resources, while the second scenario applies to the management of the network density of collocated UDNs that belong to multiple operators sharing the same frequency spectrum. Simulation results demonstrate how effectively the algorithms can adapt the allocation of the spectrum allocation and the density of active access nodes over space and time.

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19

Ateya, Abdelhamied A., Sergey Bushelenkov, Ammar Muthanna, Alexander Paramonov, Andrey Koucheryavy, Samia Allaoua Chelloug, and Ahmed A. Abd El-Latif. "Multipath Routing Scheme for Optimum Data Transmission in Dense Internet of Things." Mathematics 11, no. 19 (October 5, 2023): 4168. http://dx.doi.org/10.3390/math11194168.

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The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models.

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20

Monakhova, Emilia A., and Oleg G. Monakhov. "Effective algorithm for finding shortest paths in dense Gaussian networks." Prikladnaya Diskretnaya Matematika, no. 58 (2023): 94–104. http://dx.doi.org/10.17223/20710410/58/9.

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As a promising topology of networks on a chip, we consider a family of Dense Gaussian Networks, which are optimal circulant degree four graphs of the form C(D2 + (D + 1)2; D, D + 1). For this family, an algorithm for finding the shortest paths between graph vertices is proposed, which uses relative addressing of vertices and, unlike a number of the known algorithms, allows to calculate the shortest paths without using the coordinates of neighboring lattice zeros in a dense tessellation of graphs on the ℤ2 plane. This reduces the memory and execution time costs compared to other algorithms when the new algorithm is implemented on a network-on-chip with a Dense Gaussian Network topology.

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Muthanna, Mohammed Saleh Ali, Ping Wang, Min Wei, Ahsan Rafiq, and Nteziriza Nkerabahizi Josbert. "Clustering Optimization of LoRa Networks for Perturbed Ultra-Dense IoT Networks." Information 12, no. 2 (February 10, 2021): 76. http://dx.doi.org/10.3390/info12020076.

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Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network’s operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution.

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Campbell, Robert A., Katherine A. Overmyer, Craig H. Selzman, Brett C. Sheridan, and Alisa S. Wolberg. "Contributions of extravascular and intravascular cells to fibrin network formation, structure, and stability." Blood 114, no. 23 (November 26, 2009): 4886–96. http://dx.doi.org/10.1182/blood-2009-06-228940.

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Abstract Fibrin is essential for hemostasis; however, abnormal fibrin formation is hypothesized to increase thrombotic risk. We previously showed that in situ thrombin generation on a cell's surface modulates the 3-dimensional structure and stability of the fibrin network. Currently, we compared the abilities of extravascular and intravascular cells to support fibrin formation, structure, and stability. Extravascular cells (fibroblasts, smooth muscle) supported formation of dense fibrin networks that resisted fibrinolysis, whereas unstimulated intravascular (endothelial) cells produced coarse networks that were susceptible to fibrinolysis. All 3 cell types produced a fibrin structural gradient, with a denser network near, versus distal to, the cell surface. Although fibrin structure depended on cellular procoagulant activity, it did not reflect interactions between integrins and fibrin. These findings contrasted with those on platelets, which influenced fibrin structure via interactions between β3 integrins and fibrin. Inflammatory cytokines that induced prothrombotic activity on endothelial cells caused the production of abnormally dense fibrin networks that resisted fibrinolysis. Blocking tissue factor activity significantly reduced the density and stability of fibrin networks produced by cytokine-stimulated endothelial cells. Together, these findings indicate fibrin structure and stability reflect the procoagulant phenotype of the endogenous cells, and suggest abnormal fibrin structure is a novel link between inflammation and thrombosis.

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23

Zang, Huaijuan, Leilei Zhu, Zhenglong Ding, Xinke Li, and Shu Zhan. "Cascaded Dense-UNet for Image Super-Resolution." Journal of Circuits, Systems and Computers 29, no. 08 (October 11, 2019): 2050121. http://dx.doi.org/10.1142/s0218126620501212.

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Recently, deep convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SISR). Especially, dense skip connections and residual learning structures promote better performance. While most existing deep CNN-based networks exploit the interpolation of upsampled original images, or do transposed convolution in the reconstruction stage, which do not fully employ the hierarchical features of the networks for final reconstruction. In this paper, we present a novel cascaded Dense-UNet (CDU) structure to take full advantage of all hierarchical features for SISR. In each Dense-UNet block (DUB), many short, dense skip pathways can facilitate the flow of information and integrate the different receptive fields. A series of DUBs are concatenated to acquire high-resolution features and capture complementary contextual information. Upsampling operators are in DUBs. Furthermore, residual learning is introduced to our network, which can fuse shallow features from low resolution (LR) image and deep features from cascaded DUBs to further boost super-resolution (SR) reconstruction results. The proposed method is evaluated quantitatively and qualitatively on four benchmark datasets, our network achieves comparable performance to state-of-the-art super-resolution approaches and obtains pleasant visualization results.

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Koudouridis, Georgios P., and Pablo Soldati. "Spectrum and Network Density Management in 5G Ultra-Dense Networks." IEEE Wireless Communications 24, no. 5 (October 2017): 30–37. http://dx.doi.org/10.1109/mwc.2017.1700087.

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Sun, Kun, Xianbin Wen, Liming Yuan, and Haixia Xu. "Dense capsule networks with fewer parameters." Soft Computing 25, no. 10 (April 12, 2021): 6927–45. http://dx.doi.org/10.1007/s00500-021-05774-6.

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Hao, Yixue, Min Chen, Long Hu, Jeungeun Song, Mojca Volk, and Iztok Humar. "Wireless Fractal Ultra-Dense Cellular Networks." Sensors 17, no. 4 (April 12, 2017): 841. http://dx.doi.org/10.3390/s17040841.

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Al-Dulaimi, Anwer, Saba Al-Rubaye, John Cosmas, and Alagan Anpalagan. "Planning of Ultra-Dense Wireless Networks." IEEE Network 31, no. 2 (March 2017): 90–96. http://dx.doi.org/10.1109/mnet.2017.1500258nm.

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Cicconetti, Claudio, Antonio La Oliva, David Chieng, and Juan Zúñiga. "Extremely dense wireless networks [Guest Editorial]." IEEE Communications Magazine 53, no. 1 (January 2015): 88–89. http://dx.doi.org/10.1109/mcom.2015.7010520.

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Soret, Beatriz, Klaus I. Pedersen, Niels T. K. Jørgensen, and Víctor Fernández-López. "Interference coordination for dense wireless networks." IEEE Communications Magazine 53, no. 1 (January 2015): 102–9. http://dx.doi.org/10.1109/mcom.2015.7010522.

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Zhang, Haijun, Chunxiao Jiang, Mehdi Bennis, Merouane Debbah, Zhu Han, and Victor C. M. Leung. "Heterogeneous Ultra-Dense Networks: Part 1." IEEE Communications Magazine 55, no. 12 (December 2017): 68–69. http://dx.doi.org/10.1109/mcom.2017.8198804.

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Chen, Jun, Hongcheng Zhuang, and Zezhou Luo. "Energy Optimization in Dense OFDM Networks." IEEE Communications Letters 20, no. 1 (January 2016): 189–92. http://dx.doi.org/10.1109/lcomm.2015.2500584.

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Kartun-Giles, Alexander, Suhanya Jayaprakasam, and Sunwoo Kim. "Euclidean Matchings in Ultra-Dense Networks." IEEE Communications Letters 22, no. 6 (June 2018): 1216–19. http://dx.doi.org/10.1109/lcomm.2018.2799207.

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Han, Chaoyi, Yiping Duan, Xiaoming Tao, and Jianhua Lu. "Dense Convolutional Networks for Semantic Segmentation." IEEE Access 7 (2019): 43369–82. http://dx.doi.org/10.1109/access.2019.2908685.

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Zhang, Haijun, Chunxiao Jiang, Mehdi Bennis, Merouane Debbah, Zhu Han, and Victor C. M. Leung. "Heterogeneous Ultra Dense Networks: Part 2." IEEE Communications Magazine 56, no. 6 (June 2018): 12–13. http://dx.doi.org/10.1109/mcom.2018.8387196.

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Al-Dulaimi, Anwer, Qiang Ni, Junwei Cao, Alan Gatherer, and Chih-Lin I. "Orchestration of Ultra-Dense 5G Networks." IEEE Communications Magazine 56, no. 8 (August 2018): 68–69. http://dx.doi.org/10.1109/mcom.2018.8436048.

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Niesen, Urs. "Interference Alignment in Dense Wireless Networks." IEEE Transactions on Information Theory 57, no. 5 (May 2011): 2889–901. http://dx.doi.org/10.1109/tit.2011.2119690.

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Schuller, D. J., A. R. Rao, and G. D. Jeong. "Fractal characteristics of dense stream networks." Journal of Hydrology 243, no. 1-2 (March 2001): 1–16. http://dx.doi.org/10.1016/s0022-1694(00)00395-4.

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Arias-Castro, Ery, and Nicolas Verzelen. "Community detection in dense random networks." Annals of Statistics 42, no. 3 (June 2014): 940–69. http://dx.doi.org/10.1214/14-aos1208.

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39

Tsuda, Koji, and Elisabeth Georgii. "Dense module enumeration in biological networks." Journal of Physics: Conference Series 197 (December 1, 2009): 012012. http://dx.doi.org/10.1088/1742-6596/197/1/012012.

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Balderas, Luis, Miguel Lastra, and José M. Benítez. "Optimizing dense feed-forward neural networks." Neural Networks 171 (March 2024): 229–41. http://dx.doi.org/10.1016/j.neunet.2023.12.015.

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Yang, Bin, Guoqiang Mao, Ming Ding, Xiaohu Ge, and Xiaofeng Tao. "Dense Small Cell Networks: From Noise-Limited to Dense Interference-Limited." IEEE Transactions on Vehicular Technology 67, no. 5 (May 2018): 4262–77. http://dx.doi.org/10.1109/tvt.2018.2794452.

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Ahmed, Khaled Galal. "From ‘Orthogonal’ Sprawl to ‘Curvilinear’ Dense: Assessing Accessibility Indices for Urban Networks of Social Housing in UAE." IOP Conference Series: Materials Science and Engineering 1203, no. 2 (November 1, 2021): 022066. http://dx.doi.org/10.1088/1757-899x/1203/2/022066.

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Abstract The shift towards designing more dense urban social housing neighbourhoods has started with the embracing of urban sustainability principles by the UAE government since the beginning of the 21st century. The assessment of the recent neighbourhoods designs still lacks concrete evidence about their expected performance especially for pedestrian mobility networks. This concern is gaining further significance with the noticeable tendency of most of the recent urban designs towards developing organic and curvilinear networks instead of the conventional orthogonal grids of the mobility networks that distinguished the traditionally designed neighbourhoods in the country. To bridge this gap, the research comparatively and quantitively analysed the accessibility performance indicators of both of the traditional and the modern urban network designs. The research adopted the Case Study method with quantitative investigation tools that are fundamental to Urban Network Analysis, especially in relation to Accessibility. The simulation of the urban networks of two selected urban social housing neighbourhood forms, representing the networks of both the traditional urban orthogonal sprawl and the recent curvilinear dense one, were utilized employing the UNA toolbox. Three complementary Accessibility Indices were analysed including: Reach, Gravity and Straightness. Through this analysis, the aspects that affected the accessibility performance of the two urban form paradigms and the problems that have been associated with the designs of the urban networks of the new social housing projects, have been revealed. It became evident that the denser urban form was not sufficient in enabling more accessible facilities in the recent neighbourhoods designs. The orthogonal grid, even with its very low Floor Area Ratio showed better performance of in the three accessibility indices especially the Straightness index, if compared with the much denser curvilinear grid with it ‘naturally longer’ pattern. The inefficient number and the inappropriate distribution locally provided facilities in relation to the pedestrian mobility networks have contributed to these disappointing results. So, it is essential to include this and/or similar urban network quantitative simulation tools to help develop genuinely sustainable urban forms for this significant type of urban development in the UAE cities.

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Yang, Jie, Li Feng, Tong Jin, Hong Liang, Fangxin Xu, and Liwei Tian. "Enabling Sector Scheduling for 5G-CPE Dense Networks." Security and Communication Networks 2020 (December 28, 2020): 1–18. http://dx.doi.org/10.1155/2020/6684681.

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5G customer premise equipment (5G-CPE) is an IoT gateway technology that integrates 5G and Wi-Fi and therefore can provide Wi-Fi connection for IoT devices and meanwhile benefit from the advantages of 5G. With the increasing number of IoT devices, transmission collisions and hidden/exposed terminal problems on the Wi-Fi connection side become more and more serious. Conventional mechanisms cannot solve these problems well. In this paper, we propose a Wi-Fi sector (Wi-FiS) design, which is compatible with Wi-Fi, to solve them fundamentally. Wi-FiS divides the whole coverage area of Wi-Fi into multiple sectors and utilizes beamforming technology and sector-based scheduling to improve system performance of Wi-Fi dense networks. For a single-cell network, Wi-FiS differentiates uplink and downlink operations and totally excludes collision in downlink. For a multicell network, Wi-FiS can avoid hidden and exposed terminal problems, while enabling parallel transmissions among multiple cells. We then develop a theoretical model to analyze Wi-FiS’s throughput. Extensive simulations verify that our theoretical model is very accurate and Wi-FiS can improve system throughput of Wi-Fi dense networks significantly.

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Dapkus, Paulius, Liudas Mažeika, and Vytautas Sliesoraitis. "A study of supervised combined neural-network-based ultrasonic method for reconstruction of spatial distribution of material properties." Information Technology And Control 49, no. 3 (September 23, 2020): 381–94. http://dx.doi.org/10.5755/j01.itc.49.3.26792.

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This paper examines the performance of the commonly used neural-network-based classifiers for investigating a structural noise in metals as grain size estimation. The biggest problem which aims to identify the object structure grain size based on metal features or the object structure itself. When the structure data is obtained, a proposed feature extraction method is used to extract the feature of the object. Afterwards, the extracted features are used as the inputs for the classifiers. This research studies is focused to use basic ultrasonic sensors to obtain objects structural grain size which are used in neural network. The performance for used neural-network-based classifier is evaluated based on recognition accuracy for individual object. Also, traditional neural networks, namely convolutions and fully connected dense networks are shown as a result of grain size estimation model. To evaluate robustness property of neural networks, the original samples data is mixed for three types of grain sizes. Experimental results show that combined convolutions and fully connected dense neural networks with classifiers outperform the others single neural networks with original samples with high SN data. The Dense neural network as itself demonstrates the best robustness property when the object samples not differ from trained datasets.

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Mejia Yautentzi, Eloy, Josefina Castañeda Camacho, Gerardo Mino Aguilar, and Ignacio Enrique Zaldivar Huerta. "Performance Analysis of Ultra-dense Networks with Frequency Reuse." International Journal of Combinatorial Optimization Problems and Informatics 15, no. 5 (November 29, 2024): 218–27. http://dx.doi.org/10.61467/2007.1558.2024.v15i5.581.

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Ultra-dense networks (UDN) have become a key technology in future communications systems by deploying a large number of low-power small cell base stations (SBS). However, ultra-dense deployment of small cells in the coverage area of conventional macrocells presents new challenges, such as inter-tier interference (interference between small cells (SC) and macro cells (MC)) and both-tier interference (interference between small cells), which may affect the overall network performance. In this work, in order to mitigate interference, frequency reuse is used. The simulation was performed with MATLAB software based on Monte Carlo methodology for discrete events, with the objective of evaluating the impact of ultra-dense networks within fixed infrastructure wireless networks. The main performance indicators are the Signal to Interference Ratio (SIR) and throughput.

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Wang, Anyi, Tao Zhu, and Qifeng Meng. "Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks." Sensors 24, no. 17 (September 6, 2024): 5792. http://dx.doi.org/10.3390/s24175792.

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To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network’s feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time–frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time–frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness.

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Adel Aly, Ahmed, Hussein M. ELAttar, Hesham ElBadawy, and Wael Abbas. "Aggregated Throughput Prediction for Collated Massive Machine-Type Communications in 5G Wireless Networks." Sensors 19, no. 17 (August 22, 2019): 3651. http://dx.doi.org/10.3390/s19173651.

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The demand for extensive data rates in dense-traffic wireless networks has expanded and needs proper controlling schemes. The fifth generation of mobile communications (5G) will accommodate these massive communications, such as massive Machine Type Communications (mMTC), which is considered to be one of its top services. To achieve optimal throughput, which is considered a mandatory quality of service (QoS) metric, the carrier sense multiple access (CSMA) transmission attempt rate needs optimization. As the gradient descent algorithms consume a long time to converge, an approximation technique that distributes a dense global network into local neighborhoods that are less complex than the global ones is presented in this paper. Newton’s method of optimization was used to achieve fast convergence rates, thus, obtaining optimal throughput. The convergence rate depended only on the size of the local networks instead of global dense ones. Additionally, polynomial interpolation was used to estimate the average throughput of the network as a function of the number of nodes and target service rates. Three-dimensional planes of the average throughput were presented to give a profound description to network’s performance. The fast convergence time of the proposed model and its lower complexity are more practical than the previous gradient descent algorithm.

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Kułacz, Łukasz, and Adrian Kliks. "Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks." Journal of Telecommunications and Information Technology 1 (March 29, 2019): 39–46. http://dx.doi.org/10.26636/jtit.2019.130618.

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This paper presents developments in the area of brain-inspired wireless communications relied upon in dense wireless networks. Classic approaches to network design are complemented, ﬁrstly, by the neuroplasticity feature enabling to add the learning ability to the network. Secondly, the microglia ability enabling to repair a network with damaged neurons is considered. When combined, these two functionalities guarantee a certain level of fault-tolerance and self-repair of the network. This work is inspired primarily by observations of extremely energy eﬃcient functions of the brain, and of the role that microglia cells play in the active immune defense system. The concept is veriﬁed by computer simulations, where messages are transferred through a dense wireless network based on the assumption of minimized energy consumption. Simulation encompasses three diﬀerent network topologies which show the impact that the location of microglia nodes and their quantity exerts on network performance. Based on the results achieved, some algorithm improvements and potential future work directions have been identiﬁed.

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Fu, Junwei, and Jun Liang. "Virtual View Generation Based on 3D-Dense-Attentive GAN Networks." Sensors 19, no. 2 (January 16, 2019): 344. http://dx.doi.org/10.3390/s19020344.

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A binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm based on generative adversarial networks (GAN) is proposed to enhance the robustness of binocular vision systems. The proposed model consists of two parts: generative network and discriminator network. To improve the quality of a virtual view, a generative network structure based on 3D convolutional neural networks (3D-CNN) and attentive mechanisms is introduced to extract the time-series features from image sequences. To avoid gradient vanish during training, the dense block structure is utilized to improve the discriminator network. Meanwhile, three kinds of image features, including image edge, depth map and optical flow are extracted to constrain the supervised training of model. The final results on KITTI and Cityscapes datasets demonstrate that our algorithm outperforms conventional methods, and the missing vision signal can be replaced by a generated virtual view.

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Kim, Seungnyun, Junwon Son, and Byonghyo Shim. "Energy-Efficient Ultra-Dense Network Using LSTM-based Deep Neural Networks." IEEE Transactions on Wireless Communications 20, no. 7 (July 2021): 4702–15. http://dx.doi.org/10.1109/twc.2021.3061577.

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