Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
In recent years, folding techniques are widely used by many architects to\nmake 3D forms from 2D sheets as an inspiration for their design, which\nenables simpler and more intuitive solutions for architectural realization. This\nresearch provides an overview of using folding techniques in architecture design,\nwith an emphasis on their new applications. In this overview, we classify\nfolding techniques as computation geometry folding techniques and manual\nfolding techniques. Finally, we provide recommendations for future development....
In this paper, we address the generation of semantic labels describing the headgear\naccessories carried out by people in a scene under surveillance, only using depth information\nobtained from a Time-of-Flight (ToF) camera placed in an overhead position. We propose a new\nmethod for headgear accessories classification based on the design of a robust processing strategy that\nincludes the estimation of a meaningful feature vector that provides the relevant information about\nthe people�s head and shoulder areas. This paper includes a detailed description of the proposed\nalgorithmic approach, and the results obtained in tests with persons with and without headgear\naccessories, and with different types of hats and caps. In order to evaluate the proposal, a wide\nexperimental validation has been carried out on a fully labeled database (that has been made available\nto the scientific community), including a broad variety of people and headgear accessories. For the\nvalidation, three different levels of detail have been defined, considering a different number of classes:\nthe first level only includes two classes (hat/cap, and no hat/cap), the second one considers three\nclasses (hat, cap and no hat/cap), and the last one includes the full class set with the five classes\n(no hat/cap, cap, small size hat, medium size hat, and large size hat). The achieved performance is\nsatisfactory in every case: the average classification rates for the first level reaches 95.25%, for the\nsecond one is 92.34%, and for the full class set equals 84.60%. In addition, the online stage processing\ntime is 5.75 ms per frame in a standard PC, thus allowing for real-time operation....
Similar to traditional wireless sensor networks (WSN), the nodes only have limited memory\nand energy in low-duty-cycle sensor networks (LDC-WSN). However, different from WSN, the nodes\nin LDC-WSN often sleep most of their time to preserve their energies. The sleeping feature causes\nserious data transmission delay. However, each source node that has sensed data needs to quickly\ndisseminate its data to other nodes in the network for redundant storage. Otherwise, data would\nbe lost due to its source node possibly being destroyed by outer forces in a harsh environment. The\nquick dissemination requirement produces a contradiction with the sleeping delay in the network.\nHow to quickly disseminate all the source data to all the nodes with limited memory in the network\nfor effective preservation is a challenging issue. In this paper, a low-latency and energy-efficient data\npreservation mechanism in LDC-WSN is proposed. The mechanism is totally distributed. The data\ncan be disseminated to the network with low latency by using a revised probabilistic broadcasting\nmechanism, and then stored by the nodes with LT (Luby Transform) codes, which are a famous\nrateless erasure code. After the process of data dissemination and storage completes, some nodes\nmay die due to being destroyed by outer forces. If a mobile sink enters the network at any time\nand from any place to collect the data, it can recover all of the source data by visiting a small\nportion of survived nodes in the network. Theoretical analyses and simulation results show that our\nmechanism outperforms existing mechanisms in the performances of data dissemination delay and\nenergy efficiency....
The literature contains several reports evaluating the abilities of deep neural networks in\ntext transfer learning. To our knowledge, however, there have been few efforts to fully realize the\npotential of deep neural networks in cross-domain product review sentiment classification. In this\npaper, we propose a two-layer convolutional neural network (CNN) for cross-domain product review\nsentiment classification (LM-CNN-LB). Transfer learning research into product review sentiment\nclassification based on deep neural networks has been limited by the lack of a large-scale corpus;\nwe sought to remedy this problem using a large-scale auxiliary cross-domain dataset collected from\nAmazon product reviews. Our proposed framework exhibits the dramatic transferability of deep\nneural networks for cross-domain product review sentiment classification and achieves state-of-the-art\nperformance. The framework also outperforms complex engineered features used with a non-deep\nneural network method. The experiments demonstrate that introducing large-scale data from similar\ndomains is an effective way to resolve the lack of training data. The LM-CNN-LB trained on the\nmulti-source related domain dataset outperformed the one trained on a single similar domain....
In wireless relaying networks, the traditional incremental cooperative relaying\nscheme (IR) could improve the system throughput enormously over fading\nchannels by exploiting relay nodes to retransmit the source data packet to the\ndestination. In order to enhance the system throughput over time-varying\nfading channels, this paper proposes an adaptive incremental cooperative relaying\nscheme (AIR) based on adaptive modulation and coding, which implements\nadaptive rate transmission for the source and relay nodes according\nto channel state information. We derive expressions for the AIR system\nthroughput, and then give a gradient-based search algorithm to find the optimized\nadaptive solution for the AIR system by maximizing throughput subject\nto the constraint of packet error rate at the data link layer. The results indicate\nthat throughput of AIR system outperforms that of traditional IR system\ngreatly for any SNR value....
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