Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
Local Binary Patterns (LBPs) have been highly used in texture classification\nfor their robustness, their ease of implementation and their low computational\ncost. Initially designed to deal with gray level images, several methods based\non them in the literature have been proposed for images having more than\none spectral band. To achieve it, whether assumption using color information\nor combining spectral band two by two was done. Those methods use micro\nstructures as texture features. In this paper, our goal was to design texture\nfeatures which are relevant to color and multicomponent texture analysis\nwithout any assumption. Based on methods designed for gray scale images,\nwe find the combination of micro and macro structures efficient for multispectral\ntexture analysis....
The application of deep learning (DL) to solve physical layer issues has emerged as a\nprominent topic. In this paper, the mitigation of clipping effects for orthogonal frequency division\nmultiplexing (OFDM) systems with the help of a Neural Network (NN) is investigated. Unlike\nconventional clipping recovery algorithms, which involve costly iterative procedures, the DL-based\nmethod learns to directly reconstruct the clipped part of the signal while the unclipped part is\nprotected. Furthermore, an interpretation of the learned weight matrices of the neural network is\npresented. It is observed that parts of the network, in effect, implement transformations very similar\nto the (Inverse) Discrete Fourier Transform (DFT/IDFT) to provide information in both the time and\nfrequency domains. The simulation results show that the proposed method outperforms existing\nalgorithms for recovering clipped OFDM signals in terms of both mean square error (MSE) and Bit\nError Rate (BER)....
High capacity long haul communication and cost-effective solutions for low loss\ntransmission are the major advantages of optical fibers, which makes them a promising solution\nto be used for backhaul network transportation. A distortion-tolerant 100 Gbps framework that\nconsists of long haul and high capacity transport based wavelength division multiplexed (WDM)\nsystem is investigated in this paper, with an analysis on different design parameters to mitigate\nthe amplified spontaneous emission (ASE) noise and nonlinear effects due to the fiber transmission.......................
This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of\nWireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon\nwith transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the realtime\nlink quality accurately. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential\nsmoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize\na complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The intermittent\nKarman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and\nonline learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are\nconducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive\nsensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area\nand confirm the validity of the detection method....
In the last years, the commercial drone/unmanned aerial vehicles market has grown due to\ntheir technological performances (provided by the multiple onboard available sensors), low price,\nand ease of use. Being very attractive for an increasing number of applications, their presence\nrepresents a major issue for public or classified areas with a special status, because of the rising\nnumber of incidents. Our paper proposes a new approach for the drone movement detection and\ncharacterization based on the ultra-wide band (UWB) sensing system and advanced signal processing\nmethods. This approach characterizes the movement of the drone using classical methods such\nas correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot\nanalysis. The obtained results are compared in terms of movement map accuracy and required\ncomputation time in order to offer a future starting point for the drone intrusion detection....
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