• Title/Summary/Keyword: sensing optimization

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A Study on the ISAR Image Reconstruction Algorithm Using Compressive Sensing Theory under Incomplete RCS Data (데이터 손실이 있는 RCS 데이터에서 압축 센싱 이론을 적용한 ISAR 영상 복원 알고리즘 연구)

  • Bae, Ji-Hoon;Kang, Byung-Soo;Kim, Kyung-Tae;Yang, Eun-Jung
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.25 no.9
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    • pp.952-958
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    • 2014
  • In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

Sensor array optimization techniques for exhaled breath analysis to discriminate diabetics using an electronic nose

  • Jeon, Jin-Young;Choi, Jang-Sik;Yu, Joon-Boo;Lee, Hae-Ryong;Jang, Byoung Kuk;Byun, Hyung-Gi
    • ETRI Journal
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    • v.40 no.6
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    • pp.802-812
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    • 2018
  • Disease discrimination using an electronic nose is achieved by measuring the presence of a specific gas contained in the exhaled breath of patients. Many studies have reported the presence of acetone in the breath of diabetic patients. These studies suggest that acetone can be used as a biomarker of diabetes, enabling diagnoses to be made by measuring acetone levels in exhaled breath. In this study, we perform a chemical sensor array optimization to improve the performance of an electronic nose system using Wilks' lambda, sensor selection based on a principal component (B4), and a stepwise elimination (SE) technique to detect the presence of acetone gas in human breath. By applying five different temperatures to four sensors fabricated from different synthetic materials, a total of 20 sensing combinations are created, and three sensing combinations are selected for the sensor array using optimization techniques. The measurements and analyses of the exhaled breath using the electronic nose system together with the optimized sensor array show that diabetic patients and control groups can be easily differentiated. The results are confirmed using principal component analysis (PCA).

A Novel Optimization Algorithm Inspired by Bacteria Behavior Patterns

  • Jung, Sung-Hoon;Kim, Tae-Geon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.3
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    • pp.392-400
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    • 2008
  • This paper proposes a novel optimization algorithm inspired by bacteria behavior patterns for foraging. Most bacteria can trace attractant chemical molecules for foraging. This tracing capability of bacteria called chemotaxis might be optimized for foraging because it has been evolved for few millenniums. From this observation, we developed a new optimization algorithm based on the chemotaxis of bacteria in this paper. We first define behavior and decision rules based on the behavior patterns of bacteria and then devise an optimization algorithm with these behavior and decision rules. Generally bacteria have a quorum sensing mechanism that makes it possible to effectively forage, but we leave its implementation as a further work for simplicity. Thereby, we call our algorithm a simple bacteria cooperative optimization (BCO) algorithm. Our simple BCO is tested with four function optimization problems on various' parameters of the algorithm. It was found from experiments that the simple BCO can be a good framework for optimization.

Optimal Throughput of Secondary Users over Two Primary Channels in Cooperative Cognitive Radio Networks

  • Vu, Ha Nguyen;Kong, Hyung-Yun
    • Journal of electromagnetic engineering and science
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    • v.12 no.1
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    • pp.1-7
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    • 2012
  • In this paper, we investigated the throughput of a cognitive radio network where two primary frequency channels (PCs) are sensed and opportunistically accessed by N secondary users. The sharing sensing member (SSM) protocol is introduced to sense both PCs simultaneously. According to the SSM protocol, N SUs (Secondary User) are divided into two groups, which allows for the simultaneous sensing of two PCs. With a frame structure, after determining whether the PCs are idle or active during a sensing slot, the SUs may use the remaining time to transmit their own data. The throughput of the network is formulated as a convex optimization problem. We then evaluated an iterative algorithm to allocate the optimal sensing time, fusion rule and the number of members in each group. The computer simulation and numerical results show that the proposed optimal allocation improves the throughput of the SU under a misdetection constraint to protect the PCs. If not, its initial date of receipt shall be nullified.

Interference Avoidance through Pilot-Based Spectrum Sensing Algorithm in Overlaid Femtocell Networks

  • Sambanthan, Padmapriya;Muthu, Tamilarasi
    • ETRI Journal
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    • v.38 no.1
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    • pp.30-40
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    • 2016
  • Co-channel interference between macro-femtocell networks is an unresolved problem, due to the frequency reuse phenomenon. To mitigate such interference, a secondary femtocell must acquire channel-state knowledge about a co-channel macrocell user and accordingly condition the maximum transmit power of femtocell user. This paper proposes a pilot-based spectrum sensing (PSS) algorithm for overlaid femtocell networks to sense the presence of a macrocell user over a channel of interest. The PSS algorithm senses the pilot tones in the received signal through the power level and the correlation metric comparisons between the received signal and the local reference pilots. On ensuring the existence of a co-channel macrocell user, the maximum transmit power of the corresponding femtocell user is optimized so as to avoid interference. Time and frequency offsets are carefully handled in our proposal. Simulation results show that the PSS algorithm outperforms existing sensing techniques, even at poor received signal quality. It requires less sensing time and provides better detection probability over existing techniques.

Optimal Strategies for Cooperative Spectrum Sensing in Multiple Cross-over Cognitive Radio Networks

  • Hu, Hang;Xu, Youyun;Liu, Zhiwen;Li, Ning;Zhang, Hang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.12
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    • pp.3061-3080
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    • 2012
  • To improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. In this paper, we focus on the optimization of cooperative spectrum sensing in which multiple cognitive users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in multiple cross-over cognitive radio networks. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, the optimal threshold of the energy detector is derived in both noncooperative single-user and cooperative multiuser sensing scenarios. Under hard information fusion, the optimal randomized rule and the optimal decision threshold are derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each cognitive user which is randomly distributed in the multiple cross-over cognitive radio networks.

An Assessment of a Random Forest Classifier for a Crop Classification Using Airborne Hyperspectral Imagery

  • Jeon, Woohyun;Kim, Yongil
    • Korean Journal of Remote Sensing
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    • v.34 no.1
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    • pp.141-150
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    • 2018
  • Crop type classification is essential for supporting agricultural decisions and resource monitoring. Remote sensing techniques, especially using hyperspectral imagery, have been effective in agricultural applications. Hyperspectral imagery acquires contiguous and narrow spectral bands in a wide range. However, large dimensionality results in unreliable estimates of classifiers and high computational burdens. Therefore, reducing the dimensionality of hyperspectral imagery is necessary. In this study, the Random Forest (RF) classifier was utilized for dimensionality reduction as well as classification purpose. RF is an ensemble-learning algorithm created based on the Classification and Regression Tree (CART), which has gained attention due to its high classification accuracy and fast processing speed. The RF performance for crop classification with airborne hyperspectral imagery was assessed. The study area was the cultivated area in Chogye-myeon, Habcheon-gun, Gyeongsangnam-do, South Korea, where the main crops are garlic, onion, and wheat. Parameter optimization was conducted to maximize the classification accuracy. Then, the dimensionality reduction was conducted based on RF variable importance. The result shows that using the selected bands presents an excellent classification accuracy without using whole datasets. Moreover, a majority of selected bands are concentrated on visible (VIS) region, especially region related to chlorophyll content. Therefore, it can be inferred that the phenological status after the mature stage influences red-edge spectral reflectance.

Power Allocation in Heterogeneous Networks: Limited Spectrum-Sensing Ability and Combined Protection

  • Ma, Yuehuai;Xu, Youyun;Zhang, Dongmei
    • Journal of Communications and Networks
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    • v.13 no.4
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    • pp.360-366
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    • 2011
  • In this paper, we investigate the problem of power allocation in a heterogeneous network that is composed of a pair of cognitive users (CUs) and an infrastructure-based primary network. Since CUs have only limited effective spectrum-sensing ability and primary users (PUs) are not active all the time in all locations and licensed bands, we set up a new multi-area model to characterize the heterogeneous network. A novel combined interference-avoidance policy corresponding to different PU-appearance situations is introduced to protect the primary network from unacceptable disturbance and to increase the spectrum secondary-reuse efficiency. We use dual decomposition to transform the original power allocation problem into a two-layer optimization problem. We propose a low-complexity joint power-optimizing method to maximize the transmission rate between CUs, taking into account both the individual power-transmission constraints and the combined interference power constraint of the PUs. Numerical results show that for various values of the system parameters, the proposed joint optimization method with combined PU protection is significantly better than the opportunistic spectrum access mode and other heuristic approaches.

Joint Opportunistic Spectrum Access and Optimal Power Allocation Strategies for Full Duplex Single Secondary User MIMO Cognitive Radio Network

  • Yue, Wenjing;Ren, Yapeng;Yang, Zhen;Chen, Zhi;Meng, Qingmin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.10
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    • pp.3887-3907
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    • 2015
  • This paper introduces a full duplex single secondary user multiple-input multiple-output (FD-SSU-MIMO) cognitive radio network, where secondary user (SU) opportunistically accesses the authorized spectrum unoccupied by primary user (PU) and transmits data based on FD-MIMO mode. Then we study the network achievable average sum-rate maximization problem under sum transmit power budget constraint at SU communication nodes. In order to solve the trade-off problem between SU's sensing time and data transmission time based on opportunistic spectrum access (OSA) and the power allocation problem based on FD-MIMO transmit mode, we propose a simple trisection algorithm to obtain the optimal sensing time and apply an alternating optimization (AO) algorithm to tackle the FD-MIMO based network achievable sum-rate maximization problem. Simulation results show that our proposed sensing time optimization and AO-based optimal power allocation strategies obtain a higher achievable average sum-rate than sequential convex approximations for matrix-variable programming (SCAMP)-based power allocation for the FD transmission mode, as well as equal power allocation for the half duplex (HD) transmission mode.

Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

  • Ning, Bing;Zhang, Aihua;Hao, Wanming;Li, Jianjun;Yang, Shouyi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.6
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    • pp.2873-2892
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    • 2019
  • Reducing the interference to the licensed mobile users and obtaining the energy efficiency are key issues in cognitive heterogeneous networks. A corresponding rate loss constraint is proposed to be used for the sensing-based spectrum sharing (SBSS) model in cognitive heterogeneous networks in this paper. Resource allocation optimization strategy is designed for the maximum energy efficiency under the proposed interference constraint together with average transmission power constraint. An efficiency algorithm is studied to maximize energy efficiency due to the nonconvex optimal problem. Furthermore, the relationship between the proposed protection criterion and the conventional interference constraint strategy under imperfect sensing condition for the SBSS model is also investigated, and we found that the conventional interference threshold can be regarded as the upper bound of the maximum rate loss that the primary user could tolerate. Simulation results have shown the effectiveness of the proposed protection criterion overcome the conventional interference power constraint.