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Journal articles on the topic 'Detection for 2D ISI Channel'
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Song, Lijun, Xia Lei, Maozhu Jin, and Zhihan Lv. "Joint Channel Estimation and Signal Detection for the OFDM System Without Cyclic Prefix Over Doubly-Selective Channels." International Journal of Bifurcation and Chaos 25, no. 14 (2015): 1540028. http://dx.doi.org/10.1142/s0218127415400283.
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In the high-speed railway wireless communication, a joint channel estimation and signal detection algorithm is proposed for the orthogonal frequency division multiplexing (OFDM) system without cyclic prefix in the doubly-selective fading channels. Our proposed method first combines the basis expansion model (BEM) and the inter symbol interference (ISI) cancellation to overcome the situation that exists with the fast time-varying channel and the normalized maximum multipath channel exceeding the length of the cyclic prefix (CP). At first, the channel estimation and signal detection can be approximated without considering the ISI. Then, the channel parameters and signal detection are updated through ISI cancellation and circular convolution reconstruction from the frequency domain. The simulations show the algorithm can improve the performance of channel estimation and signal detection.
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Sripimanwat, K., H. Weinrichter, R. M. A. P. Rajatheva, and K. Ahmed. "Soft-detection phase precoding with MPSK-TCM for ISI channel." IEEE Communications Letters 5, no. 4 (2001): 163–65. http://dx.doi.org/10.1109/4234.917101.
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Modenini, Andrea, Fredrik Rusek, and Giulio Colavolpe. "Optimal Transmit Filters for ISI Channels under Channel Shortening Detection." IEEE Transactions on Communications 61, no. 12 (2013): 4997–5005. http://dx.doi.org/10.1109/tcomm.2013.110813.130385.
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Wo, Tianbin, and Peter Adam Hoeher. "Low-Complexity Gaussian Detection for MIMO Systems." Journal of Electrical and Computer Engineering 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/609509.
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For single-carrier transmission over delay-spread multi-input multi-output (MIMO) channels, the computational complexity of the receiver is often considered as a bottleneck with respect to (w.r.t.) practical implementations. Multi-antenna interference (MAI) together with intersymbol interference (ISI) provides fundamental challenges for efficient and reliable data detection. In this paper, we carry out a systematic study on the interference structure of MIMO-ISI channels, and sequentially deduce three different Gaussian approximations to simplify the calculation of the global likelihood function. Using factor graphs as a general framework and applying the Gaussian approximation, three low-complexity iterative detection algorithms are derived, and their performances are compared by means of Monte Carlo simulations. After a careful inspection of their merits and demerits, we propose a graph-based iterative Gaussian detector (GIGD) for severely delay-spread MIMO channels. The GIGD is characterized by a strictly linear computational complexity w.r.t. the effective channel memory length, the number of transmit antennas, and the number of receive antennas. When the channel has a sparse ISI structure, the complexity of the GIGD is strictly proportional to the number of nonzero channel taps. Finally, the GIGD provides a near-optimum performance in terms of the bit error rate (BER) for repetition encoded MIMO systems.
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A. Alhaidari, Fahd, Saleh A. Al-Dossary, Ilyas A. Salih, et al. "Automatic Channel Detection Using DNN on 2D Seismic Data." Computer Systems Science and Engineering 36, no. 1 (2021): 57–67. http://dx.doi.org/10.32604/csse.2021.013843.
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Hu, Sha, Xiang Gao, and Fredrik Rusek. "Linear Precoder Design for MIMO-ISI Broadcasting Channels Under Channel Shortening Detection." IEEE Signal Processing Letters 23, no. 9 (2016): 1207–11. http://dx.doi.org/10.1109/lsp.2016.2592968.
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Wang, Xinlei, Zhenqiang Wu, and Zhen Jia. "Improving Reliability Performance of Molecular Communication Based on Drift Diffusion with Ratio Detection Algorithm." International Journal of Nanoscience 20, no. 03 (2021): 2150026. http://dx.doi.org/10.1142/s0219581x21500265.
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Reliability is a vital issue in the area of communication. In this paper, we particularly investigate the reliability issue for molecular communication based on drift diffusion (MCD2). Since molecules easily accumulate in the channel to produce strong internal symbol interference (ISI), the receiver nanomachine will generate high bit error rate (BER) for the process of decode information. Based on this problem, on the premise of considering channel diffusion noise and ISI noise, the expression of channel BER is deduced to analyze reliability. Then a ratio detection algorithm (RDA) is proposed to reduce BER to improve the reliability performance that enables the receiver nanomachine to adapt the channel condition. Furthermore, an expression of signal to interference plus noise ratio is defined in numerical simulation to verify our goal with different parameters, as well as with the adoption of RDA. The results indicate that the performance of RDA in reducing BER works well in general case in improving reliability performance for MCD2.
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Li, Zhi Yong, Xiang Lu Meng, Kai Dong, Shuai Yan, and De Qiang Wang. "Layered Channel Estimation for High Data Rate DS-UWB." Advanced Materials Research 926-930 (May 2014): 1966–69. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1966.
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In this paper, a layered channel estimation method is proposed for DS-UWB systems, where both ISI and IPI are taken into consideration. The multipath components are classified into layers with respect to their time delays and estimated layer-by-layer iteratively. In each layer, the ISI and IPI are mitigated by using the inherent characteristics of bipolar symbols and prior knowledge of the spreading code sequence and pilot symbols. A detection-directed refinement procedure is used to improve the estimation accuracy further. Simulation results show that the proposed scheme can be used to gain precise channel state information.
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Sun, Wei, and Hongbin Li. "Blind channel estimation and detection for space–time coded CDMA in ISI channels." Digital Signal Processing 17, no. 1 (2007): 280–96. http://dx.doi.org/10.1016/j.dsp.2004.04.004.
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Schober, R., and L. Lampe. "Sequence Detection and Adaptive Channel Estimation for ISI Channels Under Class-A Impulsive Noise." IEEE Transactions on Communications 52, no. 9 (2004): 1523–31. http://dx.doi.org/10.1109/tcomm.2004.833197.
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Myint, Lin Min Min, and Chanon Warisarn. "Equalizer Design for Bit-Patterned Media Recording System Based on ISI and ITI Estimations by Cross Correlation Functions." Applied Mechanics and Materials 781 (August 2015): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amm.781.223.
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Due to ultra-high density, a bit-patterned media recording (BPMR) system experiences two-dimensional (2D) interference--inter-symbol interference (ISI) and inter-track interference (ITI)--which degrades the overall performance of a recording systems. Therefore, we propose a novel single-track equalization and a single track detection that can perform these duties almost equally well. Our novel equalizer design uses ISI and ITI estimation schemes with the help of cross-correlation functions. The simulation result shows that our proposed method is able to achieve a significant performance gain over the one-dimensional (1D) equalization method and the conventional joint-track equalization, especially at higher recording density.
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Kwon, SoonSub, and TaeHyoung Park. "Channel-Based Network for Fast Object Detection of 3D LiDAR." Electronics 9, no. 7 (2020): 1122. http://dx.doi.org/10.3390/electronics9071122.
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Currently, there are various methods of LiDAR-based object detection networks. In this paper, we propose a channel-based object detection network using LiDAR channel information. The proposed method is a 2D convolution network with data alignment processing stages including a single-step detection stage. The network consists of a channel internal convolution network, channel external convolution network and detection network. First, the convolutional network within the channel divides the LiDAR data for each channel to find features within the channel. Second, the convolutional network outside the channel combines the LiDAR data divided for each channel to find features between the channels. Finally, the detection network finds objects with the features obtained. We evaluate our proposed network using our 16-channel lidar and popular KITTI dataset. We can confirm that the proposed method detects objects quickly while maintaining performance when compared with the existing network.
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Galarreta, Betty C., Mohammadali Tabatabaei, Valérie Guieu, Eric Peyrin, and François Lagugné-Labarthet. "Microfluidic channel with embedded SERS 2D platform for the aptamer detection of ochratoxin A." Analytical and Bioanalytical Chemistry 405, no. 5 (2012): 1613–21. http://dx.doi.org/10.1007/s00216-012-6557-7.
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Wang, Li, Ruifeng Li, Hezi Shi, et al. "Multi-Channel Convolutional Neural Network Based 3D Object Detection for Indoor Robot Environmental Perception." Sensors 19, no. 4 (2019): 893. http://dx.doi.org/10.3390/s19040893.
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Environmental perception is a vital feature for service robots when working in an indoor environment for a long time. The general 3D reconstruction is a low-level geometric information description that cannot convey semantics. In contrast, higher level perception similar to humans requires more abstract concepts, such as objects and scenes. Moreover, the 2D object detection based on images always fails to provide the actual position and size of an object, which is quite important for a robot’s operation. In this paper, we focus on the 3D object detection to regress the object’s category, 3D size, and spatial position through a convolutional neural network (CNN). We propose a multi-channel CNN for 3D object detection, which fuses three input channels including RGB, depth, and bird’s eye view (BEV) images. We also propose a method to generate 3D proposals based on 2D ones in the RGB image and semantic prior. Training and test are conducted on the modified NYU V2 dataset and SUN RGB-D dataset in order to verify the effectiveness of the algorithm. We also carry out the actual experiments in a service robot to utilize the proposed 3D object detection method to enhance the environmental perception of the robot.
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Ting, Yu-Ching, Fang-Wen Lo, and Pei-Yun Tsai. "Implementation for Fetal ECG Detection from Multi-channel Abdominal Recordings with 2D Convolutional Neural Network." Journal of Signal Processing Systems 93, no. 9 (2021): 1101–13. http://dx.doi.org/10.1007/s11265-021-01676-w.
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Leftah, Hussein. "Performance Evaluation of DHT Based Optical OFDM for IM/DD Transmission Over Diffused Multipath Optical Wireless Channel." Iraqi Journal for Electrical and Electronic Engineering 15, no. 1 (2019): 72–75. http://dx.doi.org/10.37917/ijeee.15.1.7.
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Optical OFDM based on discrete Hartley transform (DHT-O-OFDM) has been proposed for large-size data mapping intensity modulation direct detection (IM/DD) scheme as an alternative to the conventional optical OFDM. This paper presents a performance analysis and evaluation of IM/DD optical DC-biased DHT-O-OFDM over diffused multipath optical wireless channels. Zero-padding guard interval along with minimum mean-square error (MMSE) equalizer are used in electrical domain after the direct detection to remove the intersymbol interference (ISI) and eliminate the deleterious effects of the multipath channels. Simulation results show that the ZP-MMSE can effectively reduce the effects of multipath channels. The results also show that the effects of optical wireless multipath channel become more serious as the data signaling order increases.
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Lou, Huihui, Chong Shen, Qun Xiang, Jiaqiang Xu, and Tianjun Lou. "FDU-12 Mesoporous Materials Detection Hg (II) Ions by QCM." Nano 11, no. 08 (2016): 1650094. http://dx.doi.org/10.1142/s1793292016500946.
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Thiol-functionalized three-dimensional (3D) mesoporous silica FDU-12 and SBA-15 with ordered pore were prepared. All the obtained materials were characterized by small angle X-ray scattering (SAXS), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Mesoporous silica FDU-12 (Fm3m) materials with various unit cell sizes, multifaceted pore were convenient for the interaction of subject and object; the 2D SBA-15 mesoporous silica materials with short and order pore channel were better than the traditional SBA-15 mesoporous silica, in which the pore channel can be used fully. 3D mesoporous silica FDU-12 is used as a sensing material to reconstruct QCM sensors to enhance the stability and this material is an ideal material to deal with heavy metal ions in water.
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Wu, Xiren, Xin Shen, Shuran Fan, et al. "The utilization of a stable 2D bilayer MOF for simultaneous study of luminescent and photocatalytic properties: experimental studies and theoretical analysis." RSC Advances 8, no. 42 (2018): 23529–38. http://dx.doi.org/10.1039/c8ra04145h.
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Karbalaali, Haleh, Abdolrahim Javaherian, Stephan Dahlke, and Siyavash Torabi. "Channel edge detection using 2D complex shearlet transform: a case study from the South Caspian Sea." Exploration Geophysics 49, no. 5 (2018): 704–12. http://dx.doi.org/10.1071/eg17057.
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Cha, Jun-Hwe, Seon-Jin Choi, Sunmoon Yu, and Il-Doo Kim. "2D WS2-edge functionalized multi-channel carbon nanofibers: effect of WS2 edge-abundant structure on room temperature NO2 sensing." Journal of Materials Chemistry A 5, no. 18 (2017): 8725–32. http://dx.doi.org/10.1039/c6ta11019c.
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WS<sub>2</sub> edge-abundant structure is successfully achieved in multi-channel carbon nanofibers, which allows 2D WS<sub>2</sub>-edge functionalization on carbon matrix toward NO<sub>2</sub> sensing at room temperature with remarkable detection property.
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Kuang, Jing-ming, Yuan Zhou, and Ze-song Fei. "Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems." Frontiers of Information Technology & Electronic Engineering 18, no. 6 (2017): 841–49. http://dx.doi.org/10.1631/fitee.1700025.
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SAKOWICZ, M., J. ŁUSAKOWSKI, K. KARPIERZ, M. GRYNBERG, and G. VALUSIS. "HIGH MAGNETIC FIELD IN THz PLASMA WAVE DETECTION BY HIGH ELECTRON MOBILITY TRANSISTORS." International Journal of Modern Physics B 23, no. 12n13 (2009): 3029–34. http://dx.doi.org/10.1142/s0217979209062761.
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The role of gated and ungated two dimensional (2D) electron plasma in THz detection by high electron mobility transistors (HEMTs) was investigated. THz response of GaAs / AlGaAs and GaN / AlGaN HEMTs was measured at 4.4K in quantizing magnetic fields with a simultaneous modulation of the gate voltage UGS. This allowed us to measure both the detection signal, S, and its derivative d S/ d UGS. Shubnikov - de-Haas oscillations (SdHO) of both S and d S/ d UGS were observed. A comparison of SdHO observed in detection and magnetoresistance measurements allows us to associate unambiguously SdHO in S and d S/ d UGS with the ungated and gated parts of the transistor channel, respectively. This allows us to conclude that the entire channel takes part in the detection process. Additionally, in the case of GaAlAs / GaAs HEMTs, a structure related to the cyclotron resonance transition was observed.
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Theoharatos, C., A. Makedonas, N. Fragoulis, V. Tsagaris, and S. Costicoglou. "DETECTION OF SHIP TARGETS IN POLARIMETRIC SAR DATA USING 2D-PCA DATA FUSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1017–24. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1017-2015.
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Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.
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Karbalaali, Haleh, Abdolrahim Javaherian, Stephan Dahlke, and Siyavash Torabi. "Channel boundary detection based on 2D shearlet transformation: An application to the seismic data in the South Caspian Sea." Journal of Applied Geophysics 146 (November 2017): 67–79. http://dx.doi.org/10.1016/j.jappgeo.2017.09.001.
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Borisova, Ekaterina, Borislav Vladimirov, and Latchezar Avramov. "5-ALA Mediated Fluorescence Detection of Gastrointestinal Tumors." Advances in Optical Technologies 2008 (August 31, 2008): 1–7. http://dx.doi.org/10.1155/2008/862081.
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Delta-aminolevulinic acid/protoporphyrin IX is applied for fluorescent tumor detection in the upper part of gastrointestinal tract. The 5-ALA is administered per os six hours before measurements at dose 20 mg/kg weight. High-power light-emitting diode at 405 nm is used as an excitation source. Special opto-mechanical device is built to use the light guide of standard video-endoscopic system. Through endoscopic instrumental channel a fiber is applied to return information about fluorescence to microspectrometer. In such way, 1D detection and 2D visualization of the lesions' fluorescence are received, and both advantages and limitations of these methodologies are discussed in relation to their clinical applicability. Comparison of the spectra received from normal mucosa, inflammatory, and tumor areas is applied to evaluate the feasibility for development of simple but effective algorithm based on dimensionless ratio of the fluorescence signals at 560 and 635 nm, for differentiation of normal/abnormal gastrointestinal tissues.
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Sharma, Mahendra, and Santhosh Kumar Singh. "Orthogonality Measurent of OFDM Signal." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 595. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp595-598.
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<p>In recent days, Orthogonal Frequency Division Multiplexing is the technique to transmit and receive the signal without any overlapping of the signal. OFDM is also a multiplexing technique as well as modulation technique. It is a multi-carrier transmission technique in which single high data stream is divided into a number of lower rate streams that are transmitted simultaneous over some narrow sub channel. In general, OFDM avoids Inter-Symbol Interference (ISI), Inter-Carrier Interference (ICI) and fault transmissions between source and destination node. To measure the performance of the system by using the parameters like Bit Error rate (BER), Spectrum analysis and signal strength detection. Based on the parameters the best system can be identified.</p>
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Khan, Rahim, Qiang Yang, Ahsan Bin Tufail, Alam Noor, and Yong-Kui Ma. "Classification of Digital Modulated COVID-19 Images in the Presence of Channel Noise Using 2D Convolutional Neural Networks." Wireless Communications and Mobile Computing 2021 (July 12, 2021): 1–15. http://dx.doi.org/10.1155/2021/5539907.
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The wireless environment poses a significant challenge to the propagation of signals. Different effects such as multipath scattering, noise, degradation, distortion, attenuation, and fading affect the distribution of signals adversely. Deep learning techniques can be used to differentiate among different modulated signals for reliable detection in a communication system. This study aims at distinguishing COVID-19 disease images that have been modulated by different digital modulation schemes and are then passed through different noise channels and classified using deep learning models. We proposed a comprehensive evaluation of different 2D Convolutional Neural Network (CNN) architectures for the task of multiclass (24-classes) classification of modulated images in the presence of noise and fading. It is used to differentiate between images modulated through Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16- and 64-Quadrature Amplitude Modulation and passed through Additive White Gaussian Noise, Rayleigh, and Rician channels. We obtained mixed results under different settings such as data augmentation, disharmony between batch normalization (BN), and dropout (DO), as well as lack of BN in the network. In this study, we found that the best performing model is a 2D-CNN model using disharmony between BN and DO techniques trained using 10-fold cross-validation (CV) with a small value of DO before softmax and after every convolution and fully connected layer along with BN layers in the presence of data augmentation, while the least performing model is the 2D-CNN model trained using 5-fold CV without augmentation.
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Vaillant de Guélis, Thibault, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu. "Two-dimensional and multi-channel feature detection algorithm for the CALIPSO lidar measurements." Atmospheric Measurement Techniques 14, no. 2 (2021): 1593–613. http://dx.doi.org/10.5194/amt-14-1593-2021.
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Abstract. In this paper, we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context-sensitive feature finder algorithm is applied to a 2-D lidar “scene”, i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2-D region of enhanced backscatter signal rises significantly above the expected “clear air” value. Using an iterated 2-D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for contiguous 2-D patterns using successively lower detection thresholds, it reports strongly scattering features separately from weakly scattering features, thus potentially offering improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2-D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e., the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds.
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Alshammri, Ghalib H., Walid K. M. Ahmed, and Victor B. Lawrence. "ARFIS: Adaptive-Receiver-Based Fuzzy Inference System for Diffusion- Based Molecular Communications." Current Nanoscience 16, no. 2 (2020): 280–89. http://dx.doi.org/10.2174/1573413715666190625114949.
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Background: The architecture and sequential learning rule-based underlying ARFIS (adaptive-receiver-based fuzzy inference system) are proposed to estimate and predict the adaptive threshold-based detection scheme for diffusion-based molecular communication (DMC). Method: The proposed system forwards an estimate of the received bits based on the current molecular cumulative concentration, which is derived using sequential training-based principle with weight and bias and an input-output mapping based on both human knowledge in the form of fuzzy IFTHEN rules. The ARFIS architecture is employed to model nonlinear molecular communication to predict the received bits over time series. Result: This procedure is suitable for binary On-OFF-Keying (Book signaling), where the receiver bio-nanomachine (Rx Bio-NM) adapts the 1/0-bit detection threshold based on all previous received molecular cumulative concentrations to alleviate the inter-symbol interference (ISI) problem and reception noise. Conclusion: Theoretical and simulation results show the improvement in diffusion-based molecular throughput and the optimal number of molecules in transmission. Furthermore, the performance evaluation in various noisy channel sources shows promising improvement in the un-coded bit error rate (BER) compared with other threshold-based detection schemes in the literature.
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van Houtum, Wim J., and Frans M. J. Willems. "Two-Dimensional Iterative Processing for DAB Receivers Based on Trellis-Decomposition." Journal of Electrical and Computer Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/394809.
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We investigate iterative trellis decoding techniques for DAB, with the objective of gaining from processing 2D-blocks in an OFDM scheme, that is, blocks based on the time and frequency dimension, and from trellis decomposition. Trellis-decomposition methods allow us to estimate the unknown channel phase since this phase relates to the sub-trellises. We will determine a-posteriori sub-trellis probabilities, and use these probabilities for weighting the a-posteriori symbol probabilities resulting from all the sub-trellises. Alternatively we can determine a dominant sub-trellis and use the a-posteriori symbol probabilities corresponding to this dominant sub-trellis. This dominant sub-trellis approach results in a significant complexity reduction. We will investigate both iterative and non-iterative methods. The advantage of non-iterative methods is that their forwardbackward procedures are extremely simple; however, also their gain of 0.7 dB, relative to two-symbol differential detection (2SDD) at a BER of10-4, is modest. Iterative procedures lead to the significantly larger gain of 3.7 dB at a BER of10-4for five iterations, where a part of this gain comes from 2D processing. Simulations of our iterative approach applied to the TU-6 (COST207) channel show that we get an improvement of 2.4 dB at a Doppler frequency of 10 Hz.
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Lajkó, Gábor, Renáta Nagyné Elek, and Tamás Haidegger. "Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery." Sensors 21, no. 16 (2021): 5412. http://dx.doi.org/10.3390/s21165412.
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Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic—acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors—or visual input data can be employed to perform objective algorithm-driven skill assessment. Kinematic data have been successfully linked with the expertise of surgeons performing Robot-Assisted Minimally Invasive Surgery (RAMIS) procedures, but for traditional, manual Minimally Invasive Surgery (MIS), they are not readily available as a method. 3D visual features-based evaluation methods tend to outperform 2D methods, but their utility is limited and not suited to MIS training, therefore our proposed solution relies on 2D features. The application of additional sensors potentially enhances the performance of either approach. This paper introduces a general 2D image-based solution that enables the creation and application of surgical skill assessment in any training environment. The 2D features were processed using the feature extraction techniques of a previously published benchmark to assess the attainable accuracy. We relied on the JHU–ISI Gesture and Skill Assessment Working Set dataset—co-developed by the Johns Hopkins University and Intuitive Surgical Inc. Using this well-established set gives us the opportunity to comparatively evaluate different feature extraction techniques. The algorithm reached up to 95.74% accuracy in individual trials. The highest mean accuracy—averaged over five cross-validation trials—for the surgical subtask of Knot-Tying was 83.54%, for Needle-Passing 84.23% and for Suturing 81.58%. The proposed method measured well against the state of the art in 2D visual-based skill assessment, with more than 80% accuracy for all three surgical subtasks available in JIGSAWS (Knot-Tying, Suturing and Needle-Passing). By introducing new visual features—such as image-based orientation and image-based collision detection—or, from the evaluation side, utilising other Support Vector Machine kernel methods, tuning the hyperparameters or using other classification methods (e.g., the boosted trees algorithm) instead, classification accuracy can be further improved. We showed the potential use of optical flow as an input for RAMIS skill assessment, highlighting the maximum accuracy achievable with these data by evaluating it with an established skill assessment benchmark, by evaluating its methods independently. The highest performing method, the Residual Neural Network, reached means of 81.89%, 84.23% and 83.54% accuracy for the skills of Suturing, Needle-Passing and Knot-Tying, respectively.
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Li, Hao, Sanyuan Zhao, Wenjun Zhao, Libin Zhang, and Jianbing Shen. "One-Stage Anchor-Free 3D Vehicle Detection from LiDAR Sensors." Sensors 21, no. 8 (2021): 2651. http://dx.doi.org/10.3390/s21082651.
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Recent one-stage 3D detection methods generate anchor boxes with various sizes and orientations in the ground plane, then determine whether these anchor boxes contain any region of interest and adjust the edges of them for accurate object bounding boxes. The anchor-based algorithm calculates the classification and regression label for each anchor box during the training process, which is inefficient and complicated. We propose a one-stage, anchor-free 3D vehicle detection algorithm based on LiDAR point clouds. The object position is encoded as a set of keypoints in the bird’s-eye view (BEV) of point clouds. We apply the voxel/pillar feature extractor and convolutional blocks to map an unstructured point cloud to a single-channel 2D heatmap. The vehicle’s Z-axis position, dimension, and orientation angle are regressed as additional attributes of the keypoints. Our method combines SmoothL1 loss and IoU (Intersection over Union) loss, and we apply (cosθ,sinθ) as angle regression labels, which achieve high average orientation similarity (AOS) without any direction classification tricks. During the target assignment and bounding box decoding process, our framework completely avoids any calculations related to anchor boxes. Our framework is end-to-end training and stands at the same performance level as the other one-stage anchor-based detectors.
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Kim, Jong Bae. "Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera." Symmetry 11, no. 10 (2019): 1205. http://dx.doi.org/10.3390/sym11101205.
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In this paper a method for detecting and estimating the distance of a vehicle driving in front using a single black-box camera installed in a vehicle was proposed. In order to apply the proposed method to autonomous vehicles, it was required to reduce the throughput and speed-up the processing. To do this, the proposed method decomposed the input image into multiple-resolution images for real-time processing and then extracted the aggregated channel features (ACFs). The idea was to extract only the most important features from images at different resolutions symmetrically. A method of detecting an object and a method of estimating a vehicle’s distance from a bird’s eye view through inverse perspective mapping (IPM) were applied. In the proposed method, ACFs were used to generate the AdaBoost-based vehicle detector. The ACFs were extracted from the LUV color, edge gradient, and orientation (histograms of oriented gradients) of the input image. Subsequently, by applying IPM and transforming a 2D input image into 3D by generating an image projected in three dimensions, the distance between the detected vehicle and the autonomous vehicle was detected. The proposed method was applied in a real-world road environment and showed accurate results for vehicle detection and distance estimation in real-time processing. Thus, it was showed that our method is applicable to autonomous vehicles.
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Xu, Chi, Jun Zhou, Wendi Cai, Yunkai Jiang, Yongbo Li, and Yi Liu. "Robust 3D Hand Detection from a Single RGB-D Image in Unconstrained Environments." Sensors 20, no. 21 (2020): 6360. http://dx.doi.org/10.3390/s20216360.
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Three-dimensional hand detection from a single RGB-D image is an important technology which supports many useful applications. Practically, it is challenging to robustly detect human hands in unconstrained environments because the RGB-D channels can be affected by many uncontrollable factors, such as light changes. To tackle this problem, we propose a 3D hand detection approach which improves the robustness and accuracy by adaptively fusing the complementary features extracted from the RGB-D channels. Using the fused RGB-D feature, the 2D bounding boxes of hands are detected first, and then the 3D locations along the z-axis are estimated through a cascaded network. Furthermore, we represent a challenging RGB-D hand detection dataset collected in unconstrained environments. Different from previous works which primarily rely on either the RGB or D channel, we adaptively fuse the RGB-D channels for hand detection. Specifically, evaluation results show that the D-channel is crucial for hand detection in unconstrained environments. Our RGB-D fusion-based approach significantly improves the hand detection accuracy from 69.1 to 74.1 comparing to one of the most state-of-the-art RGB-based hand detectors. The existing RGB- or D-based methods are unstable in unseen lighting conditions: in dark conditions, the accuracy of the RGB-based method significantly drops to 48.9, and in back-light conditions, the accuracy of the D-based method dramatically drops to 28.3. Compared with these methods, our RGB-D fusion based approach is much more robust without accuracy degrading, and our detection results are 62.5 and 65.9, respectively, in these two extreme lighting conditions for accuracy.
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Koonkarnkhai, Santi, Piya Kovintavewat, and Phongsak Keeratiwintakorn. "Trellis-Based Detecting the Insertion and Deletion Bits for Bit-Patterned Media Recording." Advanced Materials Research 979 (June 2014): 54–57. http://dx.doi.org/10.4028/www.scientific.net/amr.979.54.
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Bit-patterned media recording (BPMR) is one of the promising technologies for realizing an areal density up to 4 Tb/in2; however, it poses new challenges to read channel design, including the two-dimensional (2D) interference, media noise, and track mis-registration. Furthermore, the BPMR system encounters the insertion, deletion and substitution errors, which are primarily caused by mis-synchronization between the write clock and the island positions. In this paper, we propose a novel detection method that exploits the trellis structure to detect the occurrence of insertion/deletion bits. Specifically, the specific marker bits are inserted periodically inside an input data sequence before recording onto a magnetic medium. Hence, the branch metric calculation is monitored during the marker bits to determine if there is any insertion/deletion error in the system. Numerical results indicate that the proposed method can performs better than the conventional one in terms of the percentage of detection and the percentage of missed detection and false-alarm, especially at low signal-to-noise ratio scenario.
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Dong, Shuai, Wei Wang, Wensheng Li, and Kun Zou. "Vectorization of Floor Plans Based on EdgeGAN." Information 12, no. 5 (2021): 206. http://dx.doi.org/10.3390/info12050206.
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A 2D floor plan (FP) often contains structural, decorative, and functional elements and annotations. Vectorization of floor plans (VFP) is an object detection task that involves the localization and recognition of different structural primitives in 2D FPs. The detection results can be used to generate 3D models directly. The conventional pipeline of VFP often consists of a series of carefully designed complex algorithms with insufficient generalization ability and suffer from low computing speed. Considering the VFP is not suitable for deep learning-based object detection frameworks, this paper proposed a new VFP framework to solve this problem based on a generative adversarial network (GAN). First, a private dataset called ZSCVFP is established. Unlike current public datasets that only own not more than 5000 black and white samples, ZSCVFP contains 10,800 colorful samples disturbed by decorative textures in different styles. Second, a new edge-extracting GAN (EdgeGAN) is designed for the new task by formulating the VFP task as an image translation task innovatively that involves the projection of the original 2D FPs into a primitive space. The output of EdgeGAN is a primitive feature map, each channel of which only contains one category of the detected primitives in the form of lines. A self-supervising term is introduced to the generative loss of EdgeGAN to ensure the quality of generated images. EdgeGAN is faster than the conventional and object-detection-framework-based pipeline with minimal performance loss. Lastly, two inspection modules that are also suitable for conventional pipelines are proposed to check the connectivity and consistency of PFM based on the subspace connective graph (SCG). The first module contains four criteria that correspond to the sufficient conditions of a fully connected graph. The second module that classifies the category of all subspaces via one single graph neural network (GNN) should be consistent with the text annotations in the original FP (if available). The reason is that GNN treats the adjacent matrix of SCG as weights directly. Thus, GNN can utilize the global layout information and achieve higher accuracy than other common classifying methods. Experimental results are given to illustrate the efficiency of the proposed EdgeGAN and inspection approaches.
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Dehnavi, Sahar, Yasser Maghsoudi, Klemen Zakšek, Mohammad Javad Valadan Zoej, Gunther Seckmeyer, and Vladimir Skripachev. "Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images." Remote Sensing 12, no. 3 (2020): 371. http://dx.doi.org/10.3390/rs12030371.
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Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.
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Iwaszczuk, D., Z. Koppanyi, N. A. Gard, B. Zha, C. Toth, and A. Yilmaz. "SEMANTIC LABELING OF STRUCTURAL ELEMENTS IN BUILDINGS BY FUSING RGB AND DEPTH IMAGES IN AN ENCODER-DECODER CNN FRAMEWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 225–32. http://dx.doi.org/10.5194/isprs-archives-xlii-1-225-2018.
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<p><strong>Abstract.</strong> In the last decade, we have observed an increasing demand for indoor scene modeling in various applications, such as mobility inside buildings, emergency and rescue operations, and maintenance. Automatically distinguishing between structural elements of buildings, such as walls, ceilings, floors, windows, doors etc., and typical objects in buildings, such as chairs, tables and shelves, is particularly important for many reasons, such as 3D building modeling or navigation. This information can be generally retrieved through semantic labeling. In the past few years, convolutional neural networks (CNN) have become the preferred method for semantic labeling. Furthermore, there is ongoing research on fusing RGB and depth images in CNN frameworks. For pixel-level labeling, encoder-decoder CNN frameworks have been shown to be the most effective. In this study, we adopt an encoder-decoder CNN architecture to label structural elements in buildings and investigate the influence of using depth information on the detection of typical objects in buildings. For this purpose, we have introduced an approach to combine depth map with RGB images by changing the color space of the original image to HSV and then substitute the V channel with the depth information (D) and use it utilize it in the CNN architecture. As further variation of this approach, we also transform back the HSD images to RGB color space and use them within the CNN. This approach allows for using a CNN, designed for three-channel image input, and directly comparing our results with RGB-based labeling within the same network. We perform our tests using the Stanford 2D-3D-Semantics Dataset (2D-3D-S), a widely used indoor dataset. Furthermore, we compare our approach with results when using four-channel input created by stacking RGB and depth (RGBD). Our investigation shows that fusing RGB and depth improves results on semantic labeling; particularly, on structural elements of buildings. On the 2D- 3D-S dataset, we achieve up to 92.1<span class="thinspace"></span>% global accuracy, compared to 90.9<span class="thinspace"></span>% using RGB only and 93.6<span class="thinspace"></span>% using RGBD. Moreover, the scores of Intersection over Union metric have improved using depth, which shows that it gives better labeling results at the boundaries.</p>
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Nasser, Gamal A., Ahmed M. R. Fath El-Bab, Ahmed L. Abdel-Mawgood, Hisham Mohamed, and Abdelatty M. Saleh. "CO2 Laser Fabrication of PMMA Microfluidic Double T-Junction Device with Modified Inlet-Angle for Cost-Effective PCR Application." Micromachines 10, no. 10 (2019): 678. http://dx.doi.org/10.3390/mi10100678.
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The formation of uniform droplets and the control of their size, shape and monodispersity are of utmost importance in droplet-based microfluidic systems. The size of the droplets is precisely tuned by the channel geometry, the surface interfacial tension, the shear force and fluid velocity. In addition, the fabrication technique and selection of materials are essential to reduce the fabrication cost and time. In this paper, for reducing the fabrication cost Polymethyl methacrylate (PMMA) sheet is used with direct write laser technique by VERSA CO2 laser VLS3.5. This laser writing technique gives minimum channel width of about 160 μ m , which limit miniaturizing the droplet. To overcome this, modification on double T-junction (DTJ) channel geometry has been done by modifying the channel inlets angles. First, a two-dimensional (2D) simulation has been done to study the effect of the new channel geometry modification on droplet size, droplets distribution inside the channel, and its throughput. The fabricated modified DTJ gives the minimum droplet diameter of 39 ± 2 μ m , while DTJ channel produced droplet diameter of 48 ± 4 μ m at the same conditions. Moreover, the modified double T-junction (MDTJ) decreases the variation in droplets diameter at the same flow rates by 4.5 – 13 % than DTJ. This low variation in the droplet diameter is suitable for repeatability of the DNA detection results. The MDTJ also enhanced the droplet generation frequency by 8 – 25 % more than the DTJ channel. The uniformity of droplet distribution inside the channel was enhanced by 3 – 20 % compared to the DTJ channel geometry. This fabrication technique eliminates the need for a photomask and cleanroom environment in addition shortening the cost and time. It takes only 20 min for fabrication. The minimum generated droplet diameter is within 40 μ m with more than 1000 droplets per second (at 10 mL / h . oil flow rate). The device is a high-throughput and low-cost micro-droplet formation aimed to be as a front-end to a dynamic droplet digital PCR (ddPCR) platform for use in resource-limited environment.
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Thybo, J., A. N. Olesen, M. Olsen, et al. "0451 Fully Automatic Detection of Sleep Disordered Breathing Events." Sleep 43, Supplement_1 (2020): A172—A173. http://dx.doi.org/10.1093/sleep/zsaa056.448.
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Abstract Introduction Evaluation of sleep apnea involves manual annotation of Polysomnography (PSG) file, a time-consuming process subject to interscorer variations. The DOSED algorithm has been shown to be helpful in detecting Central Sleep Apnea (CSA), Obstructive Sleep Apnea (OSA), and Hypopnea when merged into a single event type. This work uses a modified version of DOSED capable of detecting each event type separately. Methods The network consists of 3 blocks of 1D convolutional layers followed by 6 blocks of 2D convolutional layers. The network has 2 classification layers, one determines the probability of each class, and the other determines the start and duration time of the event with the highest probability. Four channels from nasal and mouth airflow and position of abdomen and thorax are used as input to the model. The model was trained using 2800 PSG from 4 different cohorts (MESA, MROS, SSC, WSC) and tested on 70 PSG, which have been scored by six technicians (Stanford, U Penn, St Louis). Results On an event by event basis, model F1-scores versus a weighted consensus score based on 6 technicians were 0.60 for OSA, 0.43 for CSA, and 0.34 for Hypopnea. Average F1-scores for the 6 technicians were 0.48 (std 0.04) for OSA, 0.29 (std 0.145) for CSA, and 0.54 (std 0.183) for Hypopnea, indicating that the model functions better on an event-by-event basis than an average technician. Correlations between indices/hr for central apnea, obstructive apnea, and hypopnea indicate excellent correlations for apneas, but poor correlation for hypopnea. We are now adding the snoring channel to explore if predictions can be improved. Conclusion The result shows that deep learning-based models can detect respiratory events with an accuracy similar to technicians. The poor agreement between technicians from different universities indicates that we need better definitions of hypopnea. Support
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Adebanjo, Ibukunoluwa Adetutu, Yekeen Olajide Olasoji, and Micheal Olorunfunmi Kolawole. "Space-Time Trellis Coding with Equalization." European Journal of Engineering Research and Science 4, no. 9 (2019): 207–11. http://dx.doi.org/10.24018/ejers.2019.4.9.1412.
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As we are entering the 5G era, high demand is made of wireless communication. Consistent effort has been ongoing in multiple-input multiple-output (MIMO) systems, which provide correlation on temporal and spatial domain, to meet the high throughput demand. To handle the characteristic nature of wireless channel effectively and improve the system performance, this paper considers the combination of diversity and equalization. Space-Time trellis code is combined with single-carrier modulation using two-choice equalization techniques, namely: minimum mean squared error (MMSE) equalizer and orthogonal triangular (QR) detection. MMSE gives an optimal balance between noise enhancement and net inter-symbol interference (ISI) in the transmitted signal. Use of these equalizers provides the platform of investigating the bit error rate (BER) and the pairwise error probability (PEP) at the receiver, as well as the effect of cyclic prefix reduction on the receivers. It was found that the MMSE receiver outperforms the QR receiver in terms of BER, while in terms of PEP; the QR receiver outperforms the MMSE receiver. When a cyclic prefix reduction test was carried out on both receivers, it yields a significant reduction in BER of both receivers but has no significant effect on the overall performance.
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Seydi, Seyd Teymoor, Mahdi Hasanlou, and Meisam Amani. "A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets." Remote Sensing 12, no. 12 (2020): 2010. http://dx.doi.org/10.3390/rs12122010.
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The diversity of change detection (CD) methods and the limitations in generalizing these techniques using different types of remote sensing datasets over various study areas have been a challenge for CD applications. Additionally, most CD methods have been implemented in two intensive and time-consuming steps: (a) predicting change areas, and (b) decision on predicted areas. In this study, a novel CD framework based on the convolutional neural network (CNN) is proposed to not only address the aforementioned problems but also to considerably improve the level of accuracy. The proposed CNN-based CD network contains three parallel channels: the first and second channels, respectively, extract deep features on the original first- and second-time imagery and the third channel focuses on the extraction of change deep features based on differencing and staking deep features. Additionally, each channel includes three types of convolution kernels: 1D-, 2D-, and 3D-dilated-convolution. The effectiveness and reliability of the proposed CD method are evaluated using three different types of remote sensing benchmark datasets (i.e., multispectral, hyperspectral, and Polarimetric Synthetic Aperture RADAR (PolSAR)). The results of the CD maps are also evaluated both visually and statistically by calculating nine different accuracy indices. Moreover, the results of the CD using the proposed method are compared to those of several state-of-the-art CD algorithms. All the results prove that the proposed method outperforms the other remote sensing CD techniques. For instance, considering different scenarios, the Overall Accuracies (OAs) and Kappa Coefficients (KCs) of the proposed CD method are better than 95.89% and 0.805, respectively, and the Miss Detection (MD) and the False Alarm (FA) rates are lower than 12% and 3%, respectively.
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Ivanov, Julian, Richard D. Miller, Pierre Lacombe, Carole D. Johnson, and John W. Lane. "Delineating a shallow fault zone and dipping bedrock strata using multichannal analysis of surface waves with a land streamer." GEOPHYSICS 71, no. 5 (2006): A39—A42. http://dx.doi.org/10.1190/1.2227521.
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The multichannel analysis of surface waves (MASW) seismic method was used to delineate a fault zone and gently dipping sedimentary bedrock at a site overlain by several meters of regolith. Seismic data were collected rapidly and inexpensively using a towed 30-channel land streamer and a rubberband-accelerated weight-drop seismic source. Data processed using the MASW method imaged the subsurface to a depth of about [Formula: see text] and allowed detection of the overburden, gross bedding features, and fault zone. The fault zone was characterized by a lower shear-wave velocity [Formula: see text] than the competent bedrock, consistent with a large-scale fault, secondary fractures, and in-situ weathering. The MASW 2D [Formula: see text] section was further interpreted to identify dipping beds consistent with local geologic mapping. Mapping of shallow-fault zones and dipping sedimentary rock substantially extends the applications of the MASW method.
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Lützenkirchen-Hecht, D., R. Wagner, S. Szillat, et al. "The multi-purpose hard X-ray beamline BL10 at the DELTA storage ring." Journal of Synchrotron Radiation 21, no. 4 (2014): 819–26. http://dx.doi.org/10.1107/s1600577514006705.
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The layout and the characteristics of the hard X-ray beamline BL10 at the superconducting asymmetric wiggler at the 1.5 GeV Dortmund Electron Accelerator DELTA are described. This beamline is equipped with a Si(111) channel-cut monochromator and is dedicated to X-ray studies in the spectral range from ∼4 keV to ∼16 keV photon energy. There are two different endstations available. While X-ray absorption studies in different detection modes (transmission, fluorescence, reflectivity) can be performed on a designated table, a six-axis kappa diffractometer is installed for X-ray scattering and reflectivity experiments. Different detector set-ups are integrated into the beamline control software,i.e.gas-filled ionization chambers, different photodiodes, as well as a Pilatus 2D-detector are permanently available. The performance of the beamline is illustrated by high-quality X-ray absorption spectra from several reference compounds. First applications include temperature-dependent EXAFS experiments from liquid-nitrogen temperature in a bath cryostat up to ∼660 K by using a dedicated furnace. Besides transmission measurements, fluorescence detection for dilute sample systems as well as surface-sensitive reflection-mode experiments are presented.
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Takehara, R., K. Sunami, K. Miyagawa, et al. "Topological charge transport by mobile dielectric-ferroelectric domain walls." Science Advances 5, no. 11 (2019): eaax8720. http://dx.doi.org/10.1126/sciadv.aax8720.
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The concept of topology has been widely applied in condensed matter physics, leading to the identification of peculiar electronic states on three-dimensional (3D) surfaces or 2D lines separating topologically distinctive regions. In the systems explored so far, the topological boundaries are built-in walls; thus, their motional degrees of freedom, which potentially bring about new paradigms, have been experimentally inaccessible. Here, working with a quasi-1D organic material with a charge-transfer instability, we show that mobile neutral-ionic (dielectric-ferroelectric) domain boundaries with topological charges carry strongly 1D-confined and anomalously large electrical conduction with an energy gap much smaller than the one-particle excitation gap. This consequence is further supported by nuclear magnetic resonance detection of spin solitons, which are required for steady current of topological charges. The present observation of topological charge transport may open a new channel for broad charge transport–related phenomena such as thermoelectric effects.
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Eichkitz, Christoph Georg, Marcellus Gregor Schreilechner, Paul de Groot, and Johannes Amtmann. "Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes." Interpretation 3, no. 1 (2015): T13—T23. http://dx.doi.org/10.1190/int-2014-0099.1.
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Texture attributes describe the spatial arrangement of neighboring amplitudes values within a given analysis window. We chose a statistical texture classification method, the gray-level co-occurrence matrix (GLCM), and its derived attributes, to produce a semiautomated description of the spatial arrangement of seismic facies. The GLCM is a measure of how often different combinations of neighboring pixel values occur. We tested the application of directional GLCM-based attributes for the detection of seismic variability within paleoriver features. Calculation of 3D GLCM-based attributes can be done in 13 space directions. The results of GLCM-based attribute calculation differed depending on the chosen GLCM parameters (number of gray levels, analysis window, and direction of calculation). We specifically focused on how the direction of calculation influenced the computation of attributes, while keeping other parameters constant. We first tested the workflow on a 2D training image and later ran on a real seismic amplitude volume from the Vienna Basin. Based on the GLCM-based attributes, we could map the channel features and extract them as geobodies. Additionally, we generated a new set of directional GLCM-based attributes to detect spatial changes in the seismic facies. By comparing these directional attributes, we could determine areas within the channel features having higher directional variability. Areas with higher tendency to directional variations might be associated with changes in lithology, seismic facies, or with seismic anisotropy.
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Xie, Shuguo, Tianheng Wang, Xuchun Hao, Meiling Yang, Yanju Zhu, and Yuanyuan Li. "Localization and Frequency Identification of Large-Range Wide-Band Electromagnetic Interference Sources in Electromagnetic Imaging System." Electronics 8, no. 5 (2019): 499. http://dx.doi.org/10.3390/electronics8050499.
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The identification and localization of large-range, wide-band electromagnetic interference (EMI) sources have always been both costly and time-consuming. The measurements at different times and places are often required before a typical system can locate a target. In this paper, we proposed a 2D electromagnetic imaging system to localize interference sources and identify the EMI frequency in real time. In this system, an offset paraboloid with a diameter of three meters is designed for large-range EMI imaging, while a multi-channel digital signal acquisition system is developed for wide-band EMI localization. The located interference source is segmented by the maximum entropy method based on particle swarm optimization, and the modified generalized regression neural network (MGRNN) is applied to identify the EMI frequency effectively by excluding misleading effects of outliers. The experiment which has been completed on our dataset indicates that our approach not only increases accuracy by 5% compared with the standard generalized regression neural network approaches for identification, but also exerts a large-range wide-band localization of the EMI source detection method.
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Song, Yunhao, Jinfeng Huang, Erik Toorman, and Guolu Yang. "Reconstruction of River Topography for 3D Hydrodynamic Modelling Using Surveyed Cross-Sections: An Improved Algorithm." Water 12, no. 12 (2020): 3539. http://dx.doi.org/10.3390/w12123539.
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Multidimensional hydrodynamic modelling becomes tricky when lacking the bathymetric data representing the continuous underwater riverbed surface. Light detection and ranging (LiDAR)-based and radar-based digital elevation models (DEMs) are often used to build the high-accuracy floodplain topography, while in most cases the submerged riverbed could not be detected because both radar and LiDAR operate at wavelengths that cannot penetrate the water. Data from other sources is therefore required to establish the riverbed topography. The inundated river channel is often surveyed with an echo sounder to obtain discrete cross-section data. In this context, an improved algorithm based on the classic flow-oriented coordinates transformation is proposed to generate the riverbed topography using surveyed cross-sections. The dimensionless channel width (DCW) processing method is developed within the algorithm to largely increase the prediction accuracy, especially for the meandering reaches. The generated riverbed topography can be merged with the floodplain DEM to create an integrated DEM for 2D and 3D hydrodynamic simulations. Two case studies are carried out: a benchmark test in the Baxter River, United States, with carefully surveyed channel–floodplain topographic data to validate the algorithm, and a 3D hydrodynamic modelling-based application in Three Gorges Reservoir (TGR) area, China. Results from the benchmark case demonstrate very good consistency between the created topography and the surveyed data with root mean square error (RMSE) = 0.17 m and the interpolation accuracy was increased by 55% compared to the traditional method without DCW processing. 3D hydrodynamic modelling results match the observed field data well, indicating that the generated DEM of the TGR area was good enough not only to predict water depths along the tributary, but also to allow the hydrodynamic model to capture the typical features of the complex density currents caused by both the topography of the tributary estuary and the operation rules of TGR.
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Zhou, Houliang, and Chen Kan. "Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things." Sensors 21, no. 12 (2021): 4173. http://dx.doi.org/10.3390/s21124173.
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Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling T2 chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the T2 control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.
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Sabot, Roland, Yoonbum Nam, Cyril Brun, et al. "Integration of an Electron Cyclotron Imaging diagnostic system on the WEST tokamak." EPJ Web of Conferences 203 (2019): 03011. http://dx.doi.org/10.1051/epjconf/201920303011.
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An Electron Cyclotron Emission Imaging (ECEI) diagnostic system for the WEST tokamak has been developed under the UNIST-WEST collaboration. This diagnostic system is designed to overcome accessibility and thermomechanical constraints for long pulse operation. The first O-mode channel will be installed in the first trimester of 2019 to probe the low field side (LFS) of the WEST plasma. Two large metallic reflective mirrors are installed inside the duct which is being used for maintenance access. They are suspended on a rail to facilitate mirror manipulation. The ex-vessel optical system (lens, detection array, etc.) is housed in a compact optical enclosure that fits in a tight free space between the port flange and tokamak access lobby. The design emphasized reproducibility of the precise alignment between in-vessel mirrors and optical enclosure since the both elements must be removed during shutdown period for maintenance access. The overall optical system was fully tested at UNIST last year. The test results demonstrated that the imaging optics can full access at any radial position on the LFS. The 2D beam pattern measurements were consistent with the design values.