Abstract: In this study, an automated building and tree detection
method is proposed using DSM data and true orthophoto image. A
multiscale matched filtering is used on DSM data. Therefore, first
watershed transform is applied. Then, Otsu’s thresholding method
is used as an adaptive threshold to segment each watershed region.
Detected objects are masked with NDVI to separate buildings and
trees. The proposed method is able to detect buildings and trees
without entering any elevation threshold. We tested our method on
ISPRS semantic labeling dataset and obtained promising results.
Abstract: Spectrum underutilization has made cognitive
radio a promising technology both for current and future
telecommunications. This is due to the ability to exploit the unused
spectrum in the bands dedicated to other wireless communication
systems, and thus, increase their occupancy. The essential function,
which allows the cognitive radio device to perceive the occupancy
of the spectrum, is spectrum sensing. In this paper, the performance
of modern adaptations of the four most widely used spectrum
sensing techniques namely, energy detection (ED), cyclostationary
feature detection (CSFD), matched filter (MF) and eigenvalues-based
detection (EBD) is compared. The implementation has been
accomplished through the PlutoSDR hardware platform and the
GNU Radio software package in very low Signal-to-Noise Ratio
(SNR) conditions. The optimal detection performance of the
examined methods in a realistic implementation-oriented model is
found for the common relevant parameters (number of observed
samples, sensing time and required probability of false alarm).
Abstract: In this paper two approaches to joint signal detection,
time of arrival (ToA) and angle of arrival (AoA) estimation in
multi-element antenna array are investigated. Two scenarios were
considered: first one, when the waveform of the useful signal
is known a priori and, second one, when the waveform of the
desired signal is unknown. For first scenario, the antenna array
signal processing based on multi-element matched filtering (MF)
with the following non-coherent detection scheme and maximum
likelihood (ML) parameter estimation blocks is exploited. For second
scenario, the signal processing based on the antenna array elements
covariance matrix estimation with the following eigenvector analysis
and ML parameter estimation blocks is applied. The performance
characteristics of both signal processing schemes are thoroughly
investigated and compared for different useful signals and noise
parameters.
Abstract: Linear convolutive filters are fast in calculation and in application, and thus, often used for real-time processing of continuous data streams. In the case of transient signals, a filter has not only to detect the presence of a specific waveform, but to estimate its arrival time as well. In this study, a measure is presented which indicates the performance of detectors in achieving both of these tasks simultaneously. Furthermore, a new sub-class of linear filters within the class of filters which minimize the quadratic response is proposed. The proposed filters are more flexible than the existing ones, like the adaptive matched filter or the minimum power distortionless response beamformer, and prove to be superior with respect to that measure in certain settings. Simulations of a real-time scenario confirm the advantage of these filters as well as the usefulness of the performance measure.
Abstract: This paper presents a method for the detection of OD in the retina which takes advantage of the powerful preprocessing techniques such as the contrast enhancement, Gabor wavelet transform for vessel segmentation, mathematical morphology and Earth Mover-s distance (EMD) as the matching process. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Vessel segmentation method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel-s feature vector. Feature vectors are composed of the pixel-s intensity and 2D Gabor wavelet transform responses taken at multiple scales. A simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity using the EMD. The minimum distance provides an estimate of the OD center coordinates. The method-s performance is evaluated on publicly available DRIVE and STARE databases. On the DRIVE database the OD center was detected correctly in all of the 40 images (100%) and on the STARE database the OD was detected correctly in 76 out of the 81 images, even in rather difficult pathological situations.
Abstract: Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
Abstract: Support Vector Machine (SVM) is a statistical
learning tool developed to a more complex concept of
structural risk minimization (SRM). In this paper, SVM is
applied to signal detection in communication systems in the
presence of channel noise in various environments in the form
of Rayleigh fading, additive white Gaussian background noise
(AWGN), and interference noise generalized as additive color
Gaussian noise (ACGN). The structure and performance of
SVM in terms of the bit error rate (BER) metric is derived and
simulated for these advanced stochastic noise models and the
computational complexity of the implementation, in terms of
average computational time per bit, is also presented. The
performance of SVM is then compared to conventional binary
signaling optimal model-based detector driven by binary
phase shift keying (BPSK) modulation. We show that the
SVM performance is superior to that of conventional matched
filter-, innovation filter-, and Wiener filter-driven detectors,
even in the presence of random Doppler carrier deviation,
especially for low SNR (signal-to-noise ratio) ranges. For
large SNR, the performance of the SVM was similar to that of
the classical detectors. However, the convergence between
SVM and maximum likelihood detection occurred at a higher
SNR as the noise environment became more hostile.
Abstract: In this paper we present a soft timing phase estimation (STPE) method for wireless mobile receivers operating in low signal to noise ratios (SNRs). Discrete Polyphase Matched (DPM) filters, a Log-maximum a posterior probability (MAP) and/or a Soft-output Viterbi algorithm (SOVA) are combined to derive a new timing recovery (TR) scheme. We apply this scheme to wireless cellular communication system model that comprises of a raised cosine filter (RCF), a bit-interleaved turbo-coded multi-level modulation (BITMM) scheme and the channel is assumed to be memory-less. Furthermore, no clock signals are transmitted to the receiver contrary to the classical data aided (DA) models. This new model ensures that both the bandwidth and power of the communication system is conserved. However, the computational complexity of ideal turbo synchronization is increased by 50%. Several simulation tests on bit error rate (BER) and block error rate (BLER) versus low SNR reveal that the proposed iterative soft timing recovery (ISTR) scheme outperforms the conventional schemes.