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Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network's focus on essential features, thereby improving the model's generalization ability. To assess the model's effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods. [ABSTRACT FROM AUTHOR]

The performance of $L$- branch scan-and-wait combining (SWC) reception systems over arbitrarily correlated and not necessarily identically distributed Nakagami- $m$ fading channels is analyzed and evaluated. First, a fast convergent infinite series representation for the SWC output signal-to-noise ratio (SNR) is presented. This expression is used to obtain analytical expressions in the form of infinite series for the average error probability performance of various modulation schemes for integer values of $m$ as well as the average number of paths estimation and the average waiting time (AWT) of $L$-branch SWC receivers for arbitrary values of $m$. The numerically obtained results have shown that the performance expressions converge very fast to their exact analytical values. It was found that the convergence speed depends on the correlation and operating SNR values as well as the Nakagami $m$- parameter. In addition to the analytical results, complementary computer simulated performance evaluation results have been obtained by means of Monte Carlo error counting techniques. The match between these two sets of results has verified the accuracy of the proposed mathematical analysis. Furthermore, it is revealed that, at the expense of a negligible AWT, the average error probability performance of SWC receivers is always superior to that of switch-and-examine combining receivers and, in certain cases, to that of maximal-ratio combining receivers. [ABSTRACT FROM PUBLISHER]

Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing traditional CNNs (convolutional neural networks), which typically suffer from slow convergence and relatively low accuracy. In this paper, we propose a novel CNN, referred to as MC-UNet (Martian Crater U-Net), wherein classical U-Net is employed as the backbone for accurate identification of Martian craters at semantic and instance levels from thermal-emission-imaging-system (THEMIS) daytime infrared images. Compared with classical U-Net, the depth of the layers of MC-UNet is expanded to six, while the maximum number of channels is decreased to one-fourth, thereby making the proposed CNN-based architecture computationally efficient while maintaining a high recognition rate of impact craters on Mars. For enhancing the operation of MC-UNet, we adopt average pooling and embed channel attention into the skip-connection process between the encoder and decoder layers at the same network depth so that large-sized Martian craters can be more accurately recognized. The proposed MC-UNet is adequately trained using 2∼32 km radii Martian craters from THEMIS daytime infrared annotated images. For the predicted Martian crater rim pixels, template matching is subsequently used to recognize Martian craters at the instance level. The experimental results indicate that MC-UNet has the potential to recognize Martian craters with a maximum radius of 31.28 km (136 pixels) with a recall of 0.7916 and F 1 -score of 0.8355. The promising performance shows that the proposed MC-UNet is on par with or even better than other classical CNN architectures, such as U-Net and Crater U-Net. [ABSTRACT FROM AUTHOR]

The performance of multibranch selection diversity (SD) receivers over L arbitrarily correlated generalised Gamma (GG) fading channels is analysed and evaluated. Following a statistical approach previously proposed for the multivariate Nakagami-m distribution, a closed-form upper bound for the joint GG probability density function (PDF) and an infinite series upper bound for the joint GG cumulative distribution function (CDF) are obtained. For the special case of the trivariate GG distribution (L=3), exact expressions in the form of infinite series for the PDF and CDF are presented. The derived bounds are used to obtain tight upper bounds for the distribution of the SD output signal-to-noise ratio (SNR). Furthermore, based on the derived analysis, novel analytical expressions that accurately approximate the distribution of the SD output SNR are presented. The bounds and approximations are used to study important performance criteria of SD receivers such as outage and average symbol error probability. The accuracy of the derived expressions has been verified, on the one hand, analytically for triple-branch (L=3) and, on the other hand, by means of computer simulations for multibranch (L>3) SD receivers. [ABSTRACT FROM AUTHOR]

The performance of switch-and-examine diversity (SED) over L arbitrarily correlated and not necessarily identically distributed Nakagami-m fading channels is studied. Analytical expressions for the distribution of the SED output signal-to-noise ratio (SNR) are obtained for the constant correlation model. For the most general case of arbitrary correlation, by assuming half-integer or integer values for the fading parameterm, analytical expressions for the distribution of the output SNR with L ≤ 3 are derived. Moreover, for L>3, analytical approximations for the output SNR are presented. The derived expressions are used to study the outage and average symbol error probability of SED receivers. Performance results obtained by numerical evaluation and verified by means of computer simulations show that the performance of the receivers under consideration is degraded with increasing branch correlation. Nevertheless, SED receivers outperform uncorrelated switch-and-stay diversity receivers, even when they operate under high branch correlation. [ABSTRACT FROM AUTHOR]

The statistical characteristics of the trivariate and quadrivariate Weibull fading distribution with arbitrary correlation, non-identical fading parameters and average powers' are analytically studied. Novel expressions for important joint statistics are derived using the Weibull power transformation. These expressions are used to evaluate the performance of selection combining (SC) and maximal ratio combining (MRC) diversity receivers in the presence of such fading channels. [ABSTRACT FROM AUTHOR]

The capacity of correlated generalized Gamma (GG) fading channels with dual-branch selection diversity (SD) reception and channel side information (CSI) available at both the transmitter and the receiver is studied and analyzed. In particular, the capacity is evaluated under four adaptive transmission policies defined according to the operational CSI: 1) optimal power and rate adaptation (OPRA); 2) constant power with optimal rate adaptation (ORA); 3) channel inversion with fixed rate (CIFR); and 4) truncated CIFR (TIFR). Under these four adaptation policies, fast converging infinite series expressions for the capacity are derived and evaluated. Furthermore, for the simplifying cases of independent GG fading channels and/or single-channel reception (i.e., no diversity), closed-form expressions have been obtained. Previously published expressions are shown to be special cases of the derived expressions. Performance evaluation results obtained by means of numerical analysis clearly demonstrate the usefulness and accuracy of the analysis. [ABSTRACT FROM AUTHOR]

This paper studies channel partitioning policies and channel resource prioritization techniques for handovers, which efficiently support multi-class traffic in low Earth orbit mobile satellite systems (LEO-MSS). Analytical methods are developed for evaluating the performance of complete partitioning (CP) and complete sharing (CS) with and without fixed channel reservation (FCR) for multi-class traffic in LEO-MSS. Furthermore, a novel threshold call admission (TCA) scheme which achieves better channel utilization and fair channel access is introduced and analyzed. [ABSTRACT FROM AUTHOR]

A novel analytical framework for the accurate and efficient evaluation of the performance of channel resource management strategies for low Earth orbit mobile satellite systems (LEO-MSSs) supporting multiparty traffic is presented. By considering a fixed channel reservation (FCR) scheme as a benchmark, an efficient and accurate analytical approach is developed for obtaining the performance of multiparty traffic under various quality-of-service (QoS) performance measure criteria. The pro- posed approach is based on a Markovian queuing model, and its correctness and accuracy have been verified by means of computer simulations. To improve the overall performance of LEO-MSS, two novel resource management techniques are introduced and analyzed. The first one is an efficient adaptive channel reservation (ACR) scheme, which allows priority to be given to handover requests that are generated by multiparty traffic. The second one is a new call queuing (NCQ) policy, which efficiently reduces the new call blocking probability with little impact on other system performance measures, such as call dropping probability and unsuccessful call probability. Various performance results show that when ACR is used in conjunction with NCQ, extremely low blocking and handover failure probabilities can be achieved for multiparty traffic. [ABSTRACT FROM AUTHOR]

The exact symbol error rates (SERs) of several M-ary modulation schemes are obtained in a unified manner for mobile radio receivers employing dual-branch coherent equal-gain combining (EGC), where the received signals on each branch follow either similar or different fading distributions. Specifically, the cases considered are Nakagami-m, Rice, and Hoyt fading on both branches, but with not necessarily identical parameters, and mixed Nakagami-m/Rice and Nakagami-m/Hoyt fading; only the integer or half-integer values of the Nakagami-m parameter are considered. While the previous exact expressions for the SERS of general M-ary modulations with L-branch EGC contained double integrals, the error probability expressions derived in this paper for the case of L = 2 contain only a single integral. In addition, the derived probability distributions also allow the direct obtaining of other useful performance measures, such as the exact level crossing rates, average fade durations, and outage probabilities. [ABSTRACT FROM AUTHOR]

In this study, a new method for digitizing a combination of different analog signals occupying significantly different bandwidths and having a very high dynamic range is proposed and analyzed. Since it is based upon signal-prediction/cancellation principles, it is referred to as adaptive prediction and cancellation digitization (APCD) method and is applied to various families of signals simultaneously received by a multistandard software radio (SWR) base-station receiver. It is shown theoretically and by means of computer simulations that the APCD method can effectively reduce the high dynamic range of the signals before digitization takes place. Hence, the stringent analog-to-digital-converter (ADC) resolution requirements imposed by the operation of such SWR base-station receivers can be significant relaxed. The signal dynamic-range reduction is achieved by applying appropriate signal processing techniques, e.g., autoregressive (AR) and periodic autoregressive (PAR) prediction. Such techniques allow accurate prediction and subsequent cancellation of high-power narrowband signals present among the composite received analog signal. As these signals usually have cyclostationary statistical characteristics, analysis and performance evaluation of AR and PAR predictors, when used to predict cyclostationary signals, were presented. A new adaptive algorithm for implementing the PAR predictor is also proposed, and its validity is justified by theoretical analysis as well as by various performance evaluation results obtained by means of computer simulations. [ABSTRACT FROM AUTHOR]

The exact analysis of dynamic channel allocation (DCA) with first-in/first-out (FIFO) queuing of handover (QH) requests is highly complex, due to the dynamic nature of channel allocation to different cells. This letter presents an approximate but accurate analytical method to evaluate the performance of DCA in conjunction with FIFO-QH in low earth orbit mobile satellite systems. The accuracy of the proposed analysis has been verified by means of computer simulation. [ABSTRACT FROM AUTHOR]

Using results from the theory of multidimensional Gaussian distributions, we provide closed-form or infinite series expressions for the envelope autocorrelation function of the output of a maximal-ratio combiner, for Rayleigh, Nakagami, and Rician fading. These expressions generalize known analytical results obtained for the case of no diversity. The power spectrum of the combined envelope is also obtained for Rayleigh and Nakagami fading. The validity of the analytical approach is verified using computer simulation results obtained with a land-mobile fading emulator. It is observed that, for the land-mobile radio channel, the use of diversity slightly increases the time correlation between the receiver inputs, but in a nonuniform manner across the range of time lags. [ABSTRACT FROM AUTHOR]

Focuses on a study which derived analytical expressions for level crossing rates and average fade durations for several diversity reception techniques and a general Nakagami fading channel. Background on fading communication channels; Numerical results and discussion; Conclusion.

Studies the statistical combining process of the multipath components in a Rayleigh fading channel. Probability of constructive addition; Beneficial properties of multipath propagation; Propagation environment.

In this paper, a novel jointly adaptive transmission strategy for buffer-aided multiple-relay non-orthogonal multiple access (NOMA) networks is proposed. By leveraging time diversity brought by the data storage buffer, and multiple relays’ beamforming gains, the source node broadcasts data in the first hop and all relays perform distributed beamforming to transmit information to users with NOMA in the second hop. Our focus is on maximizing the average network throughput (ANT) by jointly optimizing the mode selection, power and rate allocation at the source node, as well as the rate allocation and distributed beamforming coefficients at the relays along the time. As a solution to this complex problem, we propose an online scheduling scheme, which first adopts the Lyapunov optimization methodology to convert the ANT maximization problem into the drift-plus-penalty function minimization problem at each time slot. Secondly, focusing on the non-convexity of the transformed problem at each time slot, a novel two-loop algorithm is proposed as an effective solution. Extensive performance evaluation results have revealed the superiority of the proposed scheme as compared to other benchmark access schemes, namely, buffer-aided single relay NOMA, maximum ratio transmission based NOMA and buffer-aided time division multiple access schemes. [ABSTRACT FROM AUTHOR]

This paper presents novel moment generating function (MGF)- and characteristic function (CHF)-based frameworks for the EC performance analysis of a generic $L_{p}$ -norm diversity combining scheme under adaptive transmission policies. The considered system operates over generalized fading channels, a maximum delay constraint and under various channel state information (CSI) conditions. Depending upon the operational CSI, four policies are studied, namely: i) Constant power with optimal rate adaptation (ORA); ii) Optimal power and rate adaptation (OPRA); iii) Channel inversion with fixed rate (CIFR); and iv) Truncated CIFR (TIFR). The $L_{p}$ -norm diversity is a generic diversity structure which includes as special cases various well-known diversity schemes, such as equal gain combining (EGC), maximal ratio combining (MRC) and amplify-and-forward (AF) relaying. Under the ORA and OPRA policies, we derive single integral expressions for evaluating the EC of $L_{p}$ -norm diversity reception directly from the MGF or the incomplete MGF of the Signal-to-Noise-Ratio (SNR) at the receiver, respectively. For the EC performance evaluation of the EGC and AF relaying systems operating under the OPRA policy, a CHF-based approach, which is computationally more efficient as compared to the MGF-based approach, is further presented. It is shown that the EC for the CIFR and TIFR policies can be directly evaluated from the MGF or the CHF of the SNR at the receiver, respectively. For the ORA policy, a novel analytical approach for the asymptotic EC performance analysis is also developed and evaluated, revealing how important system operation parameters affect the overall system performance. The mathematical formalism is validated with selected numerical and equivalent simulation performance evaluation results, thus confirming the correctness of the proposed analytical methodology. [ABSTRACT FROM AUTHOR]

In this paper, a novel three-dimensional (3-D) indoor positioning scheme is proposed for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Its operation is based upon a hybrid received signal strength and angle of arrival (RSS-AoA) positioning scheme, which employs only a single access point equipped with a large-scale uniform cylindrical array. To reduce the high computational complexity imposed by the large number of antennas used in mmWave massive MIMO (M3-MIMO) systems, we firstly propose a novel channel compression method. By proper quantization and selection of the received mmWave signals, which exhibit quasioptical and sparse multipath characteristics, the channel compression method reduces the dimension of the received signal space while maintaining the accuracy of the position estimation. Then, we propose a beamspace transformation approach to transform signal vectors in the element space to the beamspace, and thus the computational complexity of the angle estimation is significantly reduced. Finally, a novel hybrid RSS-AoA positioning scheme is designed for the computations of the 3-D coordinates of the target mobile terminal. Simulation results have shown that the proposed indoor positioning scheme is capable of achieving high accuracy as well as significantly lower computational complexity as compared to other previously known indoor positioning techniques. [ABSTRACT FROM AUTHOR]

This paper proposes an encoding–decoding network with a pyramidal representation module, which will be referred to as EDPNet, and is designed for efficient semantic image segmentation. On the one hand, during the encoding process of the proposed EDPNet, the enhancement of the Xception network, i.e., Xception+ is employed as a backbone to learn the discriminative feature maps. The obtained discriminative features are then fed into the pyramidal representation module, from which the context-augmented features are learned and optimized by leveraging a multi-level feature representation and aggregation process. On the other hand, during the image restoration decoding process, the encoded semantic-rich features are progressively recovered with the assistance of a simplified skip connection mechanism, which performs channel concatenation between high-level encoded features with rich semantic information and low-level features with spatial detail information. The proposed hybrid representation employing the proposed encoding–decoding and pyramidal structures has a global-aware perception and captures fine-grained contours of various geographical objects very well with high computational efficiency. The performance of the proposed EDPNet has been compared against PSPNet, DeepLabv3, and U-Net, employing four benchmark datasets, namely eTRIMS, Cityscapes, PASCAL VOC2012, and CamVid. EDPNet acquired the highest accuracy of 83.6% and 73.8% mIoUs on eTRIMS and PASCAL VOC2012 datasets, while its accuracy on the other two datasets was comparable to that of PSPNet, DeepLabv3, and U-Net models. EDPNet achieved the highest efficiency among the compared models on all datasets. [ABSTRACT FROM AUTHOR]

The effective and automated parsing of building facades from terrestrial laser scanning (TLS) point clouds of urban environments is an important research topic in the GIS and remote sensing fields. It is also challenging because of the complexity and great variety of the available 3D building facade layouts as well as the noise and data missing of the input TLS point clouds. In this paper, we introduce a novel methodology for the accurate and computationally efficient parsing of urban building facades from TLS point clouds. The main novelty of the proposed methodology is that it is a systematic and hierarchical approach that considers, in an adaptive way, the semantic and underlying structures of the urban facades for segmentation and subsequent accurate modeling. Firstly, the available input point cloud is decomposed into depth planes based on a data-driven method; such layer decomposition enables similarity detection in each depth plane layer. Secondly, the labeling of the facade elements is performed using the SVM classifier in combination with our proposed BieS-ScSPM algorithm. The labeling outcome is then augmented with weak architectural knowledge. Thirdly, least-squares fitted normalized gray accumulative curves are applied to detect regular structures, and a binarization dilation extraction algorithm is used to partition facade elements. A dynamic line-by-line division is further applied to extract the boundaries of the elements. The 3D geometrical façade models are then reconstructed by optimizing facade elements across depth plane layers. We have evaluated the performance of the proposed method using several TLS facade datasets. Qualitative and quantitative performance comparisons with several other state-of-the-art methods dealing with the same facade parsing problem have demonstrated its superiority in performance and its effectiveness in improving segmentation accuracy. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a common used noninvasive test to quickly detect the heart problem with high precision. However, devices used to collect continuous ECG waveforms under free-living conditions have several operational difficulties. As an alternative, photoplethysmography (PPG) signals can be conveniently collected by pulse sensors, which can be mounted onto wearable devices. In this paper, we study the relation between ECG waveforms and PPG signals by proposing a novel growing hybrid polynomial network (GHPN)). Based on the projections between adjacent layers, the network is designed to approximate the target signals with the recorded inputs and the multi-scale dynamics of the input series are explored gradually through the growth of layers. Two public datasets are employed to evaluate the performance of the proposed approach on the accuracy of waveform reconstruction and heart rate (HR) detection with the widely used metrics. Compared with the reference ECG waveforms, the normalized mean square error (NMSE) of the proposed approach is 0.248 and 0.216 for PPG-Dalia and CapnoBase datasets, which is smaller than the comparison approaches. The average absolute value (AAE) between the detected HR and reference HR is 0.93 and 1.05 for PPG-Dalia and CapnoBase datasets, which exhibit higher HR detection accuracy. Experimental results obtained from benchmark datasets clearly show that the proposed method can achieve higher similarity on the reconstructed morphology with higher HR detection accuracy. Moreover, the proposed approach make it possible to employ PPG sensors for long-term monitoring of the heart actions with higher precision. [ABSTRACT FROM AUTHOR]

To visualize large urban models efficiently, this paper presents a framework for generalizing urban building footprints and facade textures by using multiple Gestalt rules and a graph-cut-based energy function. First, an urban scene is divided into different blocks by main road networks. In each block, the building footprints are partitioned into potential Gestalt groups. A footprint may satisfy several Gestalt principles. We employ the graph-cut-based optimization function to obtain a consistent segmentation of the buildings into optimal Gestalt groups with minimal energy. The building footprints in each Gestalt group are aggregated into different levels of detail (LODs). Building facade textures are also abstracted and simplified into multiple LODs using the same approach as the building footprint simplification. An effective data structure termed SceneTree is introduced to manage these aggregated building footprints and facade textures. Combined with the parallelization scheme, the rendering efficiency of large-scale urban buildings is improved. Compared with other methods, our presented method can efficiently visualize large urban models and maintain the city's image. [ABSTRACT FROM AUTHOR]

Motivated by the importance of hardware implementation in practical turbo decoders, a simplified, yet effective, n-input \max^\ast approximation algorithm is proposed with the aim being its efficient implementation for very low-complexity turbo decoder hardware architectures. The simplification is obtained using an appropriate digital circuit for finding the first two maximum values in a set of n data that embeds the computation of a correction term. Various implementation results show that the proposed architecture is simpler by 30%, on average, than the constant logarithmic-maximum a posteriori (Log-MAP) one, in terms of chip area with the same delay. This comes at the expense of very small performance degradation, in the order of 0.1 dB for up to moderate bit error rates, e.g., 10^-5, assuming binary turbo codes. However, when applying scaling to the extrinsic information, the proposed algorithm achieves almost identical Log-MAP turbo code performance for both binary and double-binary turbo codes, without increasing noticeably the implementation complexity. [ABSTRACT FROM PUBLISHER]

In this paper, novel ergodic capacity (EC) performance evaluation results of a power beacon (PB)-assisted multi-input multi-output (MIMO) wireless powered communication network are presented. In the considered system, the energy harvesting node harvests energy from the radio-frequency signals sent by the dedicated PB and uses this energy to communicate with the destination node. To accurately model the combined effect of multi-path fading and shadowing, it is assumed that the energy transfer link is subject to $\kappa$ - $\mu$ shadowed fading. Performance evaluation results are presented for two cases, depending upon the availability of channel state information (CSI) at the PB, namely, $no$ CSI and $full$ CSI. In the former case, equal power allocation is assumed, whereas, in the later case, energy beamforming is employed to increase energy transfer efficiency. For the performance evaluation of EC under $full$ CSI, a closed-form approximation for the probability density function of the maximum eigenvalue of a $\kappa$ - $\mu$ shadowed distributed random matrix is derived. For both $no$ CSI and $full$ CSI cases, lower and upper bounds on the achievable EC are derived in closed-form. Moreover, in order to obtain further insights on the impact of key parameters on the system performance, asymptotic EC expressions which become very tight at low- and high-signal-to-noise ratio regimes, are obtained. Using the proposed EC lower bound as well as these asymptotic results, simple closed-form expressions for the optimal time split that maximize the achievable EC are derived. Numerically evaluated results accompanied with Monte-Carlo simulations are further presented to corroborate the theoretical analysis. [ABSTRACT FROM AUTHOR]

Digital satellite multimedia broadcasting services in Europe are specified in the Mobile Satellite Services (MSS) frequency band by two recently developed standards, namely the European Telecommunications Standards Institute (ETSI) Satellite Digital Radio (SDR) and the Digital Video Broadcasting—Satellite services to Handhelds (DVB-SH) standards. Commercial deployment of operational systems based on these standards is foreseen in the coming years targeting mainly six large European markets. For these standards, a state-of-the-art channel code is deployed based on the 3rd Generation Partnership Project 2 (3GPP2) specifications having a wide range of coding rates. The purpose of this paper is twofold: (i) It studies and compares, for the first time, the Bit Error Rate (BER) performance of the 3GPP2 turbo code used in the aforementioned systems, in order to serve as a benchmark for system design engineers; and (ii) It investigates novel alternative channel coding schemes, including other state-of-the-art turbo code configurations and also Low-Density Parity-Check (LDPC) codes with linear-time encoding, such as the Irregular Repeat-Accumulate (IRA) codes, in order to improve the performance and/or reduce the complexity in future mobile satellite broadcasting systems. Extensive performance evaluation results in the Additive White Gaussian Noise (AWGN) and uncorrelated Rayleigh/Rician fading channels have shown that the use of turbo codes with higher number of states improves performance against the 3GPP2 turbo codes of up to 0.25 dB but the decoding complexity is almost doubled. Furthermore, the use of duo-binary turbo codes reduces decoding latency and makes them more robust in puncturing with performance improvement against the 3GPP2 turbo codes of up to 0.3 dB but performance degrades at very low coding rates. On the other hand, the use of Rate Compatible (RC)-IRA codes results in smaller performance improvement against the 3GPP2 turbo codes, i.e. up to 0.1 dB, but rate compatibility is used to obtain different coding rates with simple puncturing/extending method. [ABSTRACT FROM PUBLISHER]

Three non-data-aided signal-to-noise ratio (SNR) estimator techniques are derived and compared for turbo decoding, in conjunction with block-coded binary-phase-shift-keying (BPSK) signals received over fully interleaved (i.e., uncorrelated) Rayleigh fading channels. These estimators, which do not require knowledge at the receiver of the channel SNR nor the power of fading amplitude, are optimized in terms of minimizing the estimation error by choosing an appropriate curve fit model and comparing their estimation errors, as well as their turbo decoding bit-error-rate (BER) performance. Turbo decoding performance evaluation results have demonstrated that the two SNR estimators, which use the ratios of the second moment to the square of the mean absolute value and the fourth moment to the fourth power of the mean absolute value of the received signals, respectively, result in comparable BER performance to the case of perfect SNR knowledge at the receiver. Furthermore, they outperform the third SNR estimator, which makes use of the ratio of the square of the second moment to the fourth moment, particularly in applications with relatively short code blocks. [ABSTRACT FROM AUTHOR]

The correlated bivariate generalized-K (KG) distribution, with not necessarily identical shaping and scaling parameters, is introduced and studied. This composite distribution is convenient for modeling multipath/shadowing correlated fading environments when the correlations between the signal envelopes and their powers are different. Generic infinite series expressions are derived for the probability density function (PDF), the cumulative distribution function (CDF) and the joint moments. Assuming identical shaping parameters, simpler expressions for the PDF, CDF and the characteristic function (CF) are provided, while the joint moments are derived in closed form. Furthermore, the PDFs of the product and ratio of two correlated KG random variables are obtained. Capitalizing on these theoretical expressions for the statistical characteristics of the correlated KG distribution, the performance analysis of various diversity reception techniques, such as maximal ratio combining (MRC), equal gain combining (EGC) and selection diversity (SD), over bivariate KG fading channels is presented. For the SD, the outage probability is studied, while for the MRC and EGC the average bit error probability is obtained. The proposed analysis is accompanied by numerical results, clearly demonstrating the usefulness of the theoretical approach as well as the appropriateness of the KG distribution to model multipath/shadowing fading channels. [ABSTRACT FROM AUTHOR]

Broadband satellite services to fixed terminals are currently offered in the forward link by the 2nd Generation (2G) Digital Video Broadcasting Satellite (DVB-S2) standard. For this standard the use of powerful Low-Density Parity-Check (LDPC) error correcting codes has been adopted performing within approximately 1 dB from the Shannon capacity limit. This paper studies and compares for the first time in a systematic manner different approximation methods used in check node update computation of DVB-S2 LDPC decoding with the aim of reducing computational complexity. Various performance evaluation results are presented for a wide range of DVB-S2 parameters, such as LDPC codeword size, coding rate, modulation format and including several decoding algorithms. It is shown that the proposed check node update approximations have a robust behavior, i.e. the resulting performance is quite independent of the DVB-S2 modulation and coding parameters. It is further shown that these approximations perform very close to the optimal Sum-Product Algorithm (SPA) in degradation, which is less than 0.2 dB. Despite this small degradation, the reduction in computational complexity compared to the optimal SPA is significant and can be as high as 40% in computational time savings. [ABSTRACT FROM AUTHOR]

A generic and novel distribution, referred to as N*Nakagami, constructed as the product of N statistically independent, but not necessarily identically distributed, Nakagami-m random variables (RVs), is introduced and analyzed. The proposed distribution turns out to be a very convenient tool for modelling cascaded Nakagami-m fading channels and analyzing the performance of digital communications systems operating over such channels. The moments-generating, probability density, cumulative distribution, and moments functions of the N*Nakagami distribution are developed in closed form using the Meijer's G-function. Using these formulas, generic closed-form expressions for the outage probability, amount of fading, and average error probabilities for several binary and multilevel modulation signals of digital communication systems operating over the N*Nakagami fading and the additive white Gaussian noise channel are presented. Complementary numerical and computer simulation performance evaluation results verify the correctness of the proposed formulation. The suitability of the N*Nakagami fading distribution to approximate the lognormal distribution is also being investigated. Using Kolmogorov-Smirnov tests, the rate of convergence of the central limit theorem as pertaining to the multiplication of Nakagami-m RVs is quantified. [ABSTRACT FROM AUTHOR]

To better understand scene images in the field of remote sensing, multi-label annotation of scene images is necessary. Moreover, to enhance the performance of deep learning models for dealing with semantic scene understanding tasks, it is vital to train them on large-scale annotated data. However, most existing datasets are annotated by a single label, which cannot describe the complex remote sensing images well because scene images might have multiple land cover classes. Few multi-label high spatial resolution remote sensing datasets have been developed to train deep learning models for multi-label based tasks, such as scene classification and image retrieval. To address this issue, in this paper, we construct a multi-label high spatial resolution remote sensing dataset named MLRSNet for semantic scene understanding with deep learning from the overhead perspective. It is composed of high-resolution optical satellite or aerial images. MLRSNet contains a total of 109,161 samples within 46 scene categories, and each image has at least one of 60 predefined labels. We have designed visual recognition tasks, including multi-label based image classification and image retrieval, in which a wide variety of deep learning approaches are evaluated with MLRSNet. The experimental results demonstrate that MLRSNet is a significant benchmark for future research, and it complements the current widely used datasets such as ImageNet, which fills gaps in multi-label image research. Furthermore, we will continue to expand the MLRSNet. MLRSNet and all related materials have been made publicly available at https://data.mendeley.com/datasets/7j9bv9vwsx/1 and https://github.com/cugbrs/MLRSNet.git. [ABSTRACT FROM AUTHOR]

Due to the large intraclass variances and complicated object distribution, recognizing objects with complex appearances and arbitrary orientations has been an active research topic and a challenging task in remote sensing fields. In this article, we formulate object recognition as a high-level feature-learning problem, and a novel supervised method is proposed to learn high-level feature representations from high-resolution remote sensing images for object recognition. Our method simultaneously and coherently achieves high-level feature learning and classifier training, which improves the recognition performance. Two constraints that enforce the label consistencies of group images and label consistencies of single images are introduced in a deep learning framework to obtain the high-level feature space. The high-level feature and a multiclass linear classifier are finally learned by an effective optimization algorithm. Experimental results demonstrate the superior performance of the proposed method over many state-of-the-art techniques in object recognition. [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology for residential building contouring from large-scale airborne point clouds. Unlike other methods that handle linearization and regularization of the linear primitives separately by imposing rigid constraints, we propose an optimization-based linearization and global regularization to form accurate, topologically error-free, and lightweight polygons. To this end, we enhance the classic density-based spatial clustering of applications with noise algorithm to segment individual building entities at the instance level. The initial contours of each individual building are then delineated and further decomposed by a novel topologically aware propagation process and a global optimization technique. The decomposed linear primitives are fed into the global regularization step, from which the regular shapes are learned and enforced hierarchically by imposing constraints, such as parallelism, homogeneity, orthogonality, and collinearity. Based on the concept of hybrid representation, the regularized and unaltered linear primitives are jointly connected in an esthetic way. Various experiments using representative buildings and large-scale residential scenes from the Dutch AHN3 data set have shown that the proposed methodology generates meaningful building contouring representation in terms of accuracy, compactness, topology, and levels of detail abstraction while being robust and scalable. [ABSTRACT FROM AUTHOR]

With the rapid development of photogrammetry, computer vision and three-dimensional (3D) modeling technologies, it is possible to efficiently construct detailed 3D urban models. Accordingly, large corpora of 3D models, such as the Google 3D Warehouse, are now becoming freely available on the web. How to find the proper 3D urban models is a challenging research issue. In this paper, we join shape descriptors and color descriptors for 3D urban model retrieval. The query objects are localized and segmented automatically from the input images by using a new selective search voting algorithm. Through combining the normalization with the light field descriptor, the Horizontal Light Descriptor is introduced to measure the shape similarity among the normalized urban models. The color descriptors are used to represent the color information of the urban models. The two types of descriptors are joined to search 3D urban models similar to the query objects. Experimental results have shown the effectiveness of our approach. [ABSTRACT FROM PUBLISHER]

With the emergence of huge volumes of high-resolution remote sensing images produced by all sorts of satellites and airborne sensors, processing and analysis of these images require effective retrieval techniques. To alleviate the dramatic variation of the retrieval accuracy among queries caused by the single image feature algorithms, we developed a novel graph-based learning method for effectively retrieving remote sensing images. The method utilizes a three-layer framework that integrates the strengths of query expansion and fusion of holistic and local features. In the first layer, two retrieval image sets are obtained by, respectively, using the retrieval methods based on holistic and local features, and the top-ranked and common images from both of the top candidate lists subsequently form graph anchors. In the second layer, the graph anchors as an expansion query retrieve six image sets from the image database using each individual feature. In the third layer, the images in the six image sets are evaluated for generating positive and negative data, and SimpleMKL is applied to learn suitable query-dependent fusion weights for achieving the final image retrieval result. Extensive experiments were performed on the UC Merced Land Use–Land Cover data set. The source code has been available at our website. Compared with other related methods, the retrieval precision is significantly enhanced without sacrificing the scalability of our approach. [ABSTRACT FROM AUTHOR]

The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computed on three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars. [ABSTRACT FROM AUTHOR]

Modeling 3-D trees from terrestrial laser scanning (TLS) point clouds remains a challenging task for several well-known reasons, including their complex structure and severe occlusions. In order to accurately reconstruct 3-D tree models from TLS point clouds that typically suffer from significant occlusions, in this paper, a novel local structure and direction-aware approach is presented to successfully complete missing structures of trees. In this method, we first extract the coarse tree skeleton from the input point cloud, and thus, the branch dominant direction and the point density of each branch are obtained. By a skeleton-based Laplacian algorithm, the point cloud is further shrunk into a skeleton point cloud to highlight the branch dominant direction of each branch. For obtaining even more accurate point densities, a dictionary-based algorithm is utilized to learn and reconstruct the local structure. Finally, the branch dominant direction and point density are integrated into an iterative optimization process to recover the missing data. Extensive experimental results have shown that the proposed method is very robust to incomplete data sets, and it is capable of accurately reconstructing 3-D trees, which are partially, or even to a large extent, missing from the input point cloud. [ABSTRACT FROM PUBLISHER]

Point cloud classification plays a critical role in point cloud processing and analysis. Accurately classifying objects on the ground in urban environments from airborne laser scanning (ALS) point clouds is a challenge because of their large variety, complex geometries, and visual appearances. In this paper, a novel framework is presented for effectively extracting the shape features of objects from an ALS point cloud, and then, it is used to classify large and small objects in a point cloud. In the framework, the point cloud is split into hierarchical clusters of different sizes based on a natural exponential function threshold. Then, to take advantage of hierarchical point cluster correlations, latent Dirichlet allocation and sparse coding are jointly performed to extract and encode the shape features of the multilevel point clusters. The features at different levels are used to capture information on the shapes of objects of different sizes. This way, robust and discriminative shape features of the objects can be identified, and thus, the precision of the classification is significantly improved, particularly for small objects. [ABSTRACT FROM AUTHOR]

The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a pointcluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars. [ABSTRACT FROM AUTHOR]

A 3-D tree structure plays an important role in many scientific fields, including forestry and agriculture. For example, terrestrial laser scanning (TLS) can efficiently capture high-precision 3-D spatial arrangements and structure of trees as a point cloud. In the past, several methods to reconstruct 3-D trees from the TLS point cloud were proposed. However, in general, they fail to process incomplete TLS data. To address such incomplete TLS data sets, a new method that is based on a structure-aware global optimization approach (SAGO) is proposed. The SAGO first obtains the approximate tree skeleton from a distance minimum spanning tree (DMst) and then defines the stretching directions of the branches on the tree skeleton. Based on these stretching directions, the SAGO recovers missing data in the incomplete TLS point cloud. The DMst is applied again to obtain the refined tree skeleton from the optimized data, and the tree skeleton is smoothed by employing a Laplacian function. To reconstruct 3-D tree models, the radius of each branch section is estimated, and leaves are added to form the crown geometry. The developed methodology has been extensively evaluated by employing a dozen TLS point clouds of various types of trees. Both qualitative and quantitative performance evaluation results have indicated that the SAGO is capable of effectively reconstructing 3-D tree models from grossly incomplete TLS point clouds with significant amounts of missing data. [ABSTRACT FROM PUBLISHER]

An optimal channel partitioning scheme that maximizes the channel utilization of low earth orbit mobile satellite systems serving multiclass traffic is presented. To this aim, the channel utilization was analytically derived and used as revenue function for the proposed optimization algorithm. The accuracy of the analysis and the effectiveness of the proposed optimization technique were verified by computer simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we deal with practical implementation issues of the \max^ \ast operation in generalized form used for decoding of both turbo and low-density-parity-check (LDPC) codes. In particular, first, a unified framework for the so-called generalized \max^\ast operation is established, which includes most of the previously published algorithms already known for turbo decoding. Next, the hardware architectures used for the practical implementation of the generalized \max^\ast operation, which is derived from this novel framework, are revealed for the first time and further analyzed, in terms of hardware complexity reduction. It is also shown how this generalized \max^\ast operation can be adopted in LDPC decoding, achieving essentially optimal bit error rate performance with small computational complexity against other algorithms in joint turbo–LDPC architectures. This solution is useful in applications where joint decoding architectures are deployed to decode both turbo and LDPC codes. An important example of such application is in software radio receivers of 4G wireless communication systems, such as those proposed in conjunction with the WiMAX standard. [ABSTRACT FROM PUBLISHER]

An analysis of the exact bit error rate (BER) performance of orthogonal space-time block codes (OSTBCs) with maximum-likelihood detection in the presence of channel estimation errors is presented. The possibly correlated coefficients of the multiple-input multiple-output (MIMO) propagation channel are assumed to be affected by flat block fading and the transmitted symbols belong to a pulse amplitude modulation or quadrature amplitude modulation signal constellation. For both square and nonsquare OSTBCs, we derive approximate and exact BER expressions, irrespective of the distribution of the fading. It is also shown how the exact expressions can be efficiently and accurately evaluated using numerical integration techniques. Their application to the arbitrarily correlated Nakagami-m fading channel is presented and an efficient importance sampling technique is derived. As the high diversity order resulting from the application of OSTBCs gives rise to small BER values, the numerical evaluation of the presented BER expressions is much faster than straightforward Monte Carlo simulations. BER results have shown the impact of both imperfect channel estimation and antenna correlation on the performance of MIMO OSTBC systems. It is also shown that under highly correlated conditions, antenna correlation is the major source of BER degradation. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust frame synchronization for TDM/TDMA systems in the presence of frequency errors, accomplished through data-aided recognition of the Unique Word (UW) preamble in the transmission flow. Robust detection design is performed applying, through approximations, the Maximum Likelihood (ML) criterion coupled to Post Detection Integration (PDI), to obtain a novel detector identified as Balanced-GPDI (B-GPDI). This new approach considerably outperforms other schemes available in the literature, at the cost of a moderate complexity increase. This is possible thanks to an optimized use of coherent accumulation, which enhances the correlation term in the decision variable, along with highly accurate modeling of the energy correction factor. To limit complexity increase and memory requirements, several approximations of the exact B-GPDI are proposed and discussed in the paper, to provide the designer with practical solutions that are still able to outperform other approaches in specific application scenarios. In particular, approximations for low and high Signal-to-Noise Ratio (SNR) are presented. Different application scenarios are considered in the paper for numerical analysis. In particular, the cases of forward link Continuous Transmission (CTX) and return link Burst Transmission (BTX) are addressed. [ABSTRACT FROM AUTHOR]

The performance of a class of generalized-selection combining (GSC) receivers operating over independent but non- identically distributed Weibull fading channels is studied. We consider the case where the two branches with the largest instantaneous signal-to-noise ratio (SNR), from a total of L available, GSC(2, L) are selected. By introducing a novel property for the product of moments of ordered Weibull random variables, convenient closed form expressions for the moments of the GSC(2,L) output SNR are derived. Using these expressions, important performance criteria, such as average output SNR and amount of fading, are obtained in closed form. Furthermore, employing the Padé approximants theory and the moment-generating function approach, outage and bit-error rate performance are studied. An attempt is also made to identify the equivalency between the Weibull and the Rice fading channel, which is typically used to model the mobile satellite channel. We present various numerical performance evaluation results for different modulation formats and channel conditions. These results are complemented by equivalent computer simulated results which validate the accuracy of the proposed analysis. [ABSTRACT FROM AUTHOR]

Ascertaining the importance of the dual selection combining (SC) receivers and the suitability of the Weibull model to describe mobile fading channels, we study the performance of a dual SC receiver over correlated Weibull fading channels with arbitrary parameters. Exact closed-form expressions are derived for the probability density function, the cumulative distribution function, and the moments of the output signal-to-noise ratio (SNR). Important performance criteria, such as average output SNR, amount of fading, outage probability, and average bit-error probability for several modulation schemes are studied. Furthermore, for these performance criteria, novel closed-form analytical expressions are derived. The proposed analysis is complemented by various performance evaluation results, including the effects of the input SNR's unbalancing, fading severity, and fading correlation on the overall system's performance. Computer simulation results have verified the validity and accuracy of the proposed analysis. [ABSTRACT FROM AUTHOR]