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Summary: It is well known that hardware impairments (HWIs) can worthy reduce the wireless system performance at high carrier frequencies by showing random effects. Most current researches for 5 GB systems assume that transmitters and receivers (transceivers) are perfectly equipped. But wireless transceivers (TxRx) are affected by HWIs in practice. Considering the previous studies in the literature, it is reported that HWIs have devastating effects on the performance of OFDM and OFDM‐index modulation (IM) systems with fading channels. In this paper, in‐phase and quadrature phase imbalance (IQI), which is the one of most HWIs between transmitter and receiver in wireless communication systems, is examined on OFDM‐IM system over Rayleigh and Nakagami‐ m fading channels. Two well‐known detectors, the maximum likelihood (ML) detector and the log‐likelihood ratio (LLR) detector are used under the effect of the IQI at TxRx. Error performance analyzes over fading channels of the IQI effect on OFDM‐IM system are realized first theoretically and then by computer simulations. Results obtained for the presence of IQI at TxRx show that a performance evaluation based only on the presence of IQI in the receiver would be optimistic and misleading in terms of the performance of real‐life OFDM‐IM systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér–Rao lower bound (CRLB), even at low SNR levels. [ABSTRACT FROM AUTHOR]

Summary This paper considers multiuser downlink transmission and detection in a multiple input‐multiple output (MIMO) visible light communication (VLC) system. We combine two promising techniques: space‐shift keying (SSK) and optical orthogonal codes (OOCs). Multiuser downlink transmission is realized by allowing each activated LED to transmit user‐specific OOC simultaneously. An OOC despreader is implemented at the front end of each user terminal (UT) to extract the desired signal and mitigate multiuser interference (MUI). The second part of UT receiver aims to extract the index of the corresponding active LED based on maximum likelihood (ML) or zero‐forcing (ZF) algorithm. The figure of merit we used to access the performance of the proposed downlink multiuser SSK system is the average symbol error rate (SER). Comprehensive Monte Carlo simulations have been undertaken for different system setup to evaluate SER at UT receiver. It is demonstrated that for a specified SER, the required SNR for ML‐based detection algorithm is 3 dB less than the ZF detector in the MUI‐free scenario. On the other hand, the ZF detector outperforms ML detector in the case with residual MUI and limited channel state information (CSI). Both algorithms are computationally attractive, hence can be put into practice in multiuser VLC MIMO system. [ABSTRACT FROM AUTHOR]

As an extension of spatial modulation (SM) technology, generalized spatial modulation (GSM) systems can achieve higher data transmission rates with limited spectrum resources due to activate multiple transmit antennas to send data in each time slot. Simultaneously activating multiple transmit antennas increase the difficulty of signal demodulation at the receiver in GSM systems. To reduce the high computational complexity of the maximum likelihood (ML) detection algorithm in GSM systems, a low-complexity GSM detection algorithm is proposed, which combines the iterative zero forcing (IZF) algorithm and the minimum mean square error (MMSE) equalization processing detection algorithm. Firstly, the zero forcing (ZF) algorithm is used to iteratively detect all transmit antenna combinations to obtain multiple unlikely transmit antenna indices. Then the number of search antenna combinations is reduced by removing these antenna combinations. Next, MMSE equalization detection and symbol quantification on the new search set are performed. Finally, the ML algorithm is used to estimate the transmit antenna combination and transmit symbols. The effectiveness of the proposed algorithm is verified by simulation experiments. The simulation results show that the computational complexity of the proposed algorithm is much lower than that of the ML algorithm, and the bit error rate (BER) performance is close to that of the ML algorithm. [ABSTRACT FROM AUTHOR]

Baccaurea is a group of fruit-producing plants found in wild forests. Many of these plant species have not yet explored their potential. The high rate of deforestation in West Sumatra poses a threat of extinction to the genetic resources of the genus Baccaurea before being explored and identified. The research aims to analyze the sequence characters and phylogenetic of Baccaurea found in West Sumatra using the MatK molecular marker. Phylogenetic analysis using the Maximum Likelihood (ML) method in MEGA X application. The results of the analysis of the six species of Baccaurea found that the sequence length ranged from 854-1019bp, the percentage of G+C base composition is 33.4%, the percentage of A+T base composition is 66.6%, the genetic distance range is 0-4% with a conservative character of 484bp and informative characters of 4bp. While the phylogenetic analysis using the ML method grouping the six species of Baccaurea to form a monophyletic clade with a bootstrap value of 100%, all species collected were in the same clade. These results reveal the first time that the MatK sequences from six species Baccaurea native to West Sumatra will be included in NCBI for use by other studies in conducting broader phylogenetic research. [ABSTRACT FROM AUTHOR]

This paper analyzes the bit error rate (BER) of multicarrier index keying—orthogonal frequency division multiplexing (MCIK-OFDM) with selection combining (SC) diversity reception. Particularly, we propose a generalized framework to derive the BER for both the low-complexity greedy detector (GD) and maximum likelihood (ML) detector. Based on this, closed-form expressions for the BERs of MCIK-OFDM with the SC using either the ML or the GD are derived in presence of the channel state information (CSI) imperfection. The asymptotic analysis is presented to gain helpful insights into effects of different CSI conditions on the BERs of these two detectors. More importantly, we theoretically provide opportunities for using the GD instead of the ML under each specific CSI uncertainty, which depend on the number of receiver antennas and the M-ary modulation size. Finally, extensive simulation results are provided in order to validate our theoretical expressions and analysis. [ABSTRACT FROM AUTHOR]

When industrial relics, such as obsolete buildings, sites, and infrastructures, enter into a process of adaptive reuse, they become transformation engines capable of shaping the urban fabric. They provide tangible and intangible links to our past and have the potential to play a significant role in today's cities' futures. One unresolved issue is the quantification of the externalities of these transformation processes. If undertaken correctly, adaptive reuse can contribute to the development of social and cultural capital, environmental sustainability, urban regeneration, and, most importantly, economic benefits to the surrounding community. In this sense, understanding the value of heritage is particularly important in light of the new European urban environmental policy movement based on the circular economy, which aims to change the way Member States consume and produce materials and energy. After a review of the externalities generated by the adaptive reuse of disused industrial heritage, the paper will concentrate on the estimation of economic benefits given by a transformation process that affected Turin's Aurora district (Northern Italy) during the last years. The hedonic pricing method (HPM) was used to investigate the effects of the construction of new headquarters and the redevelopment of an old power plant converted into a museum and conference center. This study used econometric models to identify a significant increase in market prices within 800 m of the site and calculated a EUR 16,650,445 capitalized benefit from the transformation on the surrounding residential building stock. The study thus contributed to the awareness that reused heritage not only improves the lives of residents, but it also has a positive impact on the real estate market, in terms of transactions, as well as market values. [ABSTRACT FROM AUTHOR]

This communication presents an efficient iterative approach for locating the near-field (NF) coherent sources. In each iteration, the covariance matrices only containing single source information are constructed by using alternating oblique projection. Then based on the principle of vector dot products, a new iterative direction-of-arrival (DOA) estimator is proposed. After the DOA of each separated signal is estimated, the paired ranges are obtained from the 1-D maximum likelihood (ML) estimator. The proposed algorithm avoids high-dimensional spectral searches, subspace extraction, and any preprocessing such as spatial smoothing, leading to low computational complexities and high estimation accuracy. Comparative simulations show the efficiency and merits of the proposed method. [ABSTRACT FROM AUTHOR]

As an extension of spatial modulation (SM) technology, generalized spatial modulation (GSM) is a promising technique for high data-rates systems. In GSM system, multiple antennas are activated in each time slot, which can greatly enhance the band efficiency. However, the usage of multiple active antennas for transmission also brings difficulties of the demodulation at the receiver. The computational complexity of the traditional maximum likelihood (ML) detector is closely related to the modulation order. Based on this point, we propose an ordered block hard-limited maximum likelihood (OB-HLML) algorithm for MQAM constellation, which is independent of modulation order. OB-HLML algorithm first sorts the possibility of transmit antenna combinations (TACs), and then detects the possible signal vectors of each ordered TAC by HLML detector. Simulation results show that the proposed OB-HLML algorithm can provide almost the same bit error rate (BER) performance as the ML algorithm with greatly reduced computational complexity. [ABSTRACT FROM AUTHOR]

In this article, the signal detection problem in indoor visible light communication (VLC) system aided by generalized space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by exploiting the sparse reconstruction algorithms in compressed sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed based on singular value decomposition (SVD), which theoretically guarantees the feasibility of utilizing CS based sparse signal detection method in indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the GSSK signals are efficiently detected in the considered indoor GSSK-VLC system. Furthermore, a more efficient detection algorithm combined with OMP and maximum likelihood (ML) is also presented especially for SSK scenario. Finally, the effectiveness of the proposed sparsity aided detection algorithms in indoor GSSK-VLC system are verified by computer simulations. The results show that the proposed algorithms can achieve better bit error rate (BER) and lower computation complexity than ML based detection method. Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed in the SSK scenario and about 5 dB in case of a GSSK scenario upon employing our proposed detection methods. [ABSTRACT FROM AUTHOR]

This article addresses the 3-D reconstruction of urban areas using a minimal number of Synthetic Aperture Radar (SAR) acquisitions, that is, a set of three images, characterized by intermediate spatial resolution features. In such extreme conditions, conventional tomographic techniques reveal unadapted to refined 3-D imaging purposes, either due to the resulting intrinsic coarse vertical resolution, or to the low dimensionality of the data set, that prevents any separation of complex mixed scattering patterns. A new high-resolution (HR) tomographic estimator, based on a polarimetric signal subspace fitting criterion, is proposed to overcome these limitations, as this method adapts to the statistical behavior of the backscattered signals using robust metrics. The optimization of the corresponding focusing criterion is led through a new polarimetric alternating projection algorithm, characterized by a low computational cost, which may also be used to optimize the polarimetric the deterministic maximum likelihood criterion. The proposed polarimetric signal subspace fitting technique is shown to outperform the other studied HR techniques over both simulated signals and data acquired by the DLR’s ESAR sensor at L-band over Dresden city, Germany. Finally full-rank polarimetric tomographic estimators are proposed that generalize nonparametric polarimetric estimators, and permit to estimate second-order polarimetric representations in 3-D, instead of unitary rank target vector with their conventional versions. This approach makes it possible to characterize polarimetric scattering mechanisms in 3-D. [ABSTRACT FROM AUTHOR]

Due to the severe impact of dynamic plasma sheath on electromagnetic (EM) waves, the communication quality of hypersonic vehicles deteriorates, even resulting in communication blackouts during the reentry process. In this article, the hypersonic vehicle downlink integrated channels that cascade the plasma sheath channel and Rice fading channel, i.e., the downlink telemetry channels, are first established, and then the demodulation method and performance of typical telemetry modulations including the M-ary phase shift keying (MPSK), M-ary amplitude shift keying (MASK), and M-ary frequency shift keying (MFSK) are studied. Considering the high dynamics of the plasma sheath, the pilot signals are used to detect the signal correctly for MPSK, and the non-coherent demodulation is adopted for MASK and MFSK. Especially, an adaptive maximum likelihood (ML) receiver for ASK is proposed based on a reduced reversible jump Markov chain Monte Carlo (RRJ-MCMC) algorithm to estimate the probability density function (pdf) of the received signal. The simulation results show that the communication performance of PSK and ASK is improved with the modulation order $M$ decreasing; the performance of non-coherent FSK is improved with the modulation order $M$ increasing. Meanwhile, the performance follows the order of NC-MFSK (M ≥ 8) > BPSK > NC-4FSK > NC-2FSK > 2ASK. For NC-8FSK, when the electron density peak value ${Ne}_{\mathrm {peak}}$ = {2, 4, 6, 8, 10} $\times 10^{18}\,\,\text{m}^{-3}$ , the signal to noise ratio (SNR) required to reduce the bit error rate (BER) to 10−6 are 17, 18, 19, 21, and 34 dB, respectively. [ABSTRACT FROM AUTHOR]

A multiple-input multiple-output (MIMO) radarwith widely separated antennas on moving platforms suffers from the effects of platform deviations on the target parameter estimation since the trajectories of the moving platforms are sensitive to environmental factors, such as strong wind. This article addresses the joint estimation of multiple target positions and velocities as well as radar system deviations to minimize the impact of platform deviations. The proposed algorithm can also be regarded as a self-calibration technique. First, the grid search dimensions for parameter estimation are reduced via a generalized maximum likelihood (GML) algorithm. Second, the adaptive gradient (AdaGrad) method is used to implement the GML estimation for multitarget echo delays and Doppler shifts. Finally, to address the nonlinear estimation problem of interest, the iterative least squares method is used to estimate the multiple target positions and velocities as well as radar system deviations based on the estimated delays and Doppler shifts. Prior information, such as target positions or the probability distribution of the echo coefficient, is not needed in the proposed method. The parameter identifiability is also derived in this article. Numerical simulations show that compared to a method without the estimation of system deviations, the proposed method is more efficient with respect to the derived Cramér–Rao bound. [ABSTRACT FROM AUTHOR]

In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position and timing information by round-trip communications to a number of anchor nodes (ANs) at known locations. Compared with the one-way TOA technique, the two-way TOA scheme is easy to implement and has higher localization and synchronization accuracy. Existing two-way TOA methods assume a stationary UD. This will cause uncompensated position and timing errors. In this article, we propose an optimal maximum likelihood (ML) based two-way TOA localization and synchronization method, namely TWLAS. Different from the existing methods, it takes the UD mobility into account to compensate the error caused by the UD motion. We analyze its estimation error and derive the Cramér-Rao lower bound (CRLB). We show that the conventional two-way TOA method is a special case of the TWLAS when the UD is stationary, and the TWLAS has high estimation accuracy than the conventional one-way TOA method. We also derive the estimation error in the case of deviated UD velocity information. Numerical result demonstrates that the estimation accuracy of the new TWLAS for a moving UD reaches CRLB, better than that of the conventional one-way TOA method, and the estimation error caused by the deviated UD velocity information is consistent with the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article deals with the problem of adaptive target detection in the presence of homogeneous Gaussian interference with frequency diverse array multiple-input multiple-output radar. Adaptive detectors are devised according to the generalized likelihood ratio test criterion, where the position of the target within each range cell is assumed unknown. To obtain the maximum likelihood estimate of the target incremental range under the $H_1$ hypothesis, three different optimization strategies are pursued. They are, respectively, based on semidefinite programming, discrete grid search, and Newton method. At the analysis stage, a detection performance comparison is carried on among the new proposed adaptive detectors, benchmark, and mismatched receivers. Numerical results corroborate the effectiveness of the developed receivers. [ABSTRACT FROM AUTHOR]

As bias estimation methods are developed, it becomes necessary to obtain the bound on bias estimation for more complex bias and sensor models. Three-dimensional (3-D) sensors, such as radars commonly used in applications, contain both scale and additive biases in sine space which result in a nonlinear estimation problem that may have poor observability and accuracy depending on the geometry of the sensors. By converting the sine space and range measurements to Cartesian using an unbiased conversion, it is possible, via creation of pseudomeasurements, to eliminate the need to estimate the target's state thereby reducing the sensor bias estimator complexity. The present paper evaluates the Cramér–Rao lower bound (CRLB) for estimating scale and additive biases in sine space for 3-D sensors and compares it with a maximum likelihood formulation implemented via iterated least squares, which is thereby shown to be statistically efficient. Additionally, the importance of measurement diversity is investigated with respect to the CRLB. [ABSTRACT FROM AUTHOR]

This paper addresses the performance characterization of a direction of arrival (DoA) estimator in the low signal-to-noise-ratio (SNR) region. The case of a sensor array simultaneously collecting signals emitted at multiple carrier frequencies by a single source is considered. A maximum likelihood (ML) approach is used as a reference method for DoA estimation and its accuracy is characterized in terms of mean square error (MSE). It is well known that for SNR values included in the so-called threshold region, the DoA estimation accuracy decreases rapidly, due to the presence of outliers. This effect can be possibly mitigated when multiple frequency channels are jointly exploited. However, the capability to predict this performance degradation is fundamental either for assessing the robustness of an existing sensor or for supporting its design. Therefore, the scope of this paper is to introduce appropriate approximations to the MSE of a multifrequency ML DoA estimator in order to provide a reliable characterization of its performance in the threshold region. Two models for the source signals are considered and separately discussed, namely the deterministic (or conditional) and stochastic (or unconditional). An extensive simulated analysis is reported to prove the tightness of the approximations and to characterize the benefits steming from the exploitation of signals emitted at multiple carriers. [ABSTRACT FROM AUTHOR]

We consider range estimation and range-Doppler imaging using signed measurements of a radar system. Known time-varying thresholds are investigated for taking the signed measurements through one-bit sampling, and are compared with unknown dithering with known probability density function. The maximum likelihood (ML) approach is considered for signal parameter estimation. Since the ML algorithm is computationally prohibitive, a relaxation-based approach, referred to as the One-Bit RELAX algorithm, is used for signal parameter estimation. The conventional RELAX algorithm proposed for high-resolution sampling is also considered for comparison purposes. Moreover, a model-order selection tool, namely the Bayesian information criterion, is used to determine the number of scatterers within the scene of interest. Both numerical and experimental examples are provided to demonstrate the performance of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper introduces sparse low-power sensor networks using passive electric potential sensors for crowd-aware smart buildings. The proposed networks can detect human presence, track their positions, and estimate room occupancy by measuring the electric field disturbances caused by human movement. In addition, the proposed passive sensing system can remotely monitor human physiological signals of nearby stationary occupants (standoff ~1.5 m) when the ambient 60-Hz fields are filtered out. Based on an experimentally calibrated source range model, we further propose an improved multiple-occupant near-field indoor localization method using a maximum likelihood algorithm combined with dividing rectangular search. The tradeoff between search efficiency and localization accuracy is also discussed. Experimental results in two different rooms for 1 week duration show the effectiveness of our proposed sensing system: human identification with an accuracy of 98.3% on six participants, occupant localization methods with room occupancy estimation accuracy of 89.03%, and average localization error less than 0.3 m. These results open up new possibilities in security, indoor navigation or positioning systems, human–machine interfaces, and tele-health applications. [ABSTRACT FROM AUTHOR]

Channel estimation is of great challenge for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. The underlying reason can be attributed to two facts. One is the huge dimension of MIMO channel matrix, since mmWave systems typically employ large antenna arrays at both the transmitter and receiver to realize sufficient link margin. The other fact is the limited number of radio-frequency chains available for transmitter/receiver array, due to the power and cost limitations. This paper proposes a two-stage accurate channel estimation scheme for mmWave MIMO. Initially, we utilize sparse Bayesian learning to obtain a coarse channel estimate. Then we refine the channel estimate by iteratively maximizing the likelihood function of channel parameters. It is shown through simulations that the proposed scheme significantly outperforms the counterparts, and can achieve accurate channel estimation performance. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of censoring outliers in a class of complex multivariate elliptically contoured distributed radar data, which is a vital issue in radar signal processing applications, such as adaptive radar detection and space-time adaptive processing. The maximum likelihood (ML) estimate of the outlier subset is derived resorting to the generalized likelihood function (GLF) criterion. Since the ML estimate involves the solution of a combinatorial problem, a reduced complexity but approximate ML (AML) procedure is also considered. At the analysis stage, the performance of the AML method is evaluated in the presence of both simulated and real radar data, also in comparison with the conventional generalized inner product (GIP) and the reiterative censored GIP (RCGIP) algorithms. The results highlight that the AML technique achieves a satisfactory performance level and can outperform both GIP and RCGIP in some situations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate maximum likelihood (ML) time delay (TD) and carrier frequency offset (CFO) synchronization in multi-node decode-and-forward cooperative relaying systems operating over time-varying channels. This new synchronization scheme is embedded into a distributed multiple input multiple output (MIMO)-relay beamforming transceiver structure to avoid the drawbacks of multidimensional ML estimation at the destination and to minimize the overhead cost. By accounting for a perfect Doppler spread value, the new synchronization solution delivers accurate TD and CFO estimates. For real-world operation, however, this new technique can be jointly implemented with any Doppler spread estimator in a new iterative scheme using a time-constant channel (TCC)-based synchronization method at the initialization step. The resulting TD and CFO estimates along with the channel estimates are then fed into a distributed MIMO-relay beamforming transceiver of $K$ single-antenna nodes, for pre-compensation at each node of the transmitted signals, to ensure constructive maximum ratio combining (MRC) at the destination. Simulation results show significant synchronization accuracy improvement over previous distributed multi-node synchronization techniques assuming TCCs. The latter translates into noticeable gains in terms of useful link-level throughput, more so at higher Doppler or with more relaying nodes. [ABSTRACT FROM AUTHOR]

The product model is assumed to be an appropriate statistical model for multilook polarimetric synthetic radar data (PolSAR). According to this model, the observed signal is considered as the product of independent random variates of a complex Gaussian speckle and a non-Gaussian texture. With different texture distributions, the product model leads to different expressions for the compound distribution considered as an infinite mixture model. In this paper, the maximum-likeli-hood (ML) estimator is derived to jointly estimate the speckle and texture parameters in the compound distribution model using the multilook polarimetric radar data. In particular, we estimate: 1) the equivalent number of looks; 2) the covariance matrix of the speckle component; and 3) the texture distribution parameters. The expectation–maximization algorithm is developed to compute the ML estimates of the unknown parameters. The hybrid Cramer–Rao bounds (HCRBs) are also derived for these parameters. First, a general HCRB expression is derived under an arbitrary texture distribution. Then, this expression is simplified for a specific texture distribution. The performance of the ML is compared with the performance of other known estimators using the simulated and real multilook PolSAR data. For real data, a goodness of fit of multilook PolSAR data histograms is used to assess the fitting accuracy of the compound distributions using different estimators. [ABSTRACT FROM AUTHOR]

The primary aim of this study is to explore and investigate the maximum likelihood (ML) estimation and the Bayesian approach to estimating the parameters of log-logistic distribution and to calculate the approximate intervals for the parameters and the survival function based on adaptive progressive type-II censored data. The ML estimators of the parameters of the probability distribution were obtained via the Newton–Raphson Method. The approximate confidence intervals for the reliability function were calculated using the delta method. The Bayes estimators based on squared error loss function (SELF) and the approximate credible intervals for the unknown parameters and the survival function using the Bayesian approach were constructed using the Markov Chain Monte Carlo (MCMC) method. A Monte Carlo study was performed to examine the proposed methods under different situations, based on mean-squared error, bias, coverage probability, and expected length-estimated criteria. The Bayesian approach appears to be better than the likelihood for estimating the log-logistic model parameters. An application to real data was included. [ABSTRACT FROM AUTHOR]

Symbol timing offset (STO) estimation constitutes an important part in orthogonal frequency-division multiplexing (OFDM) signal synchronization, and many blind methods based on the maximum likelihood (ML) principle have been proposed. A theoretical analysis of the estimation performance, however, is very difficult, due in part to the discrete nature of the STO. As a result, most studies on OFDM STO estimation have relied only on computer simulation for performance evaluation. This paper presents a theoretical performance analysis for ML-based blind OFDM STO estimation. We start with a review of the formulation of ML blind STO estimation problem and its solution. We then conduct the said performance analysis. The analysis turns out to involve a study of the eigenvalues of some matrix functions of the autocorrelation of the received signal. As the general case leads to highly complicated mathematics, we treat the case of full-band OFDM in additive white Gaussian noise in more detail. In this case, we are able to obtain mathematically tractable bounds and approximation to the estimation error probability and the corresponding mean-square error. In high signal-to-noise ratio (SNR), these bounds and approximation show inverse power-law dependence on the SNR. We also compare the bounds with simulation results and with an existing approximation in the literature. [ABSTRACT FROM AUTHOR]

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry approach based on maximum likelihood estimation is developed for refined topographic mapping in mountainous areas. In the algorithm, maximum likelihood (ML) height estimation is used to measure the topographic details and avoid the complicated phase unwrapping process. In order to be well-adapted to the mountainous terrain conditions, the prior height probability is re-defined to take the local terrain conditions and neighboring height constraint into consideration in the algorithm. In addition, three strategies are used to optimize the maximum likelihood height estimation process to obtain higher computational efficiency, so that this method is more suitable for spaceborne InSAR data. The strategies include substituting a rational function model into the complicated conversion process from candidate height to interferometric phase, discretizing the continuous height likelihood probability, and searching for the maximum likelihood height with a flexible step length. The experiment with simulated data is designed to verify the improvement of the ML height estimation accuracy with the re-defined prior height distribution. Then the optimized processing procedure is tested with the multi-baseline L-band ALOS/PALSAR data covering the Mount Tai area in China. The height accuracy of the generated multi-baseline InSAR DEM can meet both standards of American DTED-2 and Chinese national 1:50,000 DEM (mountain) Level 2. [ABSTRACT FROM AUTHOR]

This paper addresses the self-interference (SI) cancellation for full-duplex operation in the presence of imperfect radio-frequency (RF) components. In particular, we develop a new scheme to jointly estimate and cancel the in-phase/quadrature mixer imbalance, power amplifier nonlinearities, up-/down-conversion phase noise, and the SI channel. First, we develop a detailed baseband model that captures the most significant transceiver RF imperfections, for both separate- and common-oscillator structures used in the up- and down-conversions. Then, a basis expansion model is derived to approximate the time-varying phase noise and to transform the problem of estimating the time-varying phase noise into the estimation of a set of time-invariant coefficients. Subsequently, the likelihood function is derived in the presence of the unknown intended signal to formulate the joint estimation of the intended channel, SI channel, nonlinear impairments, and phase noise, under the maximum likelihood (ML) criterion. An iterative procedure is developed to find the ML estimate of the different parameters based on the known transmitted data, the known pilot symbols, and the statistics of the unknown intended signal received from the intended transmitter. The full use of the received signal significantly reduces the required number of pilot symbols as compared to training-based techniques. We consider the two pilot-insertion structures used in LTE for the frequency-multiplexed pilots and the time-multiplexed pilots. Simulation results indicate that the proposed ML algorithms can offer a superior SI-cancellation performance with the resulting intended-signal-to-SI-and-noise ratio very close to the intended-signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Some practical systems such as blast furnaces and turbofan engines have degradation processes with memory effects. The term of memory effects implies that the future states of the degradation processes depend on both the current state and the past states because of the interaction with environments. However, most works generally used a memoryless Markovian process to model the degradation processes. To characterize the memory effects in practical systems, we develop a new type of degradation model, in which the diffusion is represented as a fractional Brownian motion (FBM). FBM is actually a special non-Markovian process with long-term dependencies. Based on the monitored data, a Monte Carlo method is used to predict the remaining useful life (RUL). The unknown parameters in the proposed model can be estimated by the maximum likelihood algorithm, and then the distribution of the RUL is predicted. The effectiveness of the proposed model is fully verified by a numerical example and a practical case study. [ABSTRACT FROM PUBLISHER]

Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) compared with conventional SAR. Moving target indication (MTI) is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SARMTI, the HRWS SAR mainly faces the problem of under-sampled data of each channel, causing single-channel imaging and processing to be infeasible. In this study, the estimation of velocity is equivalent to the estimation of the cone angle according to their relationship. The maximum likelihood (ML) based algorithm is proposed to estimate the radial velocity in the existence of Doppler ambiguities. After that, the signal reconstruction and compensation for the phase offset caused by radial velocity are processed for a moving target. Finally, the traditional imaging algorithm is applied to obtain a focused moving target image. Experiments are conducted to evaluate the accuracy and effectiveness of the estimator under different signal-to-noise ratios (SNR). Furthermore, the performance is analyzed with respect to the motion ship that experiences interference due to different distributions of sea clutter. The results verify that the proposed algorithm is accurate and efficient with low computational complexity. This paper aims at providing a solution to the velocity estimation problem in the future HRWS SAR systems with multiple receive channels. [ABSTRACT FROM AUTHOR]

• Tracking of multiple fast-moving targets in MIMO radar is investigated. • Target locally linear motion approximation model is used. • The spread spectrum effect of the fast moving target is mitigated. • The target resolution and the tracking accuracy are improved. • The CRLBs of the estimated DoDs and DoAs are derived. In this paper, we propose a maximum likelihood (ML)-based scheme to accurately localize and track multiple fast-moving targets in multiple-input multiple-output (MIMO) radar systems. For the problem of tracking, the motion of the target will cause a spread spectrum effect, which broadens the beam width and skews the beam steering, rendering accurately localizing and tracking the fast moving targets extremely challenging. To tackle this problem, we use a locally linear model to approximate the real motion states of the target and define a vector to describe its motion state. As such, the motion states of targets at adjacent times can be accurately inferred within a short term based on this approximation model. To incorporate the effect of target motion when performing tracking, we estimate the motion state vector at a reference time from a batch of snapshots by using an ML estimator, instead of estimating the time-varying angular parameters directly as usual. Then the motion state vectors at other times can be inferred based on the estimated one by using the locally linear approximation model. The Cramér-Rao lower bound (CRLB) of the estimated parameters are also derived for performance evaluation. Numerical results validate the performance improvement of the proposed scheme compared to the benchmarks. [ABSTRACT FROM AUTHOR]

In indoor visible light communications (VLC) systems, line of sight (LoS) blockage severely impairs the received power of users. To address this issue, in this paper, a paradigm of indoor space shift keying (SSK) VLC system aided by mirror array is investigated. Firstly, the mirror array reflects the light signal received from LEDs to the user's photo-detector (PD) by the virtue of mirror reflection characteristics. And then, a method of power focusing is presented by adjusting the angle of the mirror according to the locations of LEDs and PDs. Additionally, the synchronous issue is analyzed theoretically to address the multipath effects. Furthermore, an orthogonal matching pursuit (OMP) detection algorithm aided by singular value decomposition (SVD) is proposed for the considered system. Finally, simulations are presented to verify the effectiveness of the proposed mirror array aided indoor SSK-VLC system. [ABSTRACT FROM AUTHOR]

Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna. [ABSTRACT FROM AUTHOR]

Transmission of system-critical control information plays a key role in efficient management of limited wireless network resources and successful reception of payload data information. This paper uses an orthogonal frequency division multiplexing (OFDM) architecture to investigate the detection performance of a time-domain approach used to detect deterministic control signalling information. It considers a type of control information chosen from a finite set of information, which is known at both transmitting and receiving wireless terminals. Unlike the maximum likelihood (ML) estimation method, which is often used, the time-domain detection technique requires no channel estimation and no pilots as it uses a form of time-domain correlation as the means of detection. Results show that when compared with the ML method, the time-domain approach improves detection performance even in the presence of synchronisation error caused by carrier frequency offset. [ABSTRACT FROM AUTHOR]

Owning to the high peak-to-average power ratio problem, the power efficiency of orthogonal-frequency-division-multiplexing (OFDM) systems is usually low. It deteriorates in millimeter wave systems in which the design of an efficient linear power amplifier is much more challenging. The linear-amplification-with-nonlinear-component (LINC) technique can serve as a remedy, decomposing the input signal into two constant-envelop component signals followed by high-efficient nonlinear amplifiers. However, the power combiner, a key component used to combine the amplified signals, is difficult to implement. Combinerless LINC systems employ two transmit antennas such that two component signals can be naturally combined at the receiver. Unfortunately, the performance of combinerless LINC-OFDM systems is seriously degraded if difference, even small, exists between the two channels. The maximum likelihood (ML) receiver can effectively solve the problem; however, its computational complexity is prohibitedly high. We propose a coded combinerless LINC-OFDM system, including a convolutional encoder and a list Viterbi algorithm (LVA) decoder, to solve the problem. The LVA can provide a small number of candidates for the ML detector, dramatically reducing the required computational complexity. We also utilize an enhanced zero-forcing equalizer such that the soft-demapping operation can be effectively conducted. Finally, we propose a simple iterative interference cancellation scheme to further enhance the performance. Simulations show that the proposed combinerless LINC-OFDM system can outperform the conventional OFDM while the consumed power is much lower. [ABSTRACT FROM AUTHOR]

Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain's response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°. [ABSTRACT FROM AUTHOR]

We consider the performance analysis of the maximum-likelihood (ML) angle-of-arrival (AOA) estimation algorithm in this paper. Based on the observation that the ML AOA spectrum is noncentral chi-square distributed with known degree of freedom and known noncentrality, we propose how to numerically evaluate the probability density function of the difference between two ML spectra associated with two different directions. By judiciously choosing the two different angles, we can analytically determine the probability that the ML spectrum at the nearest grid is greater than the ML spectrum at the second nearest grid by arbitrary value. Numerical examples are used to validate the derived expressions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a maximum likelihood method for jointly estimating carrier frequency offset (CFO) and sampling frequency offset (SFO) in receivers for orthogonal frequency division multiplexing signals. The proposed estimator exploits the fact that a single source (such as a crystal oscillator or voltage controlled oscillator) is typically used in modern wireless receivers to provide a reference frequency for both the sampling clock and the radio frequency synthesizers. The proposed CFO estimator is capable of a large CFO estimation range (i.e., more than half of intercarrier spacing) without extending the length of the training sequence. The Crámer–Rao lower bound (CRLB) of the variance of the estimated joint CFO and SFO is derived and compared to the CRLBs of the CFO and SFO estimators in the literature. More importantly, the proposed method is validated on a real-time wireless test bed. [ABSTRACT FROM PUBLISHER]

Multiple access broadcasting (MABC) is a spectral efficient protocol in a two-way relay channel (TWRC). The drawback of this protocol in decode-and-forward (DF) mode is decoding complexity at the relay side. Erroneous decoding at the relay can lead to error propagation that degrades the performance of all relay networks operating in the DF mode. In this paper, a maximum likelihood (ML) detector at the multiple-antenna relay is used, and it is proved that this decoding rule can achieve full diversity order at the multiple access (MAC) phase of the MABC protocol. Then, a new beamforming algorithm named as channel alignment is proposed, and it is shown that it achieves full diversity order. A new relay selection algorithm is proposed, and a tight upper bound for the bit error rate (BER) of this scheme is derived. It is proved that it achieves full diversity order. The analytical results and BER performance of the proposed schemes are investigated and compared through simulations. [ABSTRACT FROM AUTHOR]

To estimate the time delay in multipath environments, a new algorithm was proposed based on importance sampling (IS) . This algorithm applied Monte Carlo method into the sampling of unknown parameters. The global maximum of the compressed likelihood function could be found, thus, the parameters were approximated by the average of samples. Therefore, the estimator proposed avoided the complex multidimensional grid search and iterative methods which depended seriously on the initial guess. The algorithm guaranteed convergence to the global maximum. Results of simulation showed that, this algorithm held better performance than EM and MUSIC algorithms, under the conditions of the same samples. In addition, the algorithm also showed superiority in terms of estimation complexity. In addition, IS-based algorithm proposed demonstrated superiority of other ones in terms of algorithm complexity. [ABSTRACT FROM AUTHOR]

We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser's datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three. [ABSTRACT FROM AUTHOR]

The ML-EM (maximum likelihood expectation maximization) algorithm is the most popular image reconstruction method when the measurement noise is Poisson distributed. This short paper considers the problem that for a given noisy projection data set, whether the ML-EM algorithm is able to provide an approximate solution that is close to the true solution. It is well-known that the ML-EM algorithm at early iterations converges towards the true solution and then in later iterations diverges away from the true solution. Therefore a potential good approximate solution can only be obtained by early termination. This short paper argues that the ML-EM algorithm is not optimal in providing such an approximate solution. In order to show that the ML-EM algorithm is not optimal, it is only necessary to provide a different algorithm that performs better. An alternative algorithm is suggested in this paper and this alternative algorithm is able to outperform the ML-EM algorithm. [ABSTRACT FROM AUTHOR]

In this paper, cooperative sensor localization using asynchronous time-of-arrival measurements is investigated. It is well known that localization performance in wireless networks using time-based ranging or pseudo-ranging methods is greatly affected by the accuracy of the timing synchronization between the nodes involved in the estimation. Commonly, the original estimation problem is broken down into two subproblems, the synchronization problem and the localization problem, in what is known as a two-step approach. However, in this paper, the joint synchronization and localization problem is considered and examined for use in cooperative networks. It is discussed that the cooperation between the source nodes eliminates the need for high anchor node densities and improves localization performance significantly. Furthermore, the Cramér-Rao lower bounds (CRLB) and the maximum likelihood (ML) estimator are derived. It is shown that the ML estimator is highly nonlinear and nonconvex and must, therefore, be solved by using computationally complex algorithms. In order to reduce the complexity of the estimation, a novel semidefinite programming (SDP) relaxation method is developed by relaxing the original nonconvex ML problem, in such a way as to reformulate the estimation problem as a convex problem. The performance of the proposed SDP method is shown through computer simulations to nearly equal that of the ML estimator. The approach is also applied to the noncooperative case where it is found to be superior in performance than the previously proposed suboptimal estimators. Finally, complexity analyses are included for the estimators under consideration. [ABSTRACT FROM PUBLISHER]

Carrier frequency offset (CFO) introduces spectrum misalignment of the transmit and receive filters, causing energy loss and distortion of received signal. A recent study presents an accurate signal model that incorporates these characteristics. However, CFO estimators in the existing literature were developed based on the signal models without these characteristics. This paper generalizes the accurate signal model with CFO to include timing offset and sampling offset and then studies what effects the accurate signal model bring to the CFO estimation and how to address them. We investigate different existing CFO estimators and find some of them lose their optimality under the accurate signal model. We also develop two new integer normalized CFO (ICFO) estimators under the accurate signal model using only one OFDM symbol; one of them is a pilot-aided estimator using both pilot and data and the other one is a blind estimator using data only. Compared with the existing approaches, both of the new estimators make better use of channel and energy loss information, thus yielding more accurate estimation. Furthermore, the proposed methods overcome the existing approaches’ limitation to phase shift keying modulation. Simulation results corroborate the advantages of the new estimators. Analytical performance results are also provided for the proposed estimators. [ABSTRACT FROM PUBLISHER]

Speckle noise in medical ultrasound (US) degrades the image quality and reduces its diagnostic value. Reduction of speckle noise is an important pre-processing step for the analysis and processing of medical ultrasound images. Knowledge of the statistics of the log-transformed speckle especially in the multi-resolution transform domain is important for developing effective homomorphic despeckling techniques, the most popular approach of speckle reduction from ultrasound images. In this paper, the bessel K-form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is introduced for estimating the parameters of the BKF pdf. The effectiveness of the proposed estimation method is demonstrated using Monte Carlo simulations. The appropriateness of the BKF pdf in modeling the speckle is first studied extensively for simulated noise of different levels in the contourlet transform domain. Next, the suitability of BKF model is investigated for the case of real US images that include neonatal brain and breast tumors. It is shown that, in general the BKF prior can model the statistics of the contourlet transform coefficients corresponding to the log-transformed speckle better than the traditionally used Gaussian, normal inverse Gaussian and generalized Nakagami pdfs. [ABSTRACT FROM AUTHOR]

Highlights: [•] The position of myriapods within arthropods has been ambiguous. [•] Transcriptomes from six myriapod species were obtained. [•] Phylogenetic trees were derived from an alignment of 181 genes/37, 425 positions. [•] Bayesian and Maximum Likelihood methods support “traditional” Mandibulata. [•] Molecular clock analyses suggest an early Cambrian origin of Myriapoda. [Copyright &y& Elsevier]

Abstract: In mammals, two carotenoid cleaving oxygenases are known; beta-carotene 15,15′-monooxygenase (BCMO1) and beta-carotene 9′,10′-oxygenase (BCO2). BCMO1 is a key enzyme in vitamin A synthesis by symmetrically cleaving beta-carotene into 2 molecules of all-trans-retinal, while BCO2 is responsible for asymmetric cleavage of a broader range of carotenoids. Here, we show that the Atlantic salmon beta-carotene oxygenase (bco) gene family contains 5 members, three bco2 and two bcmo1 paralogs. Using public sequence databases, multiple bco genes were also found in several additional teleost species. Phylogenetic analysis indicates that bco2a and bco2b originate from the teleost fish specific genome duplication (FSGD or 3R), while the third and more distant paralog, bco2 like, might stem from a prior duplication event in the teleost lineage. The two bcmo1 paralogs (bcmo1 and bcmo1 like) appear to be the result of an ancient duplication event that took place before the divergence of ray-finned (Actinopterygii) and lobe-finned fish (Sarcopterygii), with subsequent nonfunctionalization and loss of one Sarcopterygii paralog. Gene expression analysis of the bcmo1 and bco2 paralogs in Atlantic salmon reveals regulatory divergence with tissue specific expression profiles, suggesting that the beta-carotene oxygenase subtypes have evolved functional divergences. We suggest that teleost fish have evolved and maintained an extended repertoire of beta-carotene oxygenases compared to the investigated Sarcopterygii species, and hypothesize that the main driver behind this functional divergence is the exposure to a diverse set of carotenoids in the aquatic environment. [Copyright &y& Elsevier]

Abstract: Transcription factor function is crucial for eukaryotic systems. The presence of transcription factor families in genomes represents a significant technical challenge for functional studies. To understand their function, we must understand how they evolved and maintained by organisms. Based on genome scale searches for homologs of LEAFY COTYLEDON-LIKE (L1L; AtNF-YB6), NF-YB transcription factor, we report the discovery and annotation of a complete repertoire of thirteen novel genes that belong to the L1L paralogous gene family of Solanum lycopersicum. Gene duplication events within the species resulted in the expansion of the L1L family. Sequence and structure-based phylogenetic analyses revealed two distinct groups of L1Ls in tomato. Natural selection appears to have contributed to the asymmetric evolution of paralogs. Our results point to key differences among SlL1L paralogs in the presence of motifs, structural features, cysteine composition and expression patterns during plant and fruit development. Furthermore, differences in the binding domains of L1L members suggest that some of them evolved new binding specificities. These results reveal dramatic functional diversification of L1L paralogs for their maintenance in tomato genome. Our comprehensive insights on tomato L1L family should provide the basis for further functional and genetic experimentation. [Copyright &y& Elsevier]