Maximum Likelihood Decision Rule Research Articles

Geometrical attacks resilient statistical watermark decoder using polar harmonic Fourier moments

This paper presents a new robust multiplicative watermark detector. Due to the strong robustness against various attacks, polar harmonic Fourier moment (PHFM) magnitudes are used as the employed watermark carrier. The distribution of PHFM magnitudes is highly non-Gaussian and can be properly modeled by a heavy-tailed probability density function (PDF). In this paper, we proved that Weibull distribution can suitably fit the distribution of PHFM magnitudes, and based on this, we presented a statistics-based watermark decoder by using the Weibull as a prior for the PHFM magnitudes. In watermark embedding, a multiplicative manner was used to embed watermark information in PHFM magnitudes of the highest entropy blocks to achieve better robustness and imperceptibility. In watermark detection, we developed a Weibull distribution-based statistical watermark decoder, which uses the maximum likelihood (ML) decision rule. Compared with Bessel K form (BKF), Cauchy, and generalized Gaussian (GG)-based decoders, the Weibull-based decoder demonstrates stronger robustness. In addition, the proposed watermark decoder is more robust against geometrical and common image processing attacks than existing statistical watermark decoders.

Image watermarking using DNST-PHFMs magnitude domain vector AGGM-HMT

Invisibility, robustness and payload are three indispensable and contradictory properties for any image watermarking systems. Therefore, in this paper a novel statistical image watermark decoder based on robust discrete nonseparable Shearlet transform (DNST)-polar harmonic Fourier moments (PHFMs) magnitude and effective vector anisotropic generalized Gaussian mixtures (AGGM)-hidden Markov tree (HMT). We begin with a detailed study on the robustness and statistical characteristics of local DNST- PHFMs magnitudes of natural images. This study reveals the excellent robustness, highly non-Gaussian marginal statistics and strong dependencies of local DNST-PHFMs magnitudes. We also find that conditioned on their generalized neighborhoods, the local DNST-PHFMs magnitudes can be approximately modeled as anisotropic generalized Gaussian variables. Based on these findings, we model local DNST-PHFMs magnitudes using a vector AGGM-HMT that can capture all interscale, interdirection, and interlocation dependencies. Meanwhile, model parameters can be estimated effectively by using localization clues guided expectation–maximization (LCGEM) approach. Finally, we develop a new statistical image watermark decoder using the vector AGGM-HMT and maximum likelihood (ML) decision rule. Extensive experimental results show the superiority of the proposed watermark decoder over several state-of-the-art statistical watermarking methods and some approaches based on convolutional neural networks.

Concurrent Transmission and Multiuser Detection of LoRa Signals

This article investigates a new model to improve the scalability of low-power long-range (LoRa) networks by allowing a group of multiple end devices (EDs) to communicate with multiple multi-antenna gateways simultaneously (i.e., in the same time slot) on the same frequency band and using the same spreading factor. The maximum-likelihood (ML) decision rule is first derived for noncoherent detection of information bits transmitted by multiple devices in a group. To overcome the high complexity of the ML detection, we propose a suboptimal two-stage detection algorithm to balance the computational complexity and error performance. In the first stage, we identify transmitted chirps (without knowing which EDs transmit them). In the second stage, we determine the EDs that transmit the specific chirps identified from the first stage. To improve the detection performance in the second stage, we also optimize the transmit powers of EDs to minimize the similarity, measured by the Jaccard coefficient, between the received powers of any pair of EDs in the same group. As the power control optimization problem is nonconvex, we use concepts from successive convex approximation to transform it to an approximate convex optimization problem that can be solved iteratively and guaranteed to reach a suboptimal solution. Simulation results demonstrate and justify the tradeoff between transmit power penalties and network scalability of the proposed LoRa network model. In particular, by grouping two or three EDs in each group for concurrent transmission, the uplink capacity of the proposed network can be doubled or tripled over that of a conventional LoRa network, albeit at the expense of additional 3.0 or 4.7 dB transmit power.

Observability Decomposition-Based Decentralized Kalman Filter and Its Application to Resilient State Estimation under Sensor Attacks.

This paper considers a discrete-time linear time invariant system in the presence of Gaussian disturbances/noises and sparse sensor attacks. First, we propose an optimal decentralized multi-sensor information fusion Kalman filter based on the observability decomposition when there is no sensor attack. The proposed decentralized Kalman filter deploys a bank of local observers who utilize their own single sensor information and generate the state estimate for the observable subspace. In the absence of an attack, the state estimate achieves the minimum variance, and the computational process does not suffer from the divergent error covariance matrix. Second, the decentralized Kalman filter method is applied in the presence of sparse sensor attacks as well as Gaussian disturbances/noises. Based on the redundant observability, an attack detection scheme by the test and a resilient state estimation algorithm by the maximum likelihood decision rule among multiple hypotheses, are presented. The secure state estimation algorithm finally produces a state estimate that is most likely to have minimum variance with an unbiased mean. Simulation results on a motor controlled multiple torsion system are provided to validate the effectiveness of the proposed algorithm.

Design and Analysis of MIMO Systems Using Energy Detectors for Sub-THz Applications

The significant amount of unused spectrum in sub-TeraHertz frequencies is contemplated to realize high rate wireless communications for beyond 5G networks. Yet, the performance of radio-frequency sub-TeraHertz systems is severely degraded by strong oscillator phase noise. Therefore, we investigate in this paper the use of multiple-input multiple-output (MIMO) systems with energy detection receivers to achieve high rate communications robust to phase noise. First, the design of the receiver detection algorithm is addressed. Two detectors are proposed for the studied nonlinear MIMO channel, either derived from the maximum likelihood decision rule by using a Gaussian approximation, or based on the use of neural networks. Second, the communication performance is assessed through numerical simulations for uncoded and coded systems. We consider a realistic scenario modeling an indoor wireless link in D-band with directive antennas and strongly correlated line-of-sight channels. Our results demonstrate that spatial multiplexing with non-coherent sub-TeraHertz transceivers can be realized on strongly correlated line-of-sight channels using the proposed detection schemes. Thereby, we highlight that high-rate radio-frequency sub-TeraHertz systems can be implemented with low-complexity and low-power architectures using MIMO systems with energy detection receivers.

List-Based OMP and an Enhanced Model for DOA Estimation With Nonuniform Arrays

This article proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called list-based maximum likelihood orthogonal matching pursuit (LBML-OMP) for direction-of-arrival estimation with nonuniform linear arrays (NLAs). The proposed EDCTM approach obtains improved estimates when Khatri–Rao product-based models are used to generate difference coarrays under the assumption of uncorrelated sources. In the proposed LBML-OMP technique, for each iteration a set of candidates is generated based on the correlation-maximization between the dictionary and the residue vector. LBML-OMP then chooses the best candidate based on a reduced-complexity asymptotic maximum likelihood decision rule. Simulations show the improved results of EDCTM over existing approaches and that LBML-OMP outperforms existing sparse recovery algorithms as well as spatial smoothing multiple signal classification with NLAs.

Filipino Native Language Identification using Markov Chain Model and Maximum Likelihood Decision Rule

The study developed a tool for identification of a Filipino Native Language given a textual data. The Filipino Language identified were Cebuano, Kapampangan and Pangasinan. It used Markov Chain Model for language modeling using bag of words (a total of 35,144 words for Cebuano, 14752 for Kapampangan, and 13969 of Pangasinan) from each language and maximum likelihood decision rule for the identification of the native language. The obtained model implementing Markov model, was applied in one hundred fifty text files with minimum length of ten words and maximum length of fifty words. The result of the evaluation shows the system’s accuracy of 86.25% and an F-Score of 90.55%.

Performance Analysis of a Receive Diversity PLC System With Imperfect CSI in Nakagami-m Noise Environment

The letter proposes a receive diversity PLC system subject to uncorrelated Rayleigh multipath channels and corrupted by Nakagami- m background noise employing M-ary phase-shift keying modulation for data transmission. The estimates of the channel gains are obtained prior to data transmission by the minimum mean square estimation technique utilizing pilot symbol transmission. A suboptimal receiver structure employing the maximum likelihood decision rule is proposed for the PLC system which is further used to obtain the closed form and the asymptotic expressions for the symbol error probability of the system via a characteristic function approach. The improvement of the system performance with the increase in the channel diversity, the effects of the pilot-to-signal strength ratio, and the noise shape parameter m of the background noise on the system performance are studied via numerical results providing various insights into the design of the PLC system with various system parameters.

Combined Non-parametric and Parametric Classification Method Depending on Normality of PDF of Training Samples

Classification method with combined nonparametric and parametric classifications which depends on the normality of Probability Density Function of training samples is proposed. The proposed classification method is also based on spatial information for high spatial resolution of satellite based optical sensor images is proposed. Also, a classification method which takes into account not only spectral but also spatial features for LANDSAT-4 and 5 Thematic Mapper (TM) data is proposed. Treatment of the spatial-spectral variability existing within a region is more important for such high spatial resolution of satellite imagery data. Standard deviations in small cells, such as 2x2, 3x3 and 4x4 pixels, were used as measures to represent the spatial-spectral variabilities. This information can be used together with conventional spectral features in a unified way, for the traditional classifier such as the pixelwise Maximum Likelihood Decision Rule (MLHDR). The classification performance of new clear cuts and alpine meadows which are very close in spectral space characteristics and difficult to distinguish them by conventional methods are focused. Through experiments, it is found that there is a substantial improvement in overall classification accuracy for TM forestry data. The Probability of Correct Classification (PCC) for the new clear cuts and the alpine meadows classes rose by 7% to 97% correct. The confusion between alpine meadows and new clear cuts was reduced from 9% to 3%.

Double-Sub-Stream M-ary Differential Chaos Shift Keying Wireless Communication System Using Chaotic Shape-Forming Filter

An M-ary Differential Chaos Shift Keying modulation using Chaotic Shape-forming Filter (CSF-M-DCSK) is proposed here to transmit two sub-streams with different system performances. The chaotic shape-forming filter is used to generate the chaotic carrier which is modulated according to the DCSK concept. The modulated chaotic signal is demodulated by a coherent matched filter receiver and the maximum likelihood decision rule is used to get the best noise performance. Compared to DCSK and its enhanced versions, the new modulation scheme offers not only better noise and multipath performances but also a higher data rate. The hardware implementation of the proposed method is as simple as that of a conventional communication system. Analytical expressions have been derived for the CSF-M-DCSK Bit Error Rate (BER) and its performance has been evaluated in AWGN and multipath channels by simulations. The new CSF-M-DCSK system has been implemented and successfully tested on a Wireless open-Access Research Platform (WARP). Results of simulations and measurements performed in real application scenarios have proven the feasibility and superiority of CSF-M-DCSK over its competitors.

Algorithmic performance-data classification of condensing heat exchangers

We describe a methodology able to extract the regimes of operation from condensing heat exchanger data. The methodology applies a Gaussian mixtures clustering algorithm to determine the number of groups directly from the data, and a maximum likelihood decision rule to classify such data into these clusters. Published measurements visually classified as dry-surface, dropwise condensation, and film condensation, are used to illustrate the applicability of the classification technique. Since some results from the algorithmic and visual classifications differ, the second part presents an independent evaluation of the allocation process by a procedure based on artificial neural networks (ANNs) and a variant of cross-validation. Results from the ANN approach to the same data show remarkable agreement with the clustering technique confirming that an algorithmic classification of complex physical phenomena is not only possible but also accurate, and represents an excellent alternative to typical visual-based procedures for the analysis of thermal and other systems.

Detection of Coral Reefs Degradation using Geospatial Techniques around Saint Martin’s Island, Bay of Bengal

Saint Martin’s is a unique island located in the Bay of Bengal where coral species are abundant due to its favorable and unique environmental conditions. Coral communities are in great danger due to global climate change, direct commercial coral extraction and unplanned tourism around the island. The present study attempts to identify the coral zone and mapping of coral reefs from the period of 1980 to 2018. The study utilized five sets of multi-temporal Landsat satellite images to examine the changes of coral reefs in Saint Martin’s Island during the study timeframe. Geospatial and image processing software was used for spatial coral colony mapping and data analysis. Satellite images processed to remove water depth impacts and supervised classification technique using the maximum likelihood decision rule suggested the spatial change of coral colonies over time. The result of the study indicates that coral colonies decreased steadily throughout the study period. A total area of 0.9342 km2 of coral colonies disappeared during the study period from 1980 to 2018. Field observations around Saint Martin’s Island indicates that destruction of coral reef habitat was basically caused by anthropogenic activities like tourism and extraction of coral by fisherman. Analysis of the historical data regarding tourism, population and households, and Sea Surface Temperature (SST) along with field observations of these parameter during 2018, suggests that anthropogenic factors are more significant than natural factors for causing coral reef degradation. It is very crucial to raise consciousness not only in the vicinity of Saint Martin’s Island but more widely regarding the significance of the biodiversity of the Island.

A new watermark decoder in DNST domain using singular values and gaussian-cauchy mixture-based vector HMT

There are three indispensable, yet contrasting requirements for a watermarking scheme: imperceptibility, robustness, and payload. Therefore, it is a challenge to obtain a tradeoff among above requirements in image watermark detection. In this paper, we propose a new statistical watermark decoder in discrete non-separable Shearlet transform (DNST) domain using singular values and Gaussian-Cauchy mixture-based vector hidden Markov tree (HMT). Our method can obtain great performance in imperceptibility, robustness and payload, and it is necessary for image copyright protection. We first perform DNST on the host image, and apply singular value decomposition (SVD) to the significant DNST domain high entropy blocks. We then embed the digital watermark into the DNST high entropy blocks by modifying the robust singular values. At the receiver, by combining the Gaussian-Cauchy mixture-based vector HMT and maximum likelihood (ML) decision, we propose a new blind image watermark decoder in DNST domain. Here, robust DNST domain singular values are firstly modeled by using Gaussian-Cauchy mixture-based vector HMT, where the Gaussian-Cauchy mixture marginal distribution and various strong dependencies of DNST domain singular values are incorporated. Then the statistical model parameters of Gaussian-Cauchy mixture-based vector HMT are estimated using parameter-expanded expectation–maximization (PXEM) approach. And finally, a blind image watermark decoder is developed using Gaussian-Cauchy mixture-based vector HMT and ML decision rule. The major contribution of this paper is the use of singular value, Gaussian-Cauchy mixture-based vector HMT and PXEM algorithms, which enhances the performance of watermarking scheme. Experimental results on some test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed method.

Satellite image analysis reveals changes in seagrass beds at Van Phong Bay, Vietnam during the last 30 years

Seagrass meadows are fragile ecosystems in the coastal zone. Natural disasters, land reclamation and various human activities seem to exert negative impacts on the distribution and biological performance of seagrass beds in Vietnam. In this present study, satellite Landsat TM/OLI image analysis was applied to determine changes in seagrass distribution at Van Phong Bay, Vietnam in the last 30 years. The maximum likelihood decision rule was used to extract seagrass bed distribution data. The error matrix using the in situ reference data for HLM image classification was 81–95% accurate, and Kappa coefficients were between 0.72 and 0.91. The results indicated that 186.2 ha (or 35.8%) of the original seagrass beds were lost in the last three decades at Van Phong Bay, and decline in each specific site may have been due to different causes. Typhoons may have caused the loss of seagrass beds at open-sea sites whereas aquaculture activities, excavation and terrigenous obliteration may have caused such losses in protected sites.

A Novel Iterative Detection Scheme of Bootstrap Signals for ATSC 3.0 System

This paper derives a maximum-likelihood decision rule to detect the signaling information conveyed by the bootstrap for an ATSC 3.0 system and proposes an iterative detection scheme to improve the detection performance. The proposed detection scheme iteratively averages the channel estimates for two consecutive bootstrap symbols by exploiting the previously detected symbol. The simulation results show that the proposed detection scheme can obtain a signal-to-noise ratio gain of approximately 2.0 dB at a frame error rate of $10^{-3}$ compared to the conventional detection scheme. Furthermore, this paper analyzes the computational complexity of the proposed detection scheme and presents the number of iterations sufficient to provide a good performance-complexity trade-off.

Performance analysis of multiple access relay channels for non-coherent modulations

One of the most important applications of network coding is the Multiple Access Relay Channel (MARC) technique in which source terminals transmit independent data blocks over one or more relay terminals to a common destination terminal. In recent years, MARC has become one of the vital network coding applications in the area of innovative wireless cooperative communications for enhancing spectral efficiency. In this paper, non-coherent binary frequency shift keying (BFSK) modulation and differential binary phase shift keying (DPSK) modulation techniques are applied to the decode-and-forward (DF)-based MARC model and the average bit error rate (BER) of the proposed system is derived theoretically by using the maximum likelihood (ML) decision rule. The simulation results show that the BER performance of DPSK modulation is better than the non-coherent BFSK and full diversity gain is achieved in both modulation techniques. We also propose a power allocation scheme for the proposed system model which optimizes the power allocated to the source and relay transmissions. Depending on the location of the relay, performance gains up to 1.25 dB are achieved with the proposed optimum power allocation (OPA) scheme. Also, the mathematical derivations are verified through Monte-Carlo simulations and the superior performance of the OPA over equal power allocation (EPA) is observed.

Spreading sequence estimation algorithms based on ML detector in DSSS communication systems

In this study, the authors address the spreading sequence estimation in direct-sequence spread-spectrum signals. At first, the maximum-likelihood (ML) estimator is derived. In order to alleviate the greater computational complexity of the ML estimator, an innovative algorithm based on the ML method is proposed. The authors' proposed algorithm uses an initial estimation with low complexity and low estimation accuracy as a result. In the second step, the estimation accuracy increases using the ML decision rule. They analyse their proposed algorithm and they derive an analytical approximation for the error probability of the proposed suboptimal algorithm. Simulation and analytical results show great performance and acceptable complexity of the proposed method.

Modeling the Spatio-temporal dynamics and evolution of land use and land cover (1984–2015) using remote sensing and GIS in Raya, Northern Ethiopia

This study models the spatio-temporal dynamics, patterns, and evolution of land use and land cover (LULC) (1984–2015) using remote sensing and GIS supported by in-suit measurements in Raya, Northern Ethiopia. Landsat thematic mapper (TM), and operational land imager (OLI) path 168/169 and row 051/52 were acquired from the United States Geological Survey Landsat archive. All necessary image pre-processing techniques were applied to remove the distortions due to sensors. Eight major LULC types based on a supervised image classification and maximum likelihood decision rule were identified. Post-classification change detection method was also applied to detect the dynamics in LULC. Significant change in forestland, shrub/bush land, built-up area, grassland, cropland, barren land, and floodplain areas were observed over the period 1984–2015. Considerable losses were observed in grasslands (36.9%), water body (8.7%), and floodplain areas (74.4%), while other LULC types increased. This explains why the study area is frequently affected by drought and other related disasters. An overall accuracy of 88.5, 86.5 and 90.5% were observed for the 1984, 1995 and 2015 LULC, respectively. The overall Kappa coefficient of 0.87, 0.85, and 0.90 were also observed for the same periods. Besides, the Pearson pairwise correlation matrix among the 1984–1995, 1995–2015 and 1984–2015 LULC shows positive and strong correlation (r = 0.916, r = 0.908, r = 0.914) at p < 0.005 significance level. Therefore, there is no much difference in identifying LULC types using TM and OLI products. This study is crucial to implement scientific land use policies and strategies in the study area.

Outage and BER analysis of dual-carrier modulation over frequency-selective Nakagami-m fading channels

PurposeThis paper aims to the increasing need for high-speed low-power data transmissions over frequency-selective fading channels has drawn attention to suggest dual-carrier modulation (DCM) for multiband orthogonal frequency division multiplexing (OFDM) transceivers for ultra wideband (UWB) wireless personal area network (WPAN).Design/methodology/approachUnder frequency-selective fading channel conditions, the decoder is not sufficient enough to decode the transmission bits of severely attenuated data tones. Hence, the authors suggest DCM for a multiband OFDM transceiver because of its multiple capability of providing both frequency diversity and coding gain. It also resulted in low bit-error-rate (BER) at a given signal- to-noise ratio when compared to conventional multiband OFDM system. To achieve an optimised BER, DCM transforms four re-ordered bits into two quaternary phase shift keying symbols and further transforms to two 16-quadrature amplitude modulation-like (16-QAM) symbols with a suitable mapping technique, and at the receiver end, they are decoded with maximum likelihood decision rule. After performing the transformation, the outage probability and average BER expressions are derived to analyse the system performance.FindingsDCM is suitable for high data rate transmission and is immune to frequency-selective fading. The outage and BER performance outstands over conventional multiband OFDM transceiver because of the inclusion of DCM mapping.Practical implicationsIt is widely used in WPANs such as high definition multimedia interface and wireless universal serial bus.Originality/valueThis paper derives novel closed-form outage probability and a tight upper bound on average BER expressions for DCM-based multiband OFDM UWB transceiver over frequency-selective Nakagami-m fading channels for any arbitrary value of m. For this, moment-generating function of sum of squared, independent and identically distributed (i.i.d.) Nakagami-m random variables are used. Further, the system performance is also validated for the case of exponential decaying power delay profile, and the simulation results are provided to check the accuracy of the derived expressions.

Template attacks exploiting static power and application to CMOS lightweight crypto‐hardware

SummaryA new class of template attacks aiming at recovering the secret key of a cryptographic core from measurements of its static power consumption is presented in this paper. These attacks exploit the dependence of the static current of Complementary metal–oxide–semiconductor Integrated Circuits on the input vector and the maximum likelihood decision rule as a statistical distinguisher. In the proposed Template Attacks Exploiting Static Power (TAESP), we take advantage of the temperature dependence of static currents in order to build a new multivariate approach able to extract relevant information from cryptographic devices. As a validation case study, we consider the PRESENT‐80 block cypher algorithm and its implementation on a 40 nm Complementary metal–oxide–semiconductor process. Monte Carlo and corner simulations at transistor level are used to show the effectiveness of the TAESP in the presence of die‐to‐die and intra‐die process variations. A real attack scenario is then built by adding Gaussian noise to current samples extracted from transistor‐level simulations. The univariate TAESP in which just one temperature is considered to build the templates is compared against the multivariate TAESP in which measurements at different controlled temperatures are exploited. This comparison shows that using just a few different temperatures to build multivariate templates allows to strongly increase the effectiveness of the attack. Copyright © 2016 John Wiley &amp; Sons, Ltd.