Optimal Detection Algorithm Research Articles

Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter

A statistical analysis that properly characterizes sea clutter processes is indispensable both for optimum detection algorithm design and for performance prediction problems in maritime surveillance applications. In this paper, we present the statistical analysis of experimental sea clutter data collected by a high-resolution coherent monopulse radar. First, we present the amplitude statistical analyses for these clutter data. The results show that the K, Pareto, and CIG distributions can each provide good fits to the clutter data for three channels of monopulse radar. The analyses on the variations of the K distribution parameters with range suggest that the scale parameter is closely associated with the clutter powers and that the shape parameter is influenced by the sea state. Then, we focus on the correlation properties. The averaged results suggest that the temporal and spatial correlation properties are similar for the clutter of all three channels. Moreover, the clutter between the sum and difference channels is almost completely correlated in elevation and is lowly correlated in azimuth. Finally, we perform a spectral analysis, highlighting the temporal and spatial variabilities of Doppler spectra. It is found that the individual Doppler spectra in all three channels can be represented by Gaussian-shaped power spectral densities, and their centroid and width can be modeled as two separate stage linear functions of spectrum intensity.

Low-Complexity Subcarrier-Wise Detection for MIMO-OFDM With Index Modulation

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) is a relatively new multicarrier transmission technique for the MIMO systems to achieve both energy and spectral efficiency. However, due to the dependence of subcarrier symbols introduced by the index modulation, existing MIMO detection techniques cannot be applied in MIMO-OFDM-IM as it will lead to erroneous detection on index information of the active subcarriers and further deteriorate the system performance. In this paper, we first develop an optimal detection algorithm with reduced complexity for MIMO-OFDM-IM by performing the subcarrier-wise detection under the constraint on the legal subcarrier combination within each OFDM-IM subblock. To further reduce the computational complexity, we then propose a low-complexity subcarrier-wise algorithm based on the deterministic sequential Monte Carlo technique to achieve near-optimal detection for MIMO-OFDM-IM. Specifically, the near-optimal detector applies the QR decomposition to generate an upper triangular structure and draw antenna-wise samples at the subcarrier level. Computational simulations and complexity analysis show that the proposed algorithms provide similar performance to optimal one with reduced computational complexity.

Enhanced and Optimal Algorithm for QRS Detection

Purpose: The electrocardiogram (ECG) segmentation step establishes the basis of the cardiac pathologies' classification. We propose here to detect the essential basic forms of this signal to optimize the cost calculation and ensure real-time application.Methods: An automatic approach for R wave's location of the ECG signal based on the Entropy Criterion (EC) of the Wavelet Transform (WT) is introduced in this paper and implemented on MATLAB platform. The method uses automatic placement of analysis window and adjusting its width by measuring entropy for signal in three localized sub-windows. The WT is applied to the ECG signal at analysis window with a first derivative Gaussian wavelet. The R-wave corresponds to the zero crossing between the two maxima of the WT.Results: The above-mentioned method (EC-WT) was tested with most noisy signals within QT databases and compared with two methods: the R wave detection method proposed by Martinez et al. and Zhang and Yong. EC-WT achieved good results attaining a sensitivity about 99.94% and a predictivity over 99.8%.Conclusion: Through this method, the problem of adjustable thresholds for R wave detection is resolved as we apply a dynamic window that fits R peak's parameters. This ensures optimization and efficiency detection in terms of computational cost and complexity.

Analysis of methods for detection of subscriber station signals in mobile communication systems

The analysis of the optimal statistical detection algorithms and neural network algorithms for the detection of subscriber station signals in mobile communication system and assess the possibility of their use for problems of space-time access are conducted. The analysis of the characteristics of the energy detection of signals on the background of Lichter noise consisting of independent components of the Gaussian white noise and a set of rings is conducted

A comparative study of target detection algorithms in hyperspectral imagery applied to agricultural crops in Colombia

Contexto: Las imágenes hiperespectrales (HSI) contienen información en alta resolución espectral, en cientos de bandas contiguas sobre un rango del espectro electromagnético. Se ha tomado ventaja de la información espectral mediante algoritmos de clasificación, detección de cambios, de anomalías o detección de objetivos. Específicamente, en las dos últimas décadas han sido propuestos diferentes algoritmos para detectar objetivos en HSI. Sin embargo, encontrar un algoritmo de detección con un desempeño óptimo para diferentes escenarios y objetivos, aún es materia de investigación, debido a la alta variabilidad espectral y diversidad de escenarios del mundo real.Objetivo: La presente investigación realiza un estudio comparativo de algoritmos de detección de objetivos en imágenes hiperespectrales aplicados a la agricultura colombiana.Método: Las evaluaciones se realizaron sobre 20 HSI reales adquiridas por el sensor satelital Hyperion y 6 HSI sintéticas con diferentes niveles de ruido. En el desarrollo de la investigación se implantaron 5 objetivos sintéticos; y se extrajeron más de 115 firmas espectrales reales, 11 de ellas fueron usadas como objetivo en la evaluación de los algoritmos, permitiendo la caracterización de 5 cultivos agrícolas del nororiente colombiano en 5 áreas de estudio diferentes. Los resultados muestran que el algoritmo de Estimación de Coherencia Adaptativo (ACE) presenta un mejor desempeño con probabilidades de detección PD > 90% para diferentes escenarios y objetivos de tipo agrícola, tanto en imágenes sintéticas como reales.Conclusiones: En aplicaciones de detección de objetivos en HSI, es crítico encontrar un algoritmo que presente un rendimiento óptimo para diferentes escenarios y objetivos, debido a la variabilidad espectral generada por las diferentes condiciones geográficas de Colombia. Por otra parte, este trabajo permite mostrar que es posible realizar nuevas investigaciones y aplicaciones a nivel nacional tomando ventaja de las técnicas de imágenes hiperespectrales y algoritmos de detección espectral; específicamente en el sector de la agricultura colombiana.

FEASIBILITY STUDY OF INEXPENSIVE THERMAL SENSORS AND SMALL UAS DEPLOYMENT FOR LIVING HUMAN DETECTION IN RESCUE MISSIONS APPLICATION SCENARIOS

Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

FEASIBILITY STUDY OF INEXPENSIVE THERMAL SENSORS AND SMALL UAS DEPLOYMENT FOR LIVING HUMAN DETECTION IN RESCUE MISSIONS APPLICATION SCENARIOS

Abstract. Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

Data Detection Algorithms for BICM Alternate-Relaying Cooperative Systems With Multiple-Antenna Destination

This paper proposes two new data detection algorithms for decode-and-forward alternate-relaying cooperative communication systems. These algorithms are discussed in the context of bit-interleaved coded modulation transmission with a multiple-antenna destination. The interference signal, resulting from the concurrent transmission of the source and one of the relays, is exploited as a beneficial resource to develop an optimal data detection algorithm. We show that the optimal algorithm can be implemented by parallel demappers, each based on a family of Bahl, Cocke, Jelinek, and Raviv algorithms, connected to parallel decoders in an iterative way. Due to the need to buffer the entire frame before decoding, the optimal algorithm suffers from long processing time. A suboptimal algorithm is also proposed to avoid this problem, where each packet is decoded by using two consecutive received packets. The suboptimal algorithm reduces processing time, complexity, bandwidth loss, and memory size with a slight increase in the bit error rate (BER). The BER performance of the proposed algorithms is evaluated via Monte Carlo simulations, and the results indicate their effectiveness in improving the BER performance.

Low‐complexity near‐optimal detection of dc 2 ‐balanced codes

Two constructions of a low-complexity near-optimal detection method of dc2-balanced codes are presented. The methods presented are improvements on Slepian's algorithm for optimal detection of permutation codes.

Tumour procurement, DNA extraction, coverage analysis and optimisation of mutation-detection algorithms for human melanoma genomes

Whole genome sequencing (WGS) of cancer patients' tumours offers the most comprehensive method of identifying both novel and known clinically-actionable genomic targets. However, the practicalities of performing WGS on clinical samples are poorly defined.This study was designed to test sample preparation, sequencing specifications and bioinformatic algorithms for their effect on accuracy and cost-efficiency in a large WGS analysis of human melanoma samples.WGS was performed on melanoma cell lines (n = 15) and melanoma fresh frozen tumours (n = 222). The appropriate level of coverage and the optimal mutation detection algorithm for the project pipeline were determined.An incremental increase in sequencing coverage from 36X to 132X in melanoma tissue samples and 30X to 103X for cell lines only resulted in a small increase (1-2%) in the number of mutations detected, and the quality scores of the additional mutations indicated a low probability that the mutations were real. The results suggest that 60X coverage for melanoma tissue and 40X for melanoma cell lines empower the detection of 98-99% of informative single nucleotide variants (SNVs), a sensitivity level at which clinical decision making or landscape research projects can be carried out with a high degree of confidence in the results. Likewise the bioinformatic mutation analysis methodology strongly influenced the number and quality of SNVs detected. Detecting mutations in the blood genomes separate to the tumour genomes generated 41% more SNVs than if the blood and melanoma tissue genomes were analysed simultaneously. Therefore, simultaneous analysis should be employed on matched melanoma tissue and blood genomes to reduce errors in mutation detection.This study provided valuable insights into the accuracy of SNV with WGS at various coverage levels in human clinical cancer specimens. Additionally, we investigated the accuracy of the publicly available mutation detection algorithms to detect cancer specific SNVs which will aid researchers and clinicians in study design and implementation of WGS for the identification of somatic mutations in other cancers.

Probabilistic dynamic distribution of wireless sensor networks with improved distribution method based on electromagnetism-like algorithm

Performance of the Wireless Sensor Networks (WSNs) depends significantly on coverage area which is determined via the effective dynamic distribution of sensors. Making mobile sensors’ dynamic distributions, which determines their positions within the network effectively, improves performances of WSNs by enabling sensors to form the coverage area more efficiently. In this paper, we initially propose the electromagnetism-like (EM) algorithm as the sensor distribution strategy to increase the coverage area of network after random distribution of sensors. Forming more effective coverage area by using mobile and stationary sensors and probabilistic detection model has been aimed by developing the Optimal Sensor Detection Algorithm that is based on the proposed EM algorithm (OSDA-EM). For this purpose, it has been thought that we would attain to more realistic results, with probabilistic detection model by forming the coverage area more effectively. Additionally, performance of the developed OSDA-EM algorithm has been compared with the Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms which was previously used in the dynamic distribution of WSNs. Simulation results have shown that the developed OSDA-EM can be preferred in dynamic distribution of WSNs that performed with probabilistic detection model.

Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data

Context. The direct detection of exoplanets with high-contrast imaging requires advanced data processing methods to disentangle potential planetary signals from bright quasi-static speckles. Among them, angular differential imaging (ADI) permits potential planetary signals with a known rotation rate to be separated from instrumental speckles that are either statics or slowly variable. The method presented in this paper, called ANDROMEDA for ANgular Differential OptiMal Exoplanet Detection Algorithm is based on a maximum likelihood approach to ADI and is used to estimate the position and the flux of any point source present in the field of view. Aims. In order to optimize and experimentally validate this previously proposed method, we applied ANDROMEDA to real VLT/NaCo data. In addition to its pure detection capability, we investigated the possibility of defining simple and efficient criteria for automatic point source extraction able to support the processing of large surveys. Methods. To assess the performance of the method, we applied ANDROMEDA on VLT/NaCo data of TYC-8979-1683-1 which is surrounded by numerous bright stars and on which we added synthetic planets of known position and flux in the field. In order to accommodate the real data properties, it was necessary to develop additional pre-processing and post-processing steps to the initially proposed algorithm. We then investigated its skill in the challenging case of a well-known target, $\beta$ Pictoris, whose companion is close to the detection limit and we compared our results to those obtained by another method based on principal component analysis (PCA). Results. Application on VLT/NaCo data demonstrates the ability of ANDROMEDA to automatically detect and characterize point sources present in the image field. We end up with a robust method bringing consistent results with a sensitivity similar to the recently published algorithms, with only two parameters to be fine tuned. Moreover, the companion flux estimates are not biased by the algorithm parameters and do not require a posteriori corrections. Conclusions. ANDROMEDA is an attractive alternative to current standard image processing methods that can be readily applied to on-sky data.

Position discovery for a system of bouncing robots

A collection of n anonymous mobile robots is deployed on a unit-perimeter ring or a unit-length line segment. Every robot starts moving at constant speed, and bounces each time it meets any other robot or segment endpoint, changing its walk direction. We study the problem of position discovery, in which the task of each robot is to detect the presence and the initial positions of all other robots. The robots cannot communicate or perceive information about the environment in any way other than by bouncing nor they have control over their walks which are determined by their initial positions and their starting directions. Each robot has a clock allowing it to observe the times of its bounces. We give complete characterizations of all initial configurations for both the ring and the segment in which no position detection algorithm exists and we design optimal position detection algorithms for all feasible configurations.

Evaluation of feature- and pixel-based methods for deflection measurements in temporary structure monitoring

Recent forensic studies have revealed quite a few construction site catastrophes are associated with failure of temporary structures (e.g., collapse of formworks, scaffoldings, etc.). This is particularly crucial for those located in dense urban areas where the failure of a temporary structure not only impacts the site itself but may also damage adjacent structures and cause injuries and casualties of passers-by. In this paper, the work of evaluating the optimum feature detection and matching algorithm is reported, which is the key in realizing a real-time visual sensing-based surveillance method in order to monitor the integrity and safety of on-site temporary structures. A series of experiments are designed and conducted to test three algorithms: population-based intelligent digital image correlation (DIC), David Lowe’s Scale-invariant feature transform, and Hessian matrix-based speeded-up robust features. In these experiments, synthetic images through 2D geometrical translation, rotation, deformation, and illumination changes are generated to provide the sample data and ground truths. As the experiment output, the accuracy and efficiency of these algorithms are measured and compared with each other. Analyses including feasibility of interest point localization with specific regions and the error estimation with respect to real-world distances are conducted. The results show that the DIC algorithm holds the most promise to be implemented in structural monitoring, but several challenges still remain, which call for efforts to further improvement of the technique.

A Near-ML MIMO Subspace Detection Algorithm

A low-complexity MIMO detection scheme is presented that decomposes a MIMO channel into multiple decoupled subsets of streams that can be detected separately. The scheme employs QL decomposition followed by elementary matrix operations to transform the channel matrix into a generalized elementary structure matching the subsets of streams to be detected. The proposed scheme avoids matrix inversion operations, and allows subsets to overlap thus achieving better diversity gain. Simulations demonstrate that this approach performs to within a few tenths of a dB from the optimum detection algorithm.

Reduced-Complexity k-best Decoder for LTE Standard

This paper presents a VLSI implementation of reduced -complexity and reconfigurable MIMO(Multiple-Input Multiple-Output) signal detector targeting 3GPP-LTE standard. In recent wireless communication system, MIMO technology is considered as the key technique in LTE to meet the target. Maximum Likelihood (ML) detection is the optimal detection algorithm for MIMO systems. FPGA implementation of ML detector becomes infeasible as its complexity grows exponentially with the increase in number of antennas. Therefore, we propose a modified K-best detector algorithm which employs parallel and distributed sorting strategy combined with bitonic sorter that has near-ML detection solution. The design was implemented targeting Xilinx Spartan 6 device and the resource utilization results are presented and the performance comparison with the literature was also done. The total on-chip power estimated is 213mW.

A low-complexity MIMO subspace detection algorithm

A low-complexity multiple-input multiple-output (MIMO) subspace detection algorithm is proposed. It is based on decomposing a MIMO channel into multiple subsets of decoupled streams that can be detected separately. The new scheme employs triangular decomposition followed by elementary matrix operations to transform the channel into a generalized elementary matrix whose structure matches the subsets of streams to be detected. The proposed approach avoids matrix inversion and allows subsets to overlap, thus achieving better diversity gain. An optimized detector architecture based on a 2-by-2 ML detector core is also presented. Simulations demonstrate that the proposed algorithm performs to within a few tenths of a dB from the optimum detection algorithm.

A merging approach of the demodulation and equalization for MAP block detection in CPM-SC-FDMA systems

CPM-SC-FDMA has been recently suggested as an attractive alternative to SC-FDMA multiple access technique which inherits the benefits of the continuous phase modulation (CPM). In this paper, we propose an optimal block detection algorithm for CPM-SC-FDMA systems that merges the frequency domain equalization (FDE), the temporal sample recovery using the inverse DFT (discrete Fourier transform) and the successive CPM decoding operations. The optimal detector derives the MAP (maximum a posteriori) estimate of JTs-length blocks of symbols by developing a state model description of the questioned problem and then applying a Bayesian detection algorithm referred as to the MAPBD (MAP block detector) algorithm. The MAPBD algorithm is actually a recursive implementation of well-known Bayes filtering equations updating the APP (a posteriori probabilities) vector related to a block of delayed symbols. Simulations show a significant improvement of the BER performance of the MAPBD receiver compared to the conventional CPM-SC-FDMA receiver using an MMSE-FDE in conjunction with a MAP symbol-by-symbol CPM decoder. For complexity requirements, a decision feedback (DF) mechanism is associated to the so-developed MAPBD algorithm as well as a pruning procedure of the APP vector.

English

The purpose of this project is to find out the accuracy of an X-Ray Detection of crack Bone using Canny Edge Detection Method. Cracked bone is a bone condition that suffered a breakdown of bone integrity. Normally, bones have elasticity and a great number of strength. Crack in the Bone only happen when the bones took a force beyond their elasticity or strength .Canny Edge Detection method is an optimal edge detection algorithm on determining the end of a line with changeable threshold and less error rate. The proposed algorithm simulate on MATLAB 12-b and results have shown how canny edge detection can help determine location of crack in the bone.

ATSC digital television signal detection with spectral correlation density

In this paper, we consider the problem of spectrum sensing for advanced television systems committee (ATSC) digital television (DTV) signal detection. To exploit the cyclostationarity of the ATSC DTV signals, we employ spectral correlation density (SCD) as the decision statistic and propose an optimal detection algorithm. The major difficulty is in obtaining the probability distribution functions of the SCD. To overcome the difficulty, we probabilistically model the pilot frequency location and employ Gaussian approximation for the SCD distribution. Then, we obtain a practically implementable detection algorithm that outperforms the industry leading systems by 2-3 dB. We also propose various techniques that greatly reduce the system complexity with performance degradation by only a few tenths of decibels. Finally, we show how robust the system is to the estimation errors of the noise power spectral density level and the probability distribution of the pilot frequency location.