Optimal Detection Performance Research Articles

Low Complexity Multi-User MIMO Detection for Uplink SCMA System Using Expectation Propagation Algorithm

Sparse code multiple access (SCMA), which combines the advantages of low density signature (LDS) and code-division multiple access (CDMA), is regarded as one of the promising modulation technique candidate for the next generation of wireless systems. Conventionally, the message passing algorithm (MPA) is used for data detector at the receiver side. However, the MPA-SCMA cannot be implemented in the next generation wireless systems, because of its unacceptable complexity cost. Specifically, the complexity of MPA-SCMA grows exponentially with the number of antennas. Considering the use of high dimensional systems in the next generation of wireless systems, such as massive multi-user MIMO systems, the conventional MPA-SCMA is prohibitive. In this paper, we propose a low complexity detector algorithm named the expectation propagation algorithm (EPA) for SCMA. Mainly, the EPA-SCMA solves the complexity problem of MPA-SCMA and enables the implementation of SCMA in massive MU-MIMO systems. For instance, the EPA-SCMA also enables the implemantation of SCMA in the next generation wireless systems. We further show that the EPA can achieve the optimal detection performance as the numbers of transmit and receive antennas grow. We also demonstrate that a rotation design in SCMA codebook is unnecessary, which is quite rather different from the general assumption

A novel design of algorithm and framework for decentralized CFAR signal detection without prior information

In this paper, a fusion detection algorithm that focuses on decentralized CFAR (Constant False Alarm Rate) signal detection problem without prior information is proposed. In the algorithm, the threshold and test statistic of the detection fusion algorithm derive from the conventional CFAR detection method. At last a framework for decentralized CFAR signal detection is designed corresponding to the fusion algorithm. Simulation results illustrate that an almost optimal detection performance is obtained by the proposed algorithm.

Pilot-Aided Channel Estimation Utilizing Intrinsic Interference for FBMC/OQAM Systems

This paper proposes a novel channel estimation technique for the offset quadrature amplitude modulation based filter bank multi-carrier (FBMC/OQAM) systems. Unlike the conventional approaches, the proposed pilot signals utilize their intrinsic interference for the channel estimation, and provide an improved power efficiency compared to the conventional auxiliary pilot method. However, due to the non-whiteness of the noise components at the output of the analysis filter banks, the power gain obtained by the proposed scheme cannot be maximized. To overcome this difficulty, we propose a semiblind channel estimation by grouping the pilot signals and imposing sign ambiguities to them. Theoretical analysis shows that the proposed semiblind channel estimation remarkably improves the power efficiency of its non-semiblind version. Finally, it is demonstrated that the near optimum detection performance is achieved by the proposed scheme at a reduced complexity.

Soft MIMO detection through sphere decoding and box optimization

Achieving optimal detection performance with low complexity is one of the major challenges, mainly in multiple-input multiple-output (MIMO) detection. This paper presents three low-complexity Soft-Output MIMO detection algorithms that are based mainly on Box Optimization (BO) techniques. The proposed methods provide good performance with low computational cost using continuous constrained optimization techniques. The first proposed algorithm is a non-optimal Soft-Output detector of reduced complexity. This algorithm has been compared with the Soft-Output Fixed Complexity (SFSD) algorithm, obtaining lower complexity and similar performance. The two remaining algorithms are employed in a turbo receiver, achieving the max-log Maximum a Posteriori (MAP) performance. The two Soft-Input Soft-Output (SISO) algorithms were proposed in a previous work for soft-output MIMO detection. This work presents its extension for iterative decoding. The SISO algorithms presented are developed and compared with the SISO Single Tree Search algorithm (STS), in terms of efficiency and computational cost. The results show that the proposed algorithms are more efficient for high order constellation than the STS algorithm.

Size-Dependent Immunochromatographic Assay with Quantum Dot Nanobeads for Sensitive and Quantitative Detection of Ochratoxin A in Corn.

Fluorescent microspheres are a novel luminescent nanomaterial proposed as an alternative probe to improve the detection sensitivity of competitive immunochromatographic assay (ICA). Quantum dot nanobeads (QBs) possess strong luminescence and resistance to matrix interference. Theoretically, large-sized QBs exhibit stronger luminescent intensity than small-sized QBs and are beneficial to ICA sensitivity. However, oversized QBs may reduce the sensitivity of competitive ICA. Thus, the relationship between the size and luminescent intensity of QBs and the competitive ICA sensitivity must be elucidated. In this study, QBs of different sizes (58, 124, 255, 365, and 598 nm) were synthesized. Ochratoxin A (OTA) was selected as the model analyte for competitive ICA. The effects of QB size on the detection performance of competitive ICA were then evaluated. The cutoff limit of QB-ICA for naked eye detection was used for qualitative analysis, and the half-maximal inhibitory concentration (IC50) and LOD were employed for quantitative analysis. Results indicated that 124 nm QBs used as labeling probes for competitive ICA showed the optimal detection performance and the lowest cutoff value of 5 ng/mL for qualitative detection and IC50 (0.39 ng/mL) for quantitative detection. Similar to commercial ELISA, QB124-ICA displayed good accuracy, specificity, reproducibility, and practicability. In summary, 124 nm QBs can be used as a new labeling probe for competitive ICA.

Optimization of Deployment Quality for Intrusion Detection in Surveillance Wireless Sensor Network

The volatile nature of wireless sensor network promise a lot of scopes in diverse disciplines. It is highly applicable in hostile environment, where nodes can be only deployed randomly, without having any predetermined statistical distribution. The fundamental challenges arise to provide maximum coverage and connectivity in hostile vicinity. A detailed analysis of node density, is required to achieve appropriate coverage. The problem of coverage is solved by optimum distribution scenario and sensing range of sensor nodes. Our goal is to achieve promising deployment quality and detection probability, by mapping the problem of intruder detection with geometric intersection domain, a tool from integral geometry and geometric probability. The proposed deployment quality analysis computes number of sensor nodes, required to attain optimum detection performance.

Centralized and decentralized detection with cost-constrained measurements

Optimal detection performance of centralized and decentralized detection systems is investigated in the presence of cost constrained measurements. For the evaluation of detection performance, Bayesian, Neyman–Pearson and J-divergence criteria are considered. The main goal for the Bayesian criterion is to minimize the probability of error (more generally, the Bayes risk) under a constraint on the total cost of the measurement devices. In the Neyman–Pearson framework, the probability of detection is to be maximized under a given cost constraint. In the distance based criterion, the J-divergence between the distributions of the decision statistics under different hypotheses is maximized subject to a total cost constraint. The probability of error expressions are obtained for both centralized and decentralized detection systems, and the optimization problems are proposed for the Bayesian criterion. The probability of detection and probability of false alarm expressions are obtained for the Neyman–Pearson strategy and the optimization problems are presented. In addition, J-divergences for both centralized and decentralized detection systems are calculated and the corresponding optimization problems are formulated. The solutions of these problems indicate how to allocate the cost budget among the measurement devices in order to achieve the optimum performance. Numerical examples are presented to discuss the results.

Event-related cerebral hemodynamics in 2-D and 3-D Visual Vigilance Tasks

Recently, Greenlee et al. (2015) demonstrated that a stereoscopic 3-D display attenuated the vigilance decrement, stabilizing optimal detection performance. Yet, the 3-D display did not halt the decline in global cerebral blood flow velocity (CBFV), an index of cortical resource utilization, which typically accompanies the vigilance decrement. One possible explanation for this enigmatic finding is that global CBFV may not have been sensitive enough to detect the neurological correlates of superior sustained performance in the 3-D condition. Perhaps a more fine-grained measure of CBFV would reveal the underlying neural markers. To explore that possibility, event-related analyses were employed to uncover moment-to-moment changes in CBFV. These analyses revealed that CBFV increased selectively in response to signal detections in the 3-D condition but not in the 2-D. Like performance in the 3-D condition, the detection-related CBFV response in that condition remained stable over time on task. These findings indicate that event-related measurement of detection-specific neural activity can uncover hemodynamic effects that may otherwise be buried by the tides of global CBFV. Implications for future CBFV research are discussed.

Enhanced M-algorithm-based maximum likelihood detectors for spatial modulation

M-algorithm to maximum likelihood (MML) detector has been considered as an excellent choice for the detection of spatial modulation (SM) signals with lower computational complexity but satisfactory detection performance compared with the optimal maximum likelihood (ML) detector. However, MML algorithm requires a delicate tuning of the “M” values in order to achieve the optimal detection performance. Unfortunately, these values can only be retrieved via off-line experiments in general. Meanwhile, the detection order of MML algorithm is restricted to the ascending order of the receive antenna indices which neglects its huge impact on the bit-to-error-rate (BER) performance. In this paper, we propose three enhanced MML-based SM detectors with online “M” selection and strategic adjustment on the detection orders of MML algorithm: (1) hMML detector rearranges the detection order of MML algorithm with respect to the status of channel conditions; (2) yMML detector performs SM signal detection with a descending order of the received signal strength; (3) hyMML detector jointly considers the detection order of both hMML and yMML algorithms. Experimental results show that all proposed detectors outperform the MML detector in terms of reception BER performance.

Design and fabrication of an optimum peripheral region for low gain avalanche detectors

Low Gain Avalanche Detectors (LGAD) represent a remarkable advance in high energy particle detection, since they provide a moderate increase (gain ~10) of the collected charge, thus leading to a notable improvement of the signal-to-noise ratio, which largely extends the possible application of Silicon detectors beyond their present working field. The optimum detection performance requires a careful implementation of the multiplication junction, in order to obtain the desired gain on the read out signal, but also a proper design of the edge termination and the peripheral region, which prevents the LGAD detectors from premature breakdown and large leakage current.This work deals with the critical technological aspects required to optimize the LGAD structure. The impact of several design strategies for the device periphery is evaluated with the aid of TCAD simulations, and compared with the experimental results obtained from the first LGAD prototypes fabricated at the IMB-CNM clean room. Solutions for the peripheral region improvement are also provided.

Subband adaptive GLRT-LTD for weak moving targets in sea clutter

In this paper, the subband detection scheme, a frequency division tactic to decompose received time series into low-rate subband time series, and the generalized likelihood ratio test linear threshold detector (GLRT-LTD) are combined to form a subband adaptive detector to find weak moving targets in sea clutter by lengthened integration time. To alleviate the conflict between the large number of integrated pulses and limited reference cells constrained by spatial inhomogeneity of sea clutter, a discrete Fourier transform modulated filter bank is used to decompose high-rate sea clutter into low-rate subband clutters. Subband clutters exhibit diversity of non-Gaussianity, and subbands are grouped into noise-dominated subbands of approximate Gaussianity, clutter-noise-mixed subbands of non-Gaussianity, and clutter-dominated subbands of strong non-Gaussianity. The subband compound-Gaussian (CG) model with inverse gamma texture is presented to characterize subband clutters, and a bipercentile method is given to estimate the shape and scale parameters of subband amplitude distributions. The GLRT-LTDs specified by the subband parameters are imposed on individual subbands to optimize detection performance. The experiments show that the subband adaptive GLRT-LTD attains better performance than the subband adaptive normalized matched filter detector, owing to the full exploitation of the subband diversity of the non-Gaussianity of sea clutter.

Efficient DS-UWB MUD Algorithm Using Code Mapping and RVM

A hybrid multiuser detection (MUD) using code mapping and a wrong code recognition based on relevance vector machine (RVM) for direct sequence ultra wide band (DS-UWB) system is developed to cope with the multiple access interference (MAI) and the computational efficiency. A new MAI suppression mechanism is studied in the following steps: firstly, code mapping, an optimal decision function, is constructed and the output candidate code of the matched filter is mapped to a feature space by the function. In the feature space, simulation results show that the error codes caused by MAI and the single user mapped codes can be classified by a threshold which is related to SNR of the receiver. Then, on the base of code mapping, use RVM to distinguish the wrong codes from the right ones and finally correct them. Compared with the traditional MUD approaches, the proposed method can considerably improve the bit error ratio (BER) performance due to its special MAI suppression mechanism. Simulation results also show that the proposed method can approximately achieve the BER performance of optimal multiuser detection (OMD) and the computational complexity approximately equals the matched filter. Moreover, the proposed method is less sensitive to the number of users.

Real-time alignment and cali bration of the LHCb Detector in Run II

Stable, precise spatial alignment and PID calibration are necessary to achieve optimal detector performance. During Run2, LHCb will have a new real-time detector alignment and calibration to allow equivalent performance in the online and offline reconstruction to be reached. This offers the opportunity to optimise the event selection by applying stronger constraints, and to use hadronic particle identification at the trigger level. The computing time constraints are met through the use of a new dedicated framework using the multi-core farm infrastructure for the trigger. The motivation for a real-time alignment and calibration of the LHCb detector is discussed from the operative and physics performance point of view. Specific challenges of this configuration are discussed, as well as the designed framework and its performance.

Enhanced multi-channel model for auditory spectrotemporal integration.

In psychoacoustics, a multi-channel model has traditionally been used to describe detection improvement for multicomponent signals. This model commonly postulates that energy or information within either the frequency or time domain is transformed into a probabilistic decision variable across the auditory channels, and that their weighted linear summation determines optimum detection performance when compared to a critical value such as a decision criterion. In this study, representative integration-based channel models, specifically focused on signal-processing properties of the auditory periphery are reviewed (e.g., Durlach's channel model). In addition, major limitations of the previous channel models are described when applied to spectral, temporal, and spectrotemporal integration performance by human listeners. Here, integration refers to detection threshold improvements as the number of brief tone bursts in a signal is increased. Previous versions of the multi-channel model underestimate listener performance in these experiments. Further, they are unable to apply a single processing unit to signals which vary simultaneously in time and frequency. Improvements to the previous channel models are proposed by considering more realistic conditions such as correlated signal responses in the auditory channels, nonlinear properties in system performance, and a peripheral processing unit operating in both time and frequency domains.

Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space.

Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originality of the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimal detection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (≃ 1250 subjects) collected at a French academic public hospital.

A Near-Optimal LLR Based Cooperative Spectrum Sensing Scheme for CRAHNs

In Cognitive Radio Ad Hoc Networks (CRAHNs), cooperative spectrum sensing schemes exploit spatial diversity of the Secondary Users (SUs), to reliably detect an unoccupied licensed spectrum. Soft energy combining schemes provide optimal detection performance by combining the actual sensed information from SUs. For reliable data fusion, these techniques mandate weight estimation for individual SUs in each sensing interval, resulting in high cooperation overhead in terms of time, processing and bandwidth. Alternately, a hard energy combining scheme offers lower cooperation overhead in which only local SU decisions are reported to the fusion center. However, it provides sub-optimal detection performance due to the information loss. In this paper, a Log-Likelihood Ratio (LLR) based cooperative spectrum sensing scheme is proposed in which each SU performs a local LLR based sensing test employing two threshold levels. The local decision and sequentially estimated SNR parameter values (for weight computation) are not reported to the fusion center if the local test result is in-between the two threshold levels. Thereby, cooperation overhead is reduced in proportion to the hard combining techniques; nevertheless simulation results show that the detection performance of the proposed scheme is close to the optimal soft combining techniques.

Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerningmoving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents thecoherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

Multi-user detection using non-parametric Bayesian estimation by feed forward neural networks

This paper is concerned with developing novel encoding techniques for implementing non-parametric neural based detectors for systems using Code Division Multiple Access. These new encoding methods on the one hand can increase the processing speed and reduce the complexity of the Feed Forward Neural Network based detector, on the other. Furthermore, we demonstrate that an asymptotically optimal detection performance can be achieved by the proposed algorithms. Due to the increased processing rate, the new scheme may further improve Spectral Efficiency. Extensive simulations and the corresponding numerical analysis demonstrate that the proposed algorithms yield near optimal performance on real channel models (COST-207).

Properties of certain optimal ratio-type fault detection performance indices with respect to sampling period

In this paper, the effect of sampling period on several well-established optimal ratio-type fault detection performance indices in sampled-data systems has been examined. It provides insight into the intrinsic properties of different optimal fault detection problems and supports the selection of a suitable sampling period. The result is theoretically interesting because the variation of the optimal H-/H∞ index with respect to the sampling period is contrary to the generally held view. As shown in the paper, if the sampling period is increased by an integer multiple, the optimal parity space index and the optimal H2/H2 index will degrade, but the optimal H-/H∞ index will become better.

Characterization of Extended Defects Observed in Cadmium Zinc Telluride (CZT) Crystal

ABSTRACTCadmium Zinc Telluride (CZT) semiconductor crystal properties have been studied extensively with a focus on correlations to their radiation detector performance. The need for defect-free CZT crystal is imperative for optimal detector performance. Extended defects like Tellurium (Te) inclusions, twins, sub-grain boundaries, and dislocations are common defects found in CZT crystals; they alter the electrical properties and, therefore, the crystal's response to high energy radiation. In this research we studied the extended defects in CZT crystals from two separate ingots grown using the low-pressure Bridgman technique. We fabricated several detectors cut from wafers of two separate ingots by dicing, lapping, polishing, etching and applying gold metal contacts on the main surfaces of the crystals. Using infrared (IR) transmission microscope we analyzed the defects observed in the CZT detectors, showing three dimensional scans and plot size distributions of Te inclusions, twins and sub-grain boundaries observed in particular regions of the CZT detectors. We characterized electrical properties of the detectors by measuring bulk resistivity and detector response to gamma radiation. We observed that CZT detectors with more extended defects showed poor opto-electrical properties compared to detectors with fewer defects.