Erasure Insertion in RS-Coded SFH MFSK Subjected to Tone Jamming and Rayleigh Fading

The achievable performance of Reed Solomon (RS) coded slow frequency hopping (SFH) assisted M-ary frequency shift keying (MFSK) using various erasure insertion (EI) schemes is investigated, when communicating over uncorrelated Rayleigh fading channels in the presence of multitone jamming. Three different EI schemes are considered, which are based on the output threshold test (OTT), on the ratio threshold test (RTT) and on the joint maximum output-ratio threshold test (MORTT). The relevant statistics of these EI schemes are investigated mathematically and based on these statistics, their performance is evaluated in the context of error-and-erasure RS decoding. It is demonstrated that the system performance can be significantly improved by using error-and-erasure decoding invoking the EI schemes considered. Index Terms—Tone jamming, OTT, RTT, MO-RTT, SFH, error-and-erasure decoding (EED)

Erasure Insertion in RS-Coded SFH MFSK Subjected to Tone Jamming and Rayleigh Fading

The achievable performance of Reed Solomon (RS) coded slow frequency hopping (SFH) assisted M-ary frequency shift keying (MFSK) using various erasure insertion (EI) schemes is investigated, when communicating over uncorrelated Rayleigh fading channels in the presence of multitone jamming. Three different EI schemes are considered, which are based on the output threshold test (OTT), on the ratio threshold test (RTT) and on the joint maximum output-ratio threshold test (MO-RTT). The relevant statistics of these EI schemes are investigated mathematically and based on these statistics, their performance is evaluated in the context of error-and-erasure RS decoding. It is demonstrated that the system performance can be significantly improved by using error-and-erasure decoding invoking the EI schemes considered

Diversity Combining for Fast Frequency Hopping Multiple Access Systems Subjected to Nakagami-m Fading

The achievable performance of various diversity combining schemes used in fast frequency hopping (FFH) aided M-ary frequency shift keying (MFSK) systems operating in a multiple access scenario subjected to Nakagami-m fading is investigated. Specifically, linear, self-normalization, hard limiting majority vote, soft limiting, product combining and order statistics-normalized envelope detection based diversity combining schemes are considered. The comparison of various diversity combining schemes is based on the achievable bit error rate versus the number of simultaneous users supported. It is shown using simulation results that although some of the combining schemes considered result in an inferior performance compared to the optimum soft limiting combiner, they offer the advantage of achieving an acceptable interference suppression performance without requiring side information

Successive Interference Cancellation in Clipped and Product Combining aided FFH Multi-User Systems

Abstract—In this contribution, we propose two successive interference cancellation (SIC) schemes for a fast frequency hopping (FFH) multiple access (MA) system using M-ary frequency shift keying (MFSK) and invoking multi-user detection (MUD). One of the proposed schemes invokes clipped combining, while the other scheme employs both product combining and clipped combining. The SIC schemes are adapted from a scheme proposed by U.-C. Fiebig in 1996. The basic principle of the SIC schemes is that detection is carried out in multiple stages and during each stage, only the most reliable symbols are detected. In subsequent stages, the interference contributed by the already detected symbols may be removed. The performance of the proposed schemes is evaluated and compared to that of Fiebig’s scheme, when the FFH-MFSK system operates in a Nakagamim fading MA channel. The simulation results demonstrate that the proposed schemes attain a better bit error rate performance than Fiebig’s scheme

Mellin Transform Based Performance Analysis of Fast Frequency Hopping Using Product Combining

Abstract—In this contribution, we analyze the bit error rate (BER) performance of fast frequency hopping (FFH) assisted M-ary frequency shift keying (MFSK) using product combining. Product combining constitutes an efficient yet low-complexity scheme that may be employed in FFH-MFSK receiver to combat the detrimental effects of interference or jamming. We propose a novel approach to the analysis of this receiver system, which is based on the Mellin transform. Using this approach, the probability density function (PDF) of the product combiner output is expressed in a closed form. Based on the resultant PDF, the BER of the FFH-MFSK product combining receiver operating in Rayleigh fading channel is evaluated analytically. It is shown that the Mellin transform simplifies the analysis of the product combining receiver

Person re-identification by robust canonical correlation analysis

Person re-identification is the task to match people in surveillance cameras at different time and location. Due to significant view and pose change across non-overlapping cameras, directly matching data from different views is a challenging issue to solve. In this letter, we propose a robust canonical correlation analysis (ROCCA) to match people from different views in a coherent subspace. Given a small training set as in most re-identification problems, direct application of canonical correlation analysis (CCA) may lead to poor performance due to the inaccuracy in estimating the data covariance matrices. The proposed ROCCA with shrinkage estimation and smoothing technique is simple to implement and can robustly estimate the data covariance matrices with limited training samples. Experimental results on two publicly available datasets show that the proposed ROCCA outperforms regularized CCA (RCCA), and achieves state-of-the-art matching results for person re-identification as compared to the most recent methods

Particle swarm optimization with composite particles in dynamic environments

This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

P11 Resonances with Dubna-Mainz-Taipei Dynamical Model for pi-N Scattering and Pion Electromagnetic Production

We present the results on P11 resonances obtained with Dubna-Mainz-Taipei (DMT) dynamical model for pion-nucleon scattering and pion electromagnetic production. The extracted values agree well, in general, with PDG values. One pole is found corresponding to the Roper resonance and two more resonances are definitely needed in DMT model. We further find indication for a narrow P11 resonance at around 1700 MeV with a width of around 50 MeV in both pi-N and gamma-pi reactions.Comment: Contribution to the Proceedings of NSTAR 2011 - The 8th International Workshop on the Physics of Excited Nucleons, May 17-20, 2011, Thomas Jefferson National Accelerator Facility, Newport News, Virginia US

Space-Time Equalisation Assisted Minimum Bit-Error Ratio Multiuser Detection for SDMA Systems

This contribution investigates a space-time equalisation assisted multiuser detection scheme designed for multiple receiver antenna aided space division multiple access (SDMA) systems. A novel minimum bit error ratio (MBER) design is invoked for the multiuser detector (MUD), which is shown to be capable of improving the attainable performance and enhancing system capacity in comparison to that of the standard minimum mean square error (MMSE) design. The adaptive MUD coefficient adjustment procedure of the MBER space-time MUD is implemented using a stochastic gradient based least bit error rate (LBER) algorithm, which consistently outperforms the classic least mean square (LMS) algorithm, while maintaining a lower computational complexity than the latter