Performance of the Smart Antenna Aided Generalized Multicarrier DS-CDMA Downlink using both Time-Domain Spreading and Steered Space-Time Spreading

In this contribution a generalized MC DS-CDMA system invoking smart antennas for improving the achievable performance in the downlink of the system is studied, which is capable of minimizing the downlink interference inflicted upon co-channel mobiles, while achieving frequency, time and spatial diversity. In the MC DS-CDMA system considered the transmitter employs multiple antenna arrays and each of the antenna arrays consists of several antenna elements. More specifically, the space-time transmitter processing scheme considered is based on the principles of Steered Space-Time Spreading (SSTS). Furthermore, the generalized MC DS-CDMA system employs time and frequency (TF)-domain spreading, where a user-grouping technique is employed for reducing the effects of multiuser interference

Mutual Interlacing and Eulerian-like Polynomials for Weyl Groups

We use the method of mutual interlacing to prove two conjectures on the real-rootedness of Eulerian-like polynomials: Brenti's conjecture on $q$-Eulerian polynomials for Weyl groups of type $D$, and Dilks, Petersen, and Stembridge's conjecture on affine Eulerian polynomials for irreducible finite Weyl groups. For the former, we obtain a refinement of Brenti's $q$-Eulerian polynomials of type $D$, and then show that these refined Eulerian polynomials satisfy certain recurrence relation. By using the Routh--Hurwitz theory and the recurrence relation, we prove that these polynomials form a mutually interlacing sequence for any positive $q$, and hence prove Brenti's conjecture. For $q=1$, our result reduces to the real-rootedness of the Eulerian polynomials of type $D$, which were originally conjectured by Brenti and recently proved by Savage and Visontai. For the latter, we introduce a family of polynomials based on Savage and Visontai's refinement of Eulerian polynomials of type $D$. We show that these new polynomials satisfy the same recurrence relation as Savage and Visontai's refined Eulerian polynomials. As a result, we get the real-rootedness of the affine Eulerian polynomials of type $D$. Combining the previous results for other types, we completely prove Dilks, Petersen, and Stembridge's conjecture, which states that, for every irreducible finite Weyl group, the affine descent polynomial has only real zeros.Comment: 28 page

Ant-colony-based multiuser detection for multifunctional-antenna-array-assisted MC DS-CDMA systems

A novel Ant Colony Optimization (ACO) based Multi-User Detector (MUD) is designed for the synchronous Multi-Functional Antenna Array (MFAA) assisted Multi-Carrier Direct-Sequence Code-Division Multiple-Access (MC DS-CDMA) uplink (UL), which supports both receiver diversity and receiver beamforming. The ACO-based MUD aims for achieving a bit-error-rate (BER) performance approaching that of the optimum maximum likelihood (ML) MUD, without carrying out an exhaustive search of the entire MC DS-CDMA search space constituted by all possible combinations of the received multi-user vectors. We will demonstrate that regardless of the number of the subcarriers or of the MFAA configuration, the system employing the proposed ACO based MUD is capable of supporting 32 users with the aid of 31-chip Gold codes used as the T-domain spreading sequence without any significant performance degradation compared to the single-user system. As a further benefit, the number of floating point operations per second (FLOPS) imposed by the proposed ACO-based MUD is a factor of 108 lower than that of the ML MUD. We will also show that at a given increase of the complexity, the MFAA will allow the ACO based MUD to achieve a higher SNR gain than the Single-Input Single-Output (SISO) MC DS-CDMA system. Index Terms—Ant Colony Optimization, Multi-User Detector, Multi-Functional Antenna Array, Multi-Carrier Direct-Sequence Code-Division Multiple-Access, Uplink, Near-Maximum Likelihood Detection

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

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