Molecular phylogenetics and character evolution of the ""sacaca"" clade: Novel relationships of Croton section Cleodora (Euphorbiaceae)

Phylogenetic relationships of Croton section Cleodora (Klotzsch) Baill. were evaluated using the nuclear ribosomal ITS and the chloroplast trnl-F and trnH-psbA regions. Our results show a strongly supported clade containing most previously recognized section Cleodora species, plus some other species morphologically similar to them. Two morphological synapomorphies that support section Cleodora as a clade include pistillate flowers in which the sepals overlap to some degree, and styles that are connate at the base to varying degrees. The evolution of vegetative and floral characters that have previously been relied on for taxonomic decisions within this group are evaluated in light of the phylogenetic hypotheses. Within section Cleodora there are two well-supported clades, which are proposed here as subsections (subsection Sphaerogyni and subsection Spruceani). The resulting phylogenetic hypothesis identifies the closest relatives of the medicinally important and essential oil-rich Croton cajucara Benth. as candidates for future screening in phytochemical and pharmacological studies. (C) 2011 Elsevier Inc. All rights reserved.

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.