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This chapter introduces an LWT-based audio watermarking scheme using fast Walsh-Hadamard transform (FWHT) and singular value decomposition (SVD) [30]. Conventional wavelet transform provides good results for its multi-resolution characteristics and perfect reconstruction. However, it is mainly calculated by convolution operation, resulting in high computation. In addition, the generated floating numbers increase the storage requirements. As a result, the LWT is designed to increase the efficiency and it is now used in digital watermarking [27]. In our proposed method, watermark information is preprocessed first using a Bernoulli map in order to improve the robustness and enhance the confidentiality of the watermark. Then the original audio is segmented into nonoverlapping frames. Watermark information is embedded into the largest singular value of the FWHT coefficients obtained from the low-frequency LWT coefficients of each frame. A blind watermark detection technique is developed to identify the embedded watermark under various attacks. The main features of the proposed scheme are: (i) it utilizes the LWT, FWHT, and SVD jointly; (ii) it uses Bernoulli map, containing the chaotic characteristic to enhance the confidentiality of the proposed scheme; (iii) watermark extraction process is blind; (iv) subjective and objective evaluations reveal that the proposed scheme maintains high audio quality; and (v) it achieves a good trade-off among imperceptibility, robustness, and data payload. Experimental results indicate that the proposed watermarking scheme is highly robust against various attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression. Moreover, it outperforms state-of-the-art methods [9–10, 14–16, 20, 23, 26, 28] in terms of imperceptibility, robustness, and data payload. The data payload of the proposed scheme is 172.39 bps, which is relatively higher than that of the state-of-the-art methods.

This chapter introduces an audio watermarking algorithm based on FWHT and LU decomposition (LUD). To the best of our knowledge, this is the first audio watermarking method based on FWHT, LUD, and quantization jointly. Initially, we preprocess the watermark data to enhance the security of the proposed algorithm. Then, the original audio is segmented into nonoverlapping frames and FWHT is applied to each frame. LUD is applied to the FWHT coefficients represented in a matrix form. Watermark data are embedded into the largest element of the upper triangular matrix obtained from the FWHT coefficients of each frame. Experimental results indicate that proposed algorithm is considerably robust and reliable against various attacks without degrading the quality of the watermarked audio. Moreover, it shows more excellent results than the state-of-the-art methods in terms of imperceptibility, robustness, and data payload. The main limitation of the existing audio watermarking techniques is the difficulty to obtain a favorable trade-off among imperceptibility, robustness, and data payload. To overcome this limitation, in this chapter, we propose a blind audio watermarking algorithm based on fast Walsh-Hadamard transform (FWHT) and LU decomposition (LUD). The main features of the proposed scheme are: (i) it utilizes the FWHT, LUD, and quantization jointly; (ii) it uses a tent map that contains the chaotic characteristic to enhance the confidentiality of the proposed algorithm; (iii) watermark is embedded into the largest element of the upper triangular matrix obtained from the FWHT coefficients of each frame by quantization; (iv) watermark extraction process is blind; and (v) it achieves a good trade-off among imperceptibility, robustness, and data payload. Experimental results indicate that the proposed watermarking algorithm shows high robustness against various attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression. Moreover, it outperforms state-of-the-art methods [9–10, 15–16, 20, 22–23, 26, 28] in terms of imperceptibility, robustness, and data payload.

Digital watermarking is a potent technique for digital right management of multimedia data. This paper aims to introduce a blind image watermarking algorithm using discrete Hilbert transform (DHT) and polar decomposition (PD). Initially, Gaussian map is utilized to preprocess the watermark image to enhance its security. Then the original image is segmented into red, green and blue channels. The blue channel of the original image is selected and it is divided into non-overlapping blocks. DHT is conducted on each block to obtain the DHT coefficients represented in matrix form. PD is then carried out to each of these matrices and watermark data is inserted into the positive semi-definite Hermitian matrix obtained from the DHT coefficients of each block by using an embedding operation. Experimental results specify that the proposed algorithm has excellent resistance to numerous attacks. In addition, it also provides good quality watermarked images. Moreover, it shows more excellent results than the recent techniques in respect of robustness and imperceptibility.

In this paper, we propose a method to improve the noise suppression performance of adaptive beamforming in environments where the speech source moves or where the noise level is very high. Among various speech enhancement methods, adaptive beamforming based on microphone array has high noise suppression performance. However, the performance of adaptive beamforming degrades in highly noisy environment and it cannot track the speech source when the speaker moves. To solve the problem, the proposed method utilizes the characteristics that the amplitude spectrum of noise varies more significantly than that of the speech in a short period. In addition to spectra obtained from each input speech signal of microphone arrays, spectra obtained at previous frames of each input speech signal are effectively utilized to find the Minimum Statistical Spectrum (MSS). From the resulting MSS, an enhanced speech signal is created and used in adaptive beamforming with the input speech signals of microphone array. Experiments show that the proposed method provides a high performance relative to the original MSS approach.

Pitch is one of the most essential features in human speech analysis. Although numerous pitch detection methods have been developed, it is still a challenge to provide a high pitch detection performance in noisy environments. In this paper, we propose an anti-noise pitch detection method that combines a speech enhancement algorithm with a spectral flattening algorithm. In the experiments, we compare the proposed method with several widely used or state-of-the-art pitch detection methods. The results show that the proposed method has the lowest gross pitch error (GPE) rate among all the methods when dealing with white-noise added male speeches. Moreover, comparing the pitches estimated by the proposed method to those estimated by the conventional lag window method, we can see that the speech enhancement algorithm helps diminish pitch errors.

In this paper, we have proposed a spectrum sensing method which exploits the combination of a comb filter with autocorrelation function in a parallel form. The primary user information is not required, but the noise variance is used, which makes the proposed method a semiblind one. This approach enhances the sensing capability of the cyclic prefix (CP) based orthogonal frequency division multiplexing (OFDM) signals in severe noise environments. The detection performance is investigated for various important parameters for OFDM based applications. A comprehensive evaluation by simulations shows that the proposed method is useful under low levels of signal-to-noise ratio (SNR).

Human facial emotion recognition (FER) has attracted the attention of the research community for its promising applications. Mapping different facial expressions to the respective emotional states are the main task in FER. The classical FER consists of two major steps: feature extraction and emotion recognition. Currently, the Deep Neural Networks, especially the Convolutional Neural Network (CNN), is widely used in FER by virtue of its inherent feature extraction mechanism from images. Several works have been reported on CNN with only a few layers to resolve FER problems. However, standard shallow CNNs with straightforward learning schemes have limited feature extraction capability to capture emotion information from high-resolution images. A notable drawback of the most existing methods is that they consider only the frontal images (i.e., ignore profile views for convenience), although the profile views taken from different angles are important for a practical FER system. For developing a highly accurate FER system, this study proposes a very Deep CNN (DCNN) modeling through Transfer Learning (TL) technique where a pre-trained DCNN model is adopted by replacing its dense upper layer(s) compatible with FER, and the model is fine-tuned with facial emotion data. A novel pipeline strategy is introduced, where the training of the dense layer(s) is followed by tuning each of the pre-trained DCNN blocks successively that has led to gradual improvement of the accuracy of FER to a higher level. The proposed FER system is verified on eight different pre-trained DCNN models (VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3 and DenseNet-161) and well-known KDEF and JAFFE facial image datasets. FER is very challenging even for frontal views alone. FER on the KDEF dataset poses further challenges due to the diversity of images with different profile views together with frontal views. The proposed method achieved remarkable accuracy on both datasets with pre-trained models. On a 10-fold cross-validation way, the best achieved FER accuracies with DenseNet-161 on test sets of KDEF and JAFFE are 96.51% and 99.52%, respectively. The evaluation results reveal the superiority of the proposed FER system over the existing ones regarding emotion detection accuracy. Moreover, the achieved performance on the KDEF dataset with profile views is promising as it clearly demonstrates the required proficiency for real-life applications.

Wireless sensor network is a self-organized network composed by a large number of autonomous and low-cost sensor nodes, and monitors regions through wireless communication. Blind equalization is used to estimate the transmitted signal over wireless sensor networks. In this paper, we consider a single-input multiple-output (SIMO) channels based static wireless sensor network and set out to estimate the transmitted signal blindly. We derive a new approach in that the best sensor output signal is identified as the easiest path by normalized error calculation. Then we estimate the transmitted signal from the corresponding sensor output. Direct and indirect calculations are considered for the proposed method, which utilize the prediction error in the normalized error calculation as the prefilter. Computer simulations validate the effectiveness of the proposed method through mean square error (MSE) and symbol error rate (SER) performance relative to the conventional method.

In this paper, we propose a new combination technique for full-reference image quality assessment (IQA) by utilizing three better-recognized IQA methods. It gives the best performance for various databases. The parameter values employed in the new IQA score are optimized using the particle swarm optimization algorithm. In the combination approach, we firstly pick up the most appropriate IQA index for image quality databases and then add other two indices which have the most dissimilar features with the first index. By experiments, it is validated that the proposed method outperforms the other previous combination methods.

In this paper, we propose a new method for improving the bit error rate (BER) and the computational complexity in Maximum Likelihood Detection employing QR decomposition and the M-algorithm (QRM-MLD) which provides high performance among the signal separation techniques for Multiple Input Multiple Output (MIMO). QRM-MLD utilizing noise power estimation is regarded as an effective method to reduce the computatonal complexity. However, the method can not cope with the gap from the average noise power at each stage. In the proposed method, we can obtain better performance by setting thresholds according to the variations in the noise power at each stage. Simulation results show that the proposed method can improve the BER and the computational complexity compared with the conventional method.

In this paper, two speech enhancement methods are presented to improve the performance of adaptive beamforming. Spectral arithmetic mean (SAM) and spectral geometric mean (SGM) are utilized to suppress noise in the structure of adaptive beamformer. Experiments validate that the two proposed methods provide a high performance when the sound source moves with time and when the noise level is very high.

In this letter, we propose a low complex data-null subcarrier switching-based peak-to-average power ratio (PAPR) reduction scheme for the orthogonal frequency division multiplexing (OFDM) systems, which provides improved bit error rate. We perform the switching between the data and null subcarriers in such a way that distance between any two switched-null subcarriers remains constant. This method can achieve up to 3 dB signal-to-noise ratio gain and shed the system complexity by more than 98% of the conventional systems, with a slight compromise of the PAPR reduction capability.

In this paper, we propose a bone-conducted (BC) speech enhancement method using a deep neural network (DNN). Focusing on that the vocal tract components appear only on the low-order cepstrum, we restore the low-order cepstrum of BC speech so as to be close to that of AC speech. The results in experiments show that BC speech processed by the proposed method becomes more similar to AC speech than the conventional method.

To clarify the effect of differences in hydrophyte life forms on methane (CH4) production and its carbon stable isotopic signature (δ13C-CH4), we analyzed CH4 and carbon dioxide (CO2) concentrations, their stable carbon isotope values, and chemical constituents dissolved in pore water in a small floating peat bog in Japan. Because eutrophication has modified the surrounding water quality, the bog vegetation on the mat has been, in part, replaced by fen-type vegetation. We hypothesized that differences in hydrophyte habitats affect redox conditions, including dissolved oxygen (DO) in water and therefore the amounts and carbon isotopic values of CH4 and CO2 dissolved in pore water. Between the habitats of two Sphagnum species, DO was considerably higher, and CH4 concentrations were significantly lower in Sphagnum cuspidatum Ehrh. habitats in hollow (DO: 0.62 ± 0.20 mg/L (standard error (SE)) and CH4: 0.18 ± 0.02 mmol/L) than in Sphagnum palustre L. habitats in hummock (DO: 0.29 ± 0.08 and CH4: 0.82 ± 0.06) in pore water (10 cm depth). Both DO and CH4 concentrations in three vascular plant habitats (Rhynchospora fauriei Franch., Phragmites australis [reed], and Menyanthes trifoliata L.) in pore water (10 cm depth) were intermediate relative to the two Sphagnum species. However, CH4 flux in M. trifoliata site was significantly higher than that at both Sphagnum sites, suggesting that the type of gas transport (diffusive or convective via root and stem) affected the depth profile of CH4 concentrations and its flux. δ13C-CH4 values in pore water also varied among the vegetation types, even within Sphagnum species (e.g., at 10 cm depth, δ13C-CH4: R. fauriei, −55.3 ± 1.8‰ (SE); P. australis, −57.5 ± 1.6‰; M. trifoliata, −56.7 ± 1.5‰; S. cuspidatum, −71.2 ± 1.4‰; and S. palustre, −60.4 ± 0.6‰). Our results suggest that significant differences arise in CH4 concentration and δ13C-CH4 values among the hydrophyte habitats even within a small peat bog and that change in vegetation relative to trophic conditions can affect CH4 emissions and associated δ13C-CH4 values.

This paper proposes a blind singular value decomposition (SVD) based audio watermarking scheme using entropy and log-polar transformation (LPT) for copyright protection of audio signal. In our proposed scheme, initially the original audio is segmented into non-overlapping frames and discrete cosine transform (DCT) is applied to each frame. Low frequency DCT coefficients are divided into sub band and entropy of each sub band is calculated. Watermark data is embedded into the Cartesian components of the largest singular value obtained from the DCT sub band with highest entropy value of each frame by quantization. Simulation results indicate that the hidden watermark data is robust against different attacks. The comparison analysis shows that the proposed scheme has high data payload and provides superior performance compared to the state-of-the-art watermarking schemes reported recently.

Conventional linear prediction (LP) analysis is known to suffer from problems in estimating the formant frequencies (vocal tract resonances) of high-pitched speech signals. The performance of conventional LP analysis deteriorates due to the harmonic structure of the glottal excitation source, especially in the case of high-pitched speech signals. Attempting to resolve this problem, a pitch-synchronous analysis technique based on a short-time energy function is presented. The proposed method has been verified to reduce the effect of the harmonic structure of the glottal excitation source. Experiments were carried out using synthetic vowels and real vowels. The results show that the proposed method yields a better performance in the estimation of formant frequencies than some previous LP analysis methods.

This paper presents a method to estimate noise variance from the power spectrum of the observed noisy image and proposes an improved Wiener Filter called parametric Wiener filter. The best performance can be obtained with the best parameters of the parametric Wiener filter. However, in practice, it is impossible to know the best parameters because the best parameters are determined depending on the characteristics of the image. Thus, to obtain the estimated best parameters for the parametric Wiener filter, we propose a method of calculating a power spectrum sparsity of the observed noisy image. For the parametric Wiener filter, an image with a large power spectrum sparsity has larger parameters compared with the image with a small power spectrum sparsity. A parametric Wiener filter with the estimated parameters is used in practice. The experimental results shows that the proposed method provides better performance than that of the conventional method.

We propose a new noise estimation method using only the current frame of noisy speech. The proposed method utilizes an inverse comb filter for noisy speech to suppress the power of speech, and estimates the noise from the resulting spectrum. It is shown by experiments that the spectral subtraction combined with the proposed noise estimation method is superior to the conventional speech enhancement methods in non-stationary noise environments.

Air-conducted speech is currently used for fundamental frequency extraction in noisy environment. However, the results are severely affected by noise. In this paper, additionally bone-conducted speech is combined with air-conducted speech and a novel pitch extraction method is derived. Experiments indicate that the proposed method provides an accurate estimate of fundamental frequency of speech in highly noisy environment.

It is well known that the iterative spectral subtraction (ISS) method is effective to eliminate white noise in speech, the fact that the ISS method is suitable for restoration from image degraded by white noise has been confirmed. In this paper, we presents an image denoising technique in which an iterative extended spectral subtraction (IESS) method is developed. We discuss the factors of the IESS that affect the restoration results, and by setting the best parameters, set out to improve the performance. We compare the performance of the IESS method with that of state of the art filtering approaches in experiments.

This paper presents a new blind audio watermarking scheme based on singular value decomposition (SVD) using Angle-Quantization. Initially, the original audio is divided into some frames and the samples of each frame are arranged into matrix. SVD is applied to the matrix for each frame. Watermark data is embedded into the angle between the largest singular value and the second largest singular value of each diagonal matrix by quantization. Date payload of the proposed scheme is 172.39 bps. Simulation results confirm the inaudibility and higher robustness against various signal processing attacks for the proposed method.

Semi-fragile watermarking has been widely used for content authentication. In this paper, we propose a semi-fragile watermarking method using slant transform (ST) and LU decomposition (LUD) for image authentication. Initially, the watermark image is scrambled using an algorithm to enhance the confidentiality of the image. The host image is separated into red, green, and blue channels. The red channel is divided into 8×8 non-overlapping blocks. ST is applied to each block to get the ST coefficients. LU decomposition is then applied to these coefficients to obtain the lower and upper triangular matrix. Watermark data is embedded into the upper triangular matrix of ST coefficients using an embedding equation. Simulation results indicate that proposed method produces high quality watermarked image, provides secure embedding, extraction and authentication. Moreover, it effectively detects malicious tempering on image while being resistant to various content preserving manipulations. Furthermore, it shows superior performance than the state-of-the-art methods in terms of imperceptibility.

Peak to Average Power Ratio (PAPR) of Orthogonal Frequency Division Multiplexing (OFDM) is a long-standing problem which has been hindering its performance for decades. In this paper, we propose a new PAPR reduction scheme based on shifting pilot locations among the data symbols. Since no side information is sent to the receiver about the pilot locations, a novel pilot detection algorithm is devised here exploiting the pilot power and the relative constant distance property of pilots. The proposed scheme attains around 1.5 dB PAPR reduction. The pilot detection accuracy is shown to be very excellent ranging from 80% to 99% at 0 dB of Signal to Noise Ratio (SNR) in different parameters. This scheme is very spectrally efficient with reduced complexity without degrading BER performance significantly, Comment: 6 pages, Proc. of 26th IEEE International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), Hong Kong, Aug-September 2015, pp. 77-82

In this paper we present a complex linear prediction analysis method for estimating the formant frequencies of noisy speech. The proposed method effectively utilizes the signal (being the analytic signal) which is ignored in the conventional complex linear prediction analysis to achieve noise reduction. Also, the covariance and forward-backward linear prediction (FBLP) methods are compared, and the FBLP method is deployed for predictive coefficients estimation in the proposed method. Experimental results show that the proposed method yields better performance of formant estimation when compared to some conventional methods in white noise. 

This paper presents an audio watermarking scheme in fast Fourier transform (FFT) domain based on singular value decomposition (SVD) and Cartesian-polar transformation (CPT). In our proposed scheme, initially the original audio is segmented into nonoverlapping frames. FFT is applied to each frame and low frequency FFT coefficients are selected. SVD is applied to the selected FFT coefficients of each frame represented in a matrix form. The highest singular values of each frame are selected and are decomposed into two components using CPT. Watermark information is embedded into each of these CPT components using an embedding function. Experimental results indicate that the proposed watermarking scheme is highly robust against various signal processing attacks. In addition, the proposed scheme has a high data payload. Moreover, it outperforms state-of-the-art audio watermarking methods in terms of imperceptibility, robustness, and data payload.

It is well known that linear predictive coding (LPC) is sensitive to additive noise and thatthe corresponding noise compensation method suffers from poor accuracy of noise power estimation. Inthis paper, a novel noise compensation LPC method based on pitch synchronous analysis is presented.Using the proposed method, a more accurate estimation of noise power is calculated on each current frame.Experimental results show that the proposed method yields better performance than some conventionalLPC analysis methods in the presence of white noise. Keywords: LPC, pitch period, pitch synchronous, noise compensation, autocorrelation 1. IntroductionLinear predictive coding (LPC) [1] is the most com-monly used technique for speech analysis, which canestimate the spectral envelope of a voiced speech sig-nal by modeling it by a set of parameters closely re-lated to the speech production transfer function. Thetransfer function used in LPC modeling is expressedby H ( z )= G 1 − ∑ Mi =1 a i z

Digital images are distorted by a variety of processes. Thus we need objective image quality assessment (IQA) equivalent to subjective assessment. Several objective IQA methods have been proposed, and recently a combination method has been derived successfully. The combination technique is able to assess distorted images correctly. However, it is weak against Meanshift images. In this paper, we propose a new objective IQA method adding another IQA score to the original combination technique. Adjustable parameters included in the new IQA measure are optimized with the genetic algorithm. By experiments, it is validated that the proposed method provides a superior performance on various images including Meanshift.

In this paper, we propose a new artificial bandwidth extension technique for telephone speech. Usually, the high frequency components of telephone speech are lost due to band limitation. The proposed method produces the high frequency components by estimating the spectral envelope of voiced speech and by restoring voiceless consonant spectrum. Objective evaluations in experiments showed that the proposed method provides better results than the conventional method.

Partial transmit sequence (PTS) is one of the most well-known techniques to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. However, a significant drawback of the conventional PTS is to transmit several side information bits. In this paper, we propose a new PTS method to reduce the PAPR without side information, which generates a set of new constellations. Corresponding to the new constellations with different phase factors, we can recover the original signal by using the proposed method at the receiver but not need side information in OFDM systems. Theoretical analysis and simulation results have shown that, compared with the conventional PTS, the proposed method could achieve further PAPR reduction of OFDM signals and provide a better bit error rate (BER) performance.

In this paper, we implement the fundamental frequency extraction by using an exponentiated band-limited amplitude spectrum of speech. The exponent and bandwidth are adjusted according to male and female differences, characteristics of noise and noise amount. Basis function of the fundamental frequency extraction is calculated by inverse Fourier transform of an exponentiated amplitude spectrum. It is shown that this function invokes a performance robust against the white and car noise. Experimental results show that this method provides better performance than the conventional method at a wide range of signal-to noise ratios.

In this paper, an image denoising method for noisy image corrupted by additive white noise is proposed. It is well known that the Adaptive Wiener Filter (AWF) is suitable for such denoising. However, some noises remain in the image processed by AWF. In order to improve the performance of the AWF, an iterative algorithm is derived. To prevent original image signal loss, a weighting parameter is used for the noise variance estimate and a technique adjusting the filter kernel is employed. Compared to the conventional AWF, the proposed filter provides better edge performance.

The hypothesis that the fruiting phenology of a plant community is affected by both fluctuating predation pressure and the seed preference of predators (risk of predation) was tested in a tropical peat swamp forest in Central Kalimantan, Indonesia. We conducted: (i) a seed removal experiment using model seeds (i.e. jackfruit seeds which are low in secondary compounds and attractive to seed predators) that investigated fluctuations in predators’ activity and (ii) a seed removal experiment using 17 natural species to clarify seed preferences of seed predators; we also monitored the fruiting phenology of the 17 study species. The removal rate of model seeds decreased during high water table, whilst it increased during low water table, indicating that flooding restricted the feeding activity of terrestrial small mammals which are the dominant seed predators in peat swamp forest. Differences in fruiting time between species with higher removal rates (preferred) and lower removal rates (less preferred) were observed. Species with high removal rates showed peaks of fruiting during flood periods and the reproductive pattern of this group was negatively regressed to the removal rate of model seeds and positively regressed to water level. The implication of this result is that these tree species produce seeds and deposit them on the ground whilst seed predators are disturbed by flooding, thus they avoid seed deposition when predators’ activity is high. Seed production amongst species with low removal rates was variable indicating that these tree species are likely more adapted to other factors that affect survival of seeds and/or seedling (i.e. regeneration niche) than predation. We were able to demonstrate another relationship between fruiting phenology and seed predation that differs from that of mast fruiting. The fact that species with preferred seeds produce their fruits when predator activity is restricted might have developed under the conditions of poor nutrient availability and frequent flooding that are characteristic of the peat swamp forest ecosystem.

We studied vegetation changes in a small floating mat bog in Mizorogaike Pond (Kyoto, Japan), which had experienced a severe decrease in the number and area of hummocks caused by nutrient loading in the 1960s and 1970s, to examine whether reducing the extent of nutrient loading can restore degraded wetland vegetation. However, nutrient loading in the region has been minimized since the 1980s. We examined the distributions of hummocks and Sphagnum cuspidatum mats in 1980, 1988, and 2006, as well as nine major vascular plants that dominated the hollows on the floating mat in 1980 and 2006. The total area of normal hummocks formed by Sphagnum palustre increased from 5865.3 m2 in 1980 to 5913.6 m2 in 1988 and 8485.2 m2 in 2006. The total area of the S. cuspidatum mats also changed, from 416.4 m2 in 1980 to 322.3 m2 in 1988 and 1012.5 m2 in 2006. Examination of the spatial distribution patterns of major plants revealed that emergent plants decreased in the northern part of the mat, but increased in the southern part. Thus, the improved pond water quality was effective at restoring hummocks, although nutrient loading may have caused some irreversible changes in the wetland vegetation.

The catenary sequence of vegetation in tropical peat swamp forest has been attributed to reduced nutrient availability along the gradient of peat depth. The objective of this study was to assess the phylogenetic structure of tropical peat swamp forest vegetation with the aim of clarifying the community assemblages. We laid out six plots along the gradient of peat depth in the upper catchment of the Sabangau river, Central Kalimantan, Indonesia and investigated forest phylogenetic structure. We found a tendency for phylogenetic diversity to decrease as peat depth increased. Higher phylogenetic diversity in the forest zone closest to the river could be attributed to competitive exclusion in a relatively benign peatland environment, whilst lower phylogenetic diversity further from the river could be attributed to the filtering effect of a more stressful environment. There was, however, unexpected low phylogenetic diversity in the tall interior community in the Sabangau forest, where environmental conditions were less extreme. This observed low phylogenetic diversity is possibly related to the history of peat development and degradation in this location as the ombrogenous peat is undergoing long-term oxidation due to reduced precipitation and lowering of the water table. We conclude that not only environmental filtering but also biotic filtering through such mechanisms as competitive exclusion contribute to the catenary sequence of tropical peat swamp forest vegetation

We investigated soil net nitrogen mineralization rate, above- and belowground biomass allocation, and nitrogen use in a Cryptomeria japonica plantation chronosequence. Total biomass accumulation showed an asymptotic accretion pattern, and the peak total biomass accumulation rate occurred approximately 30 years after afforestation. Soil net nitrogen mineralization rate was lowest 30 years after afforestation. Between years 30 and 88, net nitrogen mineralization increased again. These results indicate that an imbalance in soil nitrogen supply and plant nitrogen demand occurred approximately 30 years after afforestation. Furthermore, leaf nitrogen concentration, which was used as an index of plant nitrogen status, was lower in mature forest than in young forest, suggesting that mature stands did not take up nitrogen as successfully. If soil resources such as nitrogen limit plant growth, plants may increase biomass allocation to fine root structure; however, fine root biomass was not higher in 30- and 88-year-old stands than in younger stands, suggesting that changes in biomass allocation may not be effective against nitrogen deficiency in a C. japonica plantation chronosequence.