The performance of the Mel-Frequency Cepstrum Coefficients (MFCC) may be affected by (1) the number of filters, (2) the shape of filters, (3) the way in which filters are spaced, and (4) the way in which the power spectrum is warped. In this paper, several comparison experiments are done to find a best implementation. The traditional MFCC calculation excludes the 0th coefficient for the reason that it is regarded as somewhat unreliable. According to the analysis and experiments, the authors find that it can be regarded as the generalized frequency band energy (FBE) and is hence useful, which results in the FBE-MFCC. The authors also propose a better analysis, namely the auto-regressive analysis, on the frame energy,which outperforms its 1st and/or 2nd order differential derivatives. Experiments with the “863”Speech Database show that, compared with the traditional MFCC with its corresponding autoregressive analysis coefficients, the FBE-MFCC and the frame energy with their corresponding auto-regressive analysis coefficients form the best combination, reducing the Chinese syllable error rate (CSER) by about 10%, while the FBE-MFCC with the corresponding auto-regressive analysis coefficients reduces CSER by 2.5%. Comparison experiments are also done with a quite casual Chinese speech database, named Chinese Annotated Spontaneous Speech (CASS)corpus. The FBE-MFCC can reduce the error rate by about 2.9% on an average.

Comparison of Different Implementations of MFCC

Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Firat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.

https://www.mdpi.com/1424-8220/19/9/1992/pdf

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.

A review natural polymeric coagulants in wastewater treatment

The use of conventional chemical coagulant in treatment of wastewater is gaining great attention. Drawbacks related to the prolonged effects on human health and environment due to the generation of by-product non-biodegradable sludge are becoming the latest topics. Transition from chemical to natural coagulant can be a good strategy to reduce the aforementioned drawbacks. Therefore, this review aims to provide critical discussions on the use of natural coagulant along with the comparative evaluation over the chemical coagulant. Treatment performances by chemical and natural coagulant have been reviewed on various types of wastewater with different success rates. Based on this review, a transition from the use of chemical to natural coagulant is highly suggested as the performance of the natural coagulant is comparable to that of the chemical coagulant and in some cases even better. The comparative advantages and disadvantages also convinced that the natural coagulant stands a great chance to be used as an alternative over the chemical coagulant. Though the current utilization of natural coagulant is encouraging, three main aspects were overlooked by researchers: active coagulant agent, extraction, and optimization due to different wastewater characteristics. Furthermore, delving into these aspects could clarify the uncertainties on the natural coagulant. Hence, it makes this transition a prospect of green technology with sustainable application towards wastewater treatment.

Recent Advances on Coagulation-Based Treatment of Wastewater: Transition from Chemical to Natural Coagulant

Fate of antibiotic resistant genes in wastewater environments and treatment strategies - A review.

Reusing waste water by appropriate treatment is an effective method for substantiating the ever increasing water demand for construction, irrigation, domestic and industrial purposes. The greywater is the domestic waste water that does not contain the toilet effluent and it is converted and used for non-potable purpose through coagulation and flocculation by using natural coagulant.The main objective of this work is to evaluate the coagulating efficiencies of various natural coagulants on synthetic greywater by varying pH, mixing speed, mixing time and coagulant dosage.Powdered coagulants obtained from tamarind seeds, moringa oleifera, banana peels and fly ash were compared with conventional commercial coagulants like alum for synthetic and real greywater.The natural coagulants have shown significant performance compared to chemicals like alum.The turbidity removal efficiency for tamarind seeds, moringa oleifera, banana peels and fly ash were found to be 61.33%, 85.75%, 90.42%, 94.27% against 96.49% obtained from alum on treating with synthetic grey water under identical conditions.

Performance of Natural Coagulants on Greywater Treatment

Oil spills in the ocean are one of the major environmental concerns as they pose significant threat to the ecosystem. In the recent year’s hyperspectral sensors have been used to detect oil spills due to their ability to capture reflected solar signal with very narrow bandwidth, that enables to differentiate materials even with subtle signature differences. However high spectral dimensionality affects classification accuracy due to insufficient training samples. Conventional methods have dealt with this problem through band (or feature) selection or feature extraction (or transformation) approaches. In the case of an oil emulsion signal, as evident from the literature the oil characteristics are present only in certain bands of a spectral signature. Hence feature selection without focusing on right bands may lead to selecting features of less significance for target identification. Moreover, in feature extraction approaches during transformation from high dimensional space to low dimension space some of the important features could be lost. Hence, the proposed research takes advantage of different but complimentary benefits of both feature selection and feature extraction methods to obtain final features effectively. In the proposed research, features are selected by modelling the oil emulsion signal using derivative spectrum and calculation of partial sum of energies. Derivative spectrum represents variation in signal energies and calculation of partial sum of signal energies for each band facilitates the application of filters bank to capture oil sensitive signal characteristics. Feature extraction is done through wavelet transform by gradual multi-scale zooming of signals through partial analysis of time (space) frequencies. The obtained features are fused together and fed to Gaussian Mixture Model (GMM) classifier to classify oil emulsions. The proposed approach gives 5% to–10% higher classification accuracy as compared to some of the conventional techniques.

A fusion approach to classify hyperspectral oil spill data

Advances in hyper spectral imaging sensors makes it possible to obtain high dimensional and high resolution data which poses challenges for computational cost and time. As the inter-band resolution is high in hyper spectral images, the spectral dimension could be reduced without significant loss of useful information. This requires careful application of dimensionality reduction techniques to reduce the dimension but preserve the original spectral characteristics. It is observed in the literature that filtering techniques can address some of the challenges concerning dimensionality reduction in hyper spectral imaging. In this paper a dimensionality reduction method adopting Discrete Wavelet Transform and Distance Classifier Correlation filter has been proposed for hyper spectral imaging. The experimental results show that the combination of ID-DWT with DCCF outperforms the other methods in dimensionality reduction and classification of given data set.

Jacintha Menezes

Papers

Performance of Natural Coagulants on Greywater Treatment

A fusion approach to classify hyperspectral oil spill data

Dimensionality reduction and classification of hyperspetral images using DWT and DCCF