scholarly journals Velody 2—Resilient High-Capacity MIDI Steganography for Organ and Harpsichord Music

2020 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Eric Järpe ◽  
Mattias Weckstén
Keyword(s):  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
High Capacity ◽  
Absolute Error ◽  
Music Technology ◽  
Alternative Methods ◽  
File Size ◽  
User Friendly ◽  
Different Levels ◽  
Better Than

A new method for musical steganography for the MIDI format is presented. The MIDI standard is a user-friendly music technology protocol that is frequently deployed by composers of different levels of ambition. There is to the author’s knowledge no fully implemented and rigorously specified, publicly available method for MIDI steganography. The goal of this study, however, is to investigate how a novel MIDI steganography algorithm can be implemented by manipulation of the velocity attribute subject to restrictions of capacity and security. Many of today’s MIDI steganography methods—less rigorously described in the literature—fail to be resilient to steganalysis. Traces (such as artefacts in the MIDI code which would not occur by the mere generation of MIDI music: MIDI file size inflation, radical changes in mean absolute error or peak signal-to-noise ratio of certain kinds of MIDI events or even audible effects in the stego MIDI file) that could catch the eye of a scrutinizing steganalyst are side-effects of many current methods described in the literature. This steganalysis resilience is an imperative property of the steganography method. However, by restricting the carrier MIDI files to classical organ and harpsichord pieces, the problem of velocities following the mood of the music can be avoided. The proposed method, called Velody 2, is found to be on par with or better than the cutting edge alternative methods regarding capacity and inflation while still possessing a better resilience against steganalysis. An audibility test was conducted to check that there are no signs of audible traces in the stego MIDI files.

Accounting for SNR in an Algorithm Using Wavelet Transform to Remove ECG Interference from EMG Signals

2019 ◽  
Vol 19 (01) ◽  
pp. 2050001
Author(s):  
Thandar Oo ◽  
Pornchai Phukpattaranont
Keyword(s):  
Wavelet Transform ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Absolute Error ◽  
Pass Filter ◽  
Ecg Signals ◽  
Emg Signal ◽  
High Pass ◽  
Better Than ◽  
Ecg Interference

When the electromyography (EMG) signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. As a result, the EMG signal can be contaminated during data collection. In this paper, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the signal-to-noise ratio (SNR). The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. The coefficients for levels 4 and 5, which are contaminated by ECG, are set to zero when their absolute values are less than or equal to a threshold determined for each SNR level. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. We evaluated the performance of the proposed algorithm using simulated EMG contaminated with both simulated and real ECG signals, at 9 SNR levels from [Formula: see text]20 to 20[Formula: see text]dB with 5[Formula: see text]dB increments. The performance based on mean absolute error, correlation coefficient and relative error shows that the DSWT method is better than a high-pass filter.

scholarly journals Charuco Board-Based Omnidirectional Camera Calibration Method

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 421 ◽  
Author(s):  
Gwon An ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Kugjin Yun ◽  
Won-Sik Cheong ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Calibration Method ◽  
Feature Points ◽  
Omnidirectional Camera ◽  
Reprojection Error ◽  
Conventional Methods ◽  
Better Than

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

scholarly journals Perona-Malik Model with Diffusion Coefficient Depending on Fractional Gradient via Caputo-Fabrizio Derivative

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Gustavo Asumu Mboro Nchama ◽  
Angela Leon Mecias ◽  
Mariano Rodriguez Ricard
Keyword(s):  
Image Processing ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Absolute Error ◽  
Natural Images ◽  
Mean Square ◽  
Signal To Noise ◽  
Major Drawback ◽  
Diffusivity Function

The Perona-Malik (PM) model is used successfully in image processing to eliminate noise while preserving edges; however, this model has a major drawback: it tends to make the image look blocky. This work proposes to modify the PM model by introducing the Caputo-Fabrizio fractional gradient inside the diffusivity function. Experiments with natural images show that our model can suppress efficiently the blocky effect. Also, our model has good performance in visual quality, high peak signal-to-noise ratio (PSNR), and lower value of mean absolute error (MAE) and mean square error (MSE).

scholarly journals A COMPARATIVE STUDY OF IMAGE FILTERING ON VARIOUS NOISY PIXELS

2013 ◽  
pp. 157-165
Author(s):  
SONALI R. MAHAKALE ◽  
NILESHSINGH V. THAKUR
Keyword(s):  
Comparative Study ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Research Work ◽  
Absolute Error ◽  
Image Filtering ◽  
Squared Error ◽  
The Comparative Study ◽  
Work Done

This paper deals with the comparative study of research work done in the field of Image Filtering. Different noises can affect the image in different ways. Although various solutions are available for denoising them, a detail study of the research is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues. An output image should be judged on the basis of Image Quality Metrics for ex-: Peak-Signal-to-Noise ratio (PSNR), Mean Squared Error (MSE) and Mean Absolute Error (MAE) and Execution Time.

scholarly journals Machine learning meets pKa

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 113 ◽  
Author(s):  
Marcel Baltruschat ◽  
Paul Czodrowski
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
External Validation ◽  
Absolute Error ◽  
Source Model ◽  
Squared Error ◽  
Fold Cross Validation ◽  
Better Than

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

scholarly journals Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4817 ◽  
Author(s):  
Quan Liu ◽  
Li Ma ◽  
Shou-Zen Fan ◽  
Maysam F. Abbod ◽  
Jiann-Shing Shieh
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Sample Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Eeg Signals ◽  
Depth Of Anaesthesia ◽  
Different Levels

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients’ anaesthetic level during surgeries.

scholarly journals Statistical Models in Estimating Air Temperature in a Mountainous Region of Greece

2017 ◽  
Vol 12 (3) ◽  
pp. 544-549 ◽  
Author(s):  
Stelios Maniatis ◽  
Kostas Chronopoulos ◽  
Aristidis Matsoukis ◽  
Athanasios Kamoutsis
Keyword(s):  
Air Temperature ◽  
Statistical Models ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Multi Layer Perceptron ◽  
Simple Linear Regression ◽  
The Mean ◽  
Of Greece ◽  
Better Than

The current work focuses on the estimation of air temperature (T) conditions in two high altitude (alt) sites (1580 m), each one at different orientation (southeast and northwest) in the mountain (Mt) Aenos in the island of Cephalonia, Greece, by using two well-known statistical models, simple linear regression (SLR) and multi-layer perceptron ( MLP), one of the most commonly used artificial neural networks. More specifically, the estimation of mean, maximum and minimum T in high alt sites was based on the respective T data of two lower alt sites (1100 m), the first at southeast and the second at northwest orientations, and was carried out separately for each orientation. The performance of both SLR and MLP models was evaluated by the coefficient of determination (R2) and the Mean Absolute Error (MAE). Results showed that the examined models (SLR and MLP) provided very satisfactory results with regard to the estimation of mean, maximum and minimum T, regarding southeast orientation (R2 ranging from 0.96 to 0.98), with mean T estimation being relatively better, as confirmed by the lowest MAE (0.83). Regarding northwest orientation, T estimation was less accurate (lower R2 and higher MAE), compared to the respective estimation of southeast orientation, but, the results were considered adequate (R2 and MAE ranging from 0.88 to 0.92 and 1.00 to 1.40, respectively). In general, the estimations of the mean T were better than those of the extreme ones (minimum and maximum T). In addition, better results (higher R2 and lower, in general, MAE) were obtained when T estimations were based on T data derived from sites located at areas with similar surroundings, as in the case of dense and tall vegetation of the sites at southeast orientation, irrespective of applied method.

SVR Model for Prediction of Incidence Influenza Based on Automated Method

2014 ◽  
Vol 926-930 ◽  
pp. 1159-1163
Author(s):  
Jia Song
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Public Health Issue ◽  
Support Vector ◽  
Automated Method ◽  
Significant Public Health ◽  
The Mean ◽  
Single Support Vector Machine ◽  
Absolute Percent Error ◽  
Better Than

As is a significant public health issue to predict the incidence of influenza, this paper present a supported vector regression (SVR) model based on an automated method which worked as the following steps: firstly, the automated method is used to select the texts which highly related to the influenza, and then the SVR algorithm will find out the nonlinear between each context. According to the result, when assessing by the root mean squared predict error, the mean absolute error and the mean absolute percent error of the whole system, the SVR performed much better than single support vector machine regression prediction. Also, the validity of this method is verified.

scholarly journals Estimating Inside Air Temperature of a Glasshouse Using Statistical Models

2017 ◽  
Vol 12 (1) ◽  
pp. 01-05 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Konstantinos Chronopoulos
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Predictor Variable ◽  
Absolute Error ◽  
Meteorological Station ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Better Than

The efficiency of applying linear regression (LR) and artificial neural network (ANN) models to estimate inside air temperature (T) of a glasshouse (37o48΄20΄΄N, 23o57΄48΄΄E), Lavreotiki, was investigated in the present work. The T data from an urban meteorological station (MS) at 37058΄55΄΄N, 23o32΄14΄΄E, Athens, Attica, Greece, about 30 Km away from the glasshouse, were used as predictor variable, taking into account the actual time of measurement (ATM) and two hours earlier (ATM-2), depending on the case. Air temperature data were monitored in each examined area (glasshouse and MS) for four successive months (July-October) and averages on a two-hour basis were used for the aforementioned estimation. Results showed that ANN were better than LR models, considering their better performance as shown in the scatterplots of the distribution of observed versus estimated inside T data of the glasshouse, in terms of both higher coefficient of determination (R2) and lower mean absolute error (MAE). The best ANN model (highest R2 and lowest MAE) was achieved by using as predictor variables the T at ATM and the T at ATM-2 from MS. The findings of our study may be a first step towards the estimation of inside T of a glasshouse in Greece, from outside T data of a remote MS. Thus, the operation of the glasshouse could be improved noticeably.

scholarly journals Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fatemeh Sayyahi ◽  
Saeed Farzin ◽  
Hojat Karami
Keyword(s):  
Multilayer Perceptron ◽  
Water Wave ◽  
Reference Evapotranspiration ◽  
Mean Absolute Error ◽  
Principal Component ◽  
Absolute Error ◽  
Swarm Optimization ◽  
The Mean ◽  
Better Than ◽  
Computing Models

The aim of this study is to evaluate the ability of soft computing models including multilayer perceptron- (MLP-) water wave optimization (MLP-WWO), MLP-particle swarm optimization (MLP-PSO), and MLP-genetic algorithm (MLP-GA), to simulate the daily and monthly reference evapotranspiration (ET) at the Aidoghmoush basin (Iran). Principal component analysis (PCA) was used to find the best input combination including the lagged ETs. According to the results, the ET values with 1, 2, and 3 (days) lags as well as those with 1, 2, and 3 (months) lags were the most effective variables in the formation of the PCs. The total variance proportion of inputs and eigenvalues was used to identify the most important variables. The accuracy of the models was assessed based on multiple statistical indices such as the mean absolute error (MAE), Nash–Sutcliff efficiency (NSE), and percent bias (PBIAS). The results showed that the performance of hybrid MLP models was better than that of the standalone MLP. The findings confirmed that the MLP-WWO could precisely predict ET.

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