Non-linear prediction model for egg production of quails the tropics with methionine supplementation

This study aimed to predict the egg production of quails receiving methionine supplementation. Two hundred and four quails were divided into two treatment diets, and six replicates with 17 quails each. The treatment diets were control (P0) and 0.12% methionine supplementation (P1). Egg production data were collected for eleven weeks, and a T-test was performed. Next, the data was plotted to get the actual egg production curve. We used a logistic regression model to predict the egg production pattern and calculated the model’s fitness with the coefficient of determination (R2). The results showed that methionine supplementation increased egg production by 9.43% (p<0.01). Based on the actual production curve, the increase in initial production to peak production of P1 was slower than P0, but P1 had a higher egg production than P0. The logistic model predicts that peak production of P1 was higher than P0 (62.74% vs. 56.79%), although the production rate of P1 was lower than P0 (0.21 vs. 0.36). In addition, the accuracy of both P0 and P1 models was 0.88 and 0.92, respectively. Thus, the logistic model can predict quail egg production in the tropics due to diet modification with high accuracy.

Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

Spatiotemporal processing of broadband signals based on the linear prediction model

The main developments in the field of radar surveillance systems are aimed at studying their functioning, taking into account the influence of various factors, such as the effect of interference. The most important thing seems to be the detection of a broadband signal, which makes it possible to increase the range and speed resolution. This raises the problem of suppressing broadband interference with existing methods. The paper develops methods for processing broadband signals in the presence of active interference as applied to the use in systems with various variants of spatio-temporal antenna elements, in particular on the example of linear antenna arrays. The approach is based on the representation of signals and interference recorded by a digital antenna array in the form of multidimensional spatiotemporal processes, i.e. functions of spatial and temporal coordinates. This is due to both the spatial distribution of the antenna array elements and the spatial distribution of interference. Bayesian signal detector is the optimal algorithm and has the best characteristics, but at the same time its practical implementation is extremely difficult, carried out in the field of spatiotemporal coordinates. The investigated processing algorithms are based on the linear prediction model, i.e. by using the model of a Markov random process to describe interference on spatially distributed antenna elements. Particular attention is paid to the development of algorithms that can be implemented with limited computing resources and work in real time, which is a problem of statistical methods of signal processing.

Monitoring Carbon Stock and Land-Use Change in 5000-Year-Old Juniper Forest Stand of Ziarat, Balochistan, through a Synergistic Approach

The Juniper forest reserve of Ziarat is one of the biggest Juniperus forests in the world. This study assessed the land-use changes and carbon stock of Ziarat. Different types of carbon pools were quantified in terms of storage in the study area in tons/ha i.e., above ground, soil, shrubs and litter. The Juniper species of this forest is putatively called Juniperus excelsa Beiberstein. To estimate above-ground biomass, different allometric equations were applied. Average above ground carbon stock of the forest was estimated as 8.34 ton/ha, 7.79 ton/ha and 8.4 ton/ha using each equation. Average carbon stock in soil, shrubs and litter was calculated as 24.35 ton/ha, 0.05 ton/ha and 1.52 ton/ha, respectively. Based on our results, soil carbon stock in the Juniper forest of Ziarat came out to be higher than the living biomass. Furthermore, the spatio-temporal classified maps for Ziarat showed that forest area has significantly decreased, while agricultural and barren lands increased from 1988 to 2018. This was supported by the fact that estimated carbon stock also showed a decreasing pattern between the evaluation periods of 1988 to 2018. Furthermore, the trend for land use and carbon stock was estimated post 2018 using a linear prediction model. The results corroborate the assumption that under a business as usual scenario, it is highly likely that the Juniperus forest will severely decline.

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

We write the mutual information between an input speech utterance and its reconstruction by a code-excited linear prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short-term predictor, the adaptive codebook, and the fixed codebook. We then show that a recently introduced quantity, the log ratio of entropy powers, can be used to estimate these mutual informations in terms of bits/sample. A key result is that for many common distributions and for Gaussian autoregressive processes, the entropy powers in the ratio can be replaced by the corresponding minimum mean squared errors. We provide examples of estimating CELP codec performance using the new results and compare these to the performance of the adaptive multirate (AMR) codec and other CELP codecs. Similar to rate distortion theory, this method only needs the input source model and the appropriate distortion measure.

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

We write the mutual information between an input speech utterance and its reconstruction by a Code-Excited Linear Prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short term predictor, the adaptive codebook, and the fixed codebook. We then show that a recently introduced quantity, the log ratio of entropy powers, can be used to estimate these mutual informations in terms of bits/sample. A key result is that for many common distributions and for Gaussian autoregressive processes, the entropy powers in the ratio can be replaced by the corresponding minimum mean squared errors. We provide examples of estimating CELP codec performance using the new results and compare to the performance of the AMR codec and other CELP codecs. Similar to rate distortion theory, this method only needs the input source model and the appropriate distortion measure.

Fusion of High-Dynamic and Low-Drift Sensors Using Kalman Filters

In practice, a high-dynamic vibration sensor is often plagued by the problem of drift, which is caused by thermal effects. Conversely, low-drift sensors typically have a limited sample rate range. This paper presents a system combining different types of sensors to address general drift problems that occur in measurements for high-dynamic vibration signals. In this paper, the hardware structure and algorithms for fusing high-dynamic and low-drift sensors are described. The algorithms include a drift state estimation and a Kalman filter based on a linear prediction model. Key issues such as the dimension of the drift state vector, the order of the linear prediction model, are analyzed in the design of algorithm. The performance of the algorithm is illustrated by a simulation example and experiments. The simulation and experimental results show that the drift can be removed while the high-dynamic measuring ability is retained. A high-dynamic vibration measuring system with the frequency range starting from 0 Hz is achieved. Meanwhile, measurement noise was improved 9.3 dB through using the linear-prediction-based Kalman filter.