FORWARD ADAPTIVE SPEECH CODING WITH LOW BIT RATES AND VARIABLE WORD LENGTH

In this paper, two scalar quantizers for the memoryless Laplacian source with low number of levels are designed and discussed. The nonuniform quantizer is designed according to the Lloyd-Max’s algorithm since it can provide an optimal performance in the minimum distortion sense. Two variants of the uniform dead-zone quantizer are designed according to the criterion of minimal distortion and the simultaneous criterion of minimal distortion and minimal bit rate. Joint design of quantizer and Huffman encoder is considered in all proposed solutions. In addition, forward adaptation of the observed quantizers is performed on frame-by-frame basis. The best performance from the point of practical implementation is obtained using a uniform dead-zone quantizer that satisfies the criterion of minimal distortion and minimal bit rate at the same time. Moreover, the theoretical results are verified via the experimental results obtained on a real speech signal

Sustainable development management seen through the prism of natural capital preservation imperative

After explaining the content coverage and elementary explanation of the basic dimensions of the paradigm of sustainable development, the concept of sustainability is considered in the light of preserving the total amount of capital in the production process, assuming no technological change and population growth. The research task set in this way brought to the fore the extremely complex question of the substitutability of produced and natural capital (stocks of natural resources and carrying capacity of the environment). The answer to it is directly related to the concept of weak and strong sustainability. The concept of poor sustainability allows substitutability between produced and natural capital, provided that the total amount of available capital does not decrease. On the contrary, the concept of strong sustainability implies a special observation of produced and natural capital. It practically eliminates the possibility of replacing one form of capital with another in the production process and from the point of view of the development economy is the only acceptable option in the long run. If unlimited substitution between natural and produced capital is allowed, then natural resources will eventually be depleted due to the creation of produced capital

SWITCHED UNIFORM SCALAR QUANTIZATION ADAPTED TO MEAN AND VARIANCE FOR SPEECH CODING

Average power and variance are widely used in adaptation techniques in signal coding. A speech signal is usually assumed to be zero-mean; thus an average signal power is equal to the signal variance. However, this assumption is valid only for longer signals with a large number of samples. When the signal is divided into frames (especially if the number of samples within the frame is small) the speech signal within the frame may not be zero-mean. Hence, frame-by-frame adaptation to signal mean might be beneficial. A switched uniform scalar quantizer with adaptation to signal mean and variance is proposed in this paper. The analysis is performed for different frame lengths and the results are compared to an adaptive uniform quantizer that uses adaptation only to average signal power, showing an improved performance. Signal to quantization noise ratio (SQNR) is used as a performance measure

Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

U ovom radu istraženo je sedam najnovijih postupaka strojnog učenja za procjenu troškova izgradnje armiranobetonskih i prednapetih betonskih mostova, uključujući umjetne neuronske mreže (ANN) i ansamble ANN, ansamble regresijskih stabala (eng. random forests, boosted and bagged regresijska stabla), metodu potpornih vektora za regresiju (SVR) i Gausov regresijski proces (GPR). Stvorena je i baza podataka o troškovima izgradnje i projektnim karakteristikama za 181 armiranobetonski i prednapeti betonski most za treniranje i ocjenu modela.Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation

HARNESSING CLOUD COMPUTING INFRASTRUCTURE FOR E-LEARNING SERVICES

This paper introduces an innovative model for harnessing cloud computing infrastructure within an e-learning ecosystem. The main goal was to design a scalable, reliable and secure IT environment that provides a plethora of e-learning services and seamless integration of the heterogeneous e-learning components through IaaS, PaaS and SaaS cloud service models. The e-learning services are tailored to foster courses for IT engineers in the areas of mobile technologies, social computing, Internet of Things and big data. The model was implemented and evaluated in the e-learning ecosystem of the E-business Lab, University of Belgrade

Variant rs745430558 in the SMAD4 gene promoter as a biomarker for adenocarcinoma of the pancreas

Background: Our previous study has identified variant rs745430558 in the SMAD4 gene promoter as potential biomarker for adenocarcinoma of the pancreas. The allele delTT (10T instead of 12T) was present in malignant pancreatic tissue with a prevalence of 88%. As analysis of cfDNA in liquid biopsy represents a noninvasive approach for the diagnosis and monitoring of malignancies, the aim of this study was to determine the presence of 12T and 10T alleles in the peripheral blood of patients with suspected pancreatic malignancy. Material and Methods: The study was performed using cell-free DNA (cfDNA) isolated from the serum of 15 patients with morphological alterations of the pancreas. The presence of 12T and 10T alleles was assessed by allele specific quantitative real-time PCR. Results: Of 15 analyzed samples, 13 were diagnosed with adenocarcinoma of the pancreas (AcP), 1 with neuroendocrine tumor (NET), and 1 with pancreatitis. The 10T allele was present in 84.7% of cases with AcP and also in the sample from the patient with NET. In patient with pancreatitis only the 12T allele was detected. Conclusion: Our research has shown that the results of liquid biopsy of patients with AcP are in agreement with tissue specimens analysis. Targeted detection of the rs745430558 10T variant in patients with suspected pancreatic malignancies could be a potential biomarker for diagnosis of AcP in the future

A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case

Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by high-fidelity ones (LES). The low-fidelity model can efficiently estimate wind pressures over a large range of wind directions, but it generally lacks accuracy. On the other hand, the high-fidelity model generally exhibits satisfactory accuracy, yet, the high computational cost can limit the number of approaching wind angles that can be considered. In order to take advantage of the main benefits of these two CFD approaches, a multi-fidelity machine learning framework is investigated that aims to ensure the simulation accuracy while maintaining the computational efficiency. The study shows that the accurate prediction of distributions of mean and rms pressure over a high-rise building for the entire wind rose can be obtained by utilizing only 3 LES-related wind directions. The artificial neural network is shown to perform best among considered machine learning models. Moreover, hyperparameter optimization significantly improves the model predictions, increasing the ��2 value in the case of rms pressure by 60%. Dominant and ineffective features are determined that provide a route to solve a similar application more effectively

Removal of crude oil from water environment – comparison between biochars and microbial cells

Cilj ispitivanja bio je da se uporedi efikasnost uklanjanja sirove nafte iz kontaminirane vodene sredine nakon mesec dana upotrebom biougljeva, biougljeva sa imobilizovanim mikrobnim ćelijama i samih mikrobnih ćelija

Promene linearnih i nelinearnih mera nizova RR i QT intervala posle uzimanja piva

BACKGROUND: /Aim. There are only several studies on the acute effect of alcoholic drinks intake on heart rhythm and this phenomenon is still not well understood. We wanted to examine whether linear and nonlinear measures of RR interval and QT interval series could quantify the effect of beer in healthy subjects. Methods. Eighteen young volunteers drank 500 mL of beer (21 g of ethanol). Electrocardiogram (ECG) recordings were taken in supine position: 20 minutes before (relaxation) and 60 minutes after drink intake. The RR interval series and the QT interval series were extracted from ECG and we calculated short-term (α1) and long-term (α2) scaling exponents and sample entropy (SampEn) for both series; low frequency (LF) and high frequency (HF) spectral components from RR interval series and QT variability (QTV). Blood pressure was measured every 10 minutes. Results. It was shown that beer induced changes in variability and correlation properties of these series. Immediate effect of beer intake was detected as a transient increase in the QT variability, heart rate and blood pressure. Delayed effects of beer were shortening of the RR and QT intervals and reduction of the HF spectral component. Beer intake also increased short-term scaling exponent (α1) of the RR time series and long-term scaling exponent (α2) of the QT time series. Conclusion. Our results suggest that acute effects of beer are reduced parasympathetic control of the heart and changed dynamic complexity of the ventricular repolarization.Uvod/Cilj. Akutni efekat uzimanja alkoholnih pića na kardiovaskularne ritmove je fenomen koji još uvek nije dovoljno razjašnjen i u literaturi postoji svega nekoliko radova na tu temu. Cilj rada je bio da se ispita da li se linearnim i nelinearnim merama nizova RR i QT intervala može kvantifikovati akutni efekat male količine piva kod zdravih osoba. Metode. Osamnaest mladih zdravih muškaraca je pilo po 500 mL piva (21 g etanola). Elektrokardiogram (EKG) je beležen u ležećem položaju: 20 minuta pre (u relaksaciji) i 60 minuta neposredno posle uzimanja pića. Iz digitalizovanog zapisa EKG-a izdvojeni su nizovi RR i QT intervala. Iz oba niza smo izračunali kratkodometni (α1) i dugo-dometni skalirajući eksponent (α2), kao i entropiju uzorka (SampEn). Iz nizova RR intervala određene su spektralne komponente niskofrekventnih (LF) i visokofrekventnih (HF) opsega, a iz nizova QT intervala varijabilnost QT intervala (QTV). Krvni pritisak je bio meren svakih 10 minuta. Rezultati. Pokazali smo da pivo menja varijabilnost i korelacione osobine ovih nizova. Neposredni efekat uzimanja piva ogleda se u prolaznim povećanjima QT varijabilnosti, srčane frekvence i krvnog pritiska, a produženi u skraćenju dužine RR i QT intervala i smanjenju spektralne komponente HF. Uzimanje piva je takođe dovelo do porasta kratkodometnog skalirajućeg eksponenta (α1) RR niza i dugodometnog skalirajućeg eksponenta (α2) QT niza. Zaključak. Akutni efekat uzimanja piva je redukovana parasimpatička kontrola srca i izmenjena kompleksnost dinamike ventrikularne repolarizacije