Electrocardiogram (ECG) is an important physiological signal for cardiac disease diagnosis. With the increasing use of modern electrocardiogram monitoring devices that generate vast amount of data requiring huge storage capacity. In order to decrease storage costs or make ECG signals suitable and ready for transmission through common communication channels, the ECG data
volume must be reduced. So an effective data compression method is required. This paper presents an efficient technique for the compression of ECG signals. In this technique, different transforms have been used to compress the ECG signals. At first, a 1-D ECG data was segmented and aligned to a 2-D data array, then 2-D mixed transform was implemented to compress the ECG data in the 2-
D form. The compression algorithms were implemented and tested using multiwavelet, wavelet and slantlet transforms to form the proposed method based on mixed transforms. Then vector quantization technique was employed to extract the mixed transform coefficients. Some selected records from MIT/BIH arrhythmia database were tested contrastively and the performance of the
proposed methods was analyzed and evaluated using MATLAB package. Simulation results showed that the proposed methods gave a high compression ratio (CR) for the ECG signals comparing with other available methods. For example, the compression of one record (record 100) yielded CR of 24.4 associated with percent root mean square difference (PRD) of 2.56% was achieved.
Microalgae widely used for bioremediation of inorganic (NH4and PO4) nutrients as single isolates. This study aims to use a mix microalgae isolated from Dijlah river for bioremediation of municipal waste water .The experiments had been start with two different concentrations of nutrients. NH4-N values were 23.4 and 31.7mg/l, phosphorus PO4-P 6.7and 10.7 mg/ l and BOD5 were 66 and 83 mg/l. both cycles of treatment showed complete removal for NH 4-N and PO4-P levels after treatment were 1.7 and 0.7 mg/l for both of treatment cycle .Biological oxygen demand reduced to lowest value were18.9 in 2nd cycle while in 1st cycle of treatment reduced to21.3mg/l.The results showed that mix microalgae have the ability to remove nutrients and organic po
... Show MoreThis paper is concerned with introducing and studying the o-space by using out degree system (resp. i-space by using in degree system) which are the core concept in this paper. In addition, the m-lower approximations, the m-upper approximations and ospace and i-space. Furthermore, we introduce near supraopen (near supraclosed) d. g.'s. Finally, the supra-lower approximation, supraupper approximation, supra-accuracy are defined and some of its properties are investigated.
New class A^* (a,c,k,β,α,γ,μ) is introduced of meromorphic univalent functions with positive coefficient f(z)=□(1/z)+∑_(n=1)^∞▒〖a_n z^n 〗,(a_n≥0,z∈U^*,∀ n∈ N={1,2,3,…}) defined by the integral operator in the punctured unit disc U^*={z∈C∶0<|z|<1}, satisfying |(z^2 (I^k (L^* (a,c)f(z)))^''+2z(I^k (L^* (a,c)f(z)))^')/(βz(I^k (L^* (a,c)f(z)))^''-α(1+γ)z(I^k (L^* (a,c)f(z)))^' )|<μ,(0<μ≤1,0≤α,γ<1,0<β≤1/2 ,k=1,2,3,… ) . Several properties were studied like coefficient estimates, convex set and weighted mean.
Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG sig
... Show MoreIn this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo
... Show MoreCognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper ,
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