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Apples of Red Chief variety after stabilization at room temperature (20±2 °C) were initially washed with chlorinated water (100 ppm sodium hypochlorite) to prevent surface contamination. After manual peeling, apples were cut in to disc shaped slices of 2 and 3 mm thickness (having uniform diameter of 20 mm) and treated with 1% ascorbic acid and 1% citric acid (anti browning agents) for studying their effect on drying time and quality of apple slices. Slice size and pre-drying treatments of ascorbic acid and citric acid has resulted in significant (P<0.05) variation for drying time, rehydration, dry matter contents, firmness, quality and total colour change. Significantly minimum time (300 minutes) for drying of apple slices, maximum rehydration ratio (4.9), and maximum firmness (11.9 RI) was recorded in case of 2 mm slices treated with 1% ascorbic acid and 1% citric acid. Maximum TSS (18.9° Brix) was recorded in 2 mm slices and 3 mm slices (18.7° Brix) treated with citric acid and ascorbic acid. Ascorbic acid and citric acid were effective to stabilize the ascorbic acid content and maximum (18.0 mg/100g) was recorded in case of slices of 2 mm thickness treated with 1 % ascorbic acid and 1% citric acid. Similarly size of slices and anti browning agents were significantly effective to reduce the total colour change in apple slices and minimum colour change was observed in 2 mm slices (10.0) treated with 1 % ascorbic acid and citric acid and 3 mm slices (12.25) treated with 1% ascorbic acid and 1% citric acid. It can be concluded that apple slices of 2 mm thickness and pre drying treated with 1% citric acid and 1% ascorbic acid as anti browning agents took minimum time for dehydration with minimum changes in colour, firmness, quality, and retained maximum compositional attributes with minimum browning. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) plays an imperative role in heart disease diagnostics, Arrhythmia is a cardiological disorder with prevalence of the world's population..The purpose of this research to discusses the electrocardiogram analysis because this problems concerning health issue which encourage the present research. The main objective of our research is to analyze the acquired ECG signals using signal processing tools such as wavelet transform and Neuro classifier, classify them.. Total 62 ECG data subjects were analyzed. These data were grouped in two classes i.e, Normal class, and Arrhythmia class respectively. In order to achieve this we have applied a back-propagation based neural network classifier. DWT coefficients are used to extract the relevant information(statistical feature) from the ECG input data which are Energy, Variance, power Spectrul Density, Mean and Standard Deviation and morphological feature from the ECG input data which are mean and standard deviation of Magnitude of P,Q,R,S,T peak & PQ, QR,RS,ST,PR,QRS interval & RR interval. Then the extracted features data is analyzed and classified using Adaptive Neuro System (ANNS) as a Neuro classifier. The proposed algorithm is implemented and also tested in MATLAB software. The ECG signal are being selected and tested from PhysioNet Database using MIT-BIH Arrhythmia Database, and & Normal Sinus Rhythm (NSR) database. 14 subjects from Normal set and 48 subjects from arrhythmia set were analyzed for feature extraction and classification and data were divided in training, testing and validation of proposed algorithm. The ANNS system successfully classifies the Normal, Arrhythmia signal with the rate of overall accuracy is 98.4%. The analysis system also can achieved the sensitivity up to 100% for normal class and 97.9% for arrhythmia class, respectively for each class tested [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder with prevalence of about 1-2% of the world's population. The hallmark of epilepsy is recurrent seizures termed "epileptic seizures". Human Brain is the most complex organ among all the systems in the human body, also the most remarkable one. It exhibits rich spatiotemporal dynamics. Electroencephalography (EEG) signal is the recording of spontaneous electrical activity of the brain over a small period of time . The term EEG refers that the brain activity emits the signal from head and being drawn. It is produced by bombardment of neurons within the brain. EEG signal provides valuable information of the brain function and neurobiological disorders as it provides a visual display of the recorded waveform and allows computer aided signal processing techniques to characterize them. This gives a prime motivation to apply the advanced digital signal processing techniques for analysis of EEG signals. The main objective of our research is to analyze the acquired EEG signals using signal processing tools such as wavelet transform and classify them into different classes. The features from the EEG are extracted using statistical analysis of parameters obtained by wavelet transform. After feature extraction secondary goal is to improve the accuracy of classification. Total 300 EEG data subjects were analyzed. These data were grouped in three classes' i.e, Normal patient class, Epileptic patient class and epileptic patient during non-seizure zone respectively. In order to achieve this we have applied a backpropgation based neural network classifier. After feature extraction secondary goal is to improve the accuracy of classification. 100 subjects from each set were analysed for feature extraction and classification and data were divided in training, testing and validation of proposed algorithm. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological condition in which is due to chronic abnormal bursts of electrical discharge in the brain. Monitoring brain activity through the electroencephalogram (EEG) has become an important tool in the diagnosis of epilepsy. The EEG recordings of patients suffering from epilepsy show two categories of abnormal activity: inter-ictal, abnormal signals recorded between epileptic seizures; and ictal, the activity recorded during an epileptic seizure. The EEG signature of an inter-ictal activity is occasional transient waveforms, as isolated spikes, spike trains, sharp waves or spike-wave complexes. EEG signature of an epileptic seizure (ictal period) is composed of a continuous discharge of polymorphic waveforms of variable amplitude and frequency, spike and sharp wave complexes, rhythmic hyper synchrony, or electro cerebral inactivity observed over a duration longer than the average duration of these abnormalities during inter-ictal periods. Generally, the detection of epilepsy can be achieved by visual scanning of EEG recordings for inter-ictal and ictal activities by an experienced neurophysiologist. However, visual review of the vast amount of EEG data has serious drawbacks. Visual inspection is very time consuming and inefficient, especially in the case of long-term recordings. In addition, disagreement among the neurophysiologists on the same recording is possible due to the subjective nature of the analysis and due to the variety of inter-ictal spikes morphology. The main objective of our research is to analyze the acquired EEG signals using auto-regressive features and classify them into different classes. After feature extraction secondary goal is to improve the accuracy of classification. In order to achieve this we have applied a backpropgation based neural network classifier.100 subjects from each set were analysed for feature extraction and classification and data were divided in training, testing and validation of proposed algorithm. [ABSTRACT FROM AUTHOR]

Twenty genotypes of Cape gooseberry were evaluated at the experimental field of Central Institute of Temperate Horticulture, Srinagar, for its variability, their interrelationship and diversity pattern based on pomological and chemical traits during 2009 to 2011 by using multivariate analysis. All the genotypes were grouped into four different clusters. Cluster III had the maximum (10) and cluster II had the minimum (2) genotypes. The highest inter cluster distance was observed between I and III. The lowest inter cluster distance was found between the cluster I and IV. Based on cluster mean value, the cluster II was found important for fruits per plant, fruit diameter, low acidity and fruit yield per plant. Cluster I for fruit weight and fruit length. Cluster III for TSS and ascorbic acid and cluster IV for juice content. The parents could be selected accordingly for hybridization programme. Pomological and chemical traits data were also subjected to principal component analysis and the first two principal components axes accounted for 59.04% of the variance among the 20 genotypes. The greater part of variance was accounted for other traits such as fruit yield, fruit diameter, fruit length and fruit weight. The high diversity in the collection showed its great potential for improving horticultural traits in cape gooseberry. [ABSTRACT FROM AUTHOR]

The influence of different floral preservatives were assessed to determine their effect on the water relations and vase life of cut tulip cv. Yellow Purissima. Uniform size scapes of tulip at bud colour break stage were kept in ten different treatments of floral preservatives comprised of sucrose-(2,4 and 6%), aluminium sulphate (100,200 and 300 ppm) and 8-HQS (100,200 and 300 ppm) along with control (distilled water). All the preservatives improved water relations and vase life of cut tulip significantly in comparison to control. The greatest cumulative water balance and maximum vase life were recorded in 8-HQS 300 ppm (10.5 g/scape and 10.1 days) followed by aluminium sulphate 300 ppm (9.67 g/scape and 8.9 days) over control (2.53 g/scape and 5.4 days), respectively. Maximum fresh weight change (10th day) was recorded in 8-HQS 300 ppm (105.13%) followed by aluminium sulphate 300 ppm (103.75%) in comparison to control (89.91%). The floral preservatives delayed the senescence of cut tulip by improving water uptake and post harvest physiology, thereby maintained better water balance leading to improved fresh weight and vase life. [ABSTRACT FROM AUTHOR]