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This work aims to analyze the complexity of surface electromyography (sEMG) signals under muscle fatigue conditions using Hjorth parameters and bubble entropy (BE). Signals are recorded from the biceps brachii muscle of 25 healthy males during dynamic and isometric contraction exercises. These signals are filtered and segmented into 10 equal parts. The first and tenth segments are considered as nonfatigue and fatigue conditions, respectively. Activity, mobility, complexity, and BE features are extracted from both segments and classified using support vector machine (SVM), Naïve bayes (NB), k-nearest neighbor (kNN), and random forest (RF). The results indicate a reduction in signal complexity during fatigue. The parameter activity is found to increase under fatigue for both dynamic and isometric contractions with mean values of 0.35 and 0.22, respectively. It is observed that mobility, complexity, and BE are lowest during fatigue for both contractions. Maximum accuracy of 95.00% is achieved with the kNN and Hjorth parameters for dynamic signals. It is also found that the reduction of signal complexity during fatigue is more significant in dynamic contractions. This study confirms that the extracted features are suitable for analyzing the complex nature of sEMG signals. Hence, the proposed approach can be used for analyzing the complex characteristics of sEMG signals under various myoneural conditions.

Cannabis is one of the commonly abused psychoactive recreational drugs, the popularity of which has exponentially increased in the recent times due to the notion that it has minimal side effects [1-3]. But of late, many studies have showed that chronic use of cannabis has cerebrovascular as well as cardio vascular complications [4,5]. As per the report of National Drug Dependence Treatment Centre (NDDTC) of the All India Institute of Medical Sciences (AIIMS), New Delhi, 2.83% of Indians aged 10–75 years (or 31 million people) were current users of cannabis products [6]. A 22-year-old, apparently healthy male student, staying alone in a rented apartment with history of chronic cannabis use was found in an unconscious state with multiple packets of cannabis (Ganja/ Marijuana). During the PME, about 250-300 grams of intraventricular hematoma was present in the lateral ventricles of the brain. Diffuse haemorrhage was present over bilateral cerebellar hemispheres. No antemortem injuries were present on the body

In this research, an attempt is made to understand the impact on strength characteristics of expansive soil treated with Road Building International Grade-81 (RBI-Grade-81) stabilizer. This stabilizer has a wide response spectrum range. A broad range of studies, such as Atterberg limit, Compaction, Unconfined compressive strength (UCS), and California bearing ratio (CBR) measures, are demonstrated on expansive soil and it is treated with percentages varying between 2 and 10% of RBI-Grade-81. Due to the creation of new cement materials, which is apparent in the SEM research micrographs, the improvement in intensity is achieved. The results suggest that considered stabiliser is effective in enhancing the engineering properties of expansive soil.

The black soils of Adilabad and Nizamabad districts of Northern Telangana zone most prominent for the cultivation of soybean. From this region of soils, sixty soil samples were collected and analysed for available sulphur and its fractions. The results indicated that available sulphur content ranged from 5.9 mg kg-1 to 52.6 mg kg-1 with a mean of 13.8 mg kg-1. The available sulphur content was low to medium in status. The extent of sulphur deficiency of soybean growing areas of Adilabad and Nizamabad are 50 and 43.4 per cent respectively. The water-soluble sulphur ranged from 5.4 mg kg-1 in rural samples of Nizamabad to 7.3 mg kg-1 in sonala soils of Adilabad district. Whereas KH2PO4 and Heat soluble sulphur content ranged from 19.2, 29.6 mg kg-1 in Armoor village to 28.7, 37.3 mg kg-1 in Sonala village respectively. The highest total sulphur content was recorded in soils of Adilabad district.

Veterinarians need knowledge of the estrus stage of the reproductive cycle to decide the exact mating time for canine breeding. Through vaginoscopy, a visual examination of the vaginal vault of female canines can be done and the mating period can also be determined. However, due to low light illumination in vaginoscopic examination, the captured images lack visual quality. This hampers accurate determination of the mating period. In this study, an attempt has been made to enhance vaginoscopic images of female canines recorded at the College of Veterinary & Animal Sciences, Mannuthy, Kerala, India. These captured images are subjected to singular value decomposition (SVD), with the aid of a color transfer algorithm in 1αβ cone space. SVD results in a singular value matrix that accommodates the majority of the intensity information of the input image. By scaling these singular values, the total intensity of the input image is improved. Further, color transfer in 1αβ cone space is carried out to get better enhanced images. These enhanced images are evaluated using various quality parameters such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy and mean. The proposed algorithm has an average MSE of 20.11, while PSNR, entropy and mean values are 35.65 dB, 7.16, and 104.15, respectively. The qualitative analysis shows that this algorithm can be used to enhance the veterinary vaginoscopic images intended for further processing. This algorithm facilitates veterinarians to clearly visualize the wrinkles and bloody discharge during oestrus cycle.

Muscle fatigue during isometric contraction of biceps brachii is analysed using synchrosqueezed continuous wavelet transform with Morlet wavelet and singular value decomposition (SVD) features. The recorded surface electromyography signals are decomposed to time frequency matrix using Morlet wavelet and the characteristics are extracted using singular value features such as maximum singular value and zero crossing frequency. The percentage difference in feature values for each segment with the progression of fatigue is calculated. Results show that the recorded signals are complex, non‐stationary, multicomponent, and random in nature. Maximum singular value represents the non‐stationarity of a signal, with an increasing trend towards the fatigue condition. Zero crossing frequency represents the complexity or randomness in the signals and it decreases with the progression of fatigue. It is found that both the features are statistically significant with p

Web Services has been enabled IT services and computing technology to perform business services more efficiently and effectively. REpresentational State Transfer (REST) is to be used for creating Web APIs/services. In the existing system, web service search engines for RESTful Web Services/Api’s provide Keyword, Tag and Semantic based search functions. One of the RESTful service discovery, referred as Test-oriented RESTful service discovery with Semantic Interface Compatibility (TASSIC) have been developed by the search of RESTful Service’s/Api’s. TASSIC approach will search the semantic characteristics of search and match interface terms in the service document. An inability to consider the classification and in finding the suitable Api’s or services are a key issue of the search space in Tassic. A new approach has proposed for reduction of the search space in restful service discovery to develop a k-Nearest Neighbor classification algorithm. it provide candidate services with ranking based on semantic similarity, and classifying of similar candidate services and service unit testing will be considered. This approach is meant for increasing search precision in the retrieval and quick search for classifying their RESTful services or Api according to user-defined criteria.

A combined experimental and numerical study was conducted to understand the effect of rotation of outer cylinder on heat transfer from a vertical heated inner cylinder. The dimensionless analysis carried out for this problem underlines that the non dimensional heat transfer depends on the rotation parameter, radius ratio and aspect ratio. Experiments were conducted for the rotational parameter ranging from 0 ≤ ζ ≤ 526 at different heat loads, keeping the aspect ratio and radius ratio unchanged. Numerical simulations have been performed (for a wide range of rotation parameter 0 ≤ ζ ≤ 526 and fixed radius ratio) using a commercial computational fluid dynamics package ANSYS CFX 14. The heat transfer was found to increase progressively with increasing rotational parameter until a certain value of rotational parameter was reached, thereafter a sharp decay in heat transfer resulting from destabilization of flow field was observed. Three laminar heat convection modes, namely; natural, mixed and forced convection dominant, have been observed during the increasing phase of Nusselt number with rotation parameter. The transport mechanism under different heat transfer regimes was explored and discussed with the aid of numerically simulated flow and thermal fields.

Neuromuscular disabilities are common in humans. The neuromuscular system includes all muscles in the body and nerves serving them. Neuromuscular disorders are that encompasses a large number of diseases with different presentation. These disorders results in muscle weakness and fatigue that progress over time. Neuromuscular clinical conditions such as fatigue, myopathy, myositis and neuropathy affect living organisms. Among these, muscle fatigue is one of the most frequently occurring. The diagnostic test includes Magnetic Resonance Imaging (MRI) scan of the brain and the spinal cord, Electromyography (EMG) to record the electrical activity of each muscle, muscle biopsy to examine a sample of muscle tissue under microscope. In this work, a method to analyze neuromuscular conditions using Surface Electromyography (sEMG) signal is proposed. It measures electrical current generated in muscles during its contraction representing neuromuscular conditions. It is a non-invasive technique used for diagnosis of different neuromuscular conditions. EMG signal is used for recording muscle activity using EKG sensor. EMG signal parameters like RMS value and mean frequency are used to indicate fatigue. The proposed project uses analysis of EMG signal using LabVIEW and MATLAB software, thus provides fatigue indication.

Synthetic signals that represent fatiguing contractions of biceps brachii muscle are generated in this work using a comprehensive mathematical model. These signals are the biomarkers of muscle electrical activity that could be recorded non-invasively on the skin surface using Surface Electromyography (sEMG). The important components of the adopted synthetic sEMG model are current source, volume conductor, motor unit recruitment, and firing behavior functions. For this study, the amplitude (A) and scaling factor (λ) of the current source function is selected appropriate to fatiguing conditions. Further, tunable Q-wavelet method is applied to compute the frequency range associated with fatigue in the synthetic signal. The resultant wavelet coefficients are obtained using multirate filter bank where the scaling factors α and β are chosen so as to meet the anticipated Q-factor and the ranges of frequency bands. The results show that synthetically generated signal is able to truly represent fatiguing and nonfatiguing conditions. The amplitude-based features of tunable Q-wavelet coefficients are able to identify the characteristic changes associated with varied fatiguing conditions. Model generated frequency responses in fatiguing conditions are in agreement with the experimental results reported elsewhere. As fatigue is a temporary failure of skeletal muscles to maintain a required force for the accomplishment of a particular task, the model proposed here could be used as a validation of sEMG measurements in health and disease.

Background: Craniopharyngioma is a tumour of the suprasellar region that histologically has two distinct variants with some differences in clinical behaviour. The papillary type is almost always seen in adults and has a more indolent course. We studied clinical and radiological characteristics, surgical resectability, clinical outcome and recurrence patterns in tertiary care center.Methods: The present study is a prospective study, comprising of 32 patients of craniopharyngiomas studies over a period of 3 years. All the patients were thoroughly evaluated for the symptoms and signs with particular stress on visual impairment and field defects.Results: There were 16 male and 16 female patients. The incidence of craniopharyngioma was observed more in the age group of 6-10 years. Fundoscopy examination revealed optic atrophy and papilloedema in (56.25%) eases. Endocrinological dysfunction were present in 31.25% cases. Appearance on computed tomography (CT) scan showed 31.25% as cystic and 68.75% was mixed respectively. Surgical approach to the tumor was through pterional approach in (75%) sub frontal in (18.75%) transphenoidal in 18.75%. There was 2 death, 10 patients were advised postoperative radiotherapy and follow up period was from 6 months to 2 years. There were 6 recurrences out of 32 patients who were under follow-up.Conclusions: Treatment should focus not only on long term control and survival, but also on the reduction of the disease and related morbidity and preservation of quality of life.

In this study, an attempt has been made to distinguish between nonfatigue and fatigue conditions in surface Electromyography (sEMG) signal using the time frequency distribution obtained from analytic Bump Continuous Wavelet Transform. For the analysis, sEMG signals from biceps brachii muscle of 22 healthy subjects are acquired during isometric contraction protocol. The signals acquired is preprocessed and partitioned into ten equal segments followed by the decomposition of selected segments using analytic Bump wavelets. Further, Singular Value Decomposition is applied to the time frequency distribution matrix and the maximum singular value and entropy feature for each segment are obtained. The usefulness of both the features is estimated using the Wilcoxon sign rank test that gives higher significance with a p < .00001. It is observed that the proposed method is capable of analyzing the fatigue regions in sEMG signals.

Human physical activities performed depend on the muscle fiber type proportions. In this study, an attempt is made to analyse muscle fiber type proportions from sEMG features. Surface electromyography (sEMG) signals are acquired using well defined exercise protocol. sEMG signals are recorded from adductor pollicis (type 1) and triceps brachii (type 2) muscles of five healthy subjects. After preprocessing, the signals are segmented using Hamming window of 250ms duration with 50% overlap. sEMG features such as root mean square(RMS), peak frequency and entropy are extracted. The features are analysed during fatigue and non fatigue conditions. Mean values of the sEMG features computed during muscle fatigue condition shows variation with muscle fiber types. Percentage variations of RMS (1.9%) and peak frequency (32.2%) are found higher for triceps brachii muscle. Entropy variation (0.9%) is found lesser for triceps brachii muscle. These features are found to be useful in analysing muscle fiber type proportion.

Muscle fatigue is a common experience for all age groups. In this work a model to fit the fatigue and non fatigue surface electromyography (sEMG) signals using sum of sines is proposed. Signals are recorded from Biceps Brachii muscle of five healthy volunteers until fatigue using a well defined protocol. The fatigue and non fatigue conditions are analysed separately by non linear dynamical model. The sum of sine model is selected for fitting the signals. The sin7 model is found to be the best non linear fit for non fatigue condition and sin8 for fatigue condition. The Root Mean Square Error (RMSE) of fatigue condition reduced by 4 from sin7 model to sin8 model. Also the fatigue signal tends to be periodic than non fatigue signal. This method may be further extended to the non linear analysis of muscular disorders.

As imaging technology advances, one of the unavoidable part of image processing is the image enhancement. Image enhancement is to improve the visual quality for specific applications. The proposed algorithm enhances the visual quality of the vaginoscopic images of bitches using Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is decomposed into four frequency bands by using DWT. The low frequency band of the transformed image is again transformed by DCT into low frequency DCT coefficients and higher DCT coefficients. New low frequency band is formed by SVD. Inverse Discrete Cosine Transform (IDCT) is applied to produce the equalized image. Inverse Discrete Wavelet Transform (IDWT) of new low frequency band and the other three frequency band are combined to give the enhanced image. Different quality measures such as Root Mean Square Error (RMSE), Peak to Signal Noise Ratio (PSNR), Entropy, mean and Standard deviation are used to evaluate the performance of the model. The proposed model is implemented using MATLAB 2017a and obtained an average RMSE of 1.645. PSNR, Entropy, mean and standard deviation values for the proposed model are 44.12dB, 6.57, 103.35, 0.11 respectively. This qualitative analysis shows that proposed method seems to be useful for enhancing veterinary images for further processing.