A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology

Edge-centric multimodal authentication system using encrypted biometric templates

Data security, complete system control, and missed storage and computing opportunities in personal portable devices are some of the major limitations of the centralized cloud environment. Among these limitations, security is a prime concern due to potential unauthorized access to private data. Biometrics, in particular, is considered sensitive data, and its usage is subject to the privacy protection law. To address this issue, a multimodal authentication system using encrypted biometrics for the edge-centric cloud environment is proposed in this study. Personal portable devices are utilized for encrypting biometrics in the proposed system, which optimizes the use of resources and tackles another limitation of the cloud environment. Biometrics is encrypted using a new method. In the proposed system, the edges transmit the encrypted speech and face for processing in the cloud. The cloud then decrypts the biometrics and performs authentication to confirm the identity of an individual. The model for speech authentication is based on two types of features, namely, Mel-frequency cepstral coefficients and perceptual linear prediction coefficients. The model for face authentication is implemented by determining the eigenfaces. The final decision about the identity of a user is based on majority voting. Experimental results show that the new encryption method can reliably hide the identity of an individual and accurately decrypt the biometrics, which is vital for errorless authentication

Biometric Security Through Visual Encryption for Fog Edge Computing

Fog and mobile edge computing have gained considerable attention from the research and development community. The problems related to security and privacy of biometric content are simpler to solve through edge computing resulting in improved security and privacy of biometric and other critically private information. Zero-watermarking has been proposed as a solution to help protect the ownership of multimedia content that is easy to copy and distribute. Visual cryptography is another approach to secure data that is to be shared through generating multiple shares. This paper is concerned with developing a biometric security solution for face images, using visual cryptography and zero-watermarking, that does not adversely impact the visual quality of the image. The original face image is not modified through the zero-watermarking and visual encryption procedures and this in turn does not adversely impact the recognition rate

Development of Electromyography Signal Signature for Forearm Muscle

AbstractElectromyography (EMG) measures muscle response or electrical activity in response to a nerve's stimulation of the muscle. EMG is generally acquired through surface and needle or wire electrodes. The needle or wire electrodes are usually used by clinicians in a clinical setting. This paper concentrates on surface electromyography (sEMG) signal that is acquired in a research laboratory since sEMG is increasingly being recognized as the gold standard for the analysis of muscle activation. The sEMG can utilized for establishing signal signature for forearm muscles that becomes an important input in development of rehabilitative devices. This paper discusses the establishment of sEMG signal signature of female and male subjects for forearm muscles such as extensor carpi radialis, flexor carpi radialis, palmaris longus and pronator teres based on movements such as wrist extension and flexion, hand open and close, and forearm supination and pronation. This was achieved through the use of Butterworth Bessel, Elliptic and Chebyshev filters. The sEMG signal signature could be useful in the development of rehabilitation device of upper extremities

Characterization of regional hydrological drought using improved precipitation records under multi-auxiliary information

Drought is a complex natural hazard that has been recurrently occurred in many regions across the globe. Therefore, precise drought characterization and its regional monitoring are key challenges for advanced water management and hydrological research. In this research, we provided a novel method to improve annual average time series data for the Standardized Drought Index (SDI)-type drought monitoring tools. We proposed multi-auxiliary information-based estimation strategy that improves annual moving average/total precipitation time series records. Therefore, we incorporated a minimum and maximum temperature as auxiliary variables under multi-auxiliary regression estimator. In summary, this study propagates a new drought index named: the Precision-Weighted Standardized Precipitation Index (PWSDI). We evaluated the performance of PWSDI for 10 meteorological stations in Pakistan. We found that improved estimates of temporal precipitation time series are good candidates for modelling and monitoring hydrological drought at the regional settings under SDI procedure

A comparison of fluoroquinolones versus other antibiotics for treating enteric fever: meta-analysis

Objectives To review evidence supporting use of fluoroquinolones as first line agents over other antibiotics for treating typhoid and paratyphoid fever (enteric fever)

Molecular characterization of Pakistani wheat cultivars using random markers

The genetic diversity among fifteen varieties of wheat was studied by random amplified polymorphic DNA  (RAPD) analysis. By applying 20 RAPD primers, 182 fragments were amplified, out of which 118 were  polymorphic (64.84%). The number of fragments amplified per primer ranged from 10 to 24 with an average of  17 fragments per primer. Primer K-17 produced the maximum number of fragments (24) and all the fragments  were polymorphic. Range of polymorphism percentage was from as low as 0% (I to 15) to as high as 100%  (K-11). The number of fragments produced per wheat genotype varied from 36 to 56 with an average of 47.2  fragments per genotype. The variety Shahkar-95 produced maximum number of fragments (56). Cluster  analysis classified fifteen varieties of wheat into two main groups; three varieties were placed in group I and  the rest of the varieties were placed in group II. Second group (group II) was further divided into three  subgroups; IIA, IIB and IIC. The pair wise similarity values ranged from 54.88 to 82.93% and showed that  genotypes Kohinoor-83 and Pak-81 were the closest with highest similarity value (82.93%), while genotypes Kohinoor-83 and Kohistan-97 were most distinct with minimum similarity value (54.88%).Key words: Cultivar, polymorphism, random amplification of polymorphic deoxyribonucleic acid (RAPD), cluster analysis, genotype