In this study, the authors propose a multimodal biometric system by combining the finger knuckle and finger vein images at feature-level fusion using fractional firefly (FFF) optimisation. Biometric characteristics, like finger knuckle and finger vein are unique and secure. Initially, the features are extracted from the finger knuckle and finger vein images using repeated line tracking method. Then, a newly developed method of feature-level fusion using FFF optimisation is used. This method is utilised to find out the optimal weight score to fuse the extracted feature sets of finger knuckle and finger vein images. Thus, the recognition is carried out by the fused feature set using layered k-SVM (k-support vector machine) which is newly developed by combining the layered SVM classifier and k-neural network classifier. The experimental results are evaluated and the performance is analysed with false acceptance ratio, false rejection ratio and accuracy. The outcome of the proposed FFF optimisation system obtains a higher accuracy of 96%.

Design of DC-DC converter for harvesting maximum power from the multiple piezoelectric energy harvesters is a challenging task. In this work, a method to obtain maximum power from the multiple piezoelectric energy harvesters for supercapacitor charging is proposed. The method involves acquiring energy from each harvester by time-multiplexed operation of the multi-input buck-boost converter. The maximum power from each harvester is extracted by operating the converter to match the impedance of each harvester to the load impedance. The impedance matching is done by operating the converter with optimal duty cycle. The proposed method is experimentally evaluated, and the charging rate of supercapacitor is found to be higher while charging by the proposed method as compared to charging directly through the rectifier. The proposed method involves a single converter circuit for extracting energy from multiple piezoelectric energy harvesters, so that the component utilisation and its associated losses are very much reduced.

Purpose Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features. Design/methodology/approach The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector. Findings Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1. Originality/value In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

The low power energy harvesters need efficient single-stage direct ac-dc conversion evading diode bridge rectifier. An active rectifier circuit is proposed for piezoelectric energy harvester working on the principle of the buck-boost converter. The active rectifier circuit provides dual output with a reduced number of components. The analysis of the active rectifier is carried out, and expression for the optimum duty cycle is derived for maximum power extraction. The active rectifier configuration is extended for connecting multiple piezoelectric energy harvesters, and maximum power extraction is achieved through time multiplexed switching of energy harvesters. Proposed active rectifier topology is validated through simulation and experimentation. The results demonstrate that the harvested power is improved by the factor of 1.4 and 3.2 for single input and multiple input configurations, respectively, as compared to the power harvested using dual output rectifier. The charging time of the supercapacitor is reduced by 17 min while charging through the single input configuration and 15 min while charging through the multiple input configuration of the proposed active rectifier circuit.

Automatic anomaly detection in surveillance videos is a trending research domain, which assures the detection of the anomalies effectively, relieves the time-consumed by the manual interpretation methods without the requirement of the domain knowledge about the anomalous object. Accordingly, this research work proposes an effective anomaly detection approach, named, TimeRide Neural network (TimeRideNN), by modifying the standard RideNN using the Taylor series such that an extra group of rider, named as timerider, is included in the standard rider optimization algorithm. Initially, the face in the videos is subjected to face detection using the Viola Jones algorithm. Then, the object tracking is performed using the knocker and holoentropy-based Bhattacharya distance, which is a modification of the Bhattacharya distance using the knocker and holoentropy. After that, the features, such as object-level features and speed-level features of the objects, are extracted and the features are employed to the proposed TimeRideNN classifier, which declares the anomalous objects in the video. The experimentation of the proposed anomaly detection method is done using the UCSD dataset (Ped1), subway dataset and QMUL junction dataset, and the analysis is performed based on accuracy, sensitivity and specificity. The proposed TimeRideNN classifier obtains the accuracy, sensitivity and specificity of 0.9724, 0.9894 and 0.9691, respectively.