Enhanced near-infrared periocular recognition through collaborative rendering of hand crafted and deep features

Periocular recognition leverage from larger feature region and lesser user cooperation, when compared against the traditional iris recognition. Moreover, in the current scenario of Covid-19, where majority of people cover their faces with masks, potential of recognizing faces gets reduced by a large extent, calling for wide applicability of periocular recognition. In view of these facts, this paper targets towards enhanced representation of near-infrared periocular images, by combined use of hand-crafted and deep features. The hand-crafted features are extracted through partitioning of periocular image followed by obtaining the local statistical properties pertaining to each partition. Whereas, deep features are extracted through the popular convolutional neural network (CNN) ResNet-101 model. The extensive set of experiments performed with a benchmark periocular database validates the promising performance of the proposed method. Additionally, investigation of cross-spectral matching framework and comparison with state-of-the-art, reveal that combination of both types of features employed could prove to be extremely effective.

     

IoT Based Design of Air Quality Monitoring System Web Server for Android Platform

Wireless Personal Communications - The web technology is rapidly increased in various fields. This paper aims to create a server on cloud platform to store data and process the information...     

An innovative image encryption scheme based on chaotic map and Vigenère scheme

Multimedia Tools and Applications - Confidentiality of images during data communication is a prime concern of many researchers, as a result, several mechanisms have been proposed for image...     

Like-minded communities: bringing the familiarity and similarity together

Community detection in social networks is a well-studied problem. A community in social network is commonly defined as a group of people whose interactions within the group are more than outside the group. It is believed that people’s behavior can be linked to the behavior of their social neighborhood. While shared characteristics of communities have been used to validate the communities found, to the best of authors’ knowledge, it is not demonstrated in the literature that communities found using social interaction data are like-minded, i.e., they behave similarly in terms of their interest in items (e.g., movie, products). In this paper, we experimentally demonstrate, on a social networking movie rating dataset, that people who are interested in an item are socially better connected than the overall graph. Motivated by this fact, we propose a method for finding communities wherein like-mindedness is an explicit objective. We find small tight groups with many shared interests using a frequent item set mining approach and use these as building blocks for the core of these like-minded communities. We show that these communities have higher similarity in their interests compared to communities found using only the interaction information. We also compare our method against a baseline where the weight of edges are defined based on similarity in interests between nodes and show that our approach achieves far higher level of like-mindedness amongst the communities compared to this baseline as well.     

Self-aligned on-chip coupled photonic devices using individual cadmium sulfide nanobelts

Nanowires (NWs) and nanobelts (NBs) have been widely studied and fabricated into a variety of nanoscale devices such as light emitting diodes (LEDs), lasers and biosensors. These unique materials have attracted sustained attention due to their novel properties, ease of growth, and the ability to fabricate highly engineered devices. However, their widespread application remains hindered due to the difficulty in integrating multiple NWs or NBs together for more complex devices. Integration of multiple NWs and NBs together on the same chip can enable the coupling of different devices to help realize complex on-chip architectures such as photonic integrated circuits or nanoscale diagnostic tools, which currently require outcoupling using larger components. In this letter we report the coupling of on-chip NB LEDs and photodetectors using a single, precisely self-aligned, cadmium sulfide (CdS) NB fabricated on a silicon-on-insulator (SOI) substrate. Electroluminescence generated by the CdS NB is waveguided and measured across the self-aligned device and demonstrates an on/off ratio of 102–103. This work describes a new method for fabricating and integrating more complex nanoscale devices that can enable advances in areas such as on-chip optical computational components and nanoscale optical biodiagnostics.

     

Increasing the Prediction Accuracy for Thyroid Disease: A Step Towards Better Health for Society

Wireless Personal Communications - A healthy life is essential for a happy society, however it is a fact that seemingly invisible diseases plague our families and people suffer. The thyroid disease...     

Photonic band gap structure microstrip patch antenna for WiMAX and Wi-Fi application

Photonic Network Communications - A compact microstrip patch antenna was designed using IE3D v.15 simulation tool and improves its performance by using PBG structure and also has to achieve the...     

A semi empirical approach for submicron GaN MESFET using an accurate velocity field relationship for high power applications

A semi empirical model has been proposed for sub-micron GaN MESFET's to calculate the I-V characteristics using an accurate velocity-field relationship obtained by fitting it with the Monte Carlo (MC) simulation. The results so obtained are compared with the experimental results to validate our model and are also compared with the results obtained from the simple saturation model to present the influence of electron drift velocity modeling on the device parameters. The model has been extended to predict the microwave parameters such as transconductance and output conductance of the device.     

Artificial Neural Network Modeling of Hydrodynamics of Liquid‐Solid Circulating Fluidized Beds

Solids holdup and solids circulation rate are the two important hydrodynamic variables affected by process conditions. These two variables have a significant influence on the performance of a liquid‐solid circulating fluidized bed (LSCFB). An artificial neural network (ANN) methodology was developed and simulated to predict the performance of the LSCFB for the experimental dataset collected under various process conditions. Different statistical parameters were applied to evaluate the prominent and unique characteristic features of the ANN‐predicted parameters. The ANN model successfully predicted the experimental observations and captured the actual nonlinear behavior noticed during the experiments. Model validation confirmed that this data‐driven technique can be used to model such nonlinear systems.