Semi-supervised annotation of faces in image collection

The objective of this work is to correctly detect and recognize faces in an image collection using a database of known faces. This has applications in photo-tagging, video indexing, surveillance and recognition in wearable computers. We propose a two-stage approach for both detection and recognition tasks. In the first stage, we generate a seed set from the given image collection using off-the-shelf face detection and recognition algorithms. In the second stage, the obtained seed set is used to improve the performance of these algorithms by adapting them to the domain at hand. We propose an exemplar-based semi-supervised framework for improving the detections. For recognition of images, we use sparse representation classifier and generate seed images based on a confidence measure. The labels of the seed set are then propagated to other faces using label propagation framework by imposing appropriate constraints. Unlike traditional approaches, our approach exploits the similarities among the faces in collection to obtain improved performance. We conduct extensive experiments on two real-world photo-album and video collections. Our approach consistently provides an improvement of \({\sim } 4\)% for detection and \(5{-}9\)% for recognition on all these datasets.     

Investigation on effects of system parameters on transmission depth in underwater wireless optical communication

Photonic Network Communications - Performance of underwater wireless optical communication (UWOC) with different vertical water channel conditions is experimentally analyzed. Experiment has been...     

A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules

Nowadays the increasing interest to perform machining operations is in dry/near-dry environments. The reason includes health and safety of operator, cost, ease of chip recyclability, etc. However one important process, which is difficult to perform in dry, is drilling. Without coolant, drilling leads to excessive thermal distortion and poor tool life. In order to tackle these conflicting requirements, the essentiality of study on machining performances with minimum quantity lubricant (MQL) becomes important.Fuzzy logic rules, which are derived based on fuzzy set theory, are used to develop fuzzy rule based model (FRBM). The performance of FRBM depends on two different aspects: structures of fuzzy rules and the associated fuzzy sets (membership function distributions, MFDs). The aim of this study is to investigate the performances of FRBMs based on Mamdani and TSK-types of fuzzy logic rules with different shapes of MFDs for prediction and performance analysis of machining with MQL in drilling of aluminum alloy. A comparison of the model predictions with experimental results and those published in the literature shows that FRBM with TSK-type fuzzy rules describes excellent trade-off with experimental measurements.     

Mitigating Performance Degradation in Congested Sensor Networks

Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called congestion-aware routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed.     

Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks

Wireless Networks - In recent years, wireless sensor networks (WSNs) have played a major role in applications such as tracking and monitoring in remote environments. Designing energy efficient...     

Two-dimensional analytical modeling of fully depleted DMG SOI MOSFET and evidence for diminished SCEs

A two-dimensional (2-D) analytical model for the surface potential variation along the channel in fully depleted dual-material gate silicon-on-insulator MOSFETs is developed to investigate the short-channel effects (SCEs). Our model includes the effects of the source/drain and body doping concentrations, the lengths of the gate metals and their work functions, applied drain and substrate biases, the thickness of the gate and buried oxide and also the silicon thin film. We demonstrate that the surface potential in the channel region exhibits a step function that ensures the screening of the drain potential variation by the gate near the drain resulting in suppressed SCEs like the hot-carrier effect and drain-induced barrier-lowering (DIBL). The model is extended to find an expression for the threshold voltage in the submicrometer regime, which predicts a desirable "rollup" in the threshold voltage with decreasing channel lengths. The accuracy of the results obtained using our analytical model is verified using 2-D numerical simulations.     

A Hybrid Fractal Metamaterial Inspired Multiband Antenna for Wireless Applications

Wireless Personal Communications - In this paper a multiband (hepta-band) antenna loaded with hybrid fractal structures and metamaterial cell (SRR/CSRR) is proposed to cover the wireless...     

Analytical modeling for 3D potential distribution of rectangular gate (RecG) gate-all-around (GAA) MOSFET in subthreshold and strong inversion regions

In this paper, we have introduced an analytical subthreshold and strong inversion 3D potential model for rectangular gate (RecG) gate-all-around (GAA) MOSFET. The subthreshold and strong inversion potential distribution in channel region of a RecG MOSFET is obtained respectively by solving 3D Laplace and 3D Poisson equations. The assumed parabolic potential distribution along the z-axis in channel direction is appropriately matched with 3D device simulator after consideration of z-depended characteristic length in subthreshold region. For accurate estimation of short channel effects (SCE), the electrostatics near source and drain is corrected. The precise gate-to-gate potential distribution is obtained after consideration of higher order term in assumed parabolic potential profile. The model compares well with numerical data obtained from the 3D ATLAS as a device simulator and deckbuild as an interactive runtime of Silvaco Inc.     

Implication of molecular orientation on charge transport and gas sensing characteristics of cobalt–phthalocyanine thin films

Charge transport and gas sensing characteristics of cobalt phthalocyanine films deposited along (ATB) and perpendicular (PTB) to the natural twin boundaries of (0 0 1) LaAlO₃ substrate have been investigated. The charge carrier mobility of ATB films (∼5 cm² V⁻¹ s⁻¹) is five orders of magnitude higher compared to that of PTB films (∼7 × 10⁻⁵ cm² V⁻¹ s⁻¹), suggesting that twin boundaries acts like a template for ordering of molecules. The ATB films on exposure to ammonia showed a reversible increase of resistance, with fast response and recovery. In contrast PTB films showed same sensitivity, but exhibits base resistance drift along with sluggish response.