Air-Suspended Silicon Micro-Bridge Structures for Metal Oxide- Based Gas Sensing

Maintaining specific temperature is a key parameter for most of the gas sensing materials, particularly for metal oxide-based thin film layers, to operate them more efficiently to detect different gaseous (or vapor) species at ppm and ppb concentration levels. For field applications, battery-operated micro-gas-sensors, power dissipation and the required temperature stability are to be maintained with a close tolerance for better signal stability and also to analyze the generated data from the sensing element. This chapter mainly focuses on several metal oxide films to highlight the base temperature and its creation on silicon-based platforms. Air-suspended structures are highlighted, and a comparison is drawn between simple and MEMS-based structures from the power dissipation point of view. Ease of fabrication and operation limitations are explained with fabrication issues

Novel hybrid framework for image compression for supportive hardware design of boosting compression

Performing the image compression over the resource constrained hardware is quite a challenging task. Although, there has been various approaches being carried out towards image compression considering the hardware aspect of it, but still there are problems associated with the memory acceleration associated with the entire operation that downgrade the performance of the hardware device. Therefore, the proposed approach presents a cost effective image compression mechanism which offers lossless compression using a unique combination of the non-linear filtering, segmentation, contour detection, followed by the optimization. The compression mechanism adapts analytical approach for significant image compression. The execution of the compression mechanism yields faster response time, reduced mean square error, improved signal quality and significant compression ratio performance

A simplified machine learning approach for recognizing human activity

With the wide ranges of real-time event feed capturing devices, there has been significant progress in the area of digital image processing towards activity detection and recognition. Irrespective of the presence of various such devices, they are not adequate to meet dynamic monitoring demands of the visual surveillance system, and their features are highly limited towards complex human activity recognition system.  Review of existing system confirms that still there is a large scope of enhancement as they lack applicability to real-life events and also doesn't offer optimal system performance. Therefore, the proposed manuscript presents a model for activity recognition system where the accuracy of recognition operation and system performance are retained with good balance. The study presents a simplified feature extraction process from spatial and temporal traits of the event feeds that is further subjected to the machine learning mechanism for boosting recognition performanc

An Efficient Activity Detection System based on Skeleton Joints Identification

The increasing criminal activities in the current world has drawn lot of interest activity recognition techniques which helps to perform the sophistical analytical operations on human activity and also helps to interface the human and computer interactions. From the existing review analysis it is found that most of the existing systems are not emphasize on computational performance but are more application specific by identifying specific problems. Hence, it is found that all the features are not required for accurate and cost effective human activity detection. Thus, the human skelton action can be considered and presented a simple and accurate process to identify the significant joints only. From the outcomes it is found that the proposed system is cost effective and computational efficient activity recognition technique for human actions

Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive Feedforward Neural Network

The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it

Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India

Sensing sulfur-containing gases using titanium and tin decorated zigzag graphene nanoribbons from first-principles

Atom implantation in graphene or graphene nanoribbons offers a rich opportunity to tune the material structure and functional properties. In this study, zigzag graphene nanoribbons with Ti or Sn adatoms stabilised on a double carbon vacancy site are theoretically studied to investigate their sensitivity to sulfur-containing gases (H2S and SO2). Due to the abundance of oxygen in the atmosphere, we also consider the sensitivity of the structures in the presence of oxygen. Density functional theory calculations are performed to determine the adsorption geometry and energetics, and nonequilibrium Green's function method is employed to compute the current–voltage characteristics of the considered systems. Our results demonstrate the sensitivity of both Ti- and Sn-doped systems to H2S, and the mild sensitivity of Ti-doped sensor systems to SO2. The Ti-doped sensor structure exhibits sensitivity to H2S with or without oxidation, while oxidation of the Sn-doped sensor structure reduces its ability to adsorb H2S and SO2 molecules. Interestingly, oxygen dissociates on the Ti-doped sensor structure, but it does not affect the sensor's response to the H2S gas species. Oxidation prevents the dissociation of the H–S bond when H2S adsorbs on the Ti-doped structure, thus enhancing its reusability for this gas species. Our study suggests the potential of Ti- and Sn-doped graphene in selective gas sensing, irrespective of the sensing performance of the bulk oxides

Self-activated ultrahigh chemosensitivity of oxide thin film nanostructures for transparent sensors

One of the top design priorities for semiconductor chemical sensors is developing simple, low-cost, sensitive and reliable sensors to be built in handheld devices. However, the need to implement heating elements in sensor devices, and the resulting high power consumption, remains a major obstacle for the realization of miniaturized and integrated chemoresistive thin film sensors based on metal oxides. Here we demonstrate structurally simple but extremely efficient all oxide chemoresistive sensors with similar to 90% transmittance at visible wavelengths. Highly effective self-activation in anisotropically self-assembled nanocolumnar tungsten oxide thin films on glass substrate with indium-tin oxide electrodes enables ultrahigh response to nitrogen dioxide and volatile organic compounds with detection limits down to parts per trillion levels and power consumption less than 0.2 microwatts. Beyond the sensing performance, high transparency at visible wavelengths creates opportunities for their use in transparent electronic circuitry and optoelectronic devices with avenues for further functional convergence.open181

A Comprehensive Review of One-Dimensional Metal-Oxide Nanostructure Photodetectors

One-dimensional (1D) metal-oxide nanostructures are ideal systems for exploring a large number of novel phenomena at the nanoscale and investigating size and dimensionality dependence of nanostructure properties for potential applications. The construction and integration of photodetectors or optical switches based on such nanostructures with tailored geometries have rapidly advanced in recent years. Active 1D nanostructure photodetector elements can be configured either as resistors whose conductions are altered by a charge-transfer process or as field-effect transistors (FET) whose properties can be controlled by applying appropriate potentials onto the gates. Functionalizing the structure surfaces offers another avenue for expanding the sensor capabilities. This article provides a comprehensive review on the state-of-the-art research activities in the photodetector field. It mainly focuses on the metal oxide 1D nanostructures such as ZnO, SnO2, Cu2O, Ga2O3, Fe2O3, In2O3, CdO, CeO2, and their photoresponses. The review begins with a survey of quasi 1D metal-oxide semiconductor nanostructures and the photodetector principle, then shows the recent progresses on several kinds of important metal-oxide nanostructures and their photoresponses and briefly presents some additional prospective metal-oxide 1D nanomaterials. Finally, the review is concluded with some perspectives and outlook on the future developments in this area