Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization

In the medical field, image segmentation is a paramount and challenging task. The head and vertebral column make up the central nervous system (CNS), which control all the paramount functions. These include thinking, speaking, and gestures. The uncontrolled growth in the CNS can affect a person’s thinking of communication or movement. The tumor is known as the uncontrolled growth of cells in brain. The tumor can be recognized by MRI image. Brain tumor detection is mostly affected with inaccurate classification. This proposed work designed a novel classification and segmentation algorithm for the brain tumor detection. The proposed system uses the Adaptive fuzzy deep neural network with frog leap optimization to detect normality and abnormality of the image. Accurate classification is achieved with error minimization strategy through our proposed method. Then, the abnormal image is segmented using adaptive flying squirrel algorithm and the size of the tumor is detected, which is used to find out the severity of the tumor. The proposed work is implemented in the MATLAB simulation platform. The proposed work Accuracy, sensitivity, specificity, false positive rate and false negative rate are 99.6%, 99.9%, 99.8%, 0.0043 and 0.543, respectively. The detection accuracy is better in our proposed system than the existing teaching and learning based algorithm, social group algorithm and deep neural network.

     

The spray forming of nanostructured Aluminum Oxide

Research Summary Nanostructured ceramics and their composites possess improved properties such as tensile strength, fatigue strength, hardness, and wear resistance. Freestanding, near-net shape, nanostructured Al₂O₃ components can be synthesized via plasma-spray forming. In this study, plasma-spray parameters were optimized and an innovative substrate cooling technique was developed to retain nanosize Al₂O₃ in the spray deposit. Nanosize Al₂O₃ particles were partially melted and trapped between the fully melted coarser, micrometersize Al₂O₃grains. Densification of the spray-deposited Al₂O₃occurred via solidification and sintering. A similar processing approach can be adopted for fabrication of near-net shapes of a variety of nanostructured materials (metals, ceramics, and intermetallics) and their combinations by selecting suitable powder-treatment and plasmaspray parameters.     

Pyramid coding based rate control for constant bit rate video streaming

In this paper, a novel pyramid coding based rate control scheme is proposed for video streaming applications constrained by a constant channel bandwidth. To achieve the target bit rate with the best quality, the initial quantization parameter (QP) is determined by the average spatio-temporal complexity of the sequence, its resolution and the target bit rate. Simple linear estimation models are then used to predict the number of bits that would be necessary to encode a frame for a given complexity and QP. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control scheme in the Joint Model (JM) reference software in terms of Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Finally, the proposed scheme is validated through experimental evaluation over a miniature test-bed.

     

Feature tracking and matching in video using programmable graphics hardware

This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.     

Effect of Starting As-cast Structure on the Microstructure–Texture Evolution During Subsequent Processing and Finally Ridging Behavior of Ferritic Stainless Steel

Metallurgical and Materials Transactions A - Effect of the initial as-cast structure on the microstructure–texture evolution during thermomechanical processing of 409L grade ferritic...     

Efficient fog removal from video

In this paper, a framework of real-time video processing for fog removal using uncalibrated single camera system is proposed. Intelligent use of temporal redundancy present in video frames paves the way for real-time implementation. Any fog removal algorithm for images acquired with uncalibrated single camera system can be extended to video using the proposed framework. For the purpose of real-time implementation, several fog removal algorithms for images are investigated and few top ranking algorithms in speed and quality are chosen. Simulation results confirm that proposed framework reduces the computation per frame significantly. Proposed fog removal framework has a wide application in navigation, transportation, and other industries.     

Preparation,structural, and characterizations of SnO2-coated TiNb2O7 anode materials for lithium-ion batteries

SnO₂-coated TiNb₂O₇ powders were synthesized via the solution coating method in the present research. The SnO₂ layers with a thickness of 3–5 nm were homogeneously coated on the surface of TiNb₂O₇ particles. TiNb₂O₇ coated with SnO₂ of 5 mol% with high Li⁺ diffusion coefficient delivered the discharge capacity of 319.5 mAh/g, which was 6.6% higher than that of the non-coated samples. The enhancement of capacity for the coated TiNb₂O₇ was owing to the low charge-transfer resistance of 17.5 Ω in contrary to the non-coated TiNb₂O₇ (27.8 Ω). SnO₂-coated TiNb₂O₇ possessed an improved capacity retention of 85.2% at 5 C after 100 cycles, superior to the non-coated TiNb₂O₇ (79.8%). On the other hand, the excessive amounts of SnO₂ coating led to the reduction in the capacity of the prepared samples. The excessive amounts of SnO₂ layers suppressed the Li⁺ diffusion and increased the charge-transfer resistance. The obtained results in this study indicated that coating of TiNb₂O₇ with appropriate amounts of SnO₂ significantly improved the electrochemical performance of TiNb₂O₇.     

Microfluorometric detection of catecholamines with multiphoton-excited fluorescence

We demonstrate sensitive spatially resolved detection of physiological chromophores that emit in the ultraviolet (<330 nm). An atypical laser source (a visible wavelength femtosecond optical parametric oscillator), and an unconventional collection geometry (a lensless detector that detects the forward-emitted fluorescence) enable this detection. We report the excitation spectra of the catecholamines dopamine and norepinephrine, together with near-UV emitters serotonin and tryptophan, in the range of 550-595 nm. We estimate the molecular two-photon action cross section of dopamine, norepinephrine, and serotonin to be 1.2 mGM (1 GM, or Goppert Mayor, is equal to 10(-58) m4 s(-1) photon(-1)), 2 mGM, and 43 mGM, respectively, at 560 nm. The sensitivity achieved by this method holds promise for the microscopic imaging of vesicular catecholamines in live cells.     

Distributed Intelligent Energy Management System for a Single-Phase High-Frequency AC Microgrid

In this paper, a single-phase high-frequency AC (HFAC) microgrid is shown as a novel solution towards integrating renewable energy sources in a distributed generation system. Better utilization of the Microgrid is achieved by solving power flow and power quality issues using p-q theory-based active filtering called universal active power line conditioner and unified power quality conditioner, respectively. A distributed intelligent energy management system (DIEMS) is implemented to optimize operating costs. As the optimization greatly depends on the power generation and the power output from renewable sources strongly depends on the weather, the forecast of power generation is required for DIEMS. A Fuzzy ARTMAP neural network is used to predict hourly day-type outputs based on which generation can be forecasted. Depending on the forecast, an optimization scheme is developed utilizing linear programming along with heuristics. The results obtained show the successful implementation of HFAC Microgrid with adequate power flow and power quality control, as well as the optimization of operation cost by the DIEMS with Fuzzy ARTMAP-based day-type forecasting. The improvement in the battery life is also achieved due to optimization of storage charge states using the proposed DIEMS