Cognitive Computing for Multimodal Sentiment Sensing and Emotion Recognition Fusion Based on Machine Learning Techniques Implemented by Computer Interface System

A multiple slot fractal antenna design has been determined communication efficiency and its multi-function activities.  High-speed small communication devices have been required for future smart chip applications, so that researchers have been employed new and creative antenna design. Antennas are key part in communication systems, those are used to improve communication parameters like gain, efficiency, and bandwidth. Consistently, modern antennas design with high bandwidth and gain balancing is very difficult, therefore an adaptive antenna array chip design is required. In this research work a coaxial fed antenna with fractal geometry design has been implemented for Wi-Fi and Radio altimeter application. The fractal geometry has been taken with multiple numbers of slots in the radiating structure for uncertain applications. The coaxial feeding location has been selected based on the good impedance matching condition (50 Ohms). The overall dimension mentioned for antenna are approximately 50X50X1.6 mm on FR4 substrate and performance characteristic analysis is performed with change in substrate material presented in this work. Dual-band resonant frequency is being emitted by the antenna with resonance at 3.1 and 4.3 GHz for FR4 substrate material and change in the resonant bands is obtained with change in substrate. The proposed Antenna is prototyped on Anritsu VNA tool and presented the comparative analysis like VSWR 12%, reflection coefficient 9.4%,3D-Gain 6.2% and surface current 9.3% had been improved

ENHANCING A NOVEL NEURAL NETWORK ALGORITHM FOR FORECASTING THE IDENTIFICATION OF SHAPES AND DEFECTS IN POLYMER CONCRETE PANELS

The increased durability and performance features of polymer concrete panels have led to their widespread application in construction. The manufacturing of precise and effective techniques for identifying forms and flaws is vital to guarantee the high quality of these panels. To increase the accuracy of structure and defect-recognition in polymer concrete panels, this study presents a new Stochastic raven roosting optimization enhanced artificial neural network (SRRO-EANN) forecasting technique. The data sample used to assess the completed model fits the training dataset is referred to the test dataset. The Gaussian filter (GF) is a tool used in the pre-processing and Principal Component Analysis (PCA) feature extraction, leading to more effective utilization and understanding the defect capturing. The findings of the research indicate the effectiveness for the future development of forecasting technologies in the realm of quality control and building material inspection

Generative Boltzmann Adversarial Network in Manet Attack Detection and QOS Enhancement with Latency

Mobile Ad-Hoc Network (MANET) are considered as self-configured network those does not have any centralized base station for the network monitoring and control. MANET environment does not control architecture for routing for the frequent maintenance of topology. The drastic development of Internet leads to adverse effect of development in MANET for different multimedia application those are sensitive to latency. Upon the effective maintenance of the QoS routing route discovery is performed to calculate queue and contention delay. However, the MANET requirement comprises of the complex procedure to withstand the Quality of Service (QoS) with Artificial Intelligence (AI). In MANET it is necessary to compute the MANET attacks with improved QoS with the reduced latency as existing model leads to higher routing and increased latency.  In this paper proposed a Generative Boltzmann Networking Weighted Graph (GBNWG) model for the QoS improvement in MANET to reduce latency. With GBNWG model the MANET model network performance are computed with the weighted graph model. The developed weighted graph computes the routes in the MANET network for the estimation of the available path in the routing metrices. The proposed GBNWG model is comparatively estimated with the conventional QOD technique. Simulation analysis stated that GBNWG scheme exhibits the improved performance in the QoS parameters. The GBNWG scheme improves the PDR value by 12%, 41% reduced control packets and 45% improved throughput value

First Low-Latency LIGO+Virgo Search for Binary Inspirals and their Electromagnetic Counterparts

Aims. The detection and measurement of gravitational-waves from coalescing neutron-star binary systems is an important science goal for ground-based gravitational-wave detectors. In addition to emitting gravitational-waves at frequencies that span the most sensitive bands of the LIGO and Virgo detectors, these sources are also amongst the most likely to produce an electromagnetic counterpart to the gravitational-wave emission. A joint detection of the gravitational-wave and electromagnetic signals would provide a powerful new probe for astronomy. Methods. During the period between September 19 and October 20, 2010, the first low-latency search for gravitational-waves from binary inspirals in LIGO and Virgo data was conducted. The resulting triggers were sent to electromagnetic observatories for followup. We describe the generation and processing of the low-latency gravitational-wave triggers. The results of the electromagnetic image analysis will be described elsewhere. Results. Over the course of the science run, three gravitational-wave triggers passed all of the low-latency selection cuts. Of these, one was followed up by several of our observational partners. Analysis of the gravitational-wave data leads to an estimated false alarm rate of once every 6.4 days, falling far short of the requirement for a detection based solely on gravitational-wave data.Comment: 13 pages, 13 figures. For a repository of data used in the publication, go to: http://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=P1100065 Also see the announcement for this paper on ligo.org at: http://www.ligo.org/science/Publication-S6CBCLowLatency