Energy Efficient System for Wireless Sensor Networks using Modified RECHS Protocol

The area of wireless sensor networks (WSNs) is one of the emerging and fast growing fields in the scientific world. This has brought about developing low cost, low-power and multi-function sensor nodes. Prolonged network lifetime, scalability, node mobility and load balancing are important requirements for many WSN applications. Clustering the sensor nodes is an effective technique to achieve these goals. Cluster-based routing protocol is currently a hot research in wireless sensor network. In this paper, we have added additional criteria for the selection of cluster heads in a Redundant and Energy-efficient Cluster head Selection Protocol(RECHS) and compared results with Energy Aware Low Energy Adaptive Clustering Hierarchy (EA-LEACH) protocol. This modified RECHS significantly increases the lifetime, reliability of the network. Simulation results show that comparison between two methods (Modified RECHS and EA- LEACH) for LEACH protocol on the basis of network lifetime (stability period), number of cluster heads are present per round, number of alive node are present per round and throughput of data transfer in the network. DOI: 10.17762/ijritcc2321-8169.15016

Simultaneous Surgical Treatment of Congenital Spinal Deformity Associated with Intraspinal Anomalies

Study Design Prospective case series. Purpose To study the safety, efficacy, and long-term outcomes of single-stage surgical intervention for congenital spinal deformity and intraspinal anomalies. Overview of literature Congenital spinal deformities associated with intraspinal anomalies are usually treated sequentially, first by treating the intraspinal anomalies followed by deformity correction after a period of 3–6 months. Recently, a single-stage approach has been reported to show better postoperative results and reduced complication rates. Methods Thirty patients (23 females and seven males) were prospectively evaluated for the simultaneous surgical treatment of congenital spinal deformity with concurrent intraspinal anomalies from May 2006 to October 2016. The average age at presentation was 9.8±3.7 years, with the average follow-up duration being 49.06±8.6 months. Clinical records were evaluated for clinical, radiological, perioperative, and postoperative data. Results The average angle of deformity was 56.53°±25.22° preoperatively, 21.13°±14.34° postoperatively, and 23.93°±14.99° at the final follow-up. The average surgical time was 232.58±53.56 minutes (range, 100–330 minutes), with a mean blood loss of 1,587.09±439.09 mL (range, 100–2,300 mL). Conclusions Single stage surgical intervention for intraspinal anomalies with congenital spinal deformity correction, including adequate intra-operative wake-up test, is a viable option in appropriately selected patients and has minimum complication rates

Mapping a complete neural population in the retina

Recording simultaneously from essentially all of the relevant neurons in a local circuit is crucial to understand how they collectively represent information. Here we show that the combination of a large, dense multielectrode array and a novel, mostly automated spike-sorting algorithm allowed us to record simultaneously from a highly overlapping population of \u3e200 ganglion cells in the salamander retina. By combining these methods with labeling and imaging, we showed that up to 95% of the ganglion cells over the area of the array were recorded. By measuring the coverage of visual space by the receptive fields of the recorded cells, we concluded that our technique captured a neural population that forms an essentially complete representation of a region of visual space. This completeness allowed us to determine the spatial layout of different cell types as well as identify a novel group of ganglion cells that responded reliably to a set of naturalistic and artificial stimuli but had no measurable receptive field. Thus, our method allows unprecedented access to the complete neural representation of visual information, a crucial step for the understanding of population coding in sensory systems

Ultra Thin White Topping

Paper consists of subsistence of highway road and improvement in low cost and increasing the strength and vitality of the pavement. Ultra-Thin White Topping may be defined as a concrete cover with closely spaced joints and bonded to an existing bituminous pavement. It consists of a fine layer of high durability, fibre-reinforced concrete laid over a clean, milled surface of distressed bituminous concrete pavement, to achieve full or partial bonding. From the degradation summary it is identified that even after 10 years, the riding quality of Ultra-Thin White Topping is the most admirable and the most desirable one without any mediation. Structural collapse emerges from the action that contrarily affects the traffic volume carrying capacity of the pavement. This structural collapse can be overcome by using Ultra-Thin White Topping pavement over bituminous pavement. Ultra-Thin White Topping achieves very low End User Cost values thus resulting in the maximization of Gross Economic Benefits than that of ordinary bitumen overlay

Complex Computation in the Retina

Elucidating the general principles of computation in neural circuits is a difficult problem requiring both a tractable model circuit as well as sophisticated measurement tools. This thesis advances our understanding of complex computation in the salamander retina and its underlying circuitry and furthers the development of advanced tools to enable detailed study of neural circuits. The retina provides an ideal model system for neural circuits in general because it is capable of producing complex representations of the visual scene, and both its inputs and outputs are accessible to the experimenter. Chapter 2 describes the biophysical mechanisms that give rise to the omitted stimulus response in retinal ganglion cells described in Schwartz et al., (2007) and Schwartz and Berry, (2008). The extra response to omitted flashes is generated at the input to bipolar cells, and is separable from the characteristic latency shift of the OSR apparent in ganglion cells, which must occur downstream in the circuit. Chapter 3 characterizes the nonlinearities at the first synapse of the ON pathway in response to high contrast flashes and develops a phenomenological model that captures the effect of synaptic activation and intracellular signaling dynamics on flash responses. This work is the first attempt to model the dynamics of the poorly characterized mGluR6 transduction cascade unique to ON bipolar cells, and explains the second lobe of the biphasic flash response. Complementary to the study of neural circuits, recent advances in wafer-scale photolithography have made possible new devices to measure the electrical and mechanical properties of neurons. Chapter 4 reports a novel piezoelectric sensor that facilitates the simultaneous measurement of electrical and mechanical signals in neural tissue. This technology could reveal the relationship between the electrical activity of neurons and their local mechanical environment, which is critical to the study of mechanoreceptors, neural development, and traumatic brain injury. Chapter 5 describes advances in the development, fabrication, and testing of a prototype silicon micropipette for patch clamp physiology. Nanoscale photolithography addresses some of the limitations of traditional glass patch electrodes, such as the rapid dialysis of the cell with internal solution, and provides a platform for integration of microfluidics and electronics into the device, which can enable novel experimental methodology

Enhancement of Communication Model for Driving Simulators by Relevant Physical Effects of Radio ropagation.

Vehicle-2-Vehicle (V2V) and Vehicle-2-Infrastructure (V2I) communication has been an emerging area of interest in the recent times. It offers applications ranging from road safety (e.g.: collision avoidance) to infotainment (e.g.: multimedia, navigation, traffic information etc.). Due to growing number of driver assistance systems, it is very important to evaluate the interaction with users and acceptance of these systems. Suitable tools for doing this are driving simulators. The goal of this master thesis is to enhance an already existing communication model for V2V communication scenarios that is used for the driving simulators of the DLR-Institute of Transportation Systems. The current communication simulation implements a simple propagation model based on free space communication and is already integrated in the simulator environment that provides all necessary information and parameters. This work focuses on the enhancement of existing communication model with two major physical effects: the multi-path propagation to provide "urban canyon" effects and the directional antenna (main lobe) to take the direction of the communication into account. Algorithms for assessing the environment are developed based on the open source graphics engine "OpenSceneGraph" and the prototype implementation is later integrated in the DLR Institute’s own system architecture called "Dominion"

Short term air quality prediction and supervised Machine Learning analysis

Monitoring air quality pollutants form an important topic of atmospheric and environmental research due to the health effects caused by the pollutants present in the urban and suburban areas. The research evaluates forecasting models for predicting air pollution by exploiting various machine learning techniques. The goal is to determine the forecasting accuracy of the particulate matter 2.5 (PM2.5) concentration in air by various time-series models and further evaluate classification models based on its capability to segregate the air pollution type. In this research data is sourced from Indian government website. The time series analysis is accomplished using time series models such as Auto Regression Integrated Moving Averages (ARIMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), TBATS model, ARIMA with multivariate regressions (ARIMAX) and Dynamic Harmonic Regression (DHR). ARIMAX performs best among all with the lowest error. For classification K nearest neighbor (KNN), Artificial Neural Network (ANN) and Ensemble model are used. Through out the research, it was found that use of ensemble model improves the performance of classifier