EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

Hysteresis‐Free 1D Network Mixed Halide‐Perovskite Semitransparent Solar Cells

The morphology of hybrid organic–inorganic perovskite films is known to strongly affect the performance of perovskite‐based solar cells. CH₃NH₃PbI_3‐xCl_x (MAPbI_3‐xCl_x) films have been previously fabricated with 100% surface coverage in glove boxes. In ambient air, fabrication generally relies on solvent engineering to obtain compact films. In contrast, this work explores the potential of altering the perovskites microstructure for solar cell engineering. This work starts with CH₃NH₃PbI_3‐xCl_x, films with grain morphology carefully controlled by varying the deposition speed during the spin‐coating process to fabricate efficient and partially transparent solar cells. Devices produced with a CH₃NH₃PbI_3‐xCl_x film and a compact thick top gold electrode reach a maximum efficiency of 10.2% but display a large photocurrent hysteresis. As it is demonstrated, the introduction of different concentrations of bromide in the precursor solution addresses the hysteresis issues and turns the film morphology into a partially transparent interconnected network of 1D microstructures. This approach leads to semitransparent solar cells with negligible hysteresis and efficiencies up to 7.2%, while allowing average transmission of 17% across the visible spectrum. This work demonstrates that the optimization of the perovskites composition can mitigate the hysteresis effects commonly attributed to the charge trapping within the perovskite film.     

螺旋管紧凑式换热器传热性能分析

数值模拟研究了紧凑式小管螺旋管换热器的流动换热特性.螺旋管换热器由35个单管(直管和弯管)构成,管外为空气冷却,管内流过不同温度的液体工质R141B.模拟结果表明,各单根螺旋管内外对流传热系数、温度分布和传热性能,主要受内外流体温度梯度、回流条件、外部空气流速和单根管的传热表面积等因素的影响.     

Electrical behaviour of fractal nanosized tin dioxide films prepared by electrodeposition for gas sensing applications

An original electrochemical process to prepare SnO₂ gas sensors is detailed and correlated to electrical behaviour under gas environment. In particular conditions, Tin material was electrodeposited on insulating substrate to form a thin film principally composed of a single layer of individual nanoaggregates (5-10 nm in size). After tin electrodeposition, these supported aggregates were oxidised at air or pressurized oxygen to induce the formation of a fractal SnO₂ film. From these resulting active films, electrical measurements were carried out in ethanol and 300 ppm CO atmospheres. The results show, a sensitivity of 400% at 227 °C in the ethanol case with a response time of 140 s. When the temperature of electrical measurements increases, response and recovery times decrease. However, the sensing amplitude was not modified (Sensitivity around 4) between 250 and 300 °C. In the case of CO, the sensor presented a typical response with a factor of about 2.5 at 250 °C. A fractal dimension between 1.4 and 1.6 is found for fractal-shaped samples allowing an increase of specific surface in contact with gases. However, its does not effect sensitivity, which depends mainly on grain size.     

End-to-End Delay and Energy Efficient Routing Protocol for Underwater Wireless Sensor Networks

Providing better communication and maximising the communication performance in a Underwater Wireless Sensor Network (UWSN) is always challenging due to the volatile characteristics of the underwater environment. Radio signals cannot properly propagate underwater, so there is a need for acoustic technology that can support better data rates and reliable underwater wireless communications. Node mobility, 3-D spaces and horizontal communication links are some critical challenges to the researcher in designing new routing protocols for UWSNs. In this paper, we have proposed a novel routing protocol called Layer by layer Angle-Based Flooding (L2-ABF) to address the issues of continuous node movements, end-to-end delays and energy consumption. In L2-ABF, every node can calculate its flooding angle to forward data packets toward the sinks without using any explicit configuration or location information. The simulation results show that L2-ABF has some advantages over some existing flooding-based techniques and also can easily manage quick routing changes where node movements are frequent.     

An optimized and power savings protocol for mobility energy-aware in wireless sensor networks

Mobility management in Wireless Sensor Networks (WSNs) is a complex problem that must be taken into account in all layers of the protocol stack. But this mobility becomes very challenging at the MAC level in order to do not degrade the energy efficiency between sensor nodes that are in communication. However, among medium access protocols, sampling protocols reflect better the dynamics of such scenarios. Nevertheless, the main problem, of such protocols, remains the management of collisions and idle listening between nodes. Previous approaches like B-MAC and X-MAC, based on sampling protocols present some shortcomings. Therefore, we address the mobility issue of WSNs that use as medium access sampling protocols. Firstly, we propose a mobile access solution based on the X-MAC protocol which remains a reference protocol. This protocol, called MoX-MAC, incorporates different mechanisms that enables to mitigate the energy consumption of mobile sensor nodes. Furthermore, we extend our former work (Ba et al. in Proc. of IEEE WMNC, 2011) by evaluating the lifetime of static nodes with respect to MoX-MAC protocol, as well determine the degree of depletion of static nodes due to the presence of mobile nodes.     

Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels Using Mathematically Modeled Acoustic Emission Failure Mechanism Data

The purpose of this research was to predict burst pressures in composite overwrapped pressure vessels (COPVs) by using mathematically modeled acoustic emission (AE) data. Both backpropagation neural network (BPNN) and multiple linear regression (MLR) analyses were performed on various subsets of the low proof pressure AE data to predict burst pressures and to determine if the two methods were comparable. AE data were collected during hydrostatic burst testing on the 15-inch diameter COPVs. Once collected, the AE data were filtered to eliminate noise then classified into AE failure mechanism data using a MATLAB Kohonen self-organizing map (SOM). The matrix cracking only amplitude distribution data were mathematically modeled using bounded Johnson distributions with the four Johnson distribution parameters – ?, λ, γ, and η – employed as inputs to make both the BPNN and MLR predictions. The burst pressure predictions generated using a MATLAB BPNN resulted in a worst case error of 1.997% as compared to ?1.666% for the MLR analysis, suggesting comparability. However, the MLR analysis required the data from all nine COPVs to get approximately the same results as the BPNN training on just five COPVs; plus, MLR analyses are intolerant to noise, whereas BPNNs are not.     

Encapsulation of docetaxel in oily core polyester nanocapsules intended for breast cancer therapy

A method to determine the effects of the geometry and lateral ordering on the electronic properties of an array of one-dimensional self-assembled quantum dots is discussed. A model that takes into account the valence-band anisotropic effective masses and strain effects must be used to describe the behavior of the photoluminescence emission, proposed as a clean tool for the characterization of dot anisotropy and/or inter-dot coupling. Under special growth conditions, such as substrate temperature and Arsenic background, 1D chains of In_0.4Ga_0.6 As quantum dots were grown by molecular beam epitaxy. Grazing-incidence X-ray diffraction measurements directly evidence the strong strain anisotropy due to the formation of quantum dot chains, probed by polarization-resolved low-temperature photoluminescence. The results are in fair good agreement with the proposed model.