A new distributed constant false alarm rate detector

A new constant false alarm rate (CFAR) test termed signal-plus-order statistic CFAR (S+OS) using distributed sensors is developed. The sensor modeling assumes that the returns of the test cells of different sensors are all independent and identically distributed In the S+OS scheme, each sensor transmits its test sample and a designated order statistic of its surrounding observations to the fusion center. At the fusion center, the sum of the samples of the test cells is compared with a constant multiplied by a function of the order statistics. For a two-sensor network, the functions considered are the minimum of the order statistics (mOS) and the maximum of the order statistics (MOS). For detecting a Rayleigh fluctuating target in Gaussian noise, closed-form expressions for the false alarm and detection probabilities are obtained. The numerical results indicate that the performance of the MOS detector is very close to that of a centralized OS-CFAR and it performs considerably better than the OS-CFAR detector with the AND or the OR fusion rule. Extension to an N-sensor network is also considered, and general equations for the false alarm probabilities under homogeneous and nonhomogeneous background noise are presented.     

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.     

A novel framework for liquid propellant engine’s cooling system design by sensitivity analysis based on RSM and multi-objective optimization using PSO

One of the challenges of combustion chamber and nozzle design in a Liquid Propellant Engine (LPE) is to predict the behavior and performance of the cooling system. Therefore, while designing, the optimization of the cooling system is always of great importance. This paper presents the multi-objective optimization of the LPE’s cooling system. To this end, a novel framework has been developed, resulting from the application of the Response Surface Method (RSM) and the correlation coefficients matrix, sensitivity analysis and the The Particle Swarm Optimization (PSO). based on this method, the input variables, constraints, objective functions, and their surfaces were identified. In terms of multi-optimization algorithms, RSM and PSO are utilized to get global optimum. In conclusion, the methodology capability is to optimize the LPE’s cooling system, 6 percentage increase in total heat transfer and 7 bar decrease cooling system pressure loss, which resulted in a 1.2-seconds increase in the specific impulse of the engine.     

Comments with reply, on `A new look at nonseparable syntheticaperture radar processing' by A. Di Cenzo

The commenters point out that the idea using a two-dimensional digital correlation technique to perform synthetic-aperture-radar (SAR) processing, presented as new in the above-titled paper (see ibid., vol.24, p.218-23, May 1988), was described by them as early as 1978 and has since been described by other authors. They discuss some of these earlier studies. The author replies that he was unaware of the earlier work, and that he did not intend to convey the impression that the nonseparable transform domain processor that he presented was the first     

Alfvén Waves in the Solar Atmosphere

Alfvén waves are considered to be viable transporters of the non-thermal energy required to heat the Sun’s quiescent atmosphere. An abundance of recent observations, from state-of-the-art facilities, have reported the existence of Alfvén waves in a range of chromospheric and coronal structures. Here, we review the progress made in disentangling the characteristics of transverse kink and torsional linear magnetohydrodynamic (MHD) waves. We outline the simple, yet powerful theory describing their basic properties in (non-)uniform magnetic structures, which closely resemble the building blocks of the real solar atmosphere.     

湿热对单向复合材料层合板Ⅱ型分层特性的影响

利用横向裂纹拉伸试验模型（Transverse Crack Tension-tension Test）,在3种湿热环境（室温、85℃和85℃/95%RH）条件下,对2种单向碳纤维增强树脂基复合材料（T300/QY8911和HTA/6376）层合板的Ⅱ型分层特性进行了试验研究。结果表明湿热环境导致复合材料层间断裂韧性降低,引起Ⅱ型应变能释放率门槛值显着下降,裂纹扩展速率大大提高,温度的影响较湿度更为明显。利用电镜（SEM）对试样分层表面进行了检测,湿度对断面特征无明显的影响。