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In large data center, virtual machines are connected dynamically and data are moved between the physical machines and the virtual machines. With this environment, the Network Layer transfers the packets to the Transport Layer. To improve the security and to increase the speed of the packet transmission in the network layer, packet-filtering is carried out by firewalls based on the decision of routers namely Network addresses, Ports or Protocols. Earlier packet filtering techniques are IP data-gram, Queuing and Scheduling. In order to overcome the limitation in security mechanisms in the above filtering techniques, hashing algorithms are used for analyzing the elements in the packets that are stored in the Hash tables. The hashing algorithms are used in Quotient filter, Bloom filter, Cuckoo filter and Adaptive Cuckoo Filter (ACF). AMDS is termed as An Approximate Membership Data Structures were used to store the fingerprints in cuckoo hash tables. In this paper an attempt is made to improve the searching strategy in Cuckoo filter. Spring constant factor and Leivy Flight theorem is introduced in the searching strategy for best solution identification path. A suit of benchmark functions are employed to verify the performance of network with respect to time, speed and the memory space in the packets transmission. The performance of the Enhanced searching strategy gives better result when compared to ACF. [ABSTRACT FROM AUTHOR]

The paper compares the politics of e‐health policy with a particular focus on the implementation of e‐health records (e‐HRs) in two SHI countries: Austria and Germany. Although these countries share many similarities in terms of political and health care regime, they differ widely with respect to health care digitalization. Thus, the question arises which configuration of factors has facilitated health system digitalization in Austria while being potentially absent in Germany. As a research strategy, we use the Advocacy Coalition Framework to identify the respective actor coalitions for and against the advancement of e‐HRs and analyze how their formation, their resources, and strategies are related to country‐specific governance structures. We find that differences in the beliefs of patient representatives; variations in the institutional access for physician associations; and, finally, differing internal dynamics within the pro e‐HRs main coalition do account for differences in health care digitalization. [ABSTRACT FROM AUTHOR]

Recently, drones have been regularly used to aid in search and rescue in places where it is normally to carry out some of the early forensic victim localization. There are many suitable human detectors for drone use, such as Histogram Oriented Gradient (HOG), You Only Looks Once (YOLO), and Aggregate Channel Features (ACF). In this paper, the height of the aerial images is analyzed for its effect on the accuracy of the detection. This works compares ACF, YOLO MobileNet, and YOLO ResNet50 using a different set of aerial images varying at 10m, 20m, and 30m heights. The results show that in a single-model test, with our proposed bounding-box standardization, YOLO MobileNet achieves significant increase in test precision (AP), with 0.7 AP recorded. For single-model test, YOLO MobileNet yield best AP using 20m training data where it obtained AP of 0.88 (10m test height), 0.82 (20m test height), and 0.91 (30m test height). [ABSTRACT FROM AUTHOR]

In intelligent transport systems, detection and identification of vehicle types enact a substantial role. In this context, this paper addresses the detection and pose classification of a specific vehicle type: auto-rickshaws which have been heavily neglected by the publicly available vehicle datasets, but remains the most commonly used and cheap form of transportation in south Asian countries. Here, we introduce a dataset for auto-rickshaws which consists of instances of varying shape, orientation, size, scale, colour, viewpoint and many more. Further, we carry out a detailed analysis on the performance of state-of-the-art detection algorithms based on both hand-designed and deep features on the proposed dataset. The introduction of pose classification along with the detection eventually results in better understanding of road scenes involving auto-rickshaws. As a matter of fact, we came up with revisions for the currently employed detection algorithms to achieve a low miss rate on the validation sets. It is evident that the findings of this study are tangible and enormously consequential to the road scene understanding and intelligent transportation of developing countries where auto-rickshaws play a pivotal role in public transportation. [ABSTRACT FROM AUTHOR]

Understanding policy change mechanisms has been a key question for scholars of public policy and collective action. However, policy scholarship mostly ignores civil society-based explanations of policy processes. In order to address this gap, this study combines the Advocacy Coalition Framework with networked collective action perspectives and analyzes a successful case of mobilization of women's rights organizations in Turkey to reverse a bill on child marriage. Study findings suggest that advocacy coalitions are not static entities. When different issues in a policy subsystem are invoked, the structure of inter-coalition networks can change substantially and these variations in inter-coalition interactions may have consequences for influencing policy change. Moreover, this paper argues that extensive street protests and online campaigns by civil society organizations have the capacity to boost the bargaining power of minority coalitions, especially in contexts that lack multiple formal venues for making policy claims. [ABSTRACT FROM AUTHOR]

The Sun produces a fair amount of solar energy caused by the different solar activities that affect the terrestrial environment. The lowest part of the sun's atmosphere is photosphere gases that can be recognized by the disk of the sun and above parts are the chromospheres and corona. The three parts of the structure of the sun's atmosphere crucial to understand the nature of solar activities. The analysis of the study segregated monthly cyclic data of green line emission corona 530.03λ nanometers by appropriate stochastic models. After identifying stationary of each cycle along with total data is observed and generate autoregressive AR (p), MA (q) and ARIMA (p, d, q) models have revealed a linear difference and ARMA for a total span based on minimization of Akaike information criterion and Schwarz Beysian criterion. The parameters are examined by ACF and the PACF for different cyclic duration of Corona from 1944 to 2008. The models (0,1,1), (1,1,1) and (2,1,0) for cycles and (1,0,1) for total time series data were found the significance. The models obtained in this paper may be useful to understand and forecast the Coronal time series data. [ABSTRACT FROM AUTHOR]

All kinds of vehicles have different ratios of width to height, which are called the aspect ratios. Most previous works, however, use a fixed aspect ratio for vehicle detection (VD). The use of a fixed vehicle aspect ratio for VD degrades the performance. Thus, the estimation of a vehicle aspect ratio is an important part of robust VD. Taking this idea into account, a new on-road vehicle detection system is proposed in this paper. The proposed method estimates the aspect ratio of the hypothesized windows to improve the VD performance. Our proposed method uses an Aggregate Channel Feature (ACF) and a support vector machine (SVM) to verify the hypothesized windows with the estimated aspect ratio. The contribution of this paper is threefold. First, the estimation of vehicle aspect ratio is inserted between the HG (hypothesis generation) and the HV (hypothesis verification). Second, a simple HG method named a signed horizontal edge map is proposed to speed up VD. Third, a new measure is proposed to represent the overlapping ratio between the ground truth and the detection results. This new measure is used to show that the proposed method is better than previous works in terms of robust VD. Finally, the Pittsburgh dataset is used to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In coal seam gas (CSG) wells, water is periodically removed from the wellbore in order to keep the bottom-hole flowing pressure at low levels, facilitating the desorption of methane gas from the coal bed. In order to calculate gas flow rate and further optimize well performance, it is necessary to accurately monitor the liquid level in real-time. This paper presents a novel method based on autocorrelation function (ACF) analysis for determining the liquid level in CSG wells under intense noise conditions. The method involves the calculation of the acoustic travel time in the annulus and processing the autocorrelation signal in order to extract the weak echo under high background noise. In contrast to previous works, the non-linear dependence of the acoustic velocity on temperature and pressure is taken into account. To locate the liquid level of a coal seam gas well the travel time is computed iteratively with the non-linear velocity model. Afterwards, the proposed method is validated using experimental laboratory investigations that have been developed for liquid level detection under two scenarios, representing the combination of low pressure, weak signal, and intense noise generated by gas flowing and leakage. By adopting an evaluation indicator called Crest Factor, the results have shown the superiority of the ACF-based method compared to Fourier filtering (FFT). In the two scenarios, the maximal measurement error from the proposed method was 0.34% and 0.50%, respectively. The latent periodic characteristic of the reflected signal can be extracted by the ACF-based method even when the noise is larger than 1.42 Pa, which is impossible for FFT-based de-noising. A case study focused on a specific CSG well is presented to illustrate the feasibility of the proposed approach, and also to demonstrate that signal processing with autocorrelation analysis can improve the sensitivity of the detection system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a constrained convex sets-based method for designing ultra-wideband (UWB) pulse. The UWB pulses designed using earlier proposed methods either do not fit well in UWB spectral mask or require extra processing circuitry for pulses to fit in the UWB spectral mask. The proposed constraint sets-based method is flexible and optimum for the UWB mask. The pulse designed by the proposed method is Federal Communication Commission compliant and has an autocorrelation function similar to the Gaussian pulse. The bit error rate performance of time hopping binary phase shift keying using the proposed method and some of the existing methods such as Norman and Lee pulses and sixth derivative Gaussian pulse is analyzed in additive white Gaussian noise and Saleh-Valenzuela channels. Pulse designed using the proposed method outperforms existing pulse design, and the same is verified through simulations. The proposed algorithm is flexible and can also be used to mitigate narrow band interference, multiband UWB pulse generation and any other Federal Communication Commission (or other regulatory body) compliant spectrum mask by modifying the parameter of the proposed constraint set(s). [ABSTRACT FROM AUTHOR]

Abstract: Frequency stepping techniques are commonly used in modern radar system to get high range resolution with the disadvantage that its autocorrelation function (ACF) yield undesirable “grating lobes”. Wider mainlobe deteriorates the range resolution capability of the waveform and higher peak sidelobe either hides the small targets or causes the false target detection. Several techniques have been used to choose the parameters of linear frequency modulated (LFM) pulse train to suppress the grating lobes without paying much attention to the mainlobe width and peak sidelobe level. In this paper a multiobjective optimization (Nondominated Sorting Genetic Algorithm-II (NSGA-II)) approach is proposed to optimize the parameters of the LFM pulse train to achieve reduced grating lobes, low peak sidelobe level and narrow mainlobe width. The optimization problem has been studied in two different ways: first one is associated with the reduction of grating lobes and the minimization of peak sidelobe level of the ACF with constraints and second one is related to the minimization of the peak sidelobe level and mainlobe width of the ACF with constraints. Simulation studies have been carried out to justify the potentiality of the proposed approach. [Copyright &y& Elsevier]

Abstract: The microimage of activated carbon fibers (ACFs) obtained from SEM or TEM has low signal to noise ratio (SNR). And the objects in image overlapped together. Image processing methods based on clustering analysis and watershed transform were applied to explore the macropores morphology structure of ACFs. The microimage shows that abundant round-shaped macropores with pore size around 50–200nm, some of which cluster in the form of ring, distribute on ACF surface. Through clustering analysis, we calculated the surface area of macropores and their distributions. The results showed that the pores characterize within three regions, (1) the pore objects smaller than 30 pixels distributing nearly uniformly; (2) those with pixels between 30 and 165 peaking at lower region and (3) those larger than 165 pixels having fewer numbers and provide the main part of clusters. According to watershed transform segmentation analysis, the overlapped pores in one cluster were separated from each other. The number and other morphology parameters of macropores are calculated automatically and accurately in this paper. [Copyright &y& Elsevier]