Accurate range free localization in multi-hop wireless sensor networks

To localize wireless sensor networks (WSN)s nodes, only the hop-based data have been so far utilized by range free techniques, with poor-accuracy, though. In this thesis, we show that localization accuracy may importantly advantage from mutual utilization, at no cost, of the information already offered by the advancing nodes (i.e., relays) between all anchors (i.e., position-aware) and sensor nodes join up. In addition, energy-based informant localization approaches are generally established corresponding to the channel path-loss models in which the noise is mostly expected to shadow Gaussian distributions. In this thesis, we signify the applied additive noise by the Gaussian mixture model and improve a localization algorithm depend on the received signal intensity to attain the greatest likelihood location, estimator. By employing Jensen’s inequality and semidefinite relaxation, the originally offered nonlinear and nonconvex estimator is relaxed into a convex optimization difficulty, which is able to be professionally resolved to acquire the totally best solution. Moreover, the resultant Cramer–Rao lower bound is originated for occurrence comparison. Simulation and experimental results show a substantial performance gain achieved by our proposed localization algorithm in wireless sensor networks. The performance is evaluated in terms of RMSE in terms of three algorithms WLS, CRLR, and GMSDP based on using the Monte Carlo simulation with account the number of anchors that varying from anchor=4 to anchor =20. Finally, the GMSDP- algorithm achieves and provides a better value of RMSEs and the greatest localization estimation errors comparing with the CRLR algorithm and WLS algorithm

Design a high gain UWB mimo uniplanar monopole antenna with FSS array for metallic object microwave imaging

Ultra-wideband (UMB) system plays an important role in microwave imaging (MWI) applications due to its broad bandwidth, non-ionizing radiation, and cost-efficiency. this study involves the design and development phases for the optimum solution of UMB antenna's issues. In the design phase, a compact uniplanar hexagon UMB monopole antenna with a coplanar waveguide (CPW) feed is designed. The proposed UMB antenna has an oscillate impedance (za) of 50Ω. A meander-line notch filter is loaded on the designed antenna that achieves a high rejection (S11=-1.75 dB) at the band of 3.0 GHz for 5G mid-band. A T-strip is inserted between the two proposed MIMO antennas to improve the isolation. Moreover, the smallest uniplanar UWB frequency selective surface (FSS) unit cell size (0.095λx0.095λ) is miniaturized on the FR4 substrate. The simulations are compared with the equivalent circuit models of the proposed solutions, then validate with the measurement results. In the development phase, the hexagonal monopole MIMO antenna, The CPW feed, the isolation T-strip, and the 3 x 7 FSS (IMAF) achieves a bandwidth of 3-11.7 GHz, unidirectional radiation patterns, mutual coupling (S21 about -27 dB) and gain (6-8.5 dBi), and it better than the existing antennas of 3.1-10.6 GHz, _20 dB, and 5.5 dBi, respectively. Additionally, the baggage-scanner scheme is developed as a case study to evaluate the IMAF is 55% higher than that of the MIMO antenna without an FSS array. Thus, the proposed IMAF detects the smallest (0.5 x 2 cm2 ) metallic object with a location accuracy of ¥ o.5 cm compared with the recent simulation study of (0.6 x 0.h cm2 and ±1.1 cm, respectively). A good agreement is observed between the simulated and measured images of the MWI. Consequently, the IMAF is proved to be applicable as part of the detection system for low-cost and non-intricate baggage-scanner imaging to detect metallic objects

Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes

We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of longitudinal signals and a Cox model to map time-to-event data with longitudinal data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions

Asian trade barriers against primary and processed commodities

Many developing countries are being encouraged to shift toward increased processing and exports of domestically produced natural resource based products now exported in primary form. But in many major markets, the structure of tariffs and nontariff barriers militate against such efforts. Zero or low tariffs are generally applied to industrial countries'imports of primary (unprocessed) commodities; duties increase, or"escalate", as the level of processing or fabrication increases. Tariff escalation produces a trade bias against processed goods. In the past, such trade barrier escalation has been attributed chiefly to industrial countries. The authors examined the structure of restrictions in Asian countries and found that most Asian countries'tariffs incorporated more escalation than do tariffs in industrial countries. Apparently tariff escalation is often reinforced by nontariff barriers on processed goods, although supporting data for this finding are less firm. This issue should be viewed as a North-South issue, contend the authors. A bias against imports of processed goods is built into trade barrier escalation among Asian countries and should be addressed in regional initiatives to liberalize intra-Asian trade barriers. The authors make three recommendations for dealing with escalation issues in multilateral negotiations: Japan, and to a lesser extent, the Republic of Korea are the keys to successful negotiations on these issues, as they have a far greater import bias against processed commodities than do all other countries with which the authors compare them. That is, Japanese and Korean trade barriers incorporate far more escalation than do trade barriers in other countries studied. Disproportionately high cuts in trade barriers for unprocessed commodities are not the solution, as they would increase effective protection for processed goodss. Any approach to trade liberalization should deal with both tariffs and nontariff barriers, to ensure that a reduction in one type of restriction is not offset by a further tightening in the other. Several Asian countries apply both types of restrictions to commodity imports.TF054105-DONOR FUNDED OPERATION ADMINISTRATION FEE INCOME AND EXPENSE ACCOUNT,Environmental Economics&Policies,Transport and Trade Logistics,Common Carriers Industry,Trade Policy

Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach

Recently there has been an increasing interest in the multivariate Gaussian process (MGP) which extends the Gaussian process (GP) to deal with multiple outputs. One approach to construct the MGP and account for non-trivial commonalities amongst outputs employs a convolution process (CP). The CP is based on the idea of sharing latent functions across several convolutions. Despite the elegance of the CP construction, it provides new challenges that need yet to be tackled. First, even with a moderate number of outputs, model building is extremely prohibitive due to the huge increase in computational demands and number of parameters to be estimated. Second, the negative transfer of knowledge may occur when some outputs do not share commonalities. In this paper we address these issues. We propose a regularized pairwise modeling approach for the MGP established using CP. The key feature of our approach is to distribute the estimation of the full multivariate model into a group of bivariate GPs which are individually built. Interestingly pairwise modeling turns out to possess unique characteristics, which allows us to tackle the challenge of negative transfer through penalizing the latent function that facilitates information sharing in each bivariate model. Predictions are then made through combining predictions from the bivariate models within a Bayesian framework. The proposed method has excellent scalability when the number of outputs is large and minimizes the negative transfer of knowledge between uncorrelated outputs. Statistical guarantees for the proposed method are studied and its advantageous features are demonstrated through numerical studies