We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.

Background For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging offers non-invasive and non-ionizing assessment of CHD patients. However, although CMR data facilitates reliable analysis of cardiac function and anatomy, clinical workflow mostly relies on manual analysis of CMR images, which is time consuming. Thus, an automated and accurate segmentation platform exclusively dedicated to pediatric CMR images can significantly improve the clinical workflow, as the present work aims to establish. Methods Training artificial intelligence (AI) algorithms for CMR analysis requires large annotated datasets, which are not readily available for pediatric subjects and particularly in CHD patients. To mitigate this issue, we devised a novel method that uses a generative adversarial network (GAN) to synthetically augment the training dataset via generating synthetic CMR images and their corresponding chamber segmentations. In addition, we trained and validated a deep fully convolutional network (FCN) on a dataset, consisting of $$64$$ 64 pediatric subjects with complex CHD, which we made publicly available. Dice metric, Jaccard index and Hausdorff distance as well as clinically-relevant volumetric indices are reported to assess and compare our platform with other algorithms including U-Net and cvi42, which is used in clinics. Results For congenital CMR dataset, our FCN model yields an average Dice metric of $$91.0\mathrm{\%}$$ 91.0 % and $$86.8\mathrm{\%}$$ 86.8 % for LV at end-diastole and end-systole, respectively, and $$84.7\mathrm{\%}$$ 84.7 % and $$80.6\mathrm{\%}$$ 80.6 % for RV at end-diastole and end-systole, respectively. Using the same dataset, the cvi42, resulted in $$73.2\mathrm{\%}$$ 73.2 % , $$71.0\mathrm{\%}$$ 71.0 % , $$54.3\mathrm{\%}$$ 54.3 % and $$53.7\mathrm{\%}$$ 53.7 % for LV and RV at end-diastole and end-systole, and the U-Net architecture resulted in $$87.4\mathrm{\%}$$ 87.4 % , $$83.9\mathrm{\%}$$ 83.9 % , $$81.8\mathrm{\%}$$ 81.8 % and $$74.8\mathrm{\%}$$ 74.8 % for LV and RV at end-diastole and end-systole, respectively. Conclusions The chambers’ segmentation results from our fully-automated method showed strong agreement with manual segmentation and no significant statistical difference was found by two independent statistical analyses. Whereas cvi42 and U-Net segmentation results failed to pass the t-test. Relying on these outcomes, it can be inferred that by taking advantage of GANs, our method is clinically relevant and can be used for pediatric and congenital CMR segmentation and analysis.

Kernel-based learning has well-documented merits in various machine learning tasks. Most of the kernel-based learning approaches rely on a pre-selected kernel, the choice of which presumes task-specific prior information. In addition, most existing frameworks assume that data are collected centrally at batch. Such a setting may not be feasible especially for largescale data sets that are collected sequentially over a network. To cope with these challenges, the present work develops an online multi-kernel learning scheme to infer the intended nonlinear function 'on the fly' from data samples that are collected in distributed locations. To address communication efficiency among distributed nodes, we study the effects of quantization and develop a distributed and quantized online multiple kernel learning algorithm. We provide regret analysis that indicates our algorithm is capable of achieving sublinear regret. Numerical tests on real datasets show the effectiveness of our algorithm.

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We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, and Divisive Clustering, on average.Index Terms-quantization, node deployment, heterogeneous wireless sensor networks.