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The input-parallel output-parallel (IPOP) connected converter system allows the use of a low-power converter module for high-power applications. However, mismatches in the IPOP circuit parameters cause uneven current sharing between the constituent modules, which makes the IPOP system unreliable for practical use. Different techniques have been proposed so far; however, they cause an increase in magnetic volume, cost, component count, and complexity of the IPOP system. In this article, a reliable and efficient magnetic integration technique is proposed for the current sharing requirements of the IPOP system based on LLC resonant converter modules. The proposed integrated magnetic current balancing transformer (ICBT) based IPOP LLC resonant converter modules ensures both the input current sharing and output current sharing between constituent LLC modules, under the open-loop condition quite effectively. The proposed ICBT is the resonant inductors of LLC resonant converter modules, coupled to perform the function of current balancing as well as generates enough leakage inductances for resonance operation, simultaneously. Thus, it can reduce the overall magnetic volume and improves power density of the IPOP LLC modules. Moreover, the current sharing performances of the proposed ICBT in both the steady-state and dynamic-state are analyzed based on the magnetic and electrical model. To validate the performance of the proposed ICBT based IPOP two LLC modules, a 2.6-kW hardware prototype was designed, fabricated, and tested successfully. [ABSTRACT FROM AUTHOR]

Lattice Reduction aided soft output MIMO detectors (LR-SOMD) have been demonstrated to offer a promising gain. This work explores the potential of implementing a LR-SOMD on a parallel programmable baseband processor. In this paper, first a LR algorithm called the Data Regularized Parallel Lattice Reduction algorithm (DRP-LR) is proposed. Afterwards, a low-complexity LR-SOMD, Radius Constrained Multi-Tree Selective Spanning (RC-MTSS) is presented. RC-MTSS uses a novel multiple-tree search approach for LR-SOMD, while combining the benefits of Sphere Detection (SD) and Selective Spanning with Fast Enumeration (SSFE). A fixed complexity LR-SOMD, Multi-Tree Selective Spanning (MTSS) is also proposed for implementation. Both the algorithms, DRP-LR and MTSS, are enabled to exploit data level parallelism (DLP) and instruction level parallelism (ILP). In order to evaluate performance, the proposed DRP-LR and MTSS are implemented on the ADRES baseband processor for a 4 \times 4 LTE system using QAM-64. DRP-LR achieves an average throughput of 33.33 M LR per second, which is comparable to recently reported ASIC implementations, while MTSS shows an average throughput of 730 Mbps on the same processor. To the best of authors' knowledge, this is the first reported implementation of a LR-SOMD algorithm on a parallel programmable baseband processor. [ABSTRACT FROM AUTHOR]

Urban search and rescue missions require rapid intervention to locate victims and survivors in the affected environments. To facilitate this activity, Unmanned Aerial Vehicles (UAVs) have been recently used to explore the environment and locate possible victims. In this paper, a UAV equipped with multiple complementary sensors is used to detect the presence of a human in an unknown environment. A novel human localization approach in unknown environments is proposed that merges information gathered from deep-learning-based human detection, wireless signal mapping, and thermal signature mapping to build an accurate global human location map. A next-best-view (NBV) approach with a proposed multi-objective utility function is used to iteratively evaluate the map to locate the presence of humans rapidly. Results demonstrate that the proposed strategy outperforms other methods in several performance measures such as the number of iterations, entropy reduction, and traveled distance. [ABSTRACT FROM AUTHOR]