State-of-the-art single-view 360° room layout reconstruction methods formulate the problem as a high-level 1D (per-column) regression task. On the other hand, traditional low-level 2D layout segmentation is simpler to learn and can represent occluded regions, but it requires complex post-processing for the targeting layout polygon and sacrifices accuracy. We present Seg2Reg to render 1D layout depth regression from the 2D segmentation map in a differentiable and occlusion-aware way, marrying the merits of both sides. Specifically, our model predicts floor-plan density for the input equirectangular 360° image. Formulating the 2D layout representation as a density field enables us to employ ‘flattened’ volume rendering to form 1D layout depth regression. In addition, we propose a novel 3D warping augmentation on layout to improve generalization. Finally, we re-implement recent room layout reconstruction methods into our codebase for benchmarking and explore modern backbones and training techniques to serve as the strong baseline. The code is at https://PanoLayoutStudio.github.io.

An autonomous wireless transmitter with a piezoelectric energy harvester is presented for home automation and Internet of Things applications. The proposed ultra-low-power transmitter and high-efficiency energy harvester facilitates battery-less remote control with a small feature size. Each shot of the PZT generator produces an energy of more than 20 μJ Moreover, the piezoelectric harvester achieves a high conversion efficiency of up to 80% through the use of an inductor-switching circuit. Finally, the wireless transmitter realizes ultra-low power frequency synthesis by means of injection locking and frequency multiplication techniques. Operating at the 433 MHz ISM band, the transmitter delivers an output power of -12.9 dBm with a power consumption of 750 μW. A prototype device is fabricated with a size of 8.5 × 3.5 × 2.5 cm3.

It is desirable to infer cellular dynamic regulation networks from gene expression profiles to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. To cope with the problem ldquomultiplicity of solutionsrdquo, a sufficient amount of data sets of time-series gene expression profiles were often used. An efficient newly-developed method iTEA was proposed to effectively obtain S-system models from a large number (e.g., 15) of simulated data sets with/without noise. In this study, we propose an extended optimization method (named iTEAP) based on iTEA to infer the S-system models of genetic networks from a time-series real data set of gene expression profiles (using SOS DNA microarray data inE.colias an example). The algorithm iTEAP generated additionally multiple data sets of gene expression profiles by perturbing the given data set. The results reveal that 1) iTEAP can obtain S-system models with high-quality profiles to best fit the observed profiles; 2) the performance of using multiple data sets is better than that of using a single data set in terms of solution quality, and 3) the effectiveness of iTEAP using a single data set is close to that of iTEA using two real data sets.