Deep learning based reconstruction enables high-resolution electrical impedance tomography for lung function assessment.

Recently, deep learning based methods have shown potential as alternative approaches for lung time difference electrical impedance tomography (tdEIT) reconstruction other than traditional regularized least square methods, that have inherent severe ill-posedness and low spatial resolution posing challenges for further interpretation. However, the validation of deep learning reconstruction quality is mainly focused on simulated data rather than in vivo human chest data, and on image quality rather than clinical indicator accuracy. In this study, a variational autoencoder is trained on high-resolution human chest simulations, and inference results on an EIT dataset collected from 22 healthy subjects performing various breathing paradigms are benchmarked with simultaneous spirometry measurements. The deep learning reconstructed global conductivity is significantly correlated with measured volume-time curves with correlation > 0.9. EIT lung function indicators from the reconstruction are also highly correlated with standard spirometry indicators with correlation > 0.75.Clinical Relevance- Our deep learning reconstruction method of lung tdEIT can predict lung volume and spirometry indicators while generating high-resolution EIT images, revealing potential of being a competitive approach in clinical settings.

Laser beam steering of 532†nm using a power-efficient focal plane array.

Laser beam steering is important for classical and quantum information processing. On-chip beam steering is a major motivation for developing large-scale photonic integrated circuits such as optical phased arrays. A major challenge for such arrays is to simultaneously control a large number of on-chip phase shifters, which requires a complicated analog control algorithm and rapidly increasing power consumption. We report a green light (532 nm) 1 × 16 focal plane array photonic integrated circuit with simple control and low power consumption. Fabricated on a silicon nitride platform, the focal plane array achieves angular beam steering over a 10° field of view, with ultra-low electrical power consumption (4 × 3.1 mW).

Solid-state 360° optical beamforming for reconfigurable multicast optical wireless communications.

Optical wireless communication is an attractive technique for data center interconnects due to its low latency line-of-sight connectivity. Multicast, on the other hand, is an important data center network function that can improve traffic throughput, reduce latency, and make efficient use of network resources. To enable reconfigurable multicast in data center optical wireless networks, we propose a novel 360° optical beamforming scheme based on the principle of superposition of orbital angular momentum modes, emitting beams from the source rack pointing towards any combination of other racks so that connections are established between the source and multiple destination racks. We experimentally demonstrate the scheme using solid state devices for a scenario where racks are arranged in a hexagonal formation in which a source rack can connect with any number of adjacent racks simultaneously, with each link transmitting 70 Gb/s on-off-keying modulations at bit error rates of <10-6 at 1.5-m and 2.0-m link distances.

FPGA-Based High-Throughput CNN Hardware Accelerator With High Computing Resource Utilization Ratio.

The field-programmable gate array (FPGA)-based CNN hardware accelerator adopting single-computing-engine (CE) architecture or multi-CE architecture has attracted great attention in recent years. The actual throughput of the accelerator is also getting higher and higher but is still far below the theoretical throughput due to the inefficient computing resource mapping mechanism and data supply problem, and so on. To solve these problems, a novel composite hardware CNN accelerator architecture is proposed in this article. To perform the convolution layer (CL) efficiently, a novel multiCE architecture based on a row-level pipelined streaming strategy is proposed. For each CE, an optimized mapping mechanism is proposed to improve its computing resource utilization ratio and an efficient data system with continuous data supply is designed to avoid the idle state of the CE. Besides, to relieve the off-chip bandwidth stress, a weight data allocation strategy is proposed. To perform the fully connected layer (FCL), a single-CE architecture based on a batch-based computing method is proposed. Based on these design methods and strategies, visual geometry group network-16 (VGG-16) and ResNet-101 are both implemented on the XC7VX980T FPGA platform. The VGG-16 accelerator consumed 3395 multipliers and got the throughput of 1 TOPS at 150 MHz, that is, about 98.15% of the theoretical throughput ( 2 ×3395 ×150 MOPS). Similarly, the ResNet-101 accelerator achieved 600 GOPS at 100 MHz, about 96.12% of the theoretical throughput ( 2 ×3121 ×100 MOPS).

Low-noise Kerr frequency comb generation with low temperature deuterated silicon nitride waveguides.

We report very low-loss deuterated silicon nitride (SiNx:D) micro-ring resonators fabricated by back-end CMOS compatible low-temperature plasma-enhanced chemical vapor deposition (PECVD) without annealing. Strong confinement micro-ring resonators with a quality factor of > 2 million are achieved, corresponding to a propagation loss in the 1460-1610 nm wavelength range of ∼ 0.17 dB/cm. We further report the generation of low-noise coherent Kerr microcomb states including different perfect soliton crystals (PSC) in PECVD SiNx:D micro-ring resonators. These results manifest the promising potential of the back-end CMOS compatible SiNx:D platform for linear and nonlinear photonic circuits that can be co-integrated with electronics.

Optical solitons, self-focusing, and wave collapse in a space-fractional Schrödinger equation with a Kerr-type nonlinearity.

We investigate the nonlinear dynamics of (1+1)-dimensional optical beam in the system described by the space-fractional Schrödinger equation with the Kerr nonlinearity. Using the variational method, the analytical soliton solutions are obtained for different values of the fractional Lévy index α. All solitons are demonstrated to be stable for 1<α≤2. However, when α=1, the beam undergoes a catastrophic collapse (blow-up) like its counterpart in the (1+2)-dimensional system at α=2. The collapse distance is analytically obtained and a physical explanation for the collapse is given.

A Selective, Colorimetric and Ratiometric Chemosensor for Hg(2+) and Its Application as Test Papers.

Chemosensors provide an efficient and low-cost approach to the detection of various metal ions and small molecules. In the present work, we present a detailed investigation of a simple, easily-prepared, yet efficient, coumarin-based sensor for Hg(2+). The sensor is based on a fluorescent group of coumarin. When exposed to Hg(2+), the sensor solution shows a significant color change from yellow to red, and a fluorescence change from green to orange. Further study revealed that the sensor responds solely to Hg(2+), and shows excellent selectivity even in the presence of other potential competition metal ions, including Na(+), K(+), Co(2+), Fe(2+), Cd(2+), Cu(2+), Ni(2+), Ag(+), Zn(2+), Mg(2+), Mn(2+), Pb(2+), Fe(3+). Furthermore, the sensor has been successfully applied to detect Hg(2+) in a form of test paper, which may promote easy and effective application of the sensor.