Life detection radar can detect human physiological signals (respiration, heartbeat, body movement, etc.) from a long distance away by penetrating nonmetal mediums (brick walls, ruins, etc.). However, interference is often caused by respiratory movements of the radar's operator when detecting vital signs of another human target. The detection accuracy can be significantly influenced by this kind of interference. In this letter, an experimental setup with a dual-frequency continuous-wave life detection radar is investigated. The system operates with different frequencies of 5.75 and 35 GHz. An adaptive filtering method is applied to suppress the interference caused by the operator's respiratory movements. Experimental results show that this method can effectively suppress respiratory interference and improve detection accuracy.

We propose a new variant of the Benes network using a merge-replace-fold approach. It is to realize a large port-count optical switch with low waveguide crossings. A bidirectional switch is built instead of a unidirectional switch. Compared to the classical Benes, it requires the same number of switches but far fewer intersections. The novel designs enable a 24-percentage reduction in waveguide intersections. Moreover, it has a worst-case insertion loss of 12.01 dB and a comparable crosstalk level of −13.67 dB between the neighbouring paths. Thus, it offers an energy-efficient way to solve the optical on-chip design problem in a bidirectional manner.

Optical interconnects are being discussed as a replacement for conventional electrical interconnects and are expected to be applied for future generations of high-performance supercomputers and data centers. Benes networks have attracted much attention because they require only 2 × 2 optical switches, which reduce the cost of rearrangeable nonblocking. However, optical power imbalances can significantly challenge receiver sensitivity. In this work, insertion loss (IL) fairness has been proposed and applied to the field of switches to achieve a relative balance of optical path data transmission in Benes networks. Fairness can be achieved when the port count is small (4 × 4) if the IL between ports is balanced. When the number of ports is moderate (8 × 8), we must use a suitable algorithm or determine the appropriate operating wavelength to minimize the power imbalance. An efficient two-step algorithm (ETS) has particular advantages in solving the path fairness problem and mitigating the power imbalance. As the number of ports increases, the switch states and topology jointly deteriorate the power imbalance. Finally, the ETS algorithm narrows the dynamic range requirement to 13.66 dB, with a 2 dB improvement. It achieves an extinction ratio of 24 dB and a bandwidth of 375 GHz, which outperforms the conventional 32 × 32 Benes network, respectively.

We propose a novel method for building ${N}$ -port nonblocking optical switches on a silicon chip. The rationale behind this is a bidirectional merge-replace-mirror method. It offers nonblocking interconnections among multiple inputs and outputs. The critical merits include $170~\mu \text{m}\,\,\times 300~\mu \text{m}$ footprint, nano-second circuit-switching, and $85.1~\mu \text{W}$ power consumption per link. The worst-case on-chip insertion loss for the 32-port optical switch is 18.02dB (all cross) and crosstalk is −16.96dB (all bars), respectively.

Making optical circuit switching suitable for handling highly dynamic and profoundly changing traffic is a considerable challenge. This motivation drives the development of a hybrid electrical/optical network towards high bandwidth and low latency. Compared with the traditional non-aggregation scheme, we provide two machine learning assisted aggregation schemes. The first one is to design optical cross-connect switches to increase the throughput of the circuit-switched network. In this solution, the optical cross-connect serves both delay-sensitive traffic flows and delay-tolerant traffic flows. As the network throughput rises rapidly, the number of ports of the optical switch remains unchanged. The second scheme is to add small port counts, which maximizes throughput while relaxing the requirements for accurate machine learning algorithms. In this paper, we have a set of four machine learning algorithms, and only the most suitable one is selected at a time. We deploy a machine learning algorithm at edge nodes instead of a central network management system. Therefore, we can simultaneously reduce network overhead and latency. Both aggregation schemes outperform the traditional non-aggregation scheme in terms of throughput, delay, and flow completion time.