In this paper, based on Equivalent Active Flow, we propose a novel technique called Approximate Fairness Dropping, which is able to approximate fairness by containing misbehaving flows' access queue opportunity with low time/space complexity. Unlike most of the existing Active Queue Management schemes (e.g., RED, BLUE, CHOKE), Approximate Fairness Dropping does not drop the packets whose arriving rate is within the maximum admitted rate, so it protects the well-behaving flows against misbehaving ones, moreover, improves the throughput and decreases the queuing delay. Our simulations and analyses demonstrate that this new technique outperforms the existing schemes and closely approximates the "ideal" case, where full state information is needed.

Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature co-occurrence for urban region function recognition, which leverages the co-occurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

A novel and compact electro-optic modulator implemented by a combination of a 12 multimode interference (MMI) coupler and an integrated Mach-Zehnder interferometer (MZI) modulator consisting of a microring and a phase modulator (PM) is presented and analyzed theoretically. It is shown that the proposed modulator offers both ultra-linearity and high output RF gain simultaneously, with no requirements for complicated and precise direct current (DC) control.

The electromagnetic environment is increasingly complex and changeable, and radar needs to meet the execution requirements of various tasks. Modern radars should improve their intelligence level and have the ability to learn independently in dynamic countermeasures. It can make the radar countermeasure strategy change from the traditional fixed anti-interference strategy to dynamically and independently implementing an efficient anti-interference strategy. Aiming at the performance optimization of target tracking in the scene where multiple signals coexist, we propose a countermeasure method of cognitive radar based on a deep Q-learning network. In this paper, we analyze the tracking performance of this method and the Markov Decision Process under the triangular frequency sweeping interference, respectively. The simulation results show that reinforcement learning has substantial autonomy and adaptability for solving such problems.

1 × N active multi-mode interferometer laser diode (MMI LD) is proposed and demonstrated to realize single-wavelength edge-emitter without using grating configuration. As the 1 × N active-MMI LDs are based on longitudinal mode interference, they have a potential of single-wavelength emission without incorporating any grating layer on/beneath active layer. The fabricated devices showed single-wavelength emission with a side mode suppression ratio (SMSR) of 12dB at a wavelength of 1.57µm.

Cloud computing enables computational resource-limited devices to economically outsource much computations to the cloud. Modular exponentiation is one of the most expensive operations in public key cryptographic protocols, and such operation may be a heavy burden for the resource-constraint devices. Previous works for secure outsourcing modular exponentiation which use one or two untrusted cloud server model or have a relatively large computational overhead, or do not support the 100% possibility for the checkability. In this letter, we propose a new efficient and verifiable algorithm for securely outsourcing modular exponentiation in the two untrusted cloud server model. The algorithm improves efficiency by generating random pairs based on EBPV generators, and the algorithm has 100% probability for the checkability while preserving the data privacy.