Reviewerly/exHarmony huggingface dataset:
Neural Graphs: an Effective Solution for the Resource Allocation in NFV Sites interconnected by Elastic Optical Networks

The paper proposes and investigates neural graph-based solution for the prediction of the processing capacities needed by the Virtual Network Function Instances in a Network Function Virtualization environment. The proposed solution is centralized and performed by the Orchestrator which: i) acquires the processing capacity values measured by the Virtual Network Function Manager; ii) builds neural graphs each one relative to a measure period and where each node of a graph represents a VNFI and it is labelled with the measured processing capacity of that VNFI; iii) evaluates the prediction of the processing capacities needed by each VNFI node by means of convolution operations that allow for a capture of spatial and temporal correlations of the processing capacities required by the VNFIs. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters.
Calibration of a Photonic Neural Network Considering Fabrication Tolerances

Photonic integrated circuits (PICs) offer high-speed and wide-bandwidth hardware solutions for deep learning applications. However, phase mismatches resulting from fabrication tolerances can significantly degrade the accuracy of these networks. Calibration is essential before network operation to address this issue. This study presents a novel circuit-level simulation technique for photonic neural networks. Furthermore, we propose a method to calibrate the linear component of the neural network, a step that can significantly improve accuracy.
The Madrid Testbed: QKD SDN Control and Key Management in a Production Network

Software Defined Networking is increasingly seen as one of the best choices to create a Quantum Key Distribution (QKD) Network, especially if the aim is also its integration into a standard telecommunications network. The degree of flexibility afforded by this paradigm allows the creation of a network whose control mechanisms can be made aware simultaneously of the capabilities installed at the classical optical transport level as well as those at the quantum level. This is interesting not only to manage the coexistence between a classical and a quantum network sharing the same infrastructure, but also for future applications beyond QKD. This flexibility is due to a formal separation between the control and data forwarding planes. The logically centralized SDN controller gathers all relevant information monitoring the behavior of the hole network. It can act on the programmable quantum or classical devices opening many possibilities to share a common infrastructure, that can reach even the level of a single fiber strand to manage the signals that go through it in a compatible way. The same controller can use this information to manage the fundamental task of a QKD network, namely the key transport. Here we briefly report on the implementation of the SDN-QKD controller used in the Madrid testbed and its use to allow multi-vendor end-to-end key transport between any two nodes in the network.
High-Speed Random Number Generation and Key Distribution in Fiber Networks Using Amplified Spontaneous Emission

Amplified spontaneous emission (ASE) noise with inherent quantum nature is promising for true random number generator (TRNG) and secure key generation and distribution (SKGD). A high-speed TRNG and a physical-layer SKGD scheme are proposed and demonstrated using the polarization dynamics of an ASE source. The ASE-based randomness is enhanced in TRNG using digital signal processing on the two orthogonal polarization modes, while the fluctuation of the state of polarization (SOP) in ASE is applied as the random source in SKGD. A 16.8 Tb/s TRNG is successfully demonstrated with spectrum slicing of ASE. An error-free SKGD with a key generation rate (KGR) of 10.1 Gb/s is demonstrated over a 10-km standard single-mode fiber channel.
Performance Analysis of Passive-Decoy State Quantum Key Distribution

The passive decoy scheme represents a quantum key distribution protocol that employs a photon number resolving detector to selectively extract the decoy states from the signal states during post-processing. This selection process ensures resilience against attacks capable of distinguishing between these two states, thereby enhancing the security of the protocol. Previous research has emphasized the good performance of the protocol under optimal conditions. However, our study focuses on numerically evaluating the protocol performance in various practical scenarios. We aim to understand the implications of these scenarios in terms of the maximum attainable attenuation and bit rate per second, considering the number of photons per pulse sent under specific experimental conditions. Our findings demonstrate that opting for a low average photon number value ensures high tolerance to attenuation during communication, albeit at the cost of a reduced bit rate per second. Conversely, for lower attenuations, it is more advantageous to increase the average number of photons, even if it deviates from the ideal behaviour of a single-photon source. The benefit of selecting the decoy states at the end of the transmission remains consistent across different configurations. This research signifies an advancement in assessing the viability of incorporating the protocol within a practical QKD network.
Free-Space QKD Link Supported by AI Algorithm

In this work, we explore a new approach for the fine-adjustment feedback loop in a free-space quantum key distribution (QKD) system. In particular, we propose a new feedback loop based on artificial intelligence (AI) to correct the turbulence effects induced by the propagation on a free-space channel. The proof of concept proposed in this paper demonstrates the feasibility of the method and paves the way toward a large deployment of free-space links in intracity scenarios.
Entanglement Assisted MIMO Quantum Radars

In this invited paper, we describe our entanglement assisted (EA) MIMO quantum radar technique with multiple entangled transmitters and coherent detection-based receivers. This scheme exploits the phase sensitive quantum correlation on the receivers’ sides to improve the receiver sensitivity. To increase overall signal-to-noise ratio, we use the spatial MIMO concept. To reduce the system cost and complexity, the optical phase conjugation (OPC) on signal photons is performed on a transmitter side, so that commercially available classical, balanced coherent detectors can be used on receiver side instead of traditional OPC-based EA receivers.
Reliable Quantum Communication

The quantum internet, which will be a hybrid of quantum and classical networks, is expected to provide information-theoretic security for data transmission. Classical networks have well-established protocols for reliable end-to-end transmission that use duplicating the classical bits implicitly. However, due to the no-cloning theorem, quantum bits (qubits) cannot be copied. In this paper, we propose to use a Universal Quantum Copying Machine (UQCM) to create imperfect clones and summarizes the Quantum Automatic Repeat Request (QARQ) protocol, which is inspired by its classical counterpart. To investigate the viability of QARQ, a simulation platform is created considering Nitrogen-vacancy (NV) center qubit platform that implements QARQ for quantum communication. The results explore the applications it is suitable for while maintaining enough quality of the qubit state. Two major quantum technologies are studied for these protocols: Direct Transmission (DT), and Teleportation (TP). It has been shown that for short distance communication DT provides better fidelity than TP while for long-distance communication TP outperforms DT and hence improves the QARQ performance as well.
Multi-dimensional reconciliation encoder with quasi-cyclic LDPC codes on FPGA

Information reconciliation (IR) is an integral part of classical data post-processing in quantum key distribution (QKD) and often constitutes a performance bottleneck. Due to the low signal-to-noise ratio, continuous-variable QKD systems require a IR scheme, such as multi-dimensional reconciliation (MDR), which is particularly computationally intensive.In this work we present the hardware architecture of an MDR encoder employing Quasi-Cyclic Low Density Parity Check (QC-LDPC) codes. We estimate the required number of flip-flops and the latency of its FPGA implementation. Finally, we investigate the computational bottlenecks and identify solutions to improve the scalability of the proposed implementation.
Prospects of Chip-Based Multi-Protocol Quantum Key Distribution Transceivers

Quantum communications enable the transmission of information in a secure way that is ensured by the laws of quantum physics.Current quantum-safe communication systems are based on quantum key distribution (QKD) technology at their core.In the most recent years, a remarkable effort has been put into practical implementations of QKD with a focus on their integration into classical optical networks, some of which are becoming commercially available.However, even with the ongoing development of QKD systems, there are efforts toward their miniaturization, power efficiency improvement, and enhancement of their flexibility and functionality.In this paper, we outline major QKD protocols and review recent advances in QKD systems based on photonic integrated circuits.Finally, we will discuss the potential feasibility of multi-protocol QKD chips leveraging the advantages of different protocols in one solution.
Challenges and Opportunities of a Vastly Distributed Cloud Computing Infrastructure – In the Context of the Dong Shu Xi Suan (DSXS) Project of China

It has been widely recognized that placing cloud data centers near clean energy sources or in cooler environments may reduce the operational cost and carbon footprint. But when the scale of the system, in terms of the overall computing power and territorial coverage, is large, such benefits may be offset by the additional cost of moving data around and the complexity of efficiently utilizing the network and computational resources. In this paper, we discuss the benefits and challenges of a vastly distributed cloud computing infrastructure, in the particular context of the east-west synergized cloud computing project in China, i.e., the Dong Shu Xi Suan Project (DSXS). We discuss the state of art research that are relevant to this topic, and explore the potential technological advances such a project may bring.
Unsupervised Full Transformer for Pose, Depth and Optical Flow Joint Learning

The use of monocular cameras for ego-motion estimation in autonomous driving is a fundamental technique with significant potential for development. Recently, unsupervised CNNs based schemes have rapidly advanced due to their label-free advantages. However, traditional CNNs lack the ability to capture global dependencies, which are essential for sequential tasks like pose, depth, and optical flow estimation. In this paper, according to the characteristics of these three-independent tasks, we designed different network frameworks fully based on the transformer separately to improve performance by taking advantage of the global dependencies of different tasks. After unsupervised training, the model can independently predict the pose, depth, and optical flow results for the vehicle, solely based on a monocular camera. The experimental results conducted on the KITTI demonstrate that the proposed method obtain satisfactory results in all three tasks.
Surface Material Classification of Robot Perception Using Trimodal Information with Multiloss Ensemble

Surface material classification (SMC) is a critical component of robot perception system for understanding the external environment. However, traditional SMC methods that rely solely on visual information are susceptible to the effects of lighting and distance, and have limitations in classification accuracy and robustness. Drawing inspiration from the human perception system, we propose a Trimodal Fusion Neural Network (TMFNN) based on multiloss ensemble. Our model first extracts visual, auditory, and haptic features using CNN and GRU, and then efficiently fuses them using self-attention mechanism. We also employ an improved multiloss ensemble to enhance model performance. Our classification results on the TUM Dataset reveal that the proposed method significantly outperforms state-of-the-art methods, achieving the highest classification accuracy of 96.56%. Furthermore, we experimentally demonstrate that our proposed multimodal fusion and multiloss ensemble approaches can significantly enhance model accuracy. Overall, our TMFNN is capable of handling more complex SMC tasks and has considerable promise for improving robot perception in a variety of environments.
Research on Image Enhancement Algorithm Under Transformer Oil Based on Standard Deviation Gray-world

In order to solve the difficulty of extracting feature points in the front-end of visual simultaneous localization and mapping (SLAM) for oil-immersed transformer robot, a standard deviation gray-world algorithm to enhance image under transformer oil is proposed in this paper. Firstly, in order to solve the problem of color distortion and low contrast of the image under transformer oil, this paper uses the traditional gray-world algorithm to enhance the image. Then in order to prevent the appearance of “excess” gain, this paper uses adaptive standard deviation to constrain the gray gain coefficient. Finally, the gray value adaptive stretching is used to solve the problems of color distortion and low contrast under the transformer oil. The experimental results show that the image under transformer oil processed by the algorithm proposed in this paper has bright color and more feature points extracted, the UIQM value of the enhanced image is increased by 65.09%, the UCIQE value is increased by 8.67%, and the number of feature points extracted is increased by 273.21%.
Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection

Existing action detection approaches do not take spatio-temporal structural relationships of action clips into account, which leads to a low applicability in real-world scenarios and can benefit detecting if exploited. To this end, this paper proposes to formulate the action detection problem as a reinforcement learning process which is rewarded by observing both the clip sampling and classification results via adjusting the detection schemes. In particular, our framework consists of a heterogeneous graph convolutional network to represent the spatio-temporal features capturing the inherent relation, a policy network which determines the probabilities of a predefined action sampling spaces, and a classification network for action clip recognition. We accomplish the network joint learning by considering the temporal intersection over union and Euclidean distance between detected clips and ground-truth. Experiments on ActivityNet v1.3 and THUMOS14 demonstrate our method.
Infrared Image Transformation via Spatial Propagation Network

In recent years, there has been an increasing demand for intelligent infrared recognition methods. As current high-precision intelligent recognition algorithms like deep networks largely rely on massive amounts of training data, the lack of infrared databases has become a major limitation for technological development, resulting in an urgent demand for intelligent infrared image simulation technology. Different from most infrared image simulation techniques which expand infrared data amount under conditions of thermal balance, this paper proposes a novel way to simulate infrared images, i.e. generating infrared images for objects in scenes under an unsteady heat conduction process along the time axis. To be specific, this paper incorporates a spatial propagation network structure to predict the equivalent thermal conductivity coefficients for the input infrared image captured at a certain time point, and then infers the infrared images at the next time points by simulating the physical heat conduction process based on the predicted conductivity coefficients. We carry out extensive experiments and analysis on the datasets composed of factual infrared photos and PDE-simulated images, demonstrating that the proposed infrared image generation method can realize the transformation simulation and dataset expansion of infrared images with high speed and high quality.
Pulse Signal Peak Detection Algorithm Based on Improved Slope Method and Partitioning Method

Chinese medicine has a long history, and pulse diagnosis can detect physiological and pathological changes in the human body. With the rise and development of artificial intelligence, the objectification and modernization of TCM has also come a long way. In the process of pulse diagnosis objectification, the extraction of feature datum points in the time domain is an indispensable step. In this paper, starting with the pulse wave time domain feature extraction method, we systematically summarize the feature point extraction methods, including single-period feature point feature extraction and multi-period feature point extraction, and then on this basis, combined with the extraction experience of the collected data, we propose an improved slope method and partition method (ISPA) for pulse wave feature extraction research.
Detection of River Floating Waste Based on Decoupled Diffusion Model

In recent years, the conservation of water resources has attracted widespread attention. The development and application of water surface robots can achieve efficient cleaning of floating waste. However, limited to the small size of floating waste on the water surface, its detection remains a great challenge in the field of object detection. Existing object detection algorithms cannot perform well, such as YOLO (You Only Look Once), SSD (Single-Shot Detector), and Faster R-CNN. In the past two years, diffusion-based networks have shown powerful capabilities in object detection. In this paper, we decouple the position and size regressions of detection boxes, to propose a novel decoupled diffusion network for detecting the floating waste in images. To further promote the detection accuracy of floating waste, we design a new box renewal strategy to obtain desired boxes during the inference stage. To evaluate the performance of the proposed methods, we test the decoupled diffusion network on a public dataset and verify the superiority compared with other object detection methods.
A Path Planning Method Based on Simplified Visibility Graph and Support Vector Machine

The paper proposes a path planning method based on a simplified visibility graph (SVG) and support vector machine (SVM). Firstly, the centroid point set of polygon obstacles is used to construct a Delaunay triangulation network. Then, all neighbors of each obstacle are obtained from the Delaunay triangulation network to construct neighbor pairs. A global environmental SVG is then constructed by simplifying the visibility relationships between vertices of neighbor obstacle pairs. Next, the start and goal points are maintained in the SVG and the path is planned using Dijkstra’s algorithm and pruning method. Additionally, an elliptical window is generated based on the globally planned path. The obstacles and the boundary of the ellipse are classified into positive and negative samples, respectively. A safe and smooth path is obtained by training the SVM based on the radial basis function (RBF). The simulation experiments show that, compared with traditional methods, the proposed method based on the Delaunay triangulation reduces the number of visual edges by more than 40% and reduces cost time by over 50%. What’s more, the optimized path obtained through the SVM is sufficiently smooth and safe.
Laser Active Alignment Algorithm based on Spot Features and Curve Fitting

This paper addresses the active alignment problem in manufacturing solid-state LiDAR systems and focuses on image processing and alignment algorithms within a visual laser auto-collimation platform. By extracting features from laser spot images, a non-linear discrete optimization is applied to determine the optimal position of the laser emitter, minimizing the laser beam divergence angle. To overcome the challenge of inaccurate size extraction of Fraunhofer diffraction patterns, Gaussian filtering, adaptive thresholding, and circle fitting based on distance transformation are employed to estimate the spot size. Active alignment algorithms are implemented using improved hill climbing, genetic, and curve fitting algorithms. Experimental comparisons demonstrate that the curve fitting-based active alignment algorithm achieves better efficiency and stability, with an average adjustment count of 29.35 and a variance of 97.50.
Recognizing Human-object Interactions for a Home Assistive Robot

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying <human, verb, object> efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot – Xiaozhi II verify its robustness.
Positioning Obstacle Avoidance Control of AGV with no Local Minimum Problem via Safety LOS Guidance

This paper studies the positioning and obstacle avoidance control of automated guided vehicles (AGV). First, we have established the kinematics model for AGV by equivalent the obstacle to a circle. Different from the conventional obstacle avoidance methods, we use the polar coordinate method to express the obstacle position. Second, for the presented AGV system, we propose a safety heading angle base on the Line of sight (LOS) method. Unlike the traditional LOS method, this paper introduces the tangent angle of the circle formed by AGV and obstacles as the guidance of LOS. Therefore, AGV's heading will not be toward obstacles to achieve obstacle avoidance control. Then, to ensure that AGVs reach the target point, we propose a positioning heading angle that can achieve obstacle avoidance based on the above safety guidance algorithm. Compared with the traditional artificial potential field (APF) obstacle avoidance method, the proposed method has no misalignment of the balance point. Finally, experiments are conducted using a ROS-based AGV experimental platform to demonstrate the algorithm's feasibility. The experimental results show that the algorithm can effectively solve the problem of misalignment of target points in the traditional artificial potential field algorithm.
Path Planning Simulation and Research of Mobile Robot in Static Environment based on Genetic Algorithm

The feature of mobile robot is to realize the purpose of completing a certain task by perceiving, receiving and processing the information of the external environment and independently planning the moving route in the static environment. This paper focuses on the path planning of mobile robots in static environment, and designs a set of static global path planning algorithm based on genetic algorithm through the research of path planning methods and related work. MATLAB is used to simulate the path planning algorithm, and analyzes the influence of related parameters on the path planning effect, as well as the basic idea of reasonable parameter adjustment. Finally, the path planning algorithm in dynamic environment is preliminarily studied and simulated.