Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Performance Analysis of P4 Programmable Devices

In our digitized society, emerging applications require highly performant and flexible networks that can adapt to satisfy varying connectivity needs. P4 as a domain-specific programming language for data plane pipelines introduces the required flexibility through easy-to-use programmability. However, the performance of P4-capable devices is still an open question that has not yet been completely addressed. Understanding whether a P4-enabled device can meet the performance requirements for a specific network function pipeline is key for planning as well as deployment scenarios in a communication provider network.In this paper, we propose a simple analytical model that can quickly predict the performance of network functions written in P4 for a given device. The programmable data plane of P4 devices is modeled as a forward queuing system with a variable service rate that depends on the complexity of the configured data path program. On top of the data plane model, the controller’s interaction is modeled as a feedback queuing system. We evaluate the accuracy of our model through a parameter study and simulation. The evaluation reveals corner cases, which are analyzed to formalize a constraint on the service rate using model parameters to guarantee stable system performance.
Hazardous Events Detection in Automatic Train Doors Vicinity Using Deep Neural Networks

In the field of train transportation, personal injuries due to train automatic doors are still a common occurrence. This paper aims at implementing a computer vision solution as part of a safety detection system to identify automatic doors-related hazardous events to reduce their occurrence and their severity. Deep anomaly detection algorithms are often applied on CCTV video feeds to identify such hazardous events. However, the anomalous events identified by those algorithms are often simpler than most common occurrences in transport environments, hindering their widespread usage. Since such events are of quite a diverse nature and no dataset featuring them exist, we create a specilically-tailored dataset composed of real-case scenarios of hazardous events near train doors. We then study an anomaly detection algorithm from the literature on this dataset and propose a set of modifications to better adapt it to our railway context and to subsequently ease its application to a wider range of use-cases.
Adaptive Acoustic Flow-Based Navigation with 3D Sonar Sensor Fusion

Navigating spatially varied and dynamic environments is one of the key tasks for autonomous agents. In this paper we present a novel method of navigating a mobile platform with one or multiple 3D-sonar sensors. Moving a mobile platform and subsequently any 3D-sonar sensor on it, will create signature variations over time of the echoed reflections in the sensor readings. An approach is presented to create a predictive model of these signature variations for any motion type. Furthermore, the model is adaptive and works for any position and orientation of one or multiple sonar sensors on a mobile platform. We propose to use this adaptive model and fuse all sensory readings to create a layered control system allowing a mobile platform to perform a set of primitive motions such as collision avoidance, obstacle avoidance, wall following and corridor following behaviours to navigate an environment with dynamically moving objects within it. This paper describes the underlying theoretical base of the entire navigation model and validates it in a simulated environment with results that shows the system is stable and delivers expected behaviour for several tested spatial configurations of one or multiple sonar sensors that can complete an autonomous navigation task.
Sliding Mode Control with Adaptive Gain of Quadrotor with Rigid Manipulator

This paper addresses the problem of stabilizing the motion of an aerial manipulator composed of a Quadrotor and a manipulator with two degrees of freedom (2 dof). The dynamic modeling of an aerial manipulator, like the one presented in this paper, shows that there is a strong physical coupling between the dynamics of the UAV and the manipulator arm. This coupling, plus external perturbations (e.g. wind gusts), considerably affect the stability of the drone's motion in flight which, consequently, affects the desired accuracy of the end effector with respect to its final task. To overcome this problem, we propose in this paper an approach based on an adaptive gain sliding mode control, a priory without the need to know its bounds, and to make the gain as small as possible but sufficient to overcome these disturbances due to both the arm motion and external uncertainties. The effectiveness of the controller and its robustness against noise are verified and analyzed using MATLAB/SIMULINK.
Hybrid Beamforming Optimization for UAV-Enabled mmWave Beamspace MIMO System

The combination of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) multiple-input multiple-out (MIMO) systems is considered as a key enabling technology for 5G networks, as it provides high data rate aerial links. However, establishing UAV-enabled mmWave MIMO communication is challenging duo to the high hardware cost in terms of radio frequency (RF) chains. As explored in this work, the joint optimization of the UAV's altitude and hybrid beamspace precoding is proposed for the UAV-enabled multiuser MIMO system, in which the discrete lens arrays (DLA) is exploited to decrease the amount of the RF chain. In the proposed scheme, the optimization problem is formulated as a minimum weighted mean squared error (MWMSE) method. Then an efficient algorithm with the penalty dual decomposition is proposed to optimize the altitude of UAV, selection of the beam and the method of digital precoding jointly. Simulation results confirm the superior performance of the explored scheme and perform close to full-digital beamforming in terms of achievable spectral efficiency.
Voltage Unbalance Compensation for Islanded Microgrids with Hybrid Control Approach

Voltage unbalance is one of the challenges of low-voltage microgrids. It is often caused by unbalanced loads connected to the microgrid. Its compensation is required to ensure an efficient microgrid operation. In this paper, a voltage unbalance compensation mechanism based on modifying the control signals is explored. A hybrid control scheme is used to control and regulate the distributed generators (DGs) inverters in an islanded low voltage microgrid. The DGs inverters are either controlled as grid-forming or grid-feeding units. A voltage unbalance compensation mechanism using local measurements is used to reduce the voltage unbalance at the different points throughout the microgrid. This voltage unbalance compensation is performed only by the grid-forming controller which uses local voltage measurements only. The compatibility between the control algorithm and the compensation mechanism is shown through different test scenarios. The simulation results obtained using Matlab/SimscapePowerSystems prove the effective performance of the proposed approach.
Joint Transceiver Optimization for Performance Improvement of Multi-User NOMA VLC System

In this paper, we propose a scheme to improve the performance of downlink multi-user non-orthogonal multiple access (NOMA) visible light communication (VLC) system by jointly adjusting the parameters of superposition constellation at the transmitter and that of successive interference cancellation decoding at the receiver. We derive the closed-form bit error rate (BER) expression of multi-user NOMA VLC system for each user. We also find a rule for the number of terms in each users' BER expression in terms of Q-function when the modulation format of each user is quadrature amplitude modulation. This rule is named as NOMA triangle. According to the derived BER expressions, we formulate a problem that optimizes the adjustment parameters to minimize the average BER among users. This problem is solved by differential evolution algorithm. The effectiveness of proposed scheme is demonstrated by extensive simulations.
A Task Modelling Formalism for Industrial Mobile Robot Applications

Industrial mobile robots are increasingly introduced in factories and warehouses. These environments are becoming more dynamic with human co-workers and other uncertainties that may interfere with the robot's actions. To uphold efficient operation, the robots should be able to autonomously plan and replan the order of their tasks. On the other hand, the robot's actions should be predictable in an industrial process. We believe the deployment and operation of robots become more robust if the experts of the industrial processes are able to understand and modify the robot's behaviour. To this end, we present an intuitive novel task modelling formalism, Robot Task Scheduling Graph (RTSG). RTSG provides building blocks for the explicit definition of alternative task sequences in a compact graph format. We present how such a graph is automatically converted to a task planning problem in two different forms, i.e., a Mixed Integer Linear Program (MILP) and a Planning Domain Definition Language specification (PDDL). Converted RTSG models of a mobile kitting application are used to experimentally compare the performance of one MILP planner and two PDDL planners. Besides providing this comparison, the experiments confirm the equivalence of the converted MILP and PDDL problem formulations. Finally, a simulation experiment verifies the assumed correlation between a cost model, based on path lengths, and the makespan.
Bio-Inspired Design of Artificial Striated Muscles Composed of Sarcomere-Like Contraction Units

Biological muscles have always attracted robotics researchers due to their efficient capabilities in compliance, force generation, and mechanical work. Many groups are working on the development of artificial muscles, however, state-of-the-art methods still fall short in performance when compared with their biological counterpart. Muscles with high force output are mostly rigid, whereas traditional soft actuators take much space and are limited in strength and producing displacement. In this work, we aim to find a reasonable trade-off between these features by mimicking the striated structure of skeletal muscles. For that, we designed an artificial pneumatic myofibril composed of multiple contraction units that combine stretchable and inextensible materials. Varying the geometric parameters and the number of units in series provides flexible adjustment of the desired muscle operation. We derived a mathematical model that predicts the relationship between the input pneumatic pressure and the generated output force. A detailed experimental study is conducted to validate the performance of the proposed bio-inspired muscle.
Ontology Based Fake News Detection for Sinhala Language

With the current trend in extensive use of internet technologies, people are accustomed to a life of being online. Similar phenomena apply for news awareness as well where social media has become one of the main sources of information of these tech-savvy people. Even though it gives easy access to information, there is a problem with the trustworthiness and authenticity of the news posted on social media. As such, fake news detection in social media platforms has become an active research area. Despite that, for the Sinhala language, there are only a few attempts carried out to detect fake news. With this background, this research project has come up with a novel idea of ontology-based fake news detection for news published using Sinhala language. We believe that content-based fake news identification is the most appropriate method to assess the truthiness of a news article and our system was able to give promising results in detecting fake news.
Short account of Los Alamos theoretical work on thermonuclear weapons, 1946- -1950

A factual account of work (mainly theoretical) on thermonuclear weapon development at the Los Alamos Scientific Laboratory from 1946 to 1950, this is an unclasnified version of a chapter (written in 1954) for a proposed technical history. It outlines the computational and theoretical work devoted to study of the Super and other thermonuclear problems.
Recognition of Video Time Watermark Based on Water-Wave Connected Domain Segmentation Algorithm

Character segmentation has always been a difficult task in the field of text recognition. The common connected domain segmentation algorithm not only repeatedly traverses the pixels, but also does not solve the problem of character adhesion segmentation. In this work, targeted at these problems, we proposed a time watermark recognition method, which can quickly realize the time watermark recognition only by labeling a few sample pictures. It is very suitable for the scene where time watermark needs to be recognized quickly. Compared with the traditional connected domain segmentation algorithm, the water wave connected domain segmentation algorithm segmented characters more accurately, and it can also handle the situation of light adhesion. The segmentation method proposed in this paper avoids the repeated stacking and popping or recursive operations of the traditional connected domain segmentation algorithm mostly, and provides a new idea for character segmentation. Based on the water wave connected domain segmentation algorithm proposed in this article, four different image processing algorithms are compared and studied: template matching [1], Hamming distance calculation [2], Euclidean distance calculation [3], and pixel comparison calculation. The recognition method proposed in this paper is compared with the CRNN [4] recognition method under the same conditions, and the results show that when a small number of samples are used, the recognition rate of the proposed method reaches 92.3%, and the CRNN recognition rate is 0. Compared with CRNN, the recognition method proposed in this article has achieved good results on very few samples.
Multi-View Fusion Network for Crop Disease Recognition

During the growth of crops, crop yields will be affected by various diseases. Automatic and accurate recognition of crop diseases and determination of disease severity are the key to crop disease prevention and control. In practice, due to noise interference in the data collection process, etc., the acquired crop disease recognition datasets are often with large intra-class differences and large inter-class similarities, which brings great challenges to the development of accurate crop disease recognition. Existing crop disease recognition methods often ignore the subtle differences in easily confused categories, which leads to limited performance. To this end, this paper proposes a multi-view fusion network (MVF-Net) for crop disease recognition. The proposed MVF-Net consists of two parallel branches: suppression branch and global branch. The suppression branch explores discriminative fine-grained features by suppressing the saliency information of the image. The global branch aims to learn the global features of the image to avoid the network paying too much attention to local features. In addition, in order to promote network learning with more discriminative features, this paper introduces an embedding loss, which is combined with the traditional multi-class cross-entropy loss function to improve the performance of the crop disease recognition model. Extensive experiments are conducted on the 2018ai_challenger crop disease recognition dataset. The results show that the proposed MVF-Net has certain advantages over other comparison methods, which lays the foundation for the prevention and control of crop diseases.
An Evolutionary Algorithm for Multi-objective Optimization Problem Based on Projection Plane: MOEA/P

Researches on multi-objective optimization pay more attention to the convergence, diversity and robustness of solutions which is oriented to the whole object solution set, large number of scattered solutions affect the efficiency and quality of the algorithms, and too many solutions let user make decision hardly, it is more prominent in many-objective optimization problems. An algorithm of solving the multi-objective optimization problem based on projection plane is proposed in this paper, which divides the objective space into projection plane and free dimension(s) according to the decision demand, carves the projection plane into several projection grids, and solves the optimal value of the free dimension(s) on each projection grid, so as to obtain the optimal solutions of the multi-objective optimization problem. The division of projection plane reduces the problem of high-dimensional multi-objective optimization to that of low-dimensional multi-objective optimization, which only solves the optimization of free dimension objects on the projection plane; The carving of projection grids determines the solutions in the specific range of the specified object values, which improves the accuracy and efficiency of the solutions. The experimental results of multi and many objective optimization show that this algorithm can effectively solve the multi-objective optimization problems, and provide effective support for the decision-making in the specific objects of many-objective optimization problems.
A Comprehensive Review on the Runtime of NVidia Deep Learning Accelerator

∗ Designing Domain Specific Accelerators (DSAs) has become a promising approach to alleviating Convolutional Neural Networks’ (CNNs) massive demands for computation and memory bandwidth. Unleashed in 2017, NVidia Deep Learning Accelerator (NVDLA) is an open-source, reconfigurable, CNN accelerator and has received a lot of attention from both research community and industry. Most of existing work focused on efficiency and operator support while the runtime of NVDLA, which is responsible for scheduling the accelerator and interacting with Operating System (OS), was largely ignored. In this work, we introduce the working mechanism and provide a comprehensive analysis on NVDLA’s runtime. Then we further assess the execution time of different workload on NVDLA. We show that the runtime contributes even more time than the hardware core. From the perspective of a whole system, only taking the efficiency of the sole accelerator into account is not enough. We believe this could shed some light on subsequent improvement or porting it to other platforms.
An actor-critic strategy for a safe and efficient human robot collaboration

Fulfilling the ISO/TS 15066 regulation is crucial for implementing a certifiable human-robot collaborative application. If not properly embedded in the definition of the control action for the robot, the application of ISO/TS 15066 requirements can lead to a conservative and inefficient behavior of the robot. In order to maximize the performance, in this paper we propose an approach based on Deep Reinforcement Learning (DRL) for integrating the safety standards in a collaborative application. The proposed strategy is experimentally validated.
Energy-Efficient Transmissions in Federated Learning-Assisted Cognitive Radio Networks

The estimation of critical parameters and catching the relationship between allocated resources, link reliability, and transmission latency to save communication resources for data transmission while avoiding data privacy issues will be realized in beyond 5G networks. Achieving this in cognitive radio networks (CRNs) requires the combination of autonomous realtime spectrum awareness and federated algorithms to improve spectral efficiency (SE) and data privacy, respectively. However, incorporating federated intelligence in CRNs requires sacrificing SE for data security. This paper analyzes the effects of incorporating federated machine learning (FML) in the operation of CRNs, which mandates the use of computation and communication resources to evaluate the implication of FML on the energy consumption economics. To achieve this, each cognitive user trains a local model using a multi-layer perceptron model and uploads an update to the serving base station, which in turn aggregates a global model and broadcasts the update to its associated cognitive users. A closed-form energy cost was derived based on time and bandwidth allocation, and the results show that when properly weighted, FML reduces the computation and communication time for cognitive users compared to noncognitive users.
Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment

To arrive at a realistic assessment of localization methods in terms of their performance in an industrial environment under various challenging conditions, we provide a benchmark to evaluate algorithms both for individual components as well as multi-sensor systems. For several sensor types, including wheel odometry, RGB cameras, RGB-D cameras, and LIDAR, potential issues were identified. The accuracy of wheel odometry, for example, when there are bumps on the track. For each sensor type, we explicitly chose a track for the benchmark dataset containing situations where the sensor fails to provide adequate measurements. Based on the acquired sensor data, localization can be achieved either using a single sensor information or sensor fusion. To help evaluate the output of associated localization algorithms, we provide a software to evaluate a set of metrics as part of the paper. An example application of the benchmark with state-of-the-art algorithms for each sensor is also provided.
Real-time temporal adaptation of dynamic movement primitives for moving targets

This work is aimed at extending the standard dynamic movement primitives (DMP) framework to adapt to real-time changes in the task execution time while preserving its style characteristics. We propose an alternative polynomial canonical system and an adaptive law allowing a higher degree of control over the execution time. The extended framework has a potential application in robotic manipulation tasks that involve moving objects demanding real-time control over the task execution time. The existing methods require a computationally expensive forward simulation of DMP at every time step which makes it undesirable for integration in realtime control systems. To address this deficiency, the behaviour of the canonical system has been adapted according to the changes in the desired execution time of the task performed. An alternative polynomial canonical system is proposed to provide increased real-time control on the temporal scaling of DMP system compared to the standard exponential canonical system. The developed method was evaluated on scenarios of tracking a moving target where the desired tracking time is varied in real-time. The results presented show that the extended version of DMP provide better control over the temporal scaling during the execution of the task. We have evaluated our approach on a UR5 robotic manipulator for tracking a moving object.