Fusaomi NagataProfessor, Department of Mechanical Engineering, Faculty of Engineering, Sanyo-Onoda City University, Japan
Speech Title: Pixel-Based Visual Feedback Controller for an Articulated Robot on a Sliding Rail
Abstract: The authors have developed a CNN&SVM design and training tool for defect detection of resin molded articles, and the usefulness and validity have been proved through several design, training and evaluation experiment of CNNs and SVMs . The tool further enables to easily design powerful CNN models based on transfer learning concept, so that a pick and place robot with a visual feedback controller and transfer learning-based CNN models is introduced . The visual feedback controller enabled to omit the complicated calibration between image and robot coordinate systems, also the transfer learning-based CNNs allowed the robot to estimate the orientation of target objects.
In this paper, a CNN acquired by transfer learning of AlexNet, which was already trained using about 1.2 million images of 1000 categories in ImageNet database, is introduced to recognize the orientations of objects. The original AlexNet is able to classify input images into one of 1,000 kinds of objects, while the transferred CNN is able to recognize the orientation of an object in images with the resolution of 15 degrees. Then, a pixel-based visual feedback control is designed and implemented into an articulated robot so that the gripper of the pick and place robot can automatically move to the position nearly just above a target object. Furthermore, a sliding rail is considered to allow the articulated robot to move around in wider working range. The visual feedback controller was extended to utilize the sliding rail. The usefulness and validity of the robot system using the sliding rail is confirmed through pick and place experiments of randomly put objects on a table.
Figure 1 Extended picking robot using a sliding rail.
 K. Nakashima, F. Nagata, H. Ochi, A. Otsuka, T. Ikeda, K. Watanabe, M.K. Habib, Detection of Minute Defects Using Transfer Learning-Based CNN Models, Artificial Life and Robotics, Vol. 26, No. 1, pp. 35-41, Springer, 2021.
 K. Miki, F. Nagata, K. Watanabe, M.K. Habib, Picking Robot of Defective Molded Articles Using Image Processing Technique and Visual Feedback Control, Procs. of 26th International Symposium on Artificial Life and Robotics (AROB 26th 2021), pp. 498-502, 2021.
Keywords: Pick and place robot, Sliding rail, Convolutional neural network, Transfer learning, Visual feedback control
Henry HexmoorProfessor, School of Computing, Southern Illinois University, USA
Speech Title: The Democratic Appeal of Blockchain for the Internet of Everything
Abstract: Blockchain offer the possibility of a pair of strangers to directly and securely perform a variety of transactions to trade over all types of goods, information, and services without reliance on the traditional trust authorities such as legal and financial institutions. Blockchain provides a distributed digital record that does not require trust or coordination between firms, allowing for secure, and standardized transactions. All records on the blockchain are cryptographically hashed and transactions are signed by participants so that all user interactions on cloud platforms are confidential under blockchain enabled signatures. Blockchain interconnects and offers a fully decentralized storage without requiring a central authority. Digitized contracts, known as smart contracts, allow cryptographic access authentication and data sharing verification. Blockchain smart contracts identify, validate requests, and grant access permissions for users by triggering transactions or messages.
We will explore what the future holds for a variety of industry 4.0 applications mediated with the latest evolution of blockchain technology heralding an effective method for information sharing in a hyper-connected, decentralized and cloud empowered digital world.
We will discuss design and implementations for efficient security including consensus mechanism and end to end communication strategies as well as data privacy and security. We will highlight demand response energy management available for the consumption and trading of electric power data in the smart grid (i.e., the internet of energy), peer-to-peer energy trading, intelligent transportation and the internet of things.
We will sum up with the prospect and a siren call for the blockchain emergence as an instrument for democratization of the internet of everything avoiding that fragility of political democracy that can be slow and unfair.
Ding WangAssociate Professor, School of Information Systems Engineering, Information Engineering University, China
Speech Title: Research on key technologies of collaborative direct position determination of sea-aero targets based on distributed arrays
Abstract: High-precision positioning for high-value sea-aero targets (including ships, aircraft, unmanned aerial vehicle, etc.) has important practical significance. This report combines the direct-position-determination (DPD) technology with the collaborative processing idea, and focuses on the key technologies of collaborative DPD for sea-aero targets based on distributed arrays. Aiming at the characteristics of sea-aero target location scenario, this report proposes four kinds of collaborative processing methods, which include the methods making use of the modulation mode of the emitter signal; the methods utilizing the spatial correlation of target group; satellite-ground collaborative positioning methods; and the methods applying the measurement information from the calibration sources. For each kind of collaborative location methods, we construct the distributed array model and propose the collaborative DPD methods. Additionally, the location performance of the new methods is verified by simulation experiments. Theoretical and simulation results show that the collaborative DPD methods can significantly improve the positioning accuracy for sea-aero targets.
Keywords: Target positioning; Sea-aero target; direct-position-determination (DPD); Collaborative positioning
Nebojša Bačanin DžakulaAssociate professor & Vice-Rector, Singidunum University, Belgrade, Serbia
Speech Title: SWARM INTELLIGENCE APPLICATIONS IN CLOUD COMPUTING TASK SCHEDULING AND LOAD BALANCING
Abstract: Cloud computing paradigm, like the paradigm of network computing, is based on the clustering of resources and on the usage of network and Internet technologies. In general, the cloud computing refers to a new way of delivering computing resources in a form of service, such as data, software and hardware components (processing elements, memory and storage).
Cloud computing is a current and important multidisciplinary field, followed by a large number of published papers in the state-of-the-art international journals, as well as in the proceedings of the world-renowned international conferences. Based on the scientific results which have been gathered from the published papers in this domain, it can be concluded that there are many challenges and problems in the cloud environment, which can be more efficiently tackled by applying improved methods, techniques and algorithms. One of the most important challenges in cloud computing is scheduling of end users' requests on a limited set of available resources (virtual machines). A scheduling problem in cloud environment can be defined as an execution schedule of tasks on a limited set of available resources, taking into account the potential constraints and objective function.
Task scheduling is performed by scheduling algorithms, which can be divided into static and dynamic. In the case of a static scheduling, where it is not possible to dynamically switch tasks from over utilized to underutilized virtual machines, tasks are being allocated for execution on available virtual machines before the scheduling algorithm execution. In the case of the dynamic scheduling methods, which are known in the literature as load balancing approaches, a workload allocation between active virtual machines is being performed during the scheduling algorithm run-time. Requests' redistribution is executed by dynamically switching from over utilized to less utilized virtual machines. Heuristics and metaheuristics optimization methods are mostly used for dynamic scheduling, where they have achieved great results.
Task scheduling and load balancing problems in cloud computing belong to the group of NP hard combinatorial and/or global problems with or without constraints. Based on the published results in the relevant literature, it can be concluded that the swarm intelligence metaheuristics have been successfully tested on benchmark and practical NP hard optimization problems, and that they have achieved better results in terms of convergence speed and the solutions' quality, than other methods, techniques and algorithms. In our experiments, it is examined whether it is possible to further improve the task scheduling and load balancing in cloud computing environment by applying swarm intelligence metaheuristics.
During the experimental research, several swarm intelligence metaheuristics were improved and adapted for solving task scheduling and load balancing problems in cloud environment. Some of the swarm algorithms that proved state-of-the-art performance in terms of results’ quality, as well as of convergence speed are monarch butterfly optimization (MBO), whale optimization algorithms (WAO), elephant herding optimization (EHO), tree growth algorithm (TGA) and grey wolf optimization (GWO). The algorithms were implemented in both, original and modified/hybridized versions. The robust environment of CloudSim platform was utilized as the simulation platform.
In this speech, I will highlight the most significant results that the swarm algorithms obtained in the domain of cloud computing task scheduling and load balancing.
Gai-Ge WangAssociate Professor, Ocean University of China, China
Speech Title: Improving Metaheuristic Algorithms with Information Feedback Models
Abstract: In most metaheuristic algorithms, the updating process fails to make use of information available from individuals in previous iterations. If this useful information could be exploited fully and used in the later optimization process, the quality of the succeeding solutions would be improved significantly. We are to reuse the valuable information available from previous individuals to guide later search. In our approach, previous useful information was fed back to the updating process. We proposed six information feedback models. In these models, individuals from previous iterations were selected in either a fixed or random manner. Their useful information was incorporated into the updating process. Accordingly, an individual at the current iteration was updated based on the basic algorithm plus some selected previous individuals by using a simple fitness weighting method.
Zhihan LvAssociate Professor, Qingdao University, China
Speech Title: An Optimized Byzantine Fault Tolerance Algorithm for Consortium Blockchain
Abstract: According to different application scenarios of blockchain system, it is generally divided into public chain, private chain and consortium chain. Consortium chain is a typical multi-center blockchain, because it has better landing, it is supported by more and more enterprises and governments. We analyze the advantages and problems of PBFT algorithm for the application scenarios of the consortium chain. In order to be more suitable for consortium chains, we propose a new optimized consensus algorithm based on PBFT. Aiming at the shortcomings of PBFT, such as the inability to dynamically join nodes, low multi-node consensus efficiency, and primary master node selection, our optimized algorithm has designed a hierarchical structure to increase scalability and improve consensus efficiency. The simulation results show that compared with PBFT and RAFT, our new consensus algorithm increases the data throughput while supporting more nodes, and effectively reducing the consensus delay and the number of communication times between nodes.
Yutaka FukuchiAssociate Professor, Tokyo University of Science, Japan
Speech Title: Stable, wavelength-tunable and amplitude-equalized rational harmonic mode-locked laser employing a short bismuth-oxide-based highly nonlinear erbium-doped fiber
Abstract: Harmonically mode-locked fiber lasers (HMLFLs) have proven to be able to generate wavelength-tunable short pulses with small timing jitter and gigahertz repetition rates. Generally, the repetition rate of the HMLFLs given by nfcav is equal to the modulation frequency fmod of the intra-cavity modulator, where n is an integer called harmonic order, and fcav is the fundamental cavity frequency. Therefore, the maximum repetition rate is normally limited by the modulator bandwidth. Recently, a technique called rational harmonic mode locking has attracted considerable interest. In this technique, a pulse train with a repetition rate of (pn+1)fcav can be generated when fmod is set at (n+1/p)fcav, where p is an integer called rational harmonic order. However, the pulse amplitude becomes uneven when p is greater than two. Uneven amplitudes create difficulties in the real application. The wavelength tuning range is also another important issue of the HMLFLs. Furthermore, typical cavity length is 10-100 m, and n is 500-5000 for 10-GHz mode locking, which results in instability owing to the external perturbation and the large supermode noise. In this presentation, we review a rational HMLFL employing a short bismuth-oxide-based highly nonlinear erbium-doped fiber (Bi-HNL-EDF). Ultra-wide wavelength tunability covering both the C- and L-bands is achieved by utilizing a broadband gain profile of the Bi-HNL-EDF. The supermode noise is suppressed effectively due to a high nonlinearity of the Bi-HNL-EDF. The pulse-amplitude equalization is also achieved by a modulator transmittance adjustment method. The fiber cavity is as short as 6 m. Thus, stable and amplitude-equalized near-TL short pulses up to 40 GHz (p = 4) are successfully observed over the entire wavelength tuning range.
Keywords: Optical fiber lasers, mode-locked lasers, rare-earth materials, nonlinear optics, wavelength tuning, noise
Miroslav JolerProfessor, University of Rijeka, Croatia
Speech Title: On Characterization of Dielectric Properties of Non-standard Materials Using a Low-cost Resonator Circuit
Abstract: In design of wearable electronics, engineers face a task of having to sufficiently accurately determine dielectric properties of non-standard substrate materials, such as various fabrics, in order to successfully design a desired circuit, e.g. a textile antenna or a substrate-integrated waveguide. It is known that determination of dielectric properties is a subtle task, even nowadays, when sophisticated hardware and software are available, due to sensitivity of the measurement on various factors that are involved in the process. Depending on the type of the material sample to be characterized, several measurement methods are widely known, each with distinctive traits. It turns out, however, that even the most advanced and costly measurement apparatuses today can offer only a modest accuracy, followed by disclaimers regarding the measurements conditions. Given that fact, a viable alternative can be to design our own, low-cost, measurement setup that could provide a satisfactory accuracy for practical purposes. One such approach is based on the resonator circuit. Although the topic has been tackled in various papers, one will hardly find a complete or transparent and detailed measurement procedure that is part of the public domain, which cognition spontaneously lead us to revisit the topic. In this talk, an overview of the measurement principle of a ring resonator structure will be introduced and some previously defined approaches commented on. Dependence of the results on the characteristics of the sample and the resonator will be discussed based on full-wave numerical solver simulations and measurements. Finally, a hybrid approach, as a combination of an analytical model and sample measurement, will be presented for determination of relative permittivity and loss tangent of a material sample.
Tatjana SibalijaProfessor, Belgrade Metropolitan University, Serbia
Speech Title: Parametric Optimization of Integrated Circuit Assembly Process: an Evolutionary Computing-Based Approach
Abstract: Strict demands for very tight tolerances and increasing complexity in the semiconductors’ assembly impose a need for an accurate parametric design that deals with multiple conflicting requirements. This paper presents application of the advanced optimization methodology, based on evolutionary algorithms (EAs), on two studies addressing parametric optimization of the wire bonding process in the semiconductors’ assembly. The methodology involves statistical pre-processing of the experimental data, followed by an accurate process modeling by artificial neural networks (ANNs). Using the neural model, the process parameters are optimized by four metaheuristics: the two most commonly used algorithms - genetic algorithm (GA) and simulated annealing (SA), and the two newly designed algorithms that have been rarely utilized in semiconductor assembly optimizations -teachinglearning based optimization (TLBO) and Jaya algorithm. The four algorithm performances in two wire bonding studies are benchmarked, considering the accuracy of the obtained solutions and the convergence rate. In addition, influence of the algorithm hyper-parameters on the algorithms effectiveness is rigorously discussed, and the directions for the algorithm selection and settings are suggested.
Maltsev AlexanderProfessor, Mobile Communications Department, University of Nizhny Novgorod (UNN), Russia
Speech Title: Phase Tracking Sequences for 5G NR in 52.6-71 GHz Band: Design and Analysis
Abstract: This paper presents a novel approach to the phase tracking reference signal (PTRS) design in the 5G NR communication systems intended to work in a new 52.6 GHz to 71 GHz frequency band. For detailed problem illustration, the phase noise (PN) compensation algorithms are discussed and explained, from the basic common phase error (CPE) compensation to the MMSE-based inter carrier interference (ICI) filtering in frequency domain. Performance of the different algorithms is investigated for the baseline PTRS structure accepted in the current 5G NR specification and compared with a new proposed approach to the PTRS design. This approach is based on nulling a few subcarriers adjacent to each reference signal to minimize influence of the ICI on the PN estimation process. It was shown that the new PTRS design outperforms currently used distributed PTRS structure. Moreover, with new PTRSs, the performance of the simple least squares (LS) phase noise estimation method reaches the performance of the semi-optimal MMSE estimator, which has significantly larger complexity. Generally, achievable level of phase noise compensation for a given PTRS overhead is determined by the filter size, while advanced algorithms and approaches may improve filter coefficient estimation accuracy.
Keywords: 5G NR, phase tracking, phase noise, PTRS, common phase error, OFDM, least squares, phase noise compensation
Acknowledgements: Authors deeply thank Dr. Woong-Kee Min from the LG Technology Center, Moscow and Dr. Joon-Kui Ahn from the LG Electronics, Seoul for the organization of fruitful cooperation between overseas teams and making this paper possible
Diogo LimaProfessor, Universidade Paulista, Brazil
Speech Title: An approach of IoT enabled by TCNet: Trellis Code Network – a new algorithm and routing protocol
Abstract: This work proposes a new scenario that changes conventional paradigms to enable the development of QoS-aware protocols in WSNs, an important Infrastructure for the IoT architecture, using the new concept of “Trellis Coded Network”- (TCNet). The model is based on finite automata or Finite State Machines (FSM). The Low complexity of Finite State Machine (FSM) network nodes (“XOR” gates and shift registers), eliminating the use of any routing tables by means of Trellis decoding allowing to demonstrate the potential applications of the TCnet algorithm in some cases as Sensor Network Virtualization (VSN) and scenarios where clusters of nodes to allows cover large areas of interest where the sensors are distributed.
Tsuyoshi MinamiProfessor, Institute of Industrial Science, The University of Tokyo, Japan
Tadeusz CzachorskiProfessor, Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Poland
Rohini DeshpandeSchool of Electronics and Communication Engineering, REVA UNIVERSITY, India
Uyen T. NguyenAssociate Professor, Lassonde School of Engineering, York University, Canada
Evizal Abdul KadirAssociate Professor, Islamic University of Riau, Indonesia
Rajkumar SoundrapandiyanAssociate Professor, School of Computer Science and Engineering, VIT University, India
SHASHI MehrotraAssociate Professor, Department of Computer Science and Engineering, KL University, India
Roman VolianskyiAssociate Professor, Electrical Engineering Department, Dniprovsk State Technical University, Ukraine
Jerry GaoProfessor and Research Center Director, Computer Engineering Department, San Jose State University, USA;
Applied Data Analytics Department, San Jose State University, USA
Speech Title: Intelligent Testing and Automation for Smart Autonomous Vehicles