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With the technology development, enterprise applications are rapidly changing from the traditional centralized application architecture to the distributed, microservices architecture (MSA). However, with the further development of MSA, enterprises are facing new challenges while enjoying the convenience brought by microservices, e.g. how to manage a large number of services, how to quickly locate and troubleshoot these services, how to avoid the performance issues, etc. As the next generation of MSA, service mesh is attracting more and more attentions and can be used to solve above problems. On top of service mesh, this paper proposes one mechanism that largely customizes service mesh implementation technologies. Furthermore, we implement this mechanism in a real project of one local large financial company. The final result illustrates that the mechanism is not only feasible but also effective to solve aforementioned service governance problems.

Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data. An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.

The article presents a method for image restoration based on a geometric model. The object of research is methods of image removal and restoration. The research is aimed at creating a high-speed and highly efficient system for recovering the lost zones of the underlying surface map image. An algorithm for removing objects and restoring images of lost areas is investigated and its software implementation is developed. The efficiency of the developed algorithm was evaluated on a test example using a statistical criterion.

Vaccination against infectious diseases has changed the future of the human species, saving millions of lives every year, both children and adults, and providing major benefits to society as a whole. Here we show, however, that national and sub-national coverage of vaccination varies greatly and major unmet needs persist. Although scientific progress opens exciting perspectives in terms of new vaccines, the pathway from discovery to sustainable implementation can be long and difficult, from the financing, development and licensing to programme implementation and public acceptance. Immunization is one of the best investments in health and should remain a priority for research, industry, public health and society. An overview of the effects of vaccines on global morbidity and mortality, vaccine safety issues, and the hurdles involved in proceeding from vaccine discovery to successful implementation.

We show the usefulness of bifurcation diagrams to implement a pseudo-random number generator (PRNG) based on chaotic maps. We provide details on the selection of the best parameter values to obtain high entropy and positive Lyapunov exponent from the bifurcation diagram of four chaotic maps, namely: Bernoulli shift map, tent, zigzag, and Borujeni maps. The binary sequences obtained from these maps are analyzed to implement a PRNG both in software and in hardware. The software implementation is realized using 32 and 64 bits microprocessor architectures, and with floating point and fixed point computer arithmetic. The hardware implementation is done by using a field-programmable gate array (FPGA) architecture. We developed a serial communication interface between the PRNG on the FPGA and a personal computer to obtain the generated sequences. We validate the randomness of the generated binary sequences with the NIST test suite 800-22-a both in floating point and fixed point arithmetic. At the end, we show that those chaotic maps are suitable to implement a PRNG but according to the hardware resources, the one based on the Bernoulli shift map is better. In addition, another advantage is that the required initial value for the sequences can be within the whole interval [ - 1 , 1 ] , including its bounds.

This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

A program that implements a simple Markov homogeneous random search algorithm of an extremum with triangular distributions is presented. This program allows solving a fairly wide class of problems of finding the global extremum of an objective function with a high accuracy.

Purpose Global competition has intensified pressure on small- and medium-sized enterprises (SMEs) to implement lean. Recently, the debate has converged to the role of lean implementation barriers (LIBs). The purpose of this paper is to contribute to this debate by exploring the LIBs in SMEs through three case studies. Design/methodology/approach A case study approach was employed followed by interpretive structural modelling (ISM) to model the interrelationship among the LIBs. Findings This study reveals that lack of management commitment, leadership and resources are the key barriers to lean implementation in SMEs in India. Furthermore, poor communication between different levels of the organisation and inadequate dissemination of the knowledge of lean benefits also creates hindrance in lean implementation. Managerial implications of the identified barriers for lean implementation in SMEs have been discussed. Originality/value The research regarding lean implementation in SMEs is scarce. This study is the first attempt of its kind to identify the lean barriers in a small industry setup through mathematical analysis.

Collaborative robotics have a large potential for use in industrial applications. Nevertheless, this potential is currently unrealized and one of the reasons is the challenges in planning and designing while considering the safety requirements of these new types of applications. In this article, we will use an exemplary application to describe the many design decisions that are made during the planning of an industrial application featuring human-robot collaboration. Our approach uses model-based systems engineering concepts for considering safety-related aspects of the application during the design phase. Using a software implementation based on our method, we will then compare design results including required floor space and cycle time for the same exemplary application and discuss the implications of our approach for planning other robotics tasks. With our method, the required separation area around the robot was reduced by up to over 66% for a situation featuring a specific robot to be operated at 100% of its maximum possible speed.

A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals (“privacy-preserving” distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10 −7 ). In comparison of the full workflow (feature selection and classification), no significant difference in ROC was found between centralized and distributed models for both studied endpoints (DeLong p > 0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models.