Quantum estimation,control and learning: Opportunities and challenges

The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.     

Temperature-aware computer systems: Opportunities and challenges

Temperature-aware design techniques have an important role to play in addition to traditional techniques like power-aware design and package- and board-level thermal engineering. The authors define the role of architecture techniques and describe hotspot, an accurate yet fast thermal model suitable for computer architecture research.     

Very low cost testers: Opportunities and challenges

Prudent application of design-for-testability guidelines can yield designs that don't require all the expensive features of traditional automated test equipment. The authors describe how the VLSI design and semiconductor test communities can cooperate to greatly reduce testing costs     

Pattern recognition for bioprocess control

Pattern recognition is used to identify a distinct state of a biopro-cess; most interesting is the recognition of physiological states. This method is an excellent means for relevant bioprocess monitoring and the respective knowledge can be effectively used for decision making. The multitude and multiplicity of data is treated as a 3 dimensional picture of a process and compared to prototypes, i.e. pictures from, for instance, a reference process. This method was successfully used to detect unique phases in bioprocesses, to initiate certain process operation activities automatically, or to detect faults on line with little time delay.     

Brain decoding: Opportunities and challenges for pattern recognition

The neuroimaging community heavily relies on statistical inference to explain measured brain activity given the experimental paradigm. Undeniably, this method has led to many results, but it is limited by the richness of the generative models that are deployed, typically in a mass-univariate way. Such an approach is suboptimal given the high-dimensional and complex spatiotemporal correlation structure of neuroimaging data.Over the recent years, techniques from pattern recognition have brought new insights into where and how information is stored in the brain by prediction of the stimulus or state from the data. Pattern recognition is intrinsically multivariate and the underlying models are data-driven. Moreover, the predictive setting is more powerful for many applications, including clinical diagnosis and brain–computer interfacing. This special issue features a number of papers that identify and tackle remaining challenges in this field. The specific problems at hand constitute opportunities for future research in pattern recognition and neurosciences.     

Mobile Edge Computing: Opportunities,solutions, and challenges

The emergence of several new computing applications, such as virtual reality and smart environments, has become possible due to availability of large pool of cloud resources and services. However, the delay-sensitive applications pose strict delay requirements that transforms euphoria into a problem. The cloud computing paradigm is unable to meet the requirements of low latency, location awareness, and mobility support. In this context, Mobile Edge Computing (MEC) was introduced to bring the cloud services and resources closer to the user proximity by leveraging the available resources in the edge networks. In this paper, we present the definitions of the MEC given by researchers. Further, motivation of the MEC is highlighted by discussing various applications. We also discuss the opportunities brought by the MEC and some of the important research challenges are highlighted in MEC environment. A brief overview of accepted papers in our Special Issue on MEC is presented. Finally we conclude this paper by highlighting the key points and summarizing the paper.     

A NASA perspective on quantum computing: Opportunities and challenges

In the last couple of decades, the world has seen several stunning instances of quantum algorithms that provably outperform the best classical algorithms. For most problems, however, it is currently unknown whether quantum algorithms can provide an advantage, and if so by how much, or how to design quantum algorithms that realize such advantages. Many of the most challenging computational problems arising in the practical world are tackled today by heuristic algorithms that have not been mathematically proven to outperform other approaches but have been shown to be effective empirically. While quantum heuristic algorithms have been proposed, empirical testing becomes possible only as quantum computation hardware is built. The next few years will be exciting as empirical testing of quantum heuristic algorithms becomes more and more feasible. While large-scale universal quantum computers are likely decades away, special-purpose quantum computational hardware has begun to emerge, which will become more powerful over time, as well as small-scale universal quantum computers.     

Monochromatic LEDs based on perovskite quantum dots: Opportunities and challenges

Perovskite quantum dots (QDs) are emerging as one of the most promising candidates for the monochromatic light‐emitting diodes (LEDs) approaching the Rec. 2020 color gamut due to their extremely narrow emission bandwidth. Another important aspect are the high photoluminescence quantum yield (PLQY) values that can be obtained either in solution or thin films making these materials as promising candidates for optoelectronic applications such as LEDs or solar cells. Considerable research efforts in chemistry, chemical engineering, solid‐state physics, and material sciences have been made in the past years. The new opportunity in the field of semiconductor quantum dots is still in the beginning phase and is expected to remain active in the following years. Here, in this invited commentary, we briefly discuss and summarize the opportunities and challenges in both fundamental and technological aspects, based on our work and the recent work in this field.     

Visual Analytics for model-based medical image segmentation: Opportunities and challenges

Segmentation of medical images is a prerequisite in clinical practice. Many segmentation algorithms use statistical shape models. Due to the lack of tools providing prior information on the data, standard models are frequently used. However, they do not necessarily describe the data in an optimal way. Model-based segmentation can be supported by Visual Analytics tools, which give the user a deeper insight into the correspondence between data and model result. Combining both approaches, better models for segmentation of organs in medical images are created.In this work, we identify the main tasks and problems in model-based image segmentation. As a proof of concept, we show that already small visual-interactive extensions can be very beneficial. Based on these results, we present research challenges for Visual Analytics in this area.     

Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies

The rapid development and increasing availability of various location acquisition technologies provide geospatial studies with both opportunities and challenges. These opportunities and challenges are discussed in this paper focusing on the following three aspects: the massive acquisition of location data and data quality, the analysis of massive location data and pattern discovery, and privacy protection for massive location data. This paper examines the current status of and the potential opportunities for geospatial research in these three areas and notes the major challenges. Finally, the development of this special issue is described, and the four articles included in this special issue are presented.     

Opportunities and challenges in improving surgical work flow

Safe and efficient surgical operations depend on a work environment larger than the individual operating room (OR) and on communications at different levels of the hospital organization. Extensive communication is needed before and during surgery to ensure that surgical rooms, equipment, and supplies; patients; surgeons; supporting personnel; and accompanying documentation are all ready at the appropriate times. In this article, we compile the results of three of our studies of communication activities outside ORs, with the goal of identifying opportunities and challenges in the workflow of surgery. The first study demonstrates the amount of communication work performed by OR coordinators. The second study demonstrates the potential functions of a whiteboard in communication. The third study describes an organizational learning strategy of proactive event reporting. We apply computer-supported cooperative work and organizational learning concepts to the findings and suggest ways in which information technology may improve surgical workflow.     

区块链生态安全挑战及解决方案研究

区块链巧妙地融合了密码学、点对点传输、共识机制等多种技术,实现了有效的价值传输体系。近年来,随着区块链各类应用的爆发式增长,其各层面的安全问题也随之凸显。区块链底层平台、区块链应用漏洞导致的风险以及各类虚拟资产犯罪行为对区块链生态的安全带来了极大的挑战。文章针对区块链生态的各类安全挑战进行深入分析,基于区块链安全技术以及区块链监管两个维度,提供了区块链全生态安全解决方案。最后依据深度学习技术的发展,提出le区块链生态安全技术及方案的未来展望和研究方向。     

Process diagnostics using trace alignment: Opportunities, issues, and challenges

Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM|one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts.     

Developing proteomics-based biomarkers for colorectal neoplasms for clinical practice: Opportunities and challenges

Colorectal cancer (CRC) arises from the normal colon epithelium through the accumulation of genetic mutations and epigenetic alterations that are associated with progression along the histological adenoma-adenocarcinoma sequence. Elucidating the molecular alterations underlying disease progression will not only provide insight into the behavior of the tumors, but also could lead to the discovery of useful biomarkers for diagnosis, monitoring treatment responsiveness, or predicting disease outcomes. In the past a few years, there have been several evaluating differentially expressed protein biomarkers by employing proteomics technologies coupled with mass spectrometry. In the current review, we will briefly summarize the results from selected recent studies using tissue or serum samples from CRC patients in the past 5 years and discuss the opportunities and challenges in translating these findings from the research setting to clinical practice.     

Emergency management perspectives on volunteered geographic information: Opportunities,challenges and change

Volunteered geographic information (VGI) refers to the widespread creation and sharing of geographic information by private citizens, often through platforms such as online mapping tools, social media, and smartphone applications. VGI has shifted the ways information is created, shared, used and experienced, with important implications for applications of geospatial data, including emergency management. Detailed interviews with 13 emergency management professionals from eight organisations across five Australian states provided insights into the impacts of VGI on official emergency management. Perceived opportunities presented by VGI included improved communication, acquisition of diverse local information, and increased community engagement in disaster management. Identified challenges included the digital divide, data management, misinformation, and liability concerns. Significantly, VGI disrupts the traditional top-down structure of emergency management and reflects a culture shift away from authoritative control of information. To capitalise on the opportunities of VGI, agencies need to share responsibility and be willing to remain flexible in supporting positive community practises, including VGI. Given the high accountability and inherently responsive nature of decision making in disaster management, it provides a useful lens through which to examine the impacts of VGI on official authoritative systems more broadly. This analysis of the perceptions of emergency management professionals suggests changes to traditional systems that involve decentralisation of power and increased empowerment of citizens, where value is increasingly recognised in both expert and citizen-produced information, initiatives and practises.     

Linearized min‐max robust model predictive control: Application to the control of a bioprocess

This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameter uncertainties. First, this work focuses on the design of a robust nonlinear model predictive control (RNMPC) law subject to model parameter uncertainties implying solving a min‐max optimization problem. Secondly, a new approach is proposed, consisting in relating the min‐max problem to a more tractable optimization problem based on the use of linearization techniques to ensure a good trade‐off between tracking accuracy and computation time. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC and nonlinear model predictive controllers. Its efficiency and its robustness against parameter uncertainties and/or perturbations are illustrated through numerical results.     

物联网专业大学生就业的机遇、挑战和对策

作为高校新设立专业,物联网专业毕业生将拥有国家政策支持、物联网产值及人才需求倍增等有利条件,但同时也面临新专业从师资、人才培养模式到物联网硬件建设以及学生主体意识增强与企业人才要求提高等方面的挑战。文中对高校物联网专业的发展方向和人才培养目标,校企合作培养模式的创新以及整合学校各方资源,完善学生就业的指导服务等方面进行了分析,以保证物联网专业学生就业工作的健康发展。     

A mid-ranging control strategy for non-stationary processes and its application to dissolved oxygen control in a bioprocess

In this study a modified mid-ranging strategy is proposed where the controller for the secondary manipulated variable uses its own output as its setpoint, possibly with an offset and/or re-scaling. This modification allows the manipulated variables to increase in unison so that the mid-ranging advantage of utilizing the fast dynamics of the primary controller to regulate the process can be achieved also in non-stationary processes, while not adding complexity to the controller. The proposed control strategy has been implemented in pilot-scale (500 l) industrial bioprocesses where it is used to control the dissolved oxygen level by manipulating agitator speed and aeration rate. The controller is demonstrated to perform well in these, outperforming a reference controller which has previously been shown to give satisfactory control performance. It is also shown in similar experiments that the strategy can easily be adapted to control dissolved oxygen in bioprocesses where the feed rate is controlled using an extremum-seeking controller. The proposed strategy is generally applicable to non-stationary processes where a mid-ranging approach is suitable.     

Manufacturing plant control challenges and issues

Enterprise control system integration between business systems, manufacturing execution systems and shop-floor process-control systems remains a key issue for facilitating the deployment of plant-wide information control systems for practical e-business-to-manufacturing industry-led issues. Achievement of the integration-in-manufacturing paradigm based on centralized/distributed hardware/software automation architectures is evolving using the intelligence-in-manufacturing paradigm addressed by IMS industry-led R&D initiatives. The remaining goal is to define and experiment with the next generation of manufacturing systems, which should be able to cope with the high degree of complexity required to implement agility, flexibility and reactivity in customized manufacturing. This introductory paper summarizes some key problems, trends and accomplishments in manufacturing plant control before emphasizing for practical purposes some rationales and forecasts in deploying automation over networks, holonic manufacturing execution systems and their related agent-based technology, and applying formal methods to ensure dependable control of these manufacturing systems.