In our envisioned future, we see engineers, analysts, and decision makers immersed in highly interactive model-centric environments using digital system models as a primary basis for system decisions. While significant progress on modeling languages, modeling practices, and modeling methods has been achieved, insufficient attention has been given to the necessary interactivity between humans and models. Models are used by humans, and that interaction influences both how models are conceived and how they are used in decisions. Accordingly, we need to understand the factors and conditions under which people trust and distrust models. Ongoing research investigation of model-centric decision making and trust, including a recent interview-based study involving 30 experts, has generated many useful insights.  This talk shares collective wisdom of experts on why we trust (or distrust) models as a basis for making systems decisions, and discusses guiding heuristics for effective human-model interaction.

Systems Theory, Systems Thinking

Abstract: This one hour talk discusses several important concepts in cybernetics, including organized complexity, transformation, feedback, and control. The talk concludes with a brief overview of three important systems thinking approaches which are based on these concepts: Jay Forrester's System Dynamics, Peter Checkland's Soft Systems Methodology (SSM), and Stafford Beer's Viable Systems Model (VSM). Using causal loops and System Dynamics, the analyst can model and simulate interactions among system elements and avoid emergent problems when a system or organization becomes operational. Using SSM, the analyst learns about the whole problem under study, the criteria for success, and all the influences and constraints. Using VSM the analyst diagnoses and controls the organization and works towards improving it.

The Role of Knowledge Management in Requirements Management

Abstract: Developers of software intensive systems have problems delivering systems within cost and schedule, and many of the delivered systems do not do what users really want. Inadequate requirements engineering is a major contributor to these problems.. There are a number of reasons why the set of specified requirements is not normally equivalent to the set of ‘true’ requirements, including: natural language is imprecise; different disciplines use inconsistent terminology; conflicts are not readily recognized and therefore not properly negotiated; and assumptions are not clearly documented. Also practitioners “use different representation schemes, even within a single discipline (drawings, tables, natural language, and semi-formal models), leading to inconsistencies and ambiguities that are not likely to be discovered until the system is operational. To address these problems, classification methods and cognitive theories are discussed.

The Human Viewpoint captures the impact of the human operators as part of the larger system architecture and informs on human capabilities and limitations for system design. The Human View methodology results in an integrated set of models that describe human tasks, roles and interactions that can be used to contribute to system development and facilitate human-system trade-off considerations. This human-focused perspective can help reduce system risk by communicating information about the balance between operator constraints, such as workload and availability, and system performance. An example of an Aerial Reconnaissance Support Team (ARST) responding to changes in combinations of sensor types as part of the Intelligence Processing, Exploitation & Dissemination (PED) process is used as an example. The Human Viewpoint, used early in the system architecting process, can help validate manpower and personnel requirements and can be used to
evaluate different architecture implementations to ameliorate stakeholder concerns.

An interactive session to explore the past, present and future of systems. This lecture is tool-agnostic to encourage the engineer or analyst to avoid being overly reliant on a tool such as SysML or system dynamics (aka: systems thinking). Any and all tools should be incorporated in a balanced cognitive paradigm along with all the other useful approaches. This lecture provides a foundation in systems analysis, traces the history of systems analysis to the iChing, and considers how a tool-agnostic approach can improve results for the client. The talk covers several foundational systems concepts and involves case studies to demonstrate the critical thinking involved.

Lecture objectives are: Provide a brief history of systemic thinking and explain some of the modern systems concepts such as information economics, behavioral economics and supply chain management. Introduce an objectives-driven cognitive approach to analyzing any system. Explain the advantages of a systemic approach versus the checklist, systematic approaches so popular in systems engineering education and training as promoted by INCOSE and others. Encourage the students to look at systems with new eyes, in ways that foster innovation. Inspire attendees to learn more about the concepts.

Building A Business Case for Systems Engineering: the 2012 SE Effectiveness Study

This briefing provides the results of the NDIA-IEEE/AES “Systems Engineering Effectiveness Study”, conducted in the 2011-2012 time frame.
The data were collected via an extensive survey of defense and commercial companies, both US and international, concernng their systems engineering involvement in programs and projects.
The survey questions were based on the widely-adopted CMMI, or Capability maturity Model Integration, with focus on the systems engineering aspects thereof.
The study was conducted to determine if there were a definitive correlation between systems engineering work performed on projects/programs, and the overall success of the project/program.

Networked systems have created new opportunities with major societal implications. At the same time, security has emerged as one of the most important socio-technical challenges confronting society. AI/machine learning (ML) techniques are expected to enable networked systems and enhance security. In this talk, I will present my recent research on networked systems and security in the age of AI/ML. First, I will introduce my ML-enabled Counter Unmanned Aircraft System(s) (C-UAS) technology that detects and safely neutralizes rogue drones without destroying them or causing them to crash. This research has been featured by 100+ news media outlets. Next I will present my follow-up research on real-time ML for quickest event (threat/intrusion/vulnerability…) detection. Then I will introduce my research on data-efficient ML, particularly distant domain transfer learning.

View more information on the lecture here.

Most systems consist of more than a single element, being composed of multiple systems and system elements. During development, the system life cycle technical processes are applied recursively from the system of interest to the systems at the next level, with the recursion continuing until a system element is reached. The life cycle processes may be conducted in parallel for system elements at the same level. The full set of life cycle processes is applied to each system or system element in turn.

Verification and Validation are technical processes that provide evidence the system being examined fulfils its specified requirements and achieves its intended use.  In the same way that the other technical processes are applied within a system of systems, verification and validation (V&V) is applied recursively within the system of systems for each system down to system elements.

This presentation describes the conduct of V&V on a system of systems, including recursive aspects and the interrelationships of V&V and other technical processes across life cycle phases. The discussion includes consideration of the role of V&V on the System of Interest during development of lower level systems and system elements and a description of System Element Interaction Analysis.

The Earth's total surface area is made up of various flow regimes of reservoirs, bays and lakes as well as soil environment, which are considered important natural resources for the maintenance of ecosystem integrity and human consumption. As the condition of environment deteriorates throughout the world, it necessitates the scientific work of monitoring environmental quality in response to its dynamic changes of quality status or flow conditions and feedbacks to our society. For this purpose, satellite remote sensing techniques with multiple in-situ ground-based sensors may be applied to collectively capture a much larger spatial coverage within relatively short time periods through various traditional or non-traditional algorithms. To improve the overall efficiency there is a tradeoff in spectral, spatial and temporal resolution of different sensors when monitoring the water, soil, and air pollutants in the changing environment. The goal of this presentation is to introduce the latest forefronts in the field and demonstrate green, smart, and sustainable management of our changing Earth environment by integrating multi-sensor satellite image fusion, data merging, and machine learning – an emerging area of importance in systems engineering. The following scientific questions are explored in this study: (1) Are fused image reflectance bands and machine-learning techniques able to accurately carry out the estimation of target environmental quality parameters under different challenges? (2) Is it feasible to have an integrative and innovative process for updating the environmental condition for early warning? (3) How microwave signals work with optical remote sensing for monitoring changing earth environment?
 

When medical devices are created for use on patients, there are robust systems engineering principles that must be applied to ensure that the devices are as safe as reasonably possible. Safety risk management is a parallel and complementary process to the product development process. This talk will provide a comprehensive overview of the relationship between product development and safety risk management throughout the product development lifecycle.

The area of data science lacks efficient computational methods with provable guarantees that can cope with the large-scale nature and the high nonlinearity of many real-world systems. Practitioners often design heuristic algorithms tailored to specific applications, but the theoretical underpinnings of these methods remain a mystery and this limits their usage in safety-critical systems. In this talk, we investigate the above issue for some canonical data-driven problems with connections to optimization and control theory. We consider the graphical Lasso which is a popular optimization method for learning graphical models from data. By analyzing the properties of this problem, we show that its true computational complexity is indeed linear for sparse graphical models, which enables designing new algorithms for this problem to be able to solve large-scale learning problems efficiently. Second, we study the problem of solving nonlinear optimization problems efficiently using low-complexity methods. Nonlinearity is ubiquitous in control theory and more recently has played a major role in deep learning and artificial intelligence. We discuss the recent advances in this area and in particular study the low-rank matrix recovery problems which arise in various complex systems.

The fully connected IoT world – both in the consumer and industrial markets - is quickly becoming a reality. But the degree of connectivity challenges may challenge the skill set of practicing system engineers. Not only should SE’s understand traditional embedded hardware and software basics but also the demands of “smart” connected systems. Specifically, SEs must add the data dimension to traditional power, performance, area and cost constraints of embedded and larger systems. Determining how and where data must be processed and analyzed is critical to IoT development.

This additional data requirement is part of the larger digitization of previously analog systems. Digitization also enables digital continuity and aids the creation digital twins. Such digital connections may put the engineering back into systems engineering via model-based designs, testing and manufacturing.

This presentation will show why a basic understanding of the IoT hardware and software related technologies must be integrated with a systems engineering approach to successfully meet the challenges ahead. This discussion does not cover certain specific technologies in detail – such as edge and cloud computing, and digital treads and twins. Rather, it uses these concepts in examples to demonstrate their importance to systems engineering in the connected age.

As Internet of Things systems become widely adopted, cyber-physical systems offer smarter environments and services than earlier networked systems by leveraging and integrating sensed data from various types of sensors. Despite adopting the benefits of the IoT technologies, cyber-physical systems have various weaknesses due to existing vulnerabilities in wireless networks. With massively connected nodes that push sensed data, the cyber threat surface leaves more nodes as target points to enter the monitoring network. This lecture will consist of the following parts: 1) A brief introduction to the vulnerabilities and countermeasures in IoT and IIoT cybersecurity with a focus on threat models and attacks surfaces in cyber-physical critical infrastructures, 2) State of the art in machine learning-based intrusion detection in IoT and cyber-physical systems in the network layer, 3) The interplay between artificial intelligence and system/application-level security issues in cyber-physical settings, 3) Open issues, challenges and future directions in this field are going to be presented for the researchers interested in this field.

Connectivity of an asymmetric network represented by a weighted digraph is investigated in this work. A novel distributed algorithm based on the subspace consensus approach is introduced to compute the generalized algebraic connectivity as connectivity measure of asymmetric networks from the viewpoint of each node. After properly transforming the Laplacian matrix of the network, two sequences of one-dimensional and two-dimensional subspaces are generated iteratively by each node in a distributed manner such that one of them converges to the desired subspace spanned by the eigenvector(s) associated with the eigenvalue(s) representing the network’s generalized algebraic connectivity. The convergence analysis of the distributed algorithm is subsequently provided under some assumptions. The efficiency of the developed algorithm in computing the network connectivity is demonstrated by simulations.

The Future Workforce – What Will the Future Enterprise Demands for Their Workforce?

The introduction of systems with unprecedented abilities in autonomous thought and action will force our future workforce to evolve our management practices. In order to optimize the output of systems that are more powerful and intelligent than us, we will need to develop ways of managing the new dynamics. One such
way is through ‘firm’ management skills; skills that will combine the ability to use soft skills, such as leadership and negotiation, with hard technical abilities.

Artificial Intelligence (AI) and Machine Learning systems industries will need to begin segmenting into specializations for personal and professional needs. Professional AI systems will be owned and maintained by organizations, requiring higher levels of privacy and specialization than Personal AI’s. This
implies the need to examine our current practices for ensuring loyalty and productivity in our companies. We will need to establish management accountabilities in this new environment.

This poses an important question: As human kind and machines integrate further through the development of AI and automatic machine learning systems; how do we maintain the upper hand?

In his presentation, Mr. Chen will share highlights of the work his fellow IEEE members around the world have done in advancing research in the fields of AI, Deep Learning, High Performance Computers, Enterprise Architecture, and Future of Education.

EV companies in the global market focused on the recent technologies towards Hydrogen (H2) and FC-VPT to improve the Tank-To-Wheel (TTW) efficiency. The inherent benefits are the cost-effective solution for 'Eco' friendly, free of emission, and high-power capacity. The power converters are crucial in boosting the fuel cell stack power through voltage conversion. Therefore, ensure the demand for the motor and transmission in the vehicles. Several converter topologies have been proposed for various vehicular applications. State-of-the-art technology, newly developed families of the unidirectional non-isolated Multistage Power Converter (MPC) configuration for Fuel Cell – Vehicular Power Train (FC-VPT), Renewables, and Microgrids application will be discussed in the presentation. Power converters with the new modified version are viable and cost-effective solutions with reduced size and increased efficiency. The comprehensive review and comparison of different topologies and suitability for various applications will be discussed in the presentation, correctly applied to the power train of a small vehicle to large vehicles (bus, trucks, etc.). Finally, the advantages/disadvantages will be pointed out in the presentation for the prominent features of each converter, its challenges, and its application for fuel cell (FC) technology, EVs, Microgrids, and Renewables. 

You can view more about the talk here. 

During these lectures, I will provide an overview of the principles and tools to help you become more organised, and more experienced in the process of engineering design. The process begins with the specification of design objectives and constraints, continues through the development, documentation, and analysis of design ideas, and ends with the process of building and testing the solution. Throughout the cycle, engineering design follows a logical process involving careful documentation and quantitative analysis using mathematical tools and scientific principles. During the design process, working and collaborating in interdisciplinary teams is essential. Moreover, I will discuss some of the latest ICT tools that can be used to enable the efficient sharing of graphics, data, and text with team members to facilitate teamwork and remote collaboration. My lectures will focus on teaching a tested, iterative design process as well as techniques to sharpen creative analysis. You should therefore be able to apply these principles to any engineering domain. I will also provide examples of real engineering problems and how these were solved using science, technology, art, and some management principles.

Artificial light production absorbs 13-14% of the world’s electricity annual production. During the last decade, SSLs (Solid-State Lighting) based on components like LEDs, OLEDs and LDs has turned into a game changer beating the conventional technologies in all aspects. As SSL technology matures, maximizing the energy savings from connected SSL systems will become increasingly dependent on successful integration into the built environment. That way, we are witnessing a transition from the conventional “analogue” lighting technologies towards “digital” lighting. Intelligent lighting ambitions becoming the backbone for smart cities, smart buildings and smart grids. That way leads smart lighting concept toward the heart of the “Internet of Things”. Further, to serve society as effectively as we could, Industry has coined a new term “human-centric lighting” (HCL) to direct its primary efforts in meeting human needs. The objective is switching to smart human-centric lighting driven by both “application efficiency” and “quality of light”. Next generation lighting systems should provide the “Right Light” with the best efficiency and quality, when and where it is needed. This talk will highlight all the above- mentioned issues and will focus on the future of smart lighting as complex system that will be integrated in a global “system of systems” in our living environments and our cities.

This presentation discusses the profound technological changes that have taken place during the last 2 decades, the main characteristics being erosion and convergence. It is pointed out that the advances are at the edge of traditional disciplines and the connections between different disciplines are becoming the core of the new technologies, in a not multi, not inter but a transdisciplinary manner.  It is argued that convergence fuels convergence and results in emergence. Four distinct phases of industrial revolution, culminating in Industry 4.0 are discussed. The emerging paradigms of big data and cyber physical systems, supported by new disruptive advances both on software and hardware side, as well as the cross-fertilization of concepts and the amalgamation of information, communication and control technology driven approaches are pointed out to. Most importantly, the changes observed in industry are discussed, together with the paradigm change from industrial electronics to industrial informatics and finally to cyber physical systems. The presentation is concluded with a look into the future that includes a discussion of symbiotic autonomous systems and the possible transition from Industry 4.0 to Industry 5.0.

Automation of border control gates, as well as easy identification in a variety of daily-life applications (ranging, e.g., from home banking to e-commerce and e-government), requires a high degree of confidence in the identification. Modern solutions are based on biometric technologies to ensure standard quality in operation, by mimicking the usual activities performed by humans in identifying individuals. Biometric technologies allow in fact for efficiently analyzing human traits (e.g., face, fingerprint, iris, palm) for identity management. 

This talk will analyze the opportunities offered by biometric technologies and their use for identity verification and recognition in automated border control systems and also in many other critical applications. The characteristics of these technologies as well as their implications on the overall system will be considered. Attention will be also given to a comprehensive system design methodology to take into account all application requirements, including the need for privacy protection. 

Biometrics concerns the study of automated methods for identifying an individual or recognizing an individual among many people by measuring one or more physical or behavioral features. Certain physical human features or behaviors are characteristics that are specific and can be uniquely associated with one person. Retinas, iris, DNA, fingerprint, palm print, or pattern of finger lengths are typical physical features that are specific to individuals. Also, the voice print, gait, or handwriting can be used to this purpose.

Nowadays biometrics is rapidly evolving. This science is getting more and more accurate in recognizing and identifying persons and behaviors. Consequently, these technologies become more and more attractive and effective in critical applications, such as to create safe personal IDs, to control the access to personal information or physical areas, to recognize terrorists or criminals, to study the movements of people, to monitor the human behavior, and to create adaptive environments.

The use of biometrics in the real-life often requires very complex signal and image processing and scene analysis, for example encompassing biometric feature extraction and identification, individual tracking, face tracking, eye tracking, liveness/anti-spoofing tests, and facial expression recognition.

This talk will review the main biometric traits and analyze the opportunities offered by biometric technologies and applications to support a broad variety of applications. Attention will be given to the current trends in research and applications.

Biometrics concerns the study of automated methods for identifying an individual by measuring one or more physical or behavioral features of him. Certain physical human features or behaviors are characteristics that are specific and can be uniquely associated with one person. Retinas, iris, DNA, fingerprint, palm print, or pattern of finger lengths are typical physical features that are specific to individuals. Also, the voice print, gait, or handwriting can be used to this purpose.

Nowadays biometrics is rapidly evolving. This science is getting more and more accurate in recognizing and identifying persons and behaviors. Consequently, these technologies become more and more attractive and effective in critical applications, such as to create safe personal IDs, to control the access to personal information or physical areas, to recognize terrorists or criminals, to study the movements of people, and to monitor the human behavior.

The use of biometrics in the real-life often requires very complex signal and image processing and scene analysis, for example encompassing biometric feature extraction and identification, individual tracking, face tracking, eye tracking, liveness/anti-spoofing tests, and facial expression recognition.

Artificial intelligence techniques (including neural networks, fuzzy logic, evolutionary computing, and multi-agent systems) have been proved to be useful and effective in addressing this kind of data processing, especially when it is difficult to identify an algorithm while sufficiently descriptive examples are available, or when fuzzy descriptions are more natural to capture the essence of the problem, or when complex non-linear optimization is needed, or when multiple agents cooperate in solving the application problem. The relevance of artificial intelligence to contribute to solving these applications has been shown both in the design process of the solution as well as the technological component of the solution itself.

This talk will review the domain of biometrics, its applications in various domains and the relevance of artificial intelligence to effectively solve various problems in these applications.

Recent years have seen a growing interest among users in the migration of their applications to the Cloud computing environments. However, due to high complexity, Cloud-based services often experience a large number of failures and security breaches, and consequently, impose numerous challenges on the dependability and resilience of users’ applications.

Unfortunately, current dependability and resilience solutions focus either on the infrastructure itself or on application analysis, but fail to consider the complex inter-dependencies between system components and application tasks.
This aspect is highly crucial especially when Cloud environments are used, as it is increasingly considered nowadays, in critical applications.

Besides, definition of application requirements, allocations of resources to application tasks, and optimization of global management parameters usually are based either on statistical approaches or on heuristics strategies typical of operating research. Computational intelligence may give additional opportunities and flexibility in specifying the requirements especially when they are defined by non-experts and in optimizing the resource allocation and the global management parameters.

This talk will discuss a user-centric, dependability- and resilience-driven framework that considers deploying and protecting users’ applications in the Cloud infrastructure so as to minimize their exposure to the vulnerabilities in the network, as well as offering fault tolerance and resilience as a service to the users who need to deploy their applications in the Cloud.

In this scenario, the talk analyzes the opportunities offered by computational intelligence to specify the characteristics and the requirements of these environments and support their management in the presence of many local optimization minima.

Adaptability and advanced services for industrial manufacturing require intelligent technological support for understanding the production process characteristics also in complex situations. Quality control is specifically one of the activities in manufacturing which is very critical for ensuring high-quality products and competitiveness on the market.

Similarly, protection of the environment requires the ability to adjust the understanding of the current status by considering the natural dynamics of the environment itself and the natural phenomena.

Artificial intelligence can provide additional flexible techniques for designing and implementing monitoring and control systems both for industrial and environmental applications, which can be configured from behavioral examples or by mimicking approximate reasoning processes to achieve adaptable systems.

This talk will analyze the opportunities offered by artificial intelligence technologies to support the realization of adaptable operations and intelligent services in industrial applications, specifically focusing on manufacturing processes and quality control, as well as in environmental monitoring, especially for land management and agriculture.

Several factors affect the efficiency of a solar plant: among them, the electrical working conditions of the panels, the local weather, and the tidiness of the panel.

Besides, the economic cost of operations to maintain and manage the plant have to be considered in its adoption.

This talk investigated the use of computational intelligence paradigms to model the behavior of a solar panel in terms of energy production forecast, Maximum Power Point (MPP) prediction and the degradation of production due to the presence of dust on the panel have been modeled,

Several prediction computational intelligence techniques have been challenged in these tasks to exploit measurements directly collectible from the panel and from the public weather station.

Adaptability and advanced services for ambient intelligence require intelligent technological support for understanding the current needs and the desires of users in the interactions with the environment for their daily use, as well as for understanding the current status of the environment also in complex situations. This infrastructure constitutes an essential base for smart living. Various technologies are nowadays converging to support the creation of efficient and effective infrastructures for ambient intelligence.

Artificial intelligence can provide flexible techniques for designing and implementing monitoring and control systems, which can be configured from behavioral examples or by mimicking approximate reasoning processes to achieve adaptable systems. Machine learning can be effective in extracting knowledge from data and learn the actual and desired behaviors and needs of individuals as well as the environment to support informed decisions in managing the environment itself and its adaptation to the people’s needs.

Biometrics can help in identifying individuals or groups: their profiles can be used for adjusting the behavior of the environment. Machine learning can be exploited for dynamically learning the preferences and needs of individuals and enrich/update the profile associated either to such individual or to the group. Biometrics can also be used to create advanced human-computer interaction frameworks.

Cloud computing environments will be instrumental in allowing for world-wide availability of knowledge about the preferences and needs of individuals as well as services for ambient intelligence to build applications easily.

This talk will analyze the opportunities offered by these technologies to support the realization of adaptable operations and intelligent services for smart living in ambient intelligent infrastructures.

The rapid advancements in Information and Communication Technologies (ICTs) have been greatly changing our life, with clear societal and economic benefits. Mobile technology, Cloud, Big Data, Internet of things, services and technologies are becoming more and more pervasive and conveniently accessible, towards to the realization of a 'smart' society.  At the heart of this evolution is the ability to collect, analyze, process and share an ever increasing amount of data, to extract knowledge for offering personalized and advanced services.  A major concern, and potential obstacle, towards the full realization of such evolution is represented by security and privacy issues. As a matter of fact, the (actual or perceived) loss of control over data and potential compromise of their confidentiality can have a strong detrimental impact on the realization of an open framework for enabling collection, processing, and sharing of data, typically stored or processed by external cloud services.  In this talk, I will illustrate some security and privacy issues arising in the emerging scenarios, addressing problems related to guaranteeing confidentiality and integrity of data stored or processed by external providers, ensuring access privacy, regulating and controlling access to data in the cloud, and performing queries on protected data.

Biometric technology was incepted in 1958 in a very non-scientific way while recording inked fingerprints of people on paper on a whim. Then biometric technology was practiced and a scientific basis was established during finding the identity of Afgan Girl Sharbat Gula by National Geographic photographer McCurry and Prof. John Daugman. The talk will introduce the basic concepts of biometric security, beginning with the history of the development of biometric technology and how it has become the backbone of security in several daily-used products like personal computers or mobile phones. Apart from such applications, the talk will emphasize how biometrics helps to find criminals in the FBI and how helps to recognize a person in India using the nationwide database AADHAAR. The latest usage of deep learning techniques for achieving superior performances from biometric systems will also be highlighted in this talk.

This research is for deep learning models and computation for resource allocation in distributed systems. For the distributed resource allocation under uncertainty, various economic theories are used such as game theory, auction, and stable marriage frameworks. In this research, distributed and optimal auction which is revenue-improved using deep learning for various emerging systems.

A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization and numerosity are some of the characteristics of complex systems. With increasing system complexity, achieving confidence in systems becomes increasingly difficult. With the recent trend towards significant footprint of complex system’s functionality being governed by machine learning based models and algorithms, there is a need to ensure that emergent behavior associated with such systems are well analyzed and understood. This presentation discusses an approach that involves developing machine learning classifier models that learns on potential negative and positive emergent behaviors. The machine learning model observes the various MOEs (Measures of Effectiveness) and MOPs (Measures of Performance) and learns the nature of emergent behavior. The approach is illustrated through two case studies – one at system level of an aircraft pitch controller, and another at system-of-system level of a swarm of UAV

During system design and development, it is a significant challenge to ensure that the right and optimal architecture/design decisions are made. Often, the learning of whether the decision is optimal or not, and the impact on the Measures of Effectiveness (MOEs) of the system, occur late in the development life cycle. System architects and designers undergo various experiential learnings during the development of many systems over the years. This presentation discusses a framework that leverages machine learning models to learn from the decision learning cycles and advise on the uncertainty of various architecture design decisions. The framework enables a decision-oriented view that factors the learning cycles and feedback loops experienced. The framework enables codification of decisions and progressive maturity of architectural knowledge base

With the rapid progress in the field of sensors and instrumentation systems, day by day, there is a good technological development in the industry, engineering, medicine, and other scientific fields. However, more sophisticated sensor systems are still required to be developed for fast measurements in an intelligent manner.

In the present talk, the development of new nano-ultrasonic and nano-acoustic sensor systems is described for measurements in a reliable manner for remote monitoring and control of the health of old age patients living in remote and hilly areas. 

Advanced acoustic/ultrasonic biomedical sensors and IoT based systems are presented for healthcare care applications, with the main emphasis on telehealth,.The newly developed diagnostic and therapeutic devices by using RFID chips, nano-scale or sensor-enabled radio technologies and sensor networks would thus be useful for solving the problem of unexplored diseases in future, as well as for controlling of the quality of medicines, drugs, equipment, and physiological event monitoring systems. WSN (Wireless Sensor Networking) is preferred to be used here with the sensing devices for remote applications.
 

As is aware, day by day, health care system usage is progressing, with the advancement in technology. Newer and newer biomedical systems are being developed, for better health care. However, more effective and reliable systems, say ubiquitous systems, are required to be developed. Advanced u-health care systems are thus presented here. Wireless sensor networking (WSN) technology is applied for these u- health care systems which can be used easily in various harsh environments.

Design and development aspects of advanced sensor systems are discussed for such healthcare applications. Different types of diagnostic and therapeutic devices and sensing systems are described in detail. U-technology is very useful to monitor, in particular, the health of old age patients, living in isolated areas like hills. Medical abnormalities are sensed and transmitted with u-sensors to main city hospital. After proper analysis by the doctor, appropriate advice/precaution is telemetered back to the patient for better critical care and therapeutic treatment, before shifting him/her to a hospital. A case study is given for detection and monitoring of strokes, which is very serious critical situation of the patient to be dealt with.  Cancer nanotechnology and high-intensity focal ultrasound system for the treatment of deep seated brain tumors, are also presented.

The advanced u-health care systems are thus very effective and useful for better health care, in a reliable manner, at low cost.