Accurately solving discrete optimization problems is still a serious challenge for classical computing systems. Despite significant progress, it is still impossible to optimally solve examples of the size found in real-world applications. This article examines the problem of scheduling tasks on one machine. Each task has an associated execution time, desired completion date, and a late penalty feature weight. The order of executing tasks should be determined in a manner that minimizes the sum of delay costs. Despite the simplicity of the formulation, this problem is NP-hard and is representative of optimization challenges encountered in the practice of production control, management, and logistics.In this work, we propose using a quantum computing environment to solve it. The main disadvantage of calculations on quantum computers is their nondeterminism. Therefore, there is no guarantee of optimality of solutions to discrete optimization problems determined on a quantum computer.We propose a novel approach to constructing hybrid algorithms that guarantee the optimality of the solution. The algorithm for solving the task scheduling problem considered here is based on the branch and bound method. Calculations are performed alternately on a classical computer and a quantum computer. The lower and upper bounds of the criterion function are determined on a quantum computer. Computational experiments were performed on the D-Wave quantum computer. In small examples, the algorithm runs faster and generates significantly fewer solution tree vertices than its counterpart running on a classic computer. For larger examples, the proposed runtime-constrained method, acting as a heuristic, is an interesting alternative to using quantum computing hardware in relation to classical approaches based on silicon computers.

In recent years, “security by design” has become a fundamental requirement for new technologies, especially for those that interconnect various systems and their components, e.g., implanted medical devices with remote configurability capabilities. As such, security of implanted medical devices has become a concern exacerbated by the tight constraints characterizing them and the need to ensure patients’ safety as these devices typically work to assure life-critical functions. To protect such systems, multiple proposals have been made to detect threats and mitigate vulnerabilities and malware intrusions. A common approach is utilizing hardware redundancy to deal with system faults, however, implanted medical devices work under tight power and area constraints that may not always allow for redundancy.In this paper, we build on an existing multi-modal based mitigation scheme for securing medical devices and propose self-healing capabilities that would allow the device to automatically reestablish functionalities compromised due to external threats.

We present an adaptive force guidance system for laparoscopic surgery skills training. This system consists of self-adjusting fuzzy sliding-mode controllers and switching mode controllers to provide proper force feedback. Using virtual fixtures, the proposed system restricts motions or guides a trainee to navigate a surgical instrument in a 3-D space in a manner that mimics a human instructor who would teach the trainees by holding their hands. The self-adjusting controllers incorporate human factors, such as different force sensitivity and proficiency levels. The proposed system was implemented and evaluated using the computer-assisted surgical trainer (CAST). The effects of the force guidance system are presented based on the experimental test results.

Depth estimation is an important challenge in the field of augmented reality. Supervised deep learning methods of depth estimation can be difficult to use in novel settings due to the need for labeled training data. The work presented in this paper overcomes the challenge in a laparoscopic surgical simulation environment by using synthetic data generation for RGB-D training data. We also provide a neural network architecture that can generate real-time 448x448 depth map outputs suitable for use in AR applications. Our approach shows satisfactory performance when tested on a non-synthetic test dataset with an RMSE of 2.50 cm, MAE of 1.04 cm, and δ < 1.25 of 0.987.

A system called the Computer-Assisted Surgical Trainer (CAST) that provides force, visual, and audio guidance with objective assessments for laparoscopic surgery skills training in a non-patient training setup was developed. To support the guidance and assessment of trainees, given an object transfer task, a fuzzy reasoning system is proposed to estimate states of objects as well as the completion of basic actions. A peg transfer task is implemented as a target application. Based on a modeling approach of this peg transfer task, fuzzy production rules are defined and a reasoning process is designed. The experimental test results show the effectiveness of the proposed fuzzy reasoning system.

Network-connected embedded systems require multiple lines of defense against malware. In addition to preventing malware by designing secure interfaces and software, anomaly-based detection is needed to detect malware that successfully infiltrates these defenses. Timing based anomaly detection strengthens embedded system security by detecting anomalies in the execution time of critical software tasks. However, existing timing based anomaly detection methods use a lumped timing model that aggregates the timing of the software, processor architecture, operating system scheduling, etc., and thereby incurs significant variability. We present a non-intrusive hardware detector supporting two novel timing models, including a lumped timing multi-range model that clusters timing into multiple range bounds, and a subcomponent timing model that defines bounds for timing subcomponents of events. Timing subcomponents include intrinsic software execution, instruction cache misses, data cache misses, and interrupts. The experimental results demonstrate that the detection based on subcomponent timing model achieves greater malware detection accuracy compared to the lumped timing model without increasing false positives.

A mathematical model of 4 degrees of freedom of forward and inverse kinematics is presented to provide visual and haptic guidance for laparoscopic surgery skills training. Also, a particle swarm optimization technique with color marker detection is used to estimate the instrument tip position accurately. Using a simple heuristic for the visual guidance, inverse kinematics solutions are calculated in real-time, which the haptic guidance uses as well. The experimental results illustrate the effectiveness of the proposed method.

A simple and robust fuzzy logic controller with a forbidden region virtual fixture is proposed for a haptic guidance system developed to assist in simulation-based training of laparoscopic surgery. A surgical training system that involves human in the control loop is discussed. It uses a controller which adjusts control outputs for different trainees based on their skill level. The system involves virtual fixtures to improve human manipulation tasks inherent in this problem domain. The proposed controller cooperates with a heuristic-based scaling factor modifier to make such adjustments. The experimental results illustrate the feasibility and effectiveness of the proposed haptic guidance system.

Traditionally the machine-learning community has viewed the No Free Lunch (NFL) theorems for search and optimization as a limitation. We review, analyze, and unify the NFL theorem with the perspectives of "blind" search and meta-learning to arrive at necessary conditions for improving black-box optimization. We survey meta-learning literature to determine when and how meta- learning can benefit machine learning. Then, we generalize meta- learning in the context of the NFL theorems, to arrive at a novel technique called anti-training with sacrificial data (ATSD). Our technique applies at the meta level to arrive at domain specific algorithms. We also show how to generate sacrificial data. An extensive case study is presented along with simulated annealing results to demonstrate the efficacy of the ATSD method.

The integration of technology into autistic classrooms has shown promising results, including an increase in attention span, creativity, and social skills. We have introduced a low-cost learning technology composed by electronic modules called eBlocks to an autistic middle-school classroom. The participants of this study had the opportunity to learn concepts in the design and implementation of electronic systems by using the eBlocks. Our initial findings show that the integration of hands-on real world based projects, centered on the design of systems for a Smart House estimulated peer-to-peer interaction and teamwork, while promoting spontaneous creative thinking. We present our experiences with six students, including summaries of our overall experiences, teacher's pre-and post-surveys, and the examination of students' work.

The Asymmetric Threat Response and Analysis Program (ATRAP) is a software system for intelligence fusion, visualization, reasoning, and prediction. ATRAP consists of a set of tools for annotating and automatically extracting entities and relationships from documents, visualizing this information in relational, geographic, and temporal dimensions, and determining future courses of action of adversaries by creating situational threat templates and applying customized prediction algorithms. In this paper, we first describe the task of analyzing data in intelligence reports, and then provide an overview of major system components: the Text Highlighter tool, the ThoughtSpaceTMvisualization environment, and the Template Builder and prediction tool. Subsequently, we describe linguistic characteristics of intelligence reports, and describe ATRAP's named entity recognition system.

In heterogeneous system design, partitioning of the functional specifications into hardware (HW) and software (SW) components is an important procedure. Often, an HW platform is chosen, and the SW is mapped onto the existing partial solution, or the actual partitioning is performed in an ad hoc manner. The partitioning approach presented is novel in that it uses Bayesian belief networks (BBNs) to categorize functional components into HW and SW classifications. The BBNpsilas ability to propagate evidence permits the effects of a classification decision that is made about one function to be felt throughout the entire network. In addition, because BBNs have a belief of hypotheses as their core, a quantitative measurement as to the correctness of a partitioning decision is achieved. A methodology for automatically generating the qualitative structural portion of BBN and the quantitative link matrices is given. A case study of a programmable thermostat is developed to illustrate the BBN approach. The outcomes of the partitioning process are discussed and placed in a larger design context, which is called model-based codesign.

The current state of military operations includes many stability and support (SASO), multi-sided conflicts. The research presented in this paper attempts to address this complex environment by creating a SASO simulation, coevolutionary generation of courses-of-actions (COAs) for each side, and visualization tools for analysis of the resulting COAs. The SASO simulation is significantly different from previous systems because it incorporates non-conventional warfare units such as terrorists and media. The coevolution algorithm is different because it allows all sides of the conflict to evolve their COAs. The visualization tools are important because SASO doctrine is not as well developed as conventional warfare doctrine. Therefore, visual analysis and understanding of a system that is not well defined provides insight for future modeling and verification.

Intelligent agents are a new paradigm for developing complex system applications. Agent systems are based on autonomous software-hardware components (technical agents) that cooperate within an environment to perform some task. An agent can be viewed as a self-contained, concurrently executing thread of control that encapsulates some states and communicates with its environment and possibly other agents via some sort of message passing. The environment may contain other agents whose environments are disjoint with or only partially overlap with the environment of a given agent. The authors present a conflict management problem in a multiagent robotic system. The multiagent robotic system consists of a group of autonomous agents on the first (lower) level of its hierarchy, and management agents on the second (upper) level of the hierarchy.

The Advanced Tactical Architecture for Combat Knowledge System (ATACKS, formerly known as ABATIS) has been designed as a visualization tool to support commander's decision-making in a complex battlespace environment. ATACKS expands standard battlefield symbology by providing symbols for Stability and Support Operation (SASO) on three dimensional (3D) abstract battlespace terrains. Furthermore, it extends normal spatial visualization through process centered displays that seek to enhance the commander's understanding of the situation by presenting qualitative data in novel formats. In addition, external decision support tools communicate with ATACKS, through an application program interface (API) that can allow communication over a network as well as between systems on differing operating systems. With this capability, ATACKS can serve as an integration point for intelligent aiding systems'.

In heterogeneous systems design, partitioning of the functional specifications into hardware and software components is an important procedure. Often, a hardware platform is chosen and the software is mapped onto the existing partial solution, or the actual partitioning is performed in an ad hoc manner. The partitioning approach presented is novel in that it uses Bayesian belief networks (BBNs) to categorize functional components into hardware and software classifications. First, the motivation and background material are presented. Then, a case study of a programmable thermostat is developed to illustrate the BBN approach. The outcomes of the partitioning process are discussed and placed in a larger design context, called model-based co-design.

This article presents an approach to embedding expert systems within an object oriented simulation environment. The basic idea is to create classes of expert system models that can be interfaced with other model classes. An expert system shell is developed within a knowledge-based design and simulation environment which combines artificial intelligence and systems modeling concepts. In the given framework, interruptible and distributed expert systems can be defined as components of simulations models. This facilitates simulation modeling of knowledge-based controls for flexible manufacturing and many other autonomous intelligent systems. Moreover, the structure of a system can be specified using a recursive system entity structure (SES) and unfolded to generate a family of hierarchical structures using an extension of SES pruning called recursive pruning. This recursive generation of hierarchical structures is especially appropriate for design of multilevel flexible factories. The article illustrates the utility of the proposed framework within the flexible manufacturing context.

A framework for aiding the construction of simulation models in system design problems is presented. The framework employs concepts of artificial intelligence and simulation modeling. A knowledge representation scheme called system entity structure expresses information about the structure of the system to be designed and its corresponding models. More specifically, the entity structure represents objects and their attributes, decompositions, and taxonomies. A knowledge base of production rules is defined for a given design domain and is incorporated in an expert system shell which recommends a feasible configuration of design objects from the system entity structure, based on specific design objectives, constraints, and requirements. This configuration is a basis for constructing a model of the system. The methodology for constructing the system entity structure and its corresponding rule base is presented. A case study based on a high level robot design problem is discussed to illustrate the conceptual framework. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

A software shell for assisting in VLSI package design is discussed. Called packaging design support environment (PDSE), the shell integrates tools for modeling and simulation of electrical characteristics of VLSI packages. Parameter extractors tools calculate inductance and capacitance for multiconductor, multidielectric, two-dimensional structures with loss dielectrics. The simulation tools compute pulse response characteristics of uniform, multiple, coupled, and lossless transmission lines. The PDSE shell provides facilities for supporting the package design cycle. Such support facilities include an interface to couple PDSE with a CAD design database, model and simulation experiment libraries, simulation management procedures that guide evaluation of alternative design models, and data analysis support tools. Expert systems techniques are being incorporated into PDSE.>

A simulation environment for prediction of electrical characteristics of integrated circuit packaging structures is described. The simulation shell, Packaging Design Support Environment (PDSE), integrates tools for modeling and simulation of electrical characteristics in VLSI packages. It also provides facilities for supporting design of VLSI packages. Two simulation tools model inductance and capacitance for multiconductor, multidielectric, two-dimensional structures with lossy dielectrics. Another accepts the L and C matrices and computes pulse response characteristics of uniform multiple, coupled, lossless transmission lines which are terminated at discrete points with R, L, and C elements. The design process in PDSE proceeds in three major phases: modeling, simulation, and evaluation. These processes are interactive and allow the designer to refine a design model, modify simulation experiments, and apply various evaluation processes.>