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Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to pose safety requirements as planning constraints in nonlinear, nonconvex optimization problems of motion synthesis. However, many implementations of this approach are limited by uncertain convergence and local optimality of the solutions achieved, affecting overall robustness. To improve upon these issues, we propose a novel two-stage optimization framework: In the first stage, we find a solution to a mixed-integer linear programming (MILP) formulation of the motion synthesis problem, the output of which initializes a second nonlinear programming (NLP) stage. The MILP stage enforces hard constraints of safety and road rule compliance generating a solution in the right subspace, while the NLP stage refines the solution within the safety bounds for feasibility and smoothness. We demonstrate the effectiveness of our framework via simulated experiments of complex urban driving scenarios, outperforming a state-of-the-art baseline in metrics of convergence, comfort, and progress. [ABSTRACT FROM AUTHOR]

A multiobjective, risk-aware framework is developed for optimal path planning of autonomous underwater vehicles operating in uncertain current. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. A new optimization framework is proposed that accounts for the full variability of the ensemble in a single optimization problem whose solution may not necessarily coincide with any of the optimal deterministic paths corresponding to individual ensemble members. We formulate stochastic problemsthat aim to minimize a risk measure of the travel time or energy consumption, using a flexible methodology that enables the user to seamlessly explore various objectives, ranging from risk neutral to risk averse. We illustrate the application of the proposed approach using two case studies based on synthetic 2-D settings, and one case involving a real-world problem in the Gulf of Aden. The results are analyzed to assess the value of stochastic solution, guide the selection of suitable risk measures, and demonstrate the impact of the risk measures on the resulting path and on the distribution of travel times. [ABSTRACT FROM AUTHOR]

The use of electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient, high-speed, on-demand air transportation within a metropolitan area is a topic of increasing interest, which is expected to bring fundamental changes to the city infrastructures and daily commutes. NASA, Uber, and Airbus have been exploring this exciting concept of Urban Air Mobility (UAM), which has the potential to provide meaningful door-to-door trip time savings compared with automobiles. However, the ability to manage many of these eVTOL aircraft safely in a congested urban area presents a challenge unprecedented in air traffic management. In order to enable safe and efficient autonomous on-demand free flight operations in UAM, a computational guidance algorithm with collision avoidance capability is designed and analyzed. The approach proposed in this paper is to formulate this problem as a Markov Decision Process (MDP) and solve it using an online algorithm Monte Carlo Tree Search (MCTS). For illustration, a high-density free flight airspace simulator is created to test the proposed algorithm’s performance. Numerical experiment results show that this proposed algorithm has fewer conflicts and near mid-air collisions than Optimal Reciprocal Collision Avoidance (ORCA), a state-of-the-art collision avoidance strategy. [ABSTRACT FROM AUTHOR]

Emerging unmanned aircraft systems (UAS) operations propose to introduce flights orders of magnitude higher than what the airspace handles today. What type of onboard control for enroute separation assurance increases productivity without compromising safety in the low-altitude airspace? This article quantifies the impact of two conflict detection and resolution approaches - pure speed control and pure direction control - on airspace productivity. We focus on small UAS traffic for goods movement in low altitude (under 400 feet) airspace. We compute three types of metrics, namely throughput, travel time extension and energy consumption, from simulations of unmanned traffic over a representative square area. As the inflow rates are increased, the corresponding outflow rates (throughput), travel time extensions and energy consumption for each type of control are measured. A representative power consumption model is developed for aircraft with both kinds of control. Without compromising safety, both direction control and speed control are equally good, unless the UAS are designed to be highly efficient while hovering. If the UAS have a hover power consumption that is equal to or less than one and a half times the minimum power consumption, speed control increases airspace productivity more than direction control. [ABSTRACT FROM AUTHOR]

A trajectory planning is a technique that is widely used to automatically generate an expected geometric path that a vehicle needs to follow within a certain future time horizon. We propose a way to generate trajectories of multiple vehicles for a short-term planning with the formal guarantee of the collision avoidance using a SAT (Satisfiability) solver. We assume that each vehicle sends the local status (e.g., speed or position) and its planned trajectory to the server (e.g., road-side units or cloud servers). The server collects the information, and determines if the group of vehicles would encounter any collision within a fixed amount of time. Then, the server calculates the modified trajectories, and sends them back to the vehicles to avoid collisions. Calculating such trajectories is generally a complex problem, which makes it challenging to find them within a short amount of time. Given a driving scenario where multiple vehicles are engaged, we propose two methods to reduce the computation time to generate the trajectories. Firstly, we introduce the grouping method which systematically identifies vehicles that are not engaged in the collision scenario, and removes the related parameters from the SAT solving process. Our case study of the ramp merging driving scenario shows the grouping method saves the computation time by 40% compared to solving the constraints with the original parameters. Secondly, we introduce the collision check point method that chooses some waypoints to maintain their previous control input to reduce the problem complexity. Our experiment shows up to 98% computation time reduction compared to the approach without this method. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) show great potential to improve both traffic efficiency and safety by sharing information. This paper addresses the problem of coordinating two strings of vehicles at highway on-ramps efficiently and safely in the longitudinal direction. A rule-based adjusting algorithm is proposed to achieve a near-optimal merging sequence for vehicles coming from the mainline and entering through the ramp. Optimality analysis indicates that the proposed method performs very well compared with the global optimal solutions. Furthermore, to investigate the effectiveness and robustness of the proposed method, simulation-based case studies are carried out under both balanced and unbalanced scenarios. The results are compared with two other control strategies (i.e., rule-based methods and optimization-based methods) in terms of throughput, delay, computational cost, and fuel consumption. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of an optimization algorithm for car motion predictive control that addresses both hybrid car dynamics and hybrid minimization criterion. Instead of solving computationally demanding nonlinear mixed-integer programming task or approximating the hybrid dynamics/criterion, the Hamiltonian-switching hybrid nonlinear predictive control algorithm developed in this paper incorporates the information about hybridity directly into the optimization routine. To decrease the time complexity, several adaptive prediction horizon approaches are proposed, and for some of them, it is shown that they preserve maneuverability-related properties of the car. All developed alternatives are verified on an example of a motion control of a racing car and compared with the approximation-based nonlinear predictive control and a commercial product. Moreover, a sensitivity analysis examining robustness of the algorithm is included as well. [ABSTRACT FROM AUTHOR]

The transcription factors (TFs) can activate or suppress gene expression by binding to specific sites, hence are crucial regulatory elements for transcription. Recently, series of discriminative motif finders have been tailored to offering promising strategy for harnessing the power of large quantities of accumulated high-throughput experimental data. However, in order to achieve high speed, these algorithms have to sacrifice accuracy by employing simplified statistical models during the searching process. In this paper, we propose a novel approach named Discriminative Motif Learning via AUC (DiscMLA) to discover motifs on high-throughput datasets. Unlike previous approaches, DiscMLA tries to optimize with a more comprehensive criterion (AUC) during motifs searching. In addition, based on an experimental observation of motif identification on large-scale datasets, some novel procedures are designed to accelerate DiscMLA. The experimental results on 52 real-world datasets demonstrate that our approach substantially outperforms previous methods on discriminative motif learning problems. DiscMLA’ stability, discriminability, and validity will help to exploit high-throughput datasets and answer many fundamental biological questions. [ABSTRACT FROM AUTHOR]

Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent. [ABSTRACT FROM AUTHOR]

With the prevalence of chromatin immunoprecipitation (ChIP) with sequencing (ChIP-Seq) technology, massive ChIP-Seq data has been accumulated. The ChIP-Seq technology measures the genome-wide occupancy of DNA-binding proteins in vivo. It is well-known that different DNA-binding protein occupancies may result in a gene being regulated in different conditions (e.g. different cell types). To fully understand a gene’s function, it is essential to develop probabilistic models on multiple ChIP-Seq profiles for deciphering the gene transcription causalities. In this work, we propose and describe two probabilistic models. Assuming the conditional independence of different DNA-binding proteins’ occupancies, the first method (SignalRanker) is developed as an intuitive method for ChIP-Seq genome-wide signal profile inference. Unfortunately, such an assumption may not always hold in some gene regulation cases. Thus, we propose and describe another method (FullSignalRanker) which does not make the conditional independence assumption. The proposed methods are compared with other existing methods on ENCODE ChIP-Seq datasets, demonstrating its regression and classification ability. The results suggest that FullSignalRanker is the best-performing method for recovering the signal ranks on the promoter and enhancer regions. In addition, FullSignalRanker is also the best-performing method for peak sequence classification. We envision that SignalRanker and FullSignalRanker will become important in the era of next generation sequencing. FullSignalRanker program is available on the following website: http://www.cs.toronto.edu/$\sim$ wkc/FullSignalRanker/ [ABSTRACT FROM PUBLISHER]

This paper presents a model predictive control (MPC) approach for multivehicle formation taking into account collision avoidance and velocity limitation with reduced computational burden. The first part of the paper constructs a formation control law using feedback linearization with MPC in order to reduce the optimal control problem to a mixed-integer quadratic programming problem for a group of unicycles. The second part constructs a new branch-and-bound (B& B) -based algorithm for collision-avoidance problems. Numerical examples and experiments show that the proposed method significantly reduces computation time. [ABSTRACT FROM PUBLISHER]

The problem of dynamic electrothermal equivalent extraction for system level and signal integrity analysis is considered in the framework of standard macromodeling techniques. The self-heating and mutual thermal impedances of the embedded elements of the system—preliminarily evaluated via 3-D thermal simulations—are reduced with passive identification methods, and equivalent electrical models are realized with a Foster multiport synthesis scheme. The identification is pursued with a convex optimization approach, which is found to be well suited for the thermal problem. As a case study, the coupled electrothermal analysis of an ultra-thin chip-stacking module including two thinned silicon chips interconnected by a copper line is fully carried out in SPICE. [ABSTRACT FROM AUTHOR]

Embedding ultrathin semiconductor dice in flexible substrates provides unique capabilities for product designers and makes products such as smart bank cards and radio-frequency identification banknotes possible. Most of the current work in this area is directed toward handling, embedding, and interconnecting the ultrathin chips. Relatively little attention is paid to another critical process step–placing the flexible and very fragile ultrathin die onto the flexible substrate reliably and in a cost-efficient manner, suitable for high throughput assembly. The presented laser-enabled technology for embedding ultrathin dice in a flexible substrate was developed at the Center for Nanoscale Science and Engineering, North Dakota State University, Fargo, ND, to address this problem. The technology has been successfully demonstrated and proven for the fabrication of an RFID tag. [ABSTRACT FROM PUBLISHER]

The exploration/exploitation balance is a major concern in the control of evolutionary algorithms (EAs) performance. Exploration is associated with the distribution of individuals on a landscape, and can be estimated by a genotypic diversity measure (GDM). In contrast, exploitation is related to individual responses, which can be described with a phenotypic diversity measure. Many diversity measures have been proposed in the literature without a comprehensive study of their differences. This paper looks at surveys of GDMs published over the years for real-coded representations, and compares them based on a new benchmark, one that allows a better description of their behavior. The results demonstrate that none of the available GDMs is able to reflect the true diversity of all search processes. Nonetheless, the normalized pairwise diversity measurement (D^NPW) proves to be the best genotypic diversity measurement for standard EAs, as it shows nondominated behavior with respect to the desired GDM requirements. [ABSTRACT FROM AUTHOR]

Identifying the unknown transcription factor binding sites (TFBSs) is a fundamental and important component for understanding gene regulation as well as life mechanisms. The corresponding de novo motif discovery problem in bioinformatics is formulated as pattern discovery from strings, where challenges come from both modeling and optimization, because the short TFBSs are weak signals in massive and noisy experimental data. While genetic algorithms have been widely applied to the problem, recent memetic algorithms (MAs) employing local operators demonstrate the superiority in both effectiveness and efficiency. In this paper, we propose and study various MA components including local operators and models for motif discovery, through the newly established MA framework. The demonstrated optimization and modeling capabilities are analyzed in-depth on real datasets and their noisy versions. Selected optimal MAs show significantly improved performance over state-of-the-art methods in extensive tests including the blind test on the eukaryotic benchmark. This paper serves as the first systematic study of MAs on de novo motif discovery, where important issues are highlighted in the analyses of MA design. The comprehensive component categorization and the MA framework provide a useful platform for future MA developments, especially on the newly emerging chromatin immunoprecipitation followed by sequencing data. [ABSTRACT FROM AUTHOR]

System integration, performance, cost and enhanced product functionality form the major driving forces behind contemporary innovations in packaging. The need for miniaturization has led to new architectures which combine a whole range of different technologies. The ultimate miniaturization goal is to incorporate all of the elements necessary to build the system in the same package. This approach of system-in-package faces two critical issues: the management of components from different sources and the cost of individual operations necessary to complete the package. Taking into account all the developments that have been made to date on wafer level packaging (WLP), it has been proposed to perform the packaging system at wafer level. Fully tested bare dice are integrated onto or into a wafer which can be pre-processed and post-processed using techniques such as micromachining, passive integration, plating of via and pad redistribution, bumping, dicing and testing. The principal objective of this paper is to present alternative technology for integrated dice coming from various foundries where design, die thickness and contact pad metallurgy are predefined. System integration at wafer level is presented and discussed in this paper. Different approaches, such as system-on-wafer (SoW) or rebuilding a wafer, are introduced and a technology status report is drawn up. [ABSTRACT FROM PUBLISHER]

Motivated by recent research on cooperative unmanned aerial vehicles (UAVs), this paper introduces a new cooperative distributed trajectory optimization approach for systems with independent dynamics but coupled objectives and hard constraints. The overall goal is to develop a distributed approach that solves small subproblems while minimizing a fleet-level objective. In the new algorithm, vehicles solve their subproblems in sequence while generating feasible modifications to the prediction of other vehicles' plans. In order to avoid reproducing the global optimization, the decisions of other vehicles are parameterized using a much smaller number of variables than in the centralized formulation. This reduced number of variables is sufficient to improve the cooperation between vehicles without significantly increasing the computational effort involved. The resulting algorithm is shown to be robustly feasible under the action of unknown but bounded disturbances. Furthermore, the fleet objective function is proven to monotonically decrease as the algorithm cycles through the vehicles in the fleet and over the time. The results from simulations and a hardware experiment demonstrate that the proposed algorithm can improve the fleet objective by temporarily having one vehicle sacrifice its individual objective, showing the cooperative behavior. [ABSTRACT FROM PUBLISHER]