Segmentation of the carotid intima-media region in B-mode ultrasound images

This paper proposes a new approach for the segmentation of both near-end and far-end intima-media regions of the common carotid artery in ultrasound images. The method requires minimal user interaction and is able to segment the near-end wall in arteries with large, hypoechogenic and irregular plaques, issues usually not considered previously due to the increased segmentation difficulty.     

Continuous free-crab gaits for hexapod robots on a natural terrain with forbidden zones: An application to humanitarian demining

Autonomous robots are leaving the laboratories to master new outdoor applications, and walking robots in particular have already shown their potential advantages in these environments, especially on a natural terrain. Gait generation is the key to success in the negotiation of natural terrain with legged robots; however, most of the algorithms devised for hexapods have been tested under laboratory conditions. This paper presents the development of crab and turning gaits for hexapod robots on a natural terrain characterized by containing uneven ground and forbidden zones. The gaits we have developed rely on two empirical rules that derive three control modules that have been tested both under simulation and by experiment. The geometrical model of the SILO-6 walking robot has been used for simulation purposes, while the real SILO-6 walking robot has been used in the experiments. This robot was built as a mobile platform for a sensory system to detect and locate antipersonnel landmines in humanitarian demining missions.     

Incorporating Time-Varying Perturbations Into the Dynamic Inoperability Input–Output Model

This paper introduces a methodology to extend the capabilities of the dynamic inoperability input-output model (DIIM) to account for perturbations to sectors of the economy that are time varying and probabilistic. The DIIM accounts for the interdependent nature of the economy in determining the impact of a disruption on the ability of each economic sector to satisfy the prevailing demand. While the original formulation of the DIIM only allows for a single perturbation vector, this paper extends the DIIM to allow a perturbation matrix to be utilized as inputs to the model. The underlying motivation for this paper is understanding the impact of a pandemic on a regional economy. As such, idiosyncrasies associated with a pandemic are also accounted for in the model formulation. These include translating workforce absenteeism into a measure of sector inoperability and accounting for the impact of long-term absenteeism on a sector's ability to deliver its as-planned production output.     

Transmission power and data rate aware routing on wireless networks

Wireless networks can vary both the transmission power and modulation of links. Existing routing protocols do not take transmission power control (TPC) and modulation adaptation (also known as rate adaptation – RA) into account at the same time, even though the performance of wireless networks can be significantly improved when routing algorithms use link characteristics to build their routes. This article proposes and evaluates extensions to routing protocols to cope with TPC and RA. The enhancements can be applied to any link state or distance vector routing protocols. An evaluation considering node density, node mobility and link error show that TPC- and RA-aware routing algorithms improve the average latency and the end-to-end throughput, while consuming less energy than traditional protocols.     

Genetic algorithms to solve the power system restoration planning problem

This study reports the use of a Genetic Algorithm (GA) to solve the Power System Restoration Planning Problem (PSRP). The solution to the PSRP is described by a series of operations or a plan to be used by the Power System operator immediately on the occurrence of a blackout in the electrical power supply. Our GA uses new initialization and crossover operators based on the electrical power network, which are able to generate and maintain the plans feasible along GA runs. This releases the Power Flow program, which represents the most computer demanding component, from computing the fitness function of unfeasible individuals. The method was designed for large transmission systems and results for three different electrical power networks are shown: IEEE 14-Bus, IEEE 30-Bus, and a large realistic system.     

Skill Memory in Biped Locomotion

Robots must be able to adapt their motor behavior to unexpected situations in order to safely move among humans. A necessary step is to be able to predict failures, which result in behavior abnormalities and may cause irrecoverable damage to the robot and its surroundings, i.e. humans. In this paper we build a predictive model of sensor traces that enables early failure detection by means of a skill memory. Specifically, we propose an architecture based on a biped locomotion solution with improved robustness due to sensory feedback, and extend the concept of Associative Skill Memories (ASM) to periodic movements by introducing several mechanisms into the training workflow, such as linear interpolation and regression into a Dynamical Motion Primitive (DMP) system such that representation becomes time invariant and easily parameterizable. The failure detection mechanism applies statistical tests to determine the optimal operating conditions. Both training and failure testing were conducted on a DARwIn-OP inside a simulation environment to assess and validate the failure detection system proposed. Results show that the system performance in terms of the compromise between sensitivity and specificity is similar with and without the proposed mechanism, while achieving a significant data size reduction due to the periodic approach taken.     

Adaptive Robot Biped Locomotion with Dynamic Motion Primitives and Coupled Phase Oscillators

In order to properly function in real-world environments, the gait of a humanoid robot must be able to adapt to new situations as well as to deal with unexpected perturbations. A promising research direction is the modular generation of movements that results from the combination of a set of basic primitives. In this paper, we present a robot control framework that provides adaptive biped locomotion by combining the modulation of dynamic movement primitives (DMPs) with rhythm and phase coordination. The first objective is to explore the use of rhythmic movement primitives for generating biped locomotion from human demonstrations. The second objective is to evaluate how the proposed framework can be used to generalize and adapt the human demonstrations by adjusting a few open control parameters of the learned model. This paper contributes with a particular view into the problem of adaptive locomotion by addressing three aspects that, in the specific context of biped robots, have not received much attention. First, the demonstrations examples are extracted from human gaits in which the human stance foot will be constrained to remain in flat contact with the ground, forcing the “bent-knee” at all times in contrast with the typical straight-legged style. Second, this paper addresses the important concept of generalization from a single demonstration. Third, a clear departure is assumed from the classical control that forces the robot’s motion to follow a predefined fixed timing into a more event-based controller. The applicability of the proposed control architecture is demonstrated by numerical simulations, focusing on the adaptation of the robot’s gait pattern to irregularities on the ground surface, stepping over obstacles and, at the same time, on the tolerance to external disturbances.     

Relational Learning with GPUs: Accelerating Rule Coverage

Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.     

An industry experiment on the effects of test-driven development on external quality and productivity

Existing empirical studies on test-driven development (TDD) report different conclusions about its effects on quality and productivity. Very few of those studies are experiments conducted with software professionals in industry. We aim to analyse the effects of TDD on the external quality of the work done and the productivity of developers in an industrial setting. We conducted an experiment with 24 professionals from three different sites of a software organization. We chose a repeated-measures design, and asked subjects to implement TDD and incremental test last development (ITLD) in two simple tasks and a realistic application close to real-life complexity. To analyse our findings, we applied a repeated-measures general linear model procedure and a linear mixed effects procedure. We did not observe a statistical difference between the quality of the work done by subjects in both treatments. We observed that the subjects are more productive when they implement TDD on a simple task compared to ITLD, but the productivity drops significantly when applying TDD to a complex brownfield task. So, the task complexity significantly obscured the effect of TDD. Further evidence is necessary to conclude whether TDD is better or worse than ITLD in terms of external quality and productivity in an industrial setting. We found that experimental factors such as selection of tasks could dominate the findings in TDD studies.