A technique for dynamic term size computation via program transformation

Knowing the size of the terms to which program variables are bound at run-time in logic programs is required in a class of applications related to program optimization such as, for example, granularity analysis and selection among different algorithms or control rules whose performance may be dependent on such size. Such size is difficult to even approximate at compile time and is thus generally computed at run-time by using (possibly predefined) predicates which traverse the terms involved. We propose a technique based on program transformation which has the potential of performing this computation much more efficiently. The technique is based on finding program procedures which are called before those in which knowledge regarding term sizes is needed and which traverse the terms whose size is to be determined, and transforming such procedures so that they compute term sizes "on the fly". We present a systematic way of determining whether a given program can be transformed in order to compute a given term size at a given program point without additional term traversal. Also, if several such transformations are possible our approach allows finding minimal transformations under certain criteria. We also discuss the advantages and applications of our technique and present some performance results.

Performance indices for robotic manipulators: a review of the state of the art

The performance indices are important tools for motion planning and design of robot manipulators. In this paper we present a collection of some of the performance indices that have generated interest in the robotics community. These indices are four different types: kinetostatic performance indices, dynamic performance indices, indices of joint limits, and finally global performance indices. In addition, we review the strategies that have been proposed to solve the problems that occur when the units of the Jacobian matrix elements are not homogeneous. At the end of this paper, we propose a set of global performance indices that can be useful in the design of robot manipulators.

Dynamic stiffness analysis of bridge abutments

Strong motion records obtained in instrumented short-span bridges show the importance of the abutments in the dynamic response of the structure. Existing models study the pier foundation influence but not the abutment performance. This work proposes two and three dimensional boundary element models in the frequency domain and studies the dimensionless dynamic stiffness of standard bridge abutments.

Simulation of the transmission performance for equalized holographic ROADM designs

A complete simulation of the transmission performance for Equalized Holographic ROADM (Reconfigurable Optical Add-Drop Multiplexer) designs is presented in this paper. These devices can address several wavelengths from the input to different output fibres, according to the holograms stored in a SLM (Spatial Light Modulator), where all the outputs are equalized in power. All combinations of the input wavelengths are possible at the different output fibres. To simulate the transmission performance of the EH-ROADM, a software program, from Optiwave, has been used. The correspondence between physical blocks of the device (grating, SLM, lens...) and those simulated in the program (filters, losses, splitters...) has been defined in order to obtain a close agreement between the theoretical transmission performance and the simulated one. To complete the review about Equalized Holographic ROADMs some guidelines about its design have been done.

Performance of pressure control valves and flow meters precision in operational irrigation-water balance accuracy

In pressure irrigation-water distribution networks, pressure regulating devices for controlling the discharged flow rate by irrigation units are needed due to the variability of flow rate. In addition, applied water volume is used controlled operating the valve during a calculated time interval, and assuming constant flow rate. In general, a pressure regulating valve PRV is the commonly used pressure regulating device in a hydrant, which, also, executes the open and close function. A hydrant feeds several irrigation units, requiring a wide range in flow rate. In addition, some flow meters are also available, one as a component of the hydrant and the rest are placed downstream. Every land owner has one flow meter for each group of field plots downstream the hydrant. Its lecture could be used for refining the water balance but its accuracy must be taken into account. Ideal PRV performance would maintain a constant downstream pressure. However, the true performance depends on both upstream pressure and the discharged flow rate. The objective of this work is to asses the influence of the performance on the applied volume during the whole irrigation events in a year. The results of the study have been obtained introducing the flow rate into a PRV model. Variations on flow rate are simulated by taking into account the consequences of variations on climate conditions and also decisions in irrigation operation, such us duration and frequency application. The model comprises continuity, dynamic and energy equations of the components of the PRV.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

A Monitoring Infrastructure for FPGA Self-Awareness and Dynamic Adaptation

Variabilities associated with CMOS evolution affect the yield and performance of current digital designs. FPGAs, which are widely used for fast prototyping and implementation of digital circuits, also suffer from these issues. Proactive approaches start to appear to achieve self-awareness and dynamic adaptation of these devices. To support these techniques we propose the employment of a multi-purpose sensor network. This infrastructure, through adequate use of configuration and automation tools, is able to obtain relevant data along the life cycle of an FPGA. This is realised at a very reduced cost, not only in terms of area or other limited resources, but also regarding the design effort required to define and deploy the measuring infrastructure. Our proposal has been validated by measuring inter-die and intra-die variability in different FPGA families.

AUTOFLY-Aid: Flight Deck Automation Support with Dynamic 4D Trajectory Management for Responsive and Adaptive Airborne Collision Avoidance

AUTOFLY-Aid Project aims to develop and demonstrate novel automation support algorithms and tools to the flight crew for flight critical collision avoidance using “dynamic 4D trajectory management”. The automation support system is envisioned to improve the primary shortcomings of TCAS, and to aid the pilot through add-on avionics/head-up displays and reality augmentation devices in dynamically evolving collision avoidance scenarios. The main theoretical innovative and novel concepts to be developed by AUTOFLY-Aid project are a) design and development of the mathematical models of the full composite airspace picture from the flight deck’s perspective, as seen/measured/informed by the aircraft flying in SESAR 2020, b) design and development of a dynamic trajectory planning algorithm that can generate at real-time (on the order of seconds) flyable (i.e. dynamically and performance-wise feasible) alternative trajectories across the evolving stochastic composite airspace picture (which includes new conflicts, blunder risks, terrain and weather limitations) and c) development and testing of the Collision Avoidance Automation Support System on a Boeing 737 NG FNPT II Flight Simulator with synthetic vision and reality augmentation while providing the flight crew with quantified and visual understanding of collision risks in terms of time and directions and countermeasures.

Power management techniques in an FPGA-Based WSN node for high performance application

In this work, the power management techniques implemented in a high-performance node for Wireless Sensor Networks (WSN) based on a RAM-based FPGA are presented. This new node custom architecture is intended for high-end WSN applications that include complex sensor management like video cameras, high compute demanding tasks such as image encoding or robust encryption, and/or higher data bandwidth needs. In the case of these complex processing tasks, yet maintaining low power design requirements, it can be shown that the combination of different techniques such as extensive HW algorithm mapping, smart management of power islands to selectively switch on and off components, smart and low-energy partial reconfiguration, an adequate set of save energy modes and wake up options, all combined, may yield energy results that may compete and improve energy usage of typical low power microcontrollers used in many WSN node architectures. Actually, results show that higher complexity tasks are in favor of HW based platforms, while the flexibility achieved by dynamic and partial reconfiguration techniques could be comparable to SW based solutions.

Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications

Learning analytics is the analysis of static and dynamic data extracted from virtual learning environments, in order to understand and optimize the learning process. Generally, this dynamic data is generated by the interactions which take place in the virtual learning environment. At the present time, many implementations for grouping of data have been proposed, but there is no consensus yet on which interactions and groups must be measured and analyzed. There is also no agreement on what is the influence of these interactions, if any, on learning outcomes, academic performance or student success. This study presents three different extant interaction typologies in e-learning and analyzes the relation of their components with students? academic performance. The three different classifications are based on the agents involved in the learning process, the frequency of use and the participation mode, respectively. The main findings from the research are: a) that agent-based classifications offer a better explanation of student academic performance; b) that at least one component in each typology predicts academic performance; and c) that student-teacher and student-student, evaluating students, and active interactions, respectively, have a significant impact on academic performance, while the other interaction types are not significantly related to academic performance.