Geometrically-constrained, parasitic-aware synthesis of analog ICs

In order to speed up the design process of analog ICs, iterations between different design stages should be avoided as much as possible. More specifically, spins between electrical and physical synthesis should be reduced for this is a very time-consuming task: if circuit performance including layout-induced degradations proves unacceptable, a re-design cycle must be entered, and electrical, physical, or both synthesis processes, would have to be repeated. It is also worth noting that if geometric optimization (e.g., area minimization) is undertaken after electrical synthesis, it may add up as another source of unexpected degradation of the circuit performance due to the impact of the geometric variables (e.g., transistor folds) on the device and the routing parasitic values. This awkward scenario is caused by the complete separation of said electrical and physical synthesis, a design practice commonly followed so far. Parasitic-aware synthesis, consisting in including parasitic estimates to the circuit netlist directly during electrical synthesis, has been proposed as solution. While most of the reported contributions either tackle parasitic-aware synthesis without paying special attention to geometric optimization or approach both issues only partially, this paper addresses the problem in a unified way. In what has been called layout-aware electrical synthesis, a simulation-based optimization algorithm explores the design space with geometric variables constrained to meet certain user-defined goals, which provides reliable estimates of layout-induced parasitics at each iteration, and, thereby, accurate evaluation of the circuit ultimate performance. This technique, demonstrated here through several design examples, requires knowing layout details beforehand; to facilitate this, procedural layout generation is used as physical synthesis approach due to its rapidness and ability to capture analog layout know-how.Ministerio de Educación y Ciencia TEC2004-0175

Physical Interaction of Autonomous Robots in Complex Environments

Recent breakthroughs in the fields of computer vision and robotics are firmly changing the people perception about robots. The idea of robots that substitute humansisnowturningintorobotsthatcollaboratewiththem. Serviceroboticsconsidersrobotsaspersonalassistants. Itsafelyplacesrobotsindomesticenvironments in order to facilitate humans daily life. Industrial robotics is now reconsidering its basic idea of robot as a worker. Currently, the primary method to guarantee the personnels safety in industrial environments is the installation of physical barriers around the working area of robots. The development of new technologies and new algorithms in the sensor field and in the robotic one has led to a new generation of lightweight and collaborative robots. Therefore, industrial robotics leveraged the intrinsic properties of this kind of robots to generate a robot co-worker that is able to safely coexist, collaborate and interact inside its workspace with both personnels and objects. This Ph.D. dissertation focuses on the generation of a pipeline for fast object pose estimation and distance computation of moving objects,in both structured and unstructured environments,using RGB-D images. This pipeline outputs the command actions which let the robot complete its main task and fulfil the safety human-robot coexistence behaviour at once. The proposed pipeline is divided into an object segmentation part,a 6D.o.F. object pose estimation part and a real-time collision avoidance part for safe human-robot coexistence. Firstly, the segmentation module finds candidate object clusters out of RGB-D images of clutter scenes using a graph-based image segmentation technique. This segmentation technique generates a cluster of pixels for each object found in the image. The candidate object clusters are then fed as input to the 6 D.o.F. object pose estimation module. The latter is in charge of estimating both the translation and the orientation in 3D space of each candidate object clusters. The object pose is then employed by the robotic arm to compute a suitable grasping policy. The last module generates a force vector field of the environment surrounding the robot, the objects and the humans. This force vector field drives the robot toward its goal while any potential collision against objects and/or humans is safely avoided. This work has been carried out at Politecnico di Torino, in collaboration with Telecom Italia S.p.A

Canopy height estimation from lidar data using open source software compared with commercial software

The goal of this study is to analyze the performance of Open Source Software (OSS) towards the generation of Digital Terrain Model (DTM) and Digital Surface Model (DSM), further on estimates the canopy height by using Light Detection and Ranging (LIDAR) data. Generation of DTM and DSM are very important in this research to ensure that better canopy height can be modeled. DTM and DSM commonly known as a digital representation of earth surface topography where DTM only represent the ground surface while DSM represent all the features including buildings and trees. Many software that have a function to generate DTM and DSM were developed recently. However, most software has been commercialized; therefore it requires a high expenditure to own the software. Advanced technology has lead to the emergence of the growing OSS. OSS is software that can be downloaded for free via the internet. By taking the forestry area of Pekan, Pahang for this research, LIDAR data for that particular area is processed by using the OSS Geographic Resources Analysis Support System (GRASS). To determine the effectiveness and capability of GRASS in the DTM and DSM generation, the same data were processed using commercial software which is TerraScan so that the result can be compared, further on better canopy height can be modele

A Robust Zero-Calibration RF-based Localization System for Realistic Environments

Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based location determination systems usually require a tedious calibration phase to construct an RF fingerprint of the area of interest. This fingerprint varies with the used mobile device, changes of the transmit power of smart access points (APs), and dynamic changes in the environment; requiring re-calibration of the area of interest; which reduces the technology ease of use. In this paper, we present IncVoronoi: a novel system that can provide zero-calibration accurate RF-based indoor localization that works in realistic environments. The basic idea is that the relative relation between the received signal strength from two APs at a certain location reflects the relative distance from this location to the respective APs. Building on this, IncVoronoi incrementally reduces the user ambiguity region based on refining the Voronoi tessellation of the area of interest. IncVoronoi also includes a number of modules to efficiently run in realtime as well as to handle practical deployment issues including the noisy wireless environment, obstacles in the environment, heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on different Android phones using the iBeacons technology in a university campus. Evaluation of IncVoronoi with a side-by-side comparison with traditional fingerprinting techniques shows that it can achieve a consistent median accuracy of 2.8m under different scenarios with a low beacon density of one beacon every 44m2. Compared to fingerprinting techniques, whose accuracy degrades by at least 156%, this accuracy comes with no training overhead and is robust to the different user devices, different transmit powers, and over temporal changes in the environment. This highlights the promise of IncVoronoi as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201

Minimizing the residual topography effect on interferograms to improve DInSAR results: estimating land subsidence in Port-Said City, Egypt

The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise

Analysis of spatial fixed PV arrays configurations to maximize energy harvesting in BIPV applications

This paper presents a new approach for efficient utilization of building integrated photovoltaic (BIPV) systems under partial shading conditions in urban areas. The aim of this study is to find out the best electrical configuration by analyzing annual energy generation of the same BIPV system, in terms of nominal power, without changing physical locations of the PV modules in the PV arrays. For this purpose, the spatial structure of the PV system including the PV modules and the surrounding obstacles is taken into account on the basis of virtual reality environment. In this study, chimneys which are located on the residential roof-top area are considered to create the effect of shading over the PV array. The locations of PV modules are kept stationary, which is the main point of this paper, while comparing the performances of the configurations with the same surrounding obstacles that causes partial shading conditions. The same spatial structure with twelve distinct PV array configurations is considered. The same settling conditions on the roof-top area allow fair comparisons between PV array configurations. The payback time analysis is also performed with considering local and global maximum power points (MPPs) of PV arrays by comparing the annual energy yield of the different configurationsPeer ReviewedPostprint (author’s final draft

Self-tuning diagnosis of routine alarms in rotating plant items

Condition monitoring of rotating plant items in the energy generation industry is often achieved through examination of vibration signals. Engineers use this data to monitor the operation of turbine generators, gas circulators and other key plant assets. A common approach in such monitoring is to trigger an alarm when a vibration deviates from a predefined envelope of normal operation. This limit-based approach, however, generates a large volume of alarms not indicative of system damage or concern, such as operational transients that result in temporary increases in vibration. In the nuclear generation context, all alarms on rotating plant assets must be analysed and subjected to auditable review. The analysis of these alarms is often undertaken manually, on a case- by-case basis, but recent developments in monitoring research have brought forward the use of intelligent systems techniques to automate parts of this process. A knowledge- based system (KBS) has been developed to automatically analyse routine alarms, where the underlying cause can be attributed to observable operational changes. The initialisation and ongoing calibration of such systems, however, is a problem, as normal machine state is not uniform throughout asset life due to maintenance procedures and the wear of components. In addition, different machines will exhibit differing vibro- acoustic dynamics. This paper proposes a self-tuning knowledge-driven analysis system for routine alarm diagnosis across the key rotating plant items within the nuclear context common to the UK. Such a system has the ability to automatically infer the causes of routine alarms, and provide auditable reports to the engineering staff