DDH-MAC: a novel dynamic de-centralized hybrid MAC protocol for cognitive radio networks

The radio spectrum (3kHz - 300GHz) has become saturated and proven to be insufficient to address the proliferation of new wireless applications. Cognitive Radio Technology which is an opportunistic network and is equipped with fully programmable wireless devices that empowers the network by OODA cycle and then make intelligent decisions by adapting their MAC and physical layer characteristics such as waveform, has appeared to be the only solution for current low spectrum availability and under utilization problem. In this paper a novel Dynamic De-Centralized Hybrid “DDH-MAC” protocol for Cognitive Radio Networks has been presented which lies between Global Common Control Channel (GCCC) and non-GCCC categories of cognitive radio MAC protocols. DDH-MAC is equipped with the best features of GCCC MAC protocols but also overcomes the saturation and security issues in GCCC. To the best of authors' knowledge, DDH-MAC is the first protocol which is hybrid between GCCC and non-GCCC family of protocols. DDH-MAC provides multiple levels of security and partially use GCCC to transmit beacon which sets and announces local control channel for exchange of free channel list (FCL) sensed by the co-operatively communicating cognitive radio nodes, subsequently providing secure transactions among participating nodes over the decided local control channel. This paper describes the framework of the DDH-MAC protocol in addition to its pseudo code for implementation; it is shown that the pre-transmission time for DDH-MAC is on average 20% better while compared to other cognitive radio MAC protocols

Template-based reverse engineering of parametric CAD models from point clouds

openEven if many Reverse Engineering techniques exist to reconstruct real objects in 3D, very few are able to deal directly and efficiently with the reconstruction of editable CAD models of assemblies of mechanical parts that can be used in the stages of Product Development Processes (PDP). In the absence of suitable segmentation tools, these approaches struggle to identify and reconstruct model the different parts that make up the assembly. The thesis aims to develop a new Reverse Engineering technique for the reconstruction of editable CAD models of mechanical parts’ assemblies. The originality lies in the use of a Simulated Annealing-based fitting technique optimization process that leverages a two-level filtering able to capture and manage the boundaries of the parts’ geometries inside the overall point cloud to allow for interface detection and local fitting of a part template to the point cloud. The proposed method uses various types of data (e.g. clouds of points, CAD models possibly stored in database together with the associated best parameter configurations for the fitting process). The approach is modular and integrates a sensitivity analysis to characterize the impact of the variations of the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud to be fitted. The evaluation of the proposed approach is performed using both real scanned point clouds and as-scanned virtually generated point clouds which incorporate several artifacts that could appear with a real scanner. Results cover several Industry 4.0 related application scenarios, ranging from the global fitting of a single part to the update of a complete Digital Mock-Up embedding assembly constraints. The proposed approach presents good capacities to help maintaining the coherence between a product/system and its digital twin.openXXXIII CICLO - INGEGNERIA MECCANICA, ENERGETICA E GESTIONALE - Meccanica, misure e robotica01/A3 - ANALISI MATEMATICA, PROBABILITA' E STATISTICA MATEMATICA01/B1 - INFORMATICA09/B2 - IMPIANTI INDUSTRIALI MECCANICIShah, GHAZANFAR AL

CSR Practices of a Company Toward Stakeholders: The Case of Pakistan Tobacco Company (PTC)

The performance of the companies in corporate sector is reliant greatly on the practices of Corporate Social Responsibility (CSR); therefore in today’s business environment companies are paying more attention to the sense of CSR. These companies also consider the aspects of socio-culture environment into business practices and compliance with other regulatory and ethical issues. However, it has been found that CSR is being practiced in Pakistani firms in tobacco industry because the concept is new for the emerging economies like Pakistan. The paper consists of brief study about the CSR practices on stakeholder dimension of Pakistan Tobacco Company (PTC). The basic aim of this paper is to examine that how companies engage their stakeholders in CSR activities and what is the role of stakeholders in CSR policies. This research was conducted by using a qualitative method and the case study of PTC.  Data has been collected from relevant scientific articles, research books, and online resources regarding CSR and stakeholders theoretical framework while empirical data was gathered through interviews and company annual reports. However, PTC products are injurious for customers’ health but their efforts for the environment and community make a good image of the company in the minds of customer and stakeholders. Keywords: Corporate Social Responsibility (CSR), Stakeholder, Health & Safety Environment (HSE), Community Involvement, Pakistan Tobacco Company (PTC)

Comparative Analysis of Flame Propagation and Flammability Limits of CH4/H2/Air Mixture with or without Nanosecond Plasma Discharges

This study investigates the kinetic modeling of CH4/H-2/Air mixture with nanosecond pulse discharge (NSPD) by varying H-2/CH4 ratios from 0 to 20% at ambient pressure and temperature. A validated version of the plasma and chemical kinetic mechanisms was used. Two numerical tools, ZDPlasKin and CHEMKIN, were combined to analyze the thermal and kinetic effects of NSPD on flame speed enhancement. The addition of H-2 and plasma excitation increased flame speed. The highest improvement (35%) was seen with 20% H-2 and 1.2 mJ plasma energy input at phi = 1. Without plasma discharge, a 20% H-2 blend only improved flame speed by 14% compared to 100% CH4. The study found that lean conditions at low flame temperature resulted in significant improvement in flame speed. With 20% H-2 and NSPD, flame speed reached 37 cm/s at flame temperature of 2040 K at phi = 0.8. Similar results were observed with 0% and 5% H-2 and a flame temperature of 2200 K at phi = 1. Lowering the flame temperature reduced NOx emissions. Combining 20% H-2 and NSPD also increased the flammability limit to phi = 0.35 at a flame temperature of 1350 K, allowing for self-sustained combustion even at low temperatures

As-scanned point clouds generation for virtual Reverse Engineering of CAD assembly models

This paper introduces a new approach for the generation of as-scanned point clouds of CAD assembly models. The resulting point clouds incorporate various realistic artifacts that would appear if the corresponding real objects were digitalized with a laser scanner. Such a virtual Reverse Engineering technique can produce a huge amount of realistic point clouds much faster than using classical time-consuming Reverse Engineering techniques on real physical objects. Here, there is no need to use a laser scanner and the post-processing steps are automatic. Using this technique, it is easy to create large databases of point clouds automatically segmented and labeled from the CAD models and which can be used for supervised machine learning. The proposed approach starts by generating a triangle mesh wrapping the CAD assembly model to be reverse engineered. The resulting watertight mesh is then sampled to obtain a more realistic distribution of points. The occlusion phenomenon is then simulated using a hidden point removal algorithm launched from several viewpoints. A misalignment procedure can optionally be used to simulate the fact that in real-life Reverse Engineering the position and orientation of the laser scanner and/or real object would have been changed to get a different scanning viewpoint. The virtual Reverse Engineering process ends by generating noise and by inserting outliers. The approach is illustrated and validated on several industrial examples

Case‑based tuning of a metaheuristic algorithm exploiting sensitivity analysis and design of experiments for reverse engineering applications

Due to its capacity to evolve in a large solution space, the Simulated Annealing (SA) algorithm has shown very promising results for the Reverse Engineering of editable CAD geometries including parametric 2D sketches, 3D CAD parts and assem blies. However, parameter setting is a key factor for its performance, but it is also awkward work. This paper addresses the way a SA-based Reverse Engineering technique can be enhanced by identifying its optimal default setting parameters for the ftting of CAD geometries to point clouds of digitized parts. The method integrates a sensitivity analysis to characterize the impact of the variations in the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud to be ftted. The principles underpinning the adopted ftting algorithm are briefy recalled. A framework that uses design of experiments (DOEs) is introduced to identify and save in a database the best setting parameter values for given CAD models. This database is then exploited when considering the ftting of a new CAD model. Using similar ity assessment, it is then possible to reuse the best setting parameter values of the most similar CAD model found in the database. The applied sensitivity analysis is described together with the comparison of the resulting sensitivity evolution curves with the changes in the CAD model parameters imposed by the SA algorithm. Possible improvements suggested by the analysis are implemented to enhance the efciency of SA-based ftting. The overall approach is illustrated on the ftting of single mechanical parts but it can be directly extended to the ftting of parts’ assemblies. It is particularly interesting in the context of the Industry 4.0 to update and maintain the coherence of the digital twins with respect to the evolution of the associated physical products and systems

Simulated annealing-based fitting of CAD models to point clouds of mechanical parts’ assemblies

This paper introduces a new ftting approach to allow an efcient part-by-part reconstruction or update of editable CAD models fitting the point cloud of a digitized mechanical parts′ assembly. The idea is to make use of parameterized CAD mod els whose dimensional parameters are to be optimized to match the acquired point cloud. Parameters may also be related to assembly constraints, e.g. the distance between two parts. The optimization kernel relies on a simulated annealing algorithm to fnd out the best values of the parameters so as to minimize the deviations between the point cloud and the CAD models to be ftted. Both global and local ftting are possible. During the optimization process, the orientation and positioning of the CAD parts are driven by an ICP algorithm. The modifcations are ensured by the batch calls to a CAD modeler which updates the models as the ftting process goes on. The modeler also handles the assembly constraints. Both single and multiple parts can be ftted, either sequentially or simultaneously. The evaluation of the proposed approach is performed using both real scanned point clouds and as-scanned virtually generated point clouds which incorporate several artifacts that could appear with a real scanner. Results cover several Industry 4.0 related application scenarios, ranging from the global ftting of a single part to the update of a complete Digital Mock-Up embedding assembly constraints. The proposed approach demonstrates good capacities to help maintaining the coherence between a product/system and its digital twi

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

This paper introduces a novel reverse engineering (RE) technique for the reconstruction of editable computer-aided design (CAD) models of mechanical parts’ assemblies. The input is a point cloud of a mechanical parts’ assembly that has been acquired as a whole, i.e., without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized CAD geometries (i.e., 2D sketches, 3D parts, or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the adapted geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the geometries’ updates while guaranteeing the possibly imposed constraints and model coherence. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices