Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation, characterisation of bamboo charcoal particles and the effect of their application on thermo-physiological comfort properties of woven fabrics

Abstract Bamboo charcoal particles were prepared at micro level and applied on three types of woven fabric, namely 100% cotton, 100% polyester and 65/35 p/c blend. The thermo-physiological comfort properties such as wetting, wicking, water vapour permeability, air permeability and thermal resistance of the control and treated fabrics were investigated to study the effect of bamboo charcoal particles application on these fabrics. The results reveal that the bamboo charcoal treated fabrics show a good improvement in wetting, wicking and water vapour permeability. The charcoal particle finished fabrics also showed a slight decrease in thermal resistance thereby contributing to a corresponding improvement in thermal conductivity with a reduction in air permeability when compared with the control fabrics. The bamboo charcoal treated fabrics showed a good fastness to washing thus exhibiting good durability on the fabrics selected for the study. Also, the charcoal treated fabrics revealed a higher wickability than the control fabrics even after 20 washes.
Defect in Protocol Analysis with BAN Logic on Man-in-the-middle Attacks

BAN logic could prove whether a cryptograghic protocol could reach expected target and found some defects or weaknesses in the protocol.A defect of BAN logic analysis on man-in-the-middle attacks was presented through proving a station to station key agreement protocol with BAN logic.In this case,non-formal methods would be needed in the analysis together.An improved station to station protocol against a man-in-the-middle attack was given.
SELF-EFFICACY TO USE ELECTRONIC INFORMATION RESOURCES. A STUDY ON THE USERS OF RESEARCH INSTITUTES OF PUNJAB

The major change in the way library stores and maintains knowledge came with the advent of computers and new technologies. Further, origin of Internet and the development of World Wide Web (WWW) have opened up new vista for communication of scholarly information and helping Libraries to create and maintain information through Libraries’ Websites, Blogs to its users. As a result, more institutions are equipped themselves with new technology and their employees are challenged to be advanced with new technologies to complete their tasks. However, employees are sometimes express less enthusiastic response to adopt new technologies, even if it may helpful to complete their tasks. At this stage, Self-efficacy plays important role in mediating the impact of computer anxiety on the use of computer and related technologies. Present study covers three aspects i.e. computer anxiety, self-efficacy and ease of use of resources.
Rare Diseases Internet Information Retrieval and Knowledge Discovery

A rare or orphan disease (RD) is that which affects only an insignificant percentage of population, so traditionally has not received the effort devoted to common diseases. In common diseases there are healing or palliative treatments while there are over 4000 RD without a known cure. In Europe, RD affect between 27 and 36 million people (Rodwell & Aymé, 2013). Any progress in this field has a great social impact that translates directly into an improved quality of life of those affected by RD (Dragusin et al., 2011). Therefore, any advance in knowledge generation in the context of RD is welcome. One of the most important developments has come from data mining (see (Groth, 1998), and (Tomar & Agarwal, 2013)). The reduced amount of data makes the application of data mining techniques not easy in the field of RDs. Although for one disease the number of patients and data is reduced, they are numerous as a whole. Several initiatives collect data from RD patients around the world using the power of Internet (Orphadata, 2015). Given singularity of RDs, it is necessary to provide the information systems, the capacity to collect data and also promote cross-flow of information between the agents (patients and researchers). In this situation, it is interesting the development of projects like the one sponsored by the Avenzoar Chair (University of Seville, Spain), known as ER2.0 Project (see details in Rabasco et al. (2013a) and Rabasco et al. (2013b)). The underlying idea is to make it real to collaborate, share, and take decisions in a web 2.0 manner between patients and researchers in RDs. This chapter contains, first, a review of the information networks associated with RD, highlighting the registry, typology, associations and consortia related as well as the existing relationships between Rare Diseases Internet Information Retrieval and Knowledge Discovery
Efficient identification of Faces in video Streams using low-Power Multi-Core Devices

Donavan Prieur, Eric Granger, Yvon Savaria, and Claude Thibeault Laboratoire d'imagerie, de vision et d'intelligence artificielle, Ecole de technologie superieure, Universite du Quebec, Montreal, Canada Groupe de recherche en microelectronique et microsystemes, Ecole Polytechnique de Montreal, Montreal, Canada Laboratoire de communications et d’integration de la microelectronique, Ecole de technologie superieure, Universite du Quebec, Montreal, Canada E-mail: eric.granger@etsmtl.ca
Rotary ultrasonic machining: effects of tool natural frequency on ultrasonic vibration amplitude

Abstract Rotary ultrasonic machining (RUM) is a hybrid machining process that combines the material removal mechanisms of grinding and ultrasonic machining. RUM has been applied to hole-making for a wide range of materials. It is known that ultrasonic vibration amplitude has significant effects on cutting force, torque, and surface finish in RUM. One experimental observation that has been reported in the literature multiple times states that different tools show different vibration amplitudes on the same ultrasonic power level. However, no analyses can be found in the literature to explain this observation. The existence of this knowledge gap makes it difficult to explain some experimentally obtained trends or to conduct more realistic physics-based modeling work. The objectives of this research are to understand the effects of tool natural frequency on ultrasonic vibration amplitude in RUM, to provide an explanation to the observation and verification of measurement methods, and also to guide tool design and selection in RUM. Ultrasonic vibration amplitudes of tools are measured by three methods and compared. It is found that tool natural frequency significantly affects ultrasonic vibration amplitude. The tool with its natural frequency closest to that of the ultrasonic power supply (20 kHz) generates the highest ultrasonic vibration amplitude on every ultrasonic power level tested.
Binder applications of cellulose nanofibrils: new developments

A key path towards the commercialization of cellulose nanomaterials is to target large-volume applications in commodity products where a successful market introduction could potentially mobilize orchestrated efforts to revitalize the forest products industry. At the Laboratory of Renewable Nanomaterials we have introduced the concept of using cellulose nanofibrils (CNF) as binder in conventional wood based compressed panels. Binder applications take advantage of impressive hydrogen bonding capacity of CNF and have many other uses. In this presentation, we will present our findings in wet-formed particleboard bonded with cellulose nanofibrils (CNF). In an effort to lower the production cost, we have also shown that lingo-cellulose nanofibrils can be produced directly from wood and can be used as binder in the formulation of traditional fiberboard panels. Thermomechanical pulp (TMP) produced using atmospheric refining was ground to isolate lignocellulose nanofibrils (LCNF). The effect of using LCNF as adhesive replacement in fiberboard was assessed. Future outlooks, potential issue and methods to implement large volume applications of CNF are discussed.
Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation

Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
Study on Modeling Software Connector

On the basis of interrelated researches,the basic definition is summarized,and a model which consists of “4+1” views is put forward to modeling connector.Then,views composing the model are discussed,and the interactive rule/protocol view is significant to the whole model.Finally,an abstract extendable framework for interaction pattern is constructed under the principle of societal behavior,which provides design blueprint for the practical implementation.
Once More Unto the Breach: Co-evolving a robot and its simulator

One of the major challenges facing evolutionary robotics is crossing the reality gap: How to transfer evolved controllers from simulated robots to real robots while maintaining the behavior observed in simulation. Most attempts to cross the reality gap have either applied massive amounts of noise to the simulation, or conducted most or all of the evolution onboard the physical robot, an approach that can be prohibitively costly or slow. In this paper we present a new co-evolutionary approach, which we call the estimation-exploration algorithm. The algorithm automatically adapts the robot simulator using behavior of the target robot, and adapts the behavior of the robot using the robot simulator. This approach has four benefits: the process of simulator and controller evolution is automatic; it requires a minimum of hardware trials on the target robot; it could be used in conjunction with other approaches to automated behavior transferal from simulation to reality; and the algorithm itself is generalizable to other problem domains. Using this approach we demonstrate a reduction of three orders of magnitude in the number of evaluations on a target robot (thousands compared to only five).
Bi-Dimensional Deflection Estimation by Embedded Fiber Bragg Gratings Sensors

In this work, we present and discuss on the deflection estimation of a bi-dimensional panel by using Fiber Bragg Gratings (FBGs) as strain sensors embedded in the structure and a method based on the classical beam theory. The existing difficulties in the direct measure of the deflection are overcome thanks to the proposed technique and a real-time indirect structural monitoring is possible both on small and large structure. In many tests the estimated deflection with the proposed method has been compared with direct deflection measurements obtained with a mechanical comparator showing good agreement. A resolution of few tens of microns over a surface of the order of 1 m2 has been reached.
Experimental Investigation on the Thermal Performance of a Heat Pipe-based Cooling System

Received: 15 November 2018 Accepted: 21 February 2019 An experimental study is carried out in order to determine the thermal performances of a water-cooled heat pipe cooling system. An experiment rig is designed, fabricated and fully instrumented to test the cooling system prototype. The results show that the maximum heat transport capacity of the heat pipe increase with the water-cooling temperature; however, its overall thermal resistance decreases. Correlations for heat transfer in the evaporator and condenser sections are proposed. A model is also developed in order to determine the capillary limit as well as the heat transfer in the heat pipe. The model can predict the experimental results within -1.7 % and +7.9 % when estimating the capillary limit and underestimates the heat pipe overall thermal resistance within -17.8 % and -9.7 %; however, it overestimates the evaporator temperature within 4.4 % and 9.5 %.
A NOVEL DATA ASSOCIATION TECHNIQUE TO IMPROVE RUN-TIME EFFICIENCY OF SLAM ALGORITHMS

Simultaneous Localization and Mapping (SLAM) problem is a very popular research area in robotic applications. EKF-SLAM and FastSLAM are widely used algorithms for SLAM problem. The greatest advantage of FastSLAM over EKF-SLAM is that it reduces the quadratic complexity of EKF-SLAM. On the other hand, increasing number of estimated landmarks naturally slows down the operation of FastSLAM. In this paper, we propose a new method called as Intelligent Data Association-SLAM (IDA-SLAM) which reduces this slowing down problem. In data association step also known as likelihood estimation, IDA-SLAM skips comparing a new landmark with all of the pre-calculated landmarks. Instead of this, it compares the newly found one with only nearby landmarks that was found previously. The simulation results indicate that the proposed algorithm significantly speeds up the operation of SLAM without a loss of state estimation accuracy. Real world experiments which have been performed in two different scenarios verify the simulation results. A runtime reduction of 43% and 52% is observed respectively for each of the test environments.
Development of Ferroelectric Hafnium Oxide for Negative Capacitance Field Effect Transistors

With the transistor being the workhorse in modern electronics it has been vastly improved since its invention. However, the previous go-to improvement method of scaling it down has reached a dead end. Power dissipation has become a large concern and is in need of a solution. In the pursuit of further improving the transistor structure and its performance new approaches are being evaluated. A very promising approach is the integration of ferroelectric materials enabling negative capacitance. With subthreshold slopes below 60 mV/decade demonstrated and hence enabling lower operation voltage a new era of transistor technology is protruding. In this thesis a new recipe for deposition of hafnium oxide thin films using plasma enhanced atomic layer deposition has been developed. Thin hafnium oxide films have been fabricated, characterized and their properties optimized. The films are embedded into a titanium nitride metal-insulator-metal (MIM) structure for electrical characterization. With the use of Poole-Frenkel, Schottky and Fowler-Nordheim models the leakage current mechanisms of the thin films have been evaluated. For the first time, observation of the ferroelectric characteristics in undoped hafnium oxide thin films grown using plasma enhanced atomic layer deposition are reported. The presence of ferroelectricity was confirmed with polarization measurements, with values of the remanent polarization $P_r$ up to 8.75$\mu$C/cm$^2$ for a 10nm thick film with a dielectric constant $\epsilon_r$ up to 30.
An Efficient Holistic Data Distribution and Storage Solution for Online Social Networks

In the past few years, Online Social Networks (OSNs) have dramatically spread over the world. Facebook [4], one of the largest worldwide OSNs, has 1.35 billion users, 82.2% of whom are outside the US [36]. The browsing and posting interactions (text content) between OSN users lead to user data reads (visits) and writes (updates) in OSN datacenters, and Facebook now serves a billion reads and tens of millions of writes per second [37]. Besides that, Facebook has become one of the top Internet traffic sources [36] by sharing tremendous number of large multimedia files including photos and videos. The servers in datacenters have limited resources (e.g. bandwidth) to supply latency efficient service for multimedia file sharing among the rapid growing users worldwide. Most online applications operate under soft real-time constraints (e.g., ≤ 300 ms latency) for good user experience, and its service latency is negatively proportional to its income. Thus, the service latency is a very important requirement for Quality of Service (QoS) to the OSN as a web service, since it is relevant to the OSN’s revenue and user experience. Also, to increase OSN revenue, OSN service providers need to constrain capital investment, operation costs, and the resource (bandwidth) usage costs. Therefore, it is critical for the OSN to supply a guaranteed QoS for both text and multimedia contents to users while minimizing its costs. To achieve this goal, in this dissertation, we address three problems. i) Data distribution among datacenters: how to allocate data (text contents) among data servers with low service latency and minimized inter-datacenter network load; ii) Efficient multimedia file sharing: how to facilitate the servers in datacenters to efficiently share multimedia files among users; iii) Cost minimized data allocation among cloud storages: how to save the infrastructure (datacenters) capital investment and operation costs by leveraging commercial cloud storage services. Data distribution among datacenters. To serve the text content, the new OSN model, which deploys datacenters globally, helps reduce service latency to worldwide distributed users and
Topological scene recognition and visual navigation for mobile robots using omnidirectional camera

Mobile robots are intelligent agents that are designed to serve people in various circumstances. With time, more and more robots will share the surrounding environments with humans, for example, service robots in families and autonomous cars on the road etc. In all these cases above, it is important that the robots are able to provide an efficient representation of the environment, which on one hand can be used as a common understanding among all involved subjects in the scenario; on the other hand, can support the robots to fulfill assigned missions. Psychological works demonstrated that humans can understand their surroundings by mostly topological meanings. I adopt this concept all across this thesis. An example can be observed as for service robots. In most situations, human may like to control the house-hold robot by naming go to “kitchen” or “grandpa’s bed”. Topological modeling of the environment would greatly help these applications. Moreover, navigation among topological nodes in the environment is essential to carry out practical services. Based on these observations, with this thesis, I present several contributions in both the fields of robotic scene recognition and visual navigation. In the first part, I consider the environment modeling using an omnidirectional camera as the only sensor. I present a topological scene recognition algorithm, which is modeled by a Dirichlet Process Mixture Model (DPMM). Topological segments with a similar appearance in video sequences can be automatically clustered and recognized in real-time. In the second part, regarding the navigation among topological nodes, waypoint-based topological modeling of the environment is presented, centered on a visual homing framework using Image-Based Visual Servoing (IBVS). A finite state-machine is then adopted, which enables topological navigation between waypoints by fusing Visual Homing and odometry motion. Experiments on datasets and real environments show the competence of the proposed algorithms against state-of-the-art.
A Transformation from Relational Databases to Big Data

We are living in an information age and there is enormous amount of data that is flowing between systems, internet, telephones, and other media. The data is being collected and stored at unprecedented rates. There is a great challenge not only to store and manage the large volume of data, but also to analyze and extract meaningful information from it. There are several approaches to collecting, storing, processing, and analyzing big data. The main focus of the paper is to draw an analogy for data management between the traditional relational database systems and the Big Data.
Minimizing the Detection Error in Cooperative Spectrum Sensing using Teaching Learning Based Optimization(TLBO)

— Cognitive radio (CR) is a new paradigm in wireless communication system which is use for efficient utilization of radio frequency (RF) spectrum or RF channel for future wireless communication. Cooperative spectrum sensing is a key technology in cognitive radio networks (CRNs) to detect spectrum holes by combining sensing result of multiple cognitive radio users. This sensing information from CR users combines at the Fusion center (common receiver) by soft combination or conventional hard combination techniques. Sensing error minimization is an important aspect of cooperative spectrum sensing that needs attention. In this paper, the use of teaching learning based optimization (TLBO) under MINI-MAX criterion is proposed to optimize the weighting coefficients vector of energy level of spectrum sensing information so that the total probability of error is minimized. The TLBO algorithm investigates the best weighting coefficient vector which minimizes total probability of error. The performance of the TLBO based method is analysed and compared with conventional soft decision fusion schemes like EGC as well as hard decision fusion method like AND,OR, Majority etc. Simulation results show that the proposed scheme minimizes the detection error compared to conventional soft decision fusion schemes