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Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.

Mandeep Singh's Résumé

This paper documents a numerical model, developed for the McGill Rocket Team based on classical chemical thermodynamics coupled with the Trebble-Bishnoi equation of state, to solve for the oxidizer tank conditions (pressure, temperature, mole flowrate and liquid/vapour equilibrium) during the operation of a hybrid rocket. This model is modular and can be coupled to fluid mechanics and combustion chamber models for a more detailed analysis of a hybrid rocket engine.

Energy consumption is one of the most critical issues in the manufacturing industry. The modeling, analysis and improvement of energy cost and consumption in multistage production system have been widely studied in many research works. To summarize the latest development of research of energy consumption, a large amount of research work have been investigated. The review work includes effect of design for the energy consumption and interactions between many aspects related to industry. This research research combines energy systems from microscopic to macroscopic, which includes machine, manufacturing line and factory level. This document begins with a review of energy consumption on machine level. Including the schema for machine states and transition of energy, the process model for energy analysis and improvement methods. These topics are further discussed in detail for different processes, eg. forming process, additive processes, etc. In the next part of the review the researches on energy consumption for the multi-machine/manufacturing line level are introduced. Include the define of manufacturing line level, the logical benchmarking of manufacturing lines, and the utilization of energy flows. At last, the detailed way for the factory level energy management are studied. Including the factory energy management system and the method of reduce energy consumption.

Mobile ad-hoc network (MANET) is a collection of mobile terminals forming an infrastructure less and quick deployable network, which can communicate to each other via multiple hops or single hop. Such ad-hoc networks have always been important for various applications like defence applications especially for countries like India having boundaries and regions with large geographical diversity. Mobility attribute is a notable one in MANETs, as this leads to frequent topology changes which are the primary cause of route failure. A route is an ordered set of links, hence for predicting future availability of any particular route, it is important to estimate the availability of its currently available constituent links. This paper explores various link availability prediction model and proposes a least square polynomial regression-based statistical approach to predict the availability of link. Proposed approach assumes that movement of nodes are based on column mobility model i.e each node in the network is linearly moving with constant speed. Each node in the network periodically broadcasts hello packets to its neighbours to inform it’s availability in the network. Neighbour node receives hello packet and uses its signal strength to estimate distance between sender and receiver of hello packet. A monotonically decreasing signal strength of hello packets at receiver node indicates that nodes are moving away from each other and link between them may break in future so it starts link residual time prediction algorithm to predict the time when the distance between them will exceed the pre-defined threshold value. The proposed algorithm is simulated using NS 2.35. The performance of the algorithm has been analyzed for identified parameters. The results are also been compared by simulating other existing link prediction approaches based on interpolation.

Lizhen Zhu's Resume Created with the Friggeri CV template