Trade-off between minimum number of wireless sensors and the accuracy of temperature profile in cold rooms: a model-based framework

Evaluation of temperature distribution in cold rooms is an important consideration in the design of food storage solutions. Two common approaches used in both industry and academia to address this question are the deployment of wireless sensors, and modelling with Computational Fluid Dynamics (CFD). However, for a realworld evaluation of temperature distribution in a cold room, both approaches have their limitations. For wireless sensors, it is economically unfeasible to carry out large-scale deployment (to obtain a high resolution of temperature distribution); while with CFD modelling, it is usually not accurate enough to get a reliable result. In this paper, we propose a model-based framework which combines the wireless sensors technique with CFD modelling technique together to achieve a satisfactory trade-off between minimum number of wireless sensors and the accuracy of temperature profile in cold rooms. A case study is presented to demonstrate the usability of the framework.

Inter-ELM evolution of the edge current density profile on the ASDEX upgrade tokamak

The sudden decrease of plasma stored energy and subsequent power deposition on the first wall of a tokamak due to edge localised modes (ELMs) is potentially detrimental to the success of a future fusion reactor. Understanding and control of ELMs is critical for the longevity of these devices and also to maximise their performance. The commonly accepted picture of ELMs posits a critical pressure gradient and current density in the plasma edge, above which coupled magnetohy drodynamic peeling-ballooning modes become unstable. Much analysis has been presented in recent years on the spatial and temporal evolution of the edge pressure gradient. However, the edge current density has typically been overlooked due to the difficulties in measuring this quantity. In this thesis, a novel method of current density recovery is presented, using the equilibrium solver CLISTE to reconstruct a high resolution equilibrium utilising both external magnetic and internal edge kinetic data measured on the ASDEX Upgrade tokamak. The evolution of the edge current density relative to an ELM crash is presented, showing that a resistive delay in the buildup of the current density is unlikely. An uncertainty analysis shows that the edge current density can be determined with an accuracy consistent with that of the kinetic data used. A comparison with neoclassical theory demonstrates excellent agreement be- tween the current density determined by CLISTE and the calculated profiles. Three ELM mitigation regimes are investigated: Type-II ELMs, ELMs sup- pressed by external magnetic perturbations, and Nitrogen seeded ELMs. In the first two cases, the current density is found to decrease as mitigation on- sets, indicating a more ballooning-like plasma behaviour. In the latter case, the flux surface averaged current density can decrease while the local current density increases, providing a mechanism to suppress both the peeling and ballooning modes.

Profiling side-channel attacks on cryptographic algorithms

Traditionally, attacks on cryptographic algorithms looked for mathematical weaknesses in the underlying structure of a cipher. Side-channel attacks, however, look to extract secret key information based on the leakage from the device on which the cipher is implemented, be it smart-card, microprocessor, dedicated hardware or personal computer. Attacks based on the power consumption, electromagnetic emanations and execution time have all been practically demonstrated on a range of devices to reveal partial secret-key information from which the full key can be reconstructed. The focus of this thesis is power analysis, more specifically a class of attacks known as profiling attacks. These attacks assume a potential attacker has access to, or can control, an identical device to that which is under attack, which allows him to profile the power consumption of operations or data flow during encryption. This assumes a stronger adversary than traditional non-profiling attacks such as differential or correlation power analysis, however the ability to model a device allows templates to be used post-profiling to extract key information from many different target devices using the power consumption of very few encryptions. This allows an adversary to overcome protocols intended to prevent secret key recovery by restricting the number of available traces. In this thesis a detailed investigation of template attacks is conducted, along with how the selection of various attack parameters practically affect the efficiency of the secret key recovery, as well as examining the underlying assumption of profiling attacks in that the power consumption of one device can be used to extract secret keys from another. Trace only attacks, where the corresponding plaintext or ciphertext data is unavailable, are then investigated against both symmetric and asymmetric algorithms with the goal of key recovery from a single trace. This allows an adversary to bypass many of the currently proposed countermeasures, particularly in the asymmetric domain. An investigation into machine-learning methods for side-channel analysis as an alternative to template or stochastic methods is also conducted, with support vector machines, logistic regression and neural networks investigated from a side-channel viewpoint. Both binary and multi-class classification attack scenarios are examined in order to explore the relative strengths of each algorithm. Finally these machine-learning based alternatives are empirically compared with template attacks, with their respective merits examined with regards to attack efficiency.