A penological critique of Christian and Islamic justifications of capital punishment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/12/2006.This thesis provides a critique of the penology of capital punishment from the perspectives of Christianity and Islam. In order to ascertain the basic theological approaches of both religions towards capital punishment, Chapters 2 and 3 examine the core Scriptural texts, laws and traditions of both Christianity and Islam respectively. These chapters reveal how different methods of Scriptural interpretation and differences in religious practice, within each faith, have led to divergent opinions regarding the legitimacy and acceptability of capital punishment. Chapters 4 and 5 examine two of the primary penological justifications for the death penalty; retributivism and deterrence. It is demonstrated how they can be used, within secular and religious frameworks, to both condemn and condone the use of the punishment. Chapter 6 considers a variety of contemporary methods used to execute offenders and asks whether the methods used have any effect on the religious acceptance or rejection of the penalty. Finally, Chapter 7 presents one of the most controversial aspects of the contemporary death penalty debate, namely the unequal application of the penalty as it pertains particularly to black offenders, indigent offenders and mentally ill offenders. This serious criticism of the death penalty is considered first in general secular terms and then in light of the teachings of both religions and it is asked how the religious arguments in favour of the death penalty stand in light of such serious violations of human rights and justice. The thesis concludes with the assertion that, while a strong case can be made from within both religions for the use of capital punishment in principle, in practice given current practices of criminal justice systems worldwide there is a strong case to be made, if not for abolition, then at least for a drastic curtailment of the practice and a long-term moratorium on capital punishment on religious grounds

Airborne lidar feature selection for urban classification using random forests

Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they return measures of variable importance for each class. The feature selection is obtained by backward elimination of features depending on their importance. This is crucial to analyze the relevance of each lidar feature for the classification of urban scenes. The Random Forests classification using selected variables provide an overall accuracy of 94.35%.

Algorithmes de routage dans les réseaux sans-fil de radios cognitives à multi-sauts

Les réseaux de radios cognitives sont composés d'appareils cognitifs et agiles capables de changer leurs configurations à la volée en se basant sur l'environnement spectral. Cette capacité offre la possibilité de concevoir des stratégies d'accès au spectre dynamiques et flexibles dans le but d'utiliser d'une manière opportuniste une portion du spectre disponible. Toutefois, la flexibilité dans l'accès au spectre engendre une complexité accrue dans la conception des protocoles de communication. Notre travail s'intéresse au problème de routage dans les réseaux de radios cognitives à multi-sauts. Dans ce document, nous proposons un protocole de routage réactif qui permet la coexistence entre les utilisateurs premiers et secondaires, la diminution des interférences et l'augmentation du débit de transmission de bout en bout. Les simulations présentées démontrent l'efficacité de l'algorithme proposé en termes de débit moyen de bout en bout et de la gestion des chemins interrompus par l'arrivée d'un utilisateur premier. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : réseaux de radios cognitives, radio cognitive, routage réactif, multi-sauts, utilisateur premier, utilisateur secondaire

On bubble clustering and energy spectra in pseudo-turbulence

3D-Particle Tracking (3D-PTV) and Phase Sensitive Constant Temperature Anemometry in pseudo-turbulence--i.e., flow solely driven by rising bubbles-- were performed to investigate bubble clustering and to obtain the mean bubble rise velocity, distributions of bubble velocities, and energy spectra at dilute gas concentrations ($\alpha \leq2.2$%). To characterize the clustering the pair correlation function $G(r,\theta)$ was calculated. The deformable bubbles with equivalent bubble diameter $d_b=4-5$ mm were found to cluster within a radial distance of a few bubble radii with a preferred vertical orientation. This vertical alignment was present at both small and large scales. For small distances also some horizontal clustering was found. The large number of data-points and the non intrusiveness of PTV allowed to obtain well-converged Probability Density Functions (PDFs) of the bubble velocity. The PDFs had a non-Gaussian form for all velocity components and intermittency effects could be observed. The energy spectrum of the liquid velocity fluctuations decayed with a power law of -3.2, different from the $\approx -5/3$ found for homogeneous isotropic turbulence, but close to the prediction -3 by \cite{lance} for pseudo-turbulence

On stratification control of the velocity fluctuations in sedimentation

International audienceWe have tested whether stratification can govern local velocity fluctuations in suspensions of sedimenting spheres. Comparison of the proposed scaling for local control of fluctuations by stratification to experimental data demonstrates that this mechanism cannot account for the reduction of the observed velocity fluctuations

Spectral Optimization of Airborne Multispectral Camera for Land Cover Classification: Automatic Feature Selection and Spectral Band Clustering

Hyperspectral imagery consists of hundreds of contiguous spectral bands. However, most of them are redundant. Thus a subset of well-chosen bands is generally sufficient for a specific problem, enabling to design adapted superspectral sensors dedicated to specific land cover classification. Related both to feature selection and extraction, spectral optimization identifies the most relevant band subset for specific applications, involving a band subset relevance score as well as a method to optimize it. This study first focuses on the choice of such relevance score. Several criteria are compared through both quantitative and qualitative analyses. To have a fair comparison, all tested criteria are compared to classic hyperspectral data sets using the same optimization heuristics: an incremental one to assess the impact of the number of selected bands and a stochastic one to obtain several possible good band subsets and to derive band importance measures out of intermediate good band subsets. Last, a specific approach is proposed to cope with the optimization of bandwidth. It consists in building a hierarchy of groups of adjacent bands, according to a score to decide which adjacent bands must be merged, before band selection is performed at the different levels of this hierarchy

Time-Space Tradeoff in Deep Learning Models for Crop Classification on Satellite Multi-Spectral Image Time Series

International audienceIn this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure of the data. Our results thus constitute a set of guidelines for the design of bespoke deep learning models for crop type classification

Fluctuations and stratification in sedimentation of dilute suspensions of spheres

International audienceWe have tested in experiments and simulations whether stratification can control velocity fluctuations in suspensions of sedimenting spheres. The initial value and early decay of the velocity fluctuations are not affected by stratification. On the other hand, in the descending front where the stratification is strong and well defined, the velocity fluctuations are inhibited according to a previously proposed scaling. In between, after the initial decay and before the arrival of the front, the local value of the stratification does not always play a role

Spreading fronts in sedimentation of dilute suspension of spheres

International audienceThe thickness of the diffuse front between a sedimenting dilute suspension and the clear fluid above grows linearly in time due to polydispersity in the size of the particles and due to a hydrodynamic effect in which randomly heavy clusters fall out of the front leaving it depleted. Experiments and simplified point-particle numerical simulations agree that these two effects are not simply linearly additive