An Ethic of Connectedness: Book Review of The Connected Self: The Ethics and Governance of the Genetic Individual

Compte-rendu / Review

Exploring geolocation governance perspectives through the study of appropriation and collective action.

Ce mémoire de maîtrise a été rédigé dans l’objectif d’explorer une inégalité. Une inégalité dans les pratiques liées à la saisie et l’exploitation des données utilisateur dans la sphère des technologies et services Web, plus particulièrement dans la sphère des GIS (Geographic Information Systems). En 2014, de nombreuses entreprises exploitent les données de leurs utilisateurs afin d’améliorer leurs services ou générer du revenu publicitaire. Du côté de la sphère publique et gouvernementale, ce changement n’a pas été effectué. Ainsi, les gouvernements fédéraux et municipaux sont démunis de données qui permettraient d’améliorer les infrastructures et services publics. Des villes à travers le monde essayent d’améliorer leurs services et de devenir « intelligentes » mais sont dépourvues de ressources et de savoir faire pour assurer une transition respectueuse de la vie privée et des souhaits des citadins. Comment une ville peut-elle créer des jeux de données géo-référencés sans enfreindre les droits des citadins ? Dans l’objectif de répondre à ces interrogations, nous avons réalisé une étude comparative entre l’utilisation d’OpenStreetMap (OSM) et de Google Maps (GM). Grâce à une série d’entretiens avec des utilisateurs de GM et d’OSM, nous avons pu comprendre les significations et les valeurs d’usages de ces deux plateformes. Une analyse mobilisant les concepts de l’appropriation, de l’action collective et des perspectives critiques variées nous a permis d’analyser nos données d’entretiens pour comprendre les enjeux et problèmes derrière l’utilisation de technologies de géolocalisation, ainsi que ceux liés à la contribution des utilisateurs à ces GIS. Suite à cette analyse, la compréhension de la contribution et de l’utilisation de ces services a été recontextualisée pour explorer les moyens potentiels que les villes ont d’utiliser les technologies de géolocalisation afin d’améliorer leurs infrastructures publiques en respectant leurs citoyens.

The Application of a Pluralist Approach of Global Administrative Law on the Governance of Doping in Sport

Plusieurs problèmes liés à l'utilisation de substances et méthodes interdites de dopage dans les sports posent de grands défis à la gouvernance antidopage. Afin de lutter contre le dopage, certains pays ont mis en oeuvre des cadres juridiques basés exclusivement sur le droit pénal tandis que d'autres pays ont plutôt misé sur des mécanismes et organismes spécialisés trouvant fondement en droit privé ou sur un régime hybride de droit public et privé. Ces différentes approches réglementaires ont pour conséquence de faire en sorte qu’il est très difficile de lutter efficacement contre le dopage dans les sports, notamment parce que leur exécution requiert un degré de collaboration internationale et une participation concertée des autorités publiques qui est difficile à mettre en place. À l’heure actuelle, on peut par exemple observer que les États n’arrivent pas à contrer efficacement la participation des syndicats et organisations transnationales liés au crime organisé dans le marché du dopage, ni à éliminer des substances et méthodes de dopage interdites par la réglementation. Par ailleurs, la gouvernance antidopage basée sur les règles prescrites par l’Agence mondiale antidopage prévoit des règles et des normes distinctes de dopage distinguant entre deux catégories de personnes, les athlètes et les autres, plaçant ainsi les premiers dans une position désavantageuse. Par exemple, le standard de responsabilité stricte sans faute ou négligence imposé aux athlètes exige moins que la preuve hors de tout doute raisonnable et permet l'utilisation de preuves circonstancielles pour établir la violation des règles antidopages. S'appliquant pour prouver le dopage, ce standard mine le principe de la présomption d'innocence et le principe suivant lequel une personne ne devrait pas se voir imposer une peine sans loi. D’ailleurs, le nouveau Code de 2015 de l’Agence attribuera aux organisations nationales antidopage (ONADs) des pouvoirs d'enquête et de collecte de renseignements et ajoutera de nouvelles catégories de dopage non-analytiques, réduisant encore plus les droits des athlètes. Dans cette thèse, nous discutons plus particulièrement du régime réglementaire de l’Agence et fondé sur le droit privé parce qu’il ne parvient pas à répondre aux besoins actuels de gouvernance mondiale antidopage. Nous préconisons donc l’adoption d’une nouvelle approche de gouvernance antidopage où la nature publique et pénale mondiale du dopage est clairement reconnue. Cette reconnaissance combiné avec un modèle de gouvernance adapté basé sur une approche pluraliste du droit administratif global produira une réglementation et une administration antidopage mieux acceptée chez les athlètes et plus efficace sur le plan des résultats. Le nouveau modèle de gouvernance que nous proposons nécessitera toutefois que tous les acteurs étatiques et non-étatiques ajustent leur cadre de gouvernance en tenant compte de cette nouvelle approche, et ce, afin de confronter les défis actuels et de régler de manière plus satisfaisante les problèmes liés à la gouvernance mondiale du dopage dans les sports.

Analysis and Design of an Adaptive Polynomial Predistorter with the Loop Delay Estimator

Most adaptive linearization circuits for the nonlinear amplifier have a feedback loop that returns the output signal oj'tne eunplifier to the lineurizer. The loop delay of the linearizer most be controlled precisely so that the convergence of the linearizer should be assured lot this Letter a delay control circuit is presented. It is a delay lock loop (ULL) with it modified early-lute gate and can he easily applied to a DSP implementation. The proposed DLL circuit is applied to an adaptive linearizer with the use of a polynomial predistorter, and the simulalion for a 16-QAM signal is performed. The simulation results show that the proposed DLL eliminates the delay between the reference input signal and the delayed feedback signal of the linearizing circuit perfectly, so that the predistorter polynomial coefficients converge into the optimum value and a high degree of linearization is achieved

Energy Management in Wireless Sensor Networks: A Crosslayer,Channel Adaptive Approach Towards Performance Optimization

The proliferation of wireless sensor networks in a large spectrum of applications had been spurered by the rapid advances in MEMS(micro-electro mechanical systems )based sensor technology coupled with low power,Low cost digital signal processors and radio frequency circuits.A sensor network is composed of thousands of low cost and portable devices bearing large sensing computing and wireless communication capabilities. This large collection of tiny sensors can form a robust data computing and communication distributed system for automated information gathering and distributed sensing.The main attractive feature is that such a sensor network can be deployed in remote areas.Since the sensor node is battery powered,all the sensor nodes should collaborate together to form a fault tolerant network so as toprovide an efficient utilization of precious network resources like wireless channel,memory and battery capacity.The most crucial constraint is the energy consumption which has become the prime challenge for the design of long lived sensor nodes.

Speech sample estimation from composite zerocrossings and encoding via adaptive switching of transforms

This thesis investigates the potential use of zerocrossing information for speech sample estimation. It provides 21 new method tn) estimate speech samples using composite zerocrossings. A simple linear interpolation technique is developed for this purpose. By using this method the A/D converter can be avoided in a speech coder. The newly proposed zerocrossing sampling theory is supported with results of computer simulations using real speech data. The thesis also presents two methods for voiced/ unvoiced classification. One of these methods is based on a distance measure which is a function of short time zerocrossing rate and short time energy of the signal. The other one is based on the attractor dimension and entropy of the signal. Among these two methods the first one is simple and reguires only very few computations compared to the other. This method is used imtea later chapter to design an enhanced Adaptive Transform Coder. The later part of the thesis addresses a few problems in Adaptive Transform Coding and presents an improved ATC. Transform coefficient with maximum amplitude is considered as ‘side information’. This. enables more accurate tfiiz assignment enui step—size computation. A new bit reassignment scheme is also introduced in this work. Finally, sum ATC which applies switching between luiscrete Cosine Transform and Discrete Walsh-Hadamard Transform for voiced and unvoiced speech segments respectively is presented. Simulation results are provided to show the improved performance of the coder

Modified Block Adaptive Predictive Coder For Speech Processing

This thesis investigated the potential use of Linear Predictive Coding in speech communication applications. A Modified Block Adaptive Predictive Coder is developed, which reduces the computational burden and complexity without sacrificing the speech quality, as compared to the conventional adaptive predictive coding (APC) system. For this, changes in the evaluation methods have been evolved. This method is as different from the usual APC system in that the difference between the true and the predicted value is not transmitted. This allows the replacement of the high order predictor in the transmitter section of a predictive coding system, by a simple delay unit, which makes the transmitter quite simple. Also, the block length used in the processing of the speech signal is adjusted relative to the pitch period of the signal being processed rather than choosing a constant length as hitherto done by other researchers. The efficiency of the newly proposed coder has been supported with results of computer simulation using real speech data. Three methods for voiced/unvoiced/silent/transition classification have been presented. The first one is based on energy, zerocrossing rate and the periodicity of the waveform. The second method uses normalised correlation coefficient as the main parameter, while the third method utilizes a pitch-dependent correlation factor. The third algorithm which gives the minimum error probability has been chosen in a later chapter to design the modified coder The thesis also presents a comparazive study beh-cm the autocorrelation and the covariance methods used in the evaluaiicn of the predictor parameters. It has been proved that the azztocorrelation method is superior to the covariance method with respect to the filter stabf-it)‘ and also in an SNR sense, though the increase in gain is only small. The Modified Block Adaptive Coder applies a switching from pitch precitzion to spectrum prediction when the speech segment changes from a voiced or transition region to an unvoiced region. The experiments cont;-:ted in coding, transmission and simulation, used speech samples from .\£=_‘ajr2_1a:r1 and English phrases. Proposal for a speaker reecgnifion syste: and a phoneme identification system has also been outlized towards the end of the thesis.

An adaptive cluster based routing scheme for mobile wireless sensor networks

Clustering schemes improve energy efficiency of wireless sensor networks. The inclusion of mobility as a new criterion for the cluster creation and maintenance adds new challenges for these clustering schemes. Cluster formation and cluster head selection is done on a stochastic basis for most of the algorithms. In this paper we introduce a cluster formation and routing algorithm based on a mobility factor. The proposed algorithm is compared with LEACH-M protocol based on metrics viz. number of cluster head transitions, average residual energy, number of alive nodes and number of messages lost

Channel Adaptive MAC Protocol with Traffic-Aware Distributed Power Management in Wireless Sensor Networks-Some Performance Issues

In Wireless Sensor Networks (WSN), neglecting the effects of varying channel quality can lead to an unnecessary wastage of precious battery resources and in turn can result in the rapid depletion of sensor energy and the partitioning of the network. Fairness is a critical issue when accessing a shared wireless channel and fair scheduling must be employed to provide the proper flow of information in a WSN. In this paper, we develop a channel adaptive MAC protocol with a traffic-aware dynamic power management algorithm for efficient packet scheduling and queuing in a sensor network, with time varying characteristics of the wireless channel also taken into consideration. The proposed protocol calculates a combined weight value based on the channel state and link quality. Then transmission is allowed only for those nodes with weights greater than a minimum quality threshold and nodes attempting to access the wireless medium with a low weight will be allowed to transmit only when their weight becomes high. This results in many poor quality nodes being deprived of transmission for a considerable amount of time. To avoid the buffer overflow and to achieve fairness for the poor quality nodes, we design a Load prediction algorithm. We also design a traffic aware dynamic power management scheme to minimize the energy consumption by continuously turning off the radio interface of all the unnecessary nodes that are not included in the routing path. By Simulation results, we show that our proposed protocol achieves a higher throughput and fairness besides reducing the delay

Coastal regulation zone rules in coastal panchayats (villages) of Kerala, India vis-a` -vis socio-economic impacts from the recently introduced peoples’ participatory program for local self-governance and sustainable development

Coastal Regulation Zone (CRZ) notification was issued by the Ministry of Environment and Forest of Government of India in February 1991 as a part of the Environmental Protection Act of 1986 to protect the coast from eroding and to preserve its natural resources. The initial notification did not distinguish the variability and diversity of various coastal states before enforcing it on the various states and Union Territories. Impact assessments were not carried out to assess its impact on socio-economic life of the coastal population. For the very same reason, it was unnoticed or rather ignored till 1994 when the Supreme Court of India made a land mark judgment on the fate of the coastal aquaculture which by then had established as an economically successful industry in many South Indian States. Coastal aquaculture in its modern form was a prohibited activity within CRZ. Lately, only various stakeholders of the coast realized the real impact of the CRZ rules on their property rights andbusiness. To overcome the initial drawbacks several amendments were made in the regulation to suit regional needs. In 1995, another great transformation took place in the State of Kerala as a part of the reorganization of the local self government institutions into a decentralized three tier system called ‘‘Panchayathi Raj System’’. In 1997, the state government also decided to transfer the power with the required budget outlay to the grass root level panchayats (villages) and municipalities to plan and implement the various projects in their localities with the full participation of the local people by constituting Grama Sabhas (Peoples’ Forum). It is called the ‘‘Peoples’ Planning Campaign’’(Peoples’ Participatory Programme—PPP for Local Level Self-Governance). The management of all the resources including the local natural resources was largely decentralized to the level of local communities and villages. Integrated, sustainable coastal zone management has become the concern of the local population. The paper assesses the socio-economic impact of the centrally enforced CRZ and the state sponsored PPP on the coastal community in Kerala and suggests measures to improve the system and living standards of the coastal people within the framework of CRZ.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Image Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain

The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

Spatially Adaptive Image Denoising Techniques Using Directionlets

The aim of the thesis was to design and develop spatially adaptive denoising techniques with edge and feature preservation, for images corrupted with additive white Gaussian noise and SAR images affected with speckle noise. Image denoising is a well researched topic. It has found multifaceted applications in our day to day life. Image denoising based on multi resolution analysis using wavelet transform has received considerable attention in recent years. The directionlet based denoising schemes presented in this thesis are effective in preserving the image specific features like edges and contours in denoising. Scope of this research is still open in areas like further optimization in terms of speed and extension of the techniques to other related areas like colour and video image denoising. Such studies would further augment the practical use of these techniques.

Anisotropic adaptive resolution of boundary layers for heat conduction problems

We deal with the numerical solution of heat conduction problems featuring steep gradients. In order to solve the associated partial differential equation a finite volume technique is used and unstructured grids are employed. A discrete maximum principle for triangulations of a Delaunay type is developed. To capture thin boundary layers incorporating steep gradients an anisotropic mesh adaptation technique is implemented. Computational tests are performed for an academic problem where the exact solution is known as well as for a real world problem of a computer simulation of the thermoregulation of premature infants.