ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES

Objective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it usingvarious post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activitiesof the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is usedby both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis ofmany neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalentneurological disorders is epilepsy and it is easily characterized by recurrent seizures.Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent ComponentAnalysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF).The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and WeightedKNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion: Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was whenLDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risklevels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types ofclassifiers for the perfect classification of epilepsy risk levels from EEG signals.Keywords: FMI, ICA, LGE, LDA, W-KNN, EE

Space debris or natural? Impacts on NASA's Long Duration Exposure Facility.

The Long Duration Exposure Facility has provided the most complete in-situ study of the near Earth environment to date. This thesis details the spacecraft conception and development culminating in a 69 month excursion into low Earth orbit. The techniques required to analyse the retrieved data are discussed, and indeed, these techniques are applicable to any spacecraft surface or terrestrial experimental hypervelocity impact project. The results have shown that all faces of the LDEF have been impacted by both natural and anthropogenic space debris to some extent. The current models employed to determine the relative proportions of these populations on the LDEF are shown to be inadequate, although the assumptions used are quite sweeping. The modelling presented shows a definite need to use a more anisotropic distribution when discussing the natural environment that incorporates both meteor streams and comet encounters. The problems and concerns surrounding the present anthropogenic space debris population is discussed in detail concluding with the need for better Earth and space borne detection systems and understanding of orbital dynamics of small particles, presently undetectable. An average particle density for interplanetary particles of lgcnr3 is derived from a comparison of data from different experimental surfaces on the Space face of LDEF. The impact direction distributions of both natural and anthropogenic space debris is illustrated, including an enhanced space debris distribution, which accounts for some of the limits in the presently tracked data sets. These data sets are discussed in terms of generation, ownership and orbital distribution

Location Privacy in Moving-Object Environments

The expanding use of location-based services has profound implications on the privacy of personal information. If no adequate protection is adopted, information about movements of specific individuals could be disclosed to unauthorized subjects or organizations, thus resulting in privacy breaches. In this paper, we propose a framework for preserving location privacy in moving-object environments. Our approach is based on the idea of sending to the service provider suitably modified location information. Such modifications, that include transformations like scaling, are performed by agents interposed between users and service providers. Agents execute data transformation and the service provider directly processes the transformed dataset. Our technique not only prevents the service provider from knowing the exact locations of users, but also protects information about user movements and locations from being disclosed to other users who are not authorized to access this information. A key characteristic of our approach is that it achieves privacy without degrading service quality. We also define a privacy model to analyze our framework, and examine our approach experimentally

Case report of aplastic anaemia detected in third trimester of pregnancy: dilemmas faced

Aplastic anaemia with pregnancy is rarely encountered. Management of aplastic anaemia in pregnancy primarily involves a multidisciplinary approach offering supportive care. Our case was challenging as she developed aplastic anaemia during the third trimester and had refractory thrombocytopenia. She required platelet transfusions on a daily basis for few weeks as well as packed red blood cells frequently. Her leucocyte count was low initially but improved quickly unlike the platelet counts. Initiation of immunosuppressive therapy turned out to be beneficial and culminated in a good outcome. After starting immunosuppressive therapy with eltrombopag and cyclosporine she drifted through term and achieved a normal vaginal delivery

The biomechanics of fast prey capture in aquatic bladderworts

Carnivorous plants match their animal prey for speed of movements and hence offer fascinating insights into the evolution of fast movements in plants. Here, we describe the mechanics of prey capture in aquatic bladderworts Utricularia stellaris, which prey on swimming insect larvae or nematodes to supplement their nitrogen intake. The closed Utricularia bladder develops lower-than-ambient internal pressures by pumping out water from the bladder and thus setting up an elastic instability in bladder walls. When the external sensory trigger hairs on their trapdoor are mechanically stimulated by moving prey, the trapdoor opens within 300–700 μs, causing strong inward flows that trap their prey. The opening time of the bladder trapdoor is faster than any recorded motion in carnivorous plants. Thus, Utricularia have evolved a unique biomechanical system to gain an advantage over their animal prey

The neural mechanisms of antennal positioning in flying moths

Summary: In diverse insects, the forward positioning of the antenna is often among the first behavioral indicators of the onset of flight. This behavior may be important for the proper acquisition of the mechanosensory and olfactory inputs by the antennae during flight. Here, we describe the neural mechanisms of antennal positioning in hawk moths from behavioral, neuroanatomical and neurophysiological perspectives. The behavioral experiments indicated that a set of sensory bristles called Bohm's bristles (or hair plates) mediate antennal positioning during flight. When these sensory structures were ablated from the basal segments of their antennae, moths were unable to bring their antennae into flight position, causing frequent collisions with the flapping wing. Fluorescent dye-fills of the underlying sensory and motor neurons revealed that the axonal arbors of the mechanosensory bristle neurons spatially overlapped with the dendritic arbors of the antennal motor neurons. Moreover, the latency between the activation of antennal muscles following stimulation of sensory bristles was also very short (<10 ms), indicating that the sensorimotor connections may be direct. Together, these data show that Bohm's bristles control antennal positioning in moths via a reflex mechanism. Because the sensory structures and motor organization are conserved across most Neoptera, the mechanisms underlying antennal positioning, as described here, are likely to be conserved in these diverse insects

Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster

The widespread use of GPS-enabled smartphones along with the popularity of micro-blogging and social networking applications, e.g., Twitter and Facebook, has resulted in the generation of huge streams of geo-tagged textual data. Many applications require real-time processing of these streams. For example, location-based e-coupon and ad-targeting systems enable advertisers to register millions of ads to millions of users. The number of users is typically very high and they are continuously moving, and the ads change frequently as well. Hence sending the right ad to the matching users is very challenging. Existing streaming systems are either centralized or are not spatial-keyword aware, and cannot efficiently support the processing of rapidly arriving spatial-keyword data streams. This paper presents Tornado, a distributed spatial-keyword stream processing system. Tornado features routing units to fairly distribute the workload, and furthermore, co-locate the data objects and the corresponding queries at the same processing units. The routing units use the Augmented-Grid, a novel structure that is equipped with an efficient search algorithm for distributing the data objects and queries. Tornado uses evaluators to process the data objects against the queries. The routing units minimize the redundant communication by not sending data updates for processing when these updates do not match any query. By applying dynamically evaluated cost formulae that continuously represent the processing overhead at each evaluator, Tornado is adaptive to changes in the workload. Extensive experimental evaluation using spatio-textual range queries over real Twitter data indicates that Tornado outperforms the non-spatio-textually aware approaches by up to two orders of magnitude in terms of the overall system throughput