32,074 research outputs found
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
We present results from a set of experiments in this pilot study to
investigate the causal influence of user activity on various environmental
parameters monitored by occupant carried multi-purpose sensors. Hypotheses with
respect to each type of measurements are verified, including temperature,
humidity, and light level collected during eight typical activities: sitting in
lab / cubicle, indoor walking / running, resting after physical activity,
climbing stairs, taking elevators, and outdoor walking. Our main contribution
is the development of features for activity and location recognition based on
environmental measurements, which exploit location- and activity-specific
characteristics and capture the trends resulted from the underlying
physiological process. The features are statistically shown to have good
separability and are also information-rich. Fusing environmental sensing
together with acceleration is shown to achieve classification accuracy as high
as 99.13%. For building applications, this study motivates a sensor fusion
paradigm for learning individualized activity, location, and environmental
preferences for energy management and user comfort.Comment: submitted to the 40th Annual Conference of the IEEE Industrial
Electronics Society (IECON
A Tutorial on the Expectation-Maximization Algorithm Including Maximum-Likelihood Estimation and EM Training of Probabilistic Context-Free Grammars
The paper gives a brief review of the expectation-maximization algorithm
(Dempster 1977) in the comprehensible framework of discrete mathematics. In
Section 2, two prominent estimation methods, the relative-frequency estimation
and the maximum-likelihood estimation are presented. Section 3 is dedicated to
the expectation-maximization algorithm and a simpler variant, the generalized
expectation-maximization algorithm. In Section 4, two loaded dice are rolled. A
more interesting example is presented in Section 5: The estimation of
probabilistic context-free grammars.Comment: Presented at the 15th European Summer School in Logic, Language and
Information (ESSLLI 2003). Example 5 extended (and partially corrected
EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy
Visual inspection of the EEG to determine the seizure onset zone (SOZ) in the context of the presurgical evaluation in epilepsy is time-consuming and often challenging or impossible. We offer an approach that uses EEG source imaging (ESI) in combination with functional connectivity analysis (FC) to localize the SOZ from ictal EEG.
Ictal low-density-scalp EEG from 111 seizures in 27 patients who were rendered-seizure free after surgery was analyzed. For every seizure, ESI (LORETA) was applied on an artifact-free epoch selected around the seizure onset. Additionally, FC was applied on the reconstructed sources. We estimated the SOZ in two ways: (i)the source with highest power after ESI and (ii)the source with the most outgoing connections after ESI and FC. For both approaches, the distance between the estimated SOZ and the resected zone (RZ) of the patient were calculated.
Using ESI alone, the SOZ was estimated inside the RZ in 31% of the seizures and within 10mm from the border of the RZ in 42%. For 18.5% of the patients, all seizures were estimated within 10mm of the RZ. Using ESI and FC, 72% of the seizures were estimated inside the RZ, and 94% within 10mm. For 85% of the patients, all seizures were estimated within 10mm of the RZ. FC provided a significant added value to ESI alone (p<0.001).
ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter
learning of parameterized logic programs, i.e. definite clause programs
containing probabilistic facts with a parameterized distribution. It extends
the traditional least Herbrand model semantics in logic programming to
distribution semantics, possible world semantics with a probability
distribution which is unconditionally applicable to arbitrary logic programs
including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM
algorithm, the graphical EM algorithm, that runs for a class of parameterized
logic programs representing sequential decision processes where each decision
is exclusive and independent. It runs on a new data structure called support
graphs describing the logical relationship between observations and their
explanations, and learns parameters by computing inside and outside probability
generalized for logic programs. The complexity analysis shows that when
combined with OLDT search for all explanations for observations, the graphical
EM algorithm, despite its generality, has the same time complexity as existing
EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside
algorithm for PCFGs, and the one for singly connected Bayesian networks that
have been developed independently in each research field. Learning experiments
with PCFGs using two corpora of moderate size indicate that the graphical EM
algorithm can significantly outperform the Inside-Outside algorithm
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Part 2: pushing the envelope. A process perspective for architecture, engineering and construction
In this article, I am building on an emerging 'process view of nature' and how biological membranes emerge through the combined action of (locally) autonomous construction agents. In Part 1, we considered the simultaneous aggregation and disaggregation of matter around embedded processes, used to create, sustain and regulate matter, energy and information gradients from which 'work' is derived for the benefit of the agents or organisms present in the system. In Part 2, I intend to demonstrate that emerging digital design, simulation and fabrication techniques, when linked to sensory and effector feedback, memory and actions, directed by pre-encoded objectives (as rules or algorithms), produce the same fundamental unit of 'agency' as biological agents possess. By understanding how biological membranes emerge in nature, as the outcome of 'negotiated agency', to regulate matter, energy and information exchange between adjacent spaces, we can begin to consider the building envelope as a biological interface or membrane from which 'work' can be derived from the environment we inhabit, as a physiological extension of ourselves
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