202 research outputs found
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
Universal Activation Function For Machine Learning
This article proposes a Universal Activation Function (UAF) that achieves
near optimal performance in quantification, classification, and reinforcement
learning (RL) problems. For any given problem, the optimization algorithms are
able to evolve the UAF to a suitable activation function by tuning the UAF's
parameters. For the CIFAR-10 classification and VGG-8, the UAF converges to the
Mish like activation function, which has near optimal performance when compared to other activation functions. For the
quantification of simulated 9-gas mixtures in 30 dB signal-to-noise ratio (SNR)
environments, the UAF converges to the identity function, which has near
optimal root mean square error of . In the
BipedalWalker-v2 RL dataset, the UAF achieves the 250 reward in
epochs, which proves that the UAF converges in the lowest number of epochs.
Furthermore, the UAF converges to a new activation function in the
BipedalWalker-v2 RL dataset
Influence of interface recombination in light emission from lateral Si-based light emitting devices
The influence of interface recombination on the electroluminescence profile of a lateral p+/p/n+ light emitting diode fabricated on Silicon On Insulator (SOI) materials has been experimentally investigated. Our device resembles a MOSFET fabricated on SOI (1), except that the source region has opposite doping to the drain. By controlling the voltage bias at the poly gate on top of active emitting region in association with a bias on the silicon substrate under the active region we were able to diminish the non-radiative recombination component at Si/SiO2 interface and therefore enhance the radiative recombination in the thin film SOI. When the diode is working under constant current condition, we observe an increased light output of ~ 20 % as the gate and/or the substrate are biased negatively. The intensity profile across the device is also strongly influenced. To understand the device thoroughly, the structure has also been simulated showing agreement with experimental results.\u
Influence of dislocation loops on the near infrared light emission from silicon diodes
The infrared light emission of forward-biased silicon diodes is studied. Through ion implantation and anneal, dislocation loops were created near the diode junction. These loops suppress the light emission at the band-to-band peak around 1.1 μm. The so-called D1 line at 1.5 μm is strongly enhanced by these dislocation loops. We report a full study of photoluminescence and electroluminescence of these diodes. The results lead to new insights for the manufacturing approach of practical infrared light sources in integrated circuit
A Machine Learning-based Approach to Vietnamese Handwritten Medical Record Recognition
Handwritten text recognition has been an active research topic within computer vision division. Existing deep-learning solutions are practical; however, recognizing Vietnamese handwriting has shown to be a challenge with the presence of extra six distinctive tonal symbols and extra vowels. Vietnam is a developing country with a population of approximately 100 million, but has only focused on digitalization transforms in recent years, and so Vietnam has a significant number of physical documents, that need to be digitized. This digitalization transform is urgent when considering the public health sector, in which medical records are mostly still in hand-written form and still are growing rapidly in number. Digitization would not only help current public health management but also allow preparation and management in future public health emergencies. Enabling the digitalization of old physical records will allow efficient and precise care, especially in emergency units. We proposed a solution to Vietnamese text recognition that is combined into an end-to-end document-digitalization system. We do so by performing segmentation to word-level and then leveraging an artificial neural network consisting of both convolutional neural network (CNN) and a long short-term memory recurrent neural network (LSTM) to propagate the sequence information. From the experiment with the records written by 12 doctors, we have obtained encouraging results of 6.47% and 19.14% of CER and WER respectively
Recognition of medication information from discharge summaries using ensembles of classifiers
BACKGROUND: Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks. METHODS: We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting. RESULTS: Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall) for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall) than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall) by the first-ranked system in the challenge. CONCLUSIONS: Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition
Semi-Sequential Probabilistic Model For Indoor Localization Enhancement
This paper proposes a semi-sequential probabilistic model (SSP) that applies
an additional short term memory to enhance the performance of the probabilistic
indoor localization. The conventional probabilistic methods normally treat the
locations in the database indiscriminately. In contrast, SSP leverages the
information of the previous position to determine the probable location since
the user's speed in an indoor environment is bounded and locations near the
previous one have higher probability than the other locations. Although the SSP
utilizes the previous location information, it does not require the exact
moving speed and direction of the user. On-site experiments using the received
signal strength indicator (RSSI) and channel state information (CSI)
fingerprints for localization demonstrate that SSP reduces the maximum error
and boosts the performance of existing probabilistic approaches by 25% - 30%
A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence
This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction (FLSVR TSP ). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR's tuning parameters, the FLSVR TSP incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the FLSVR TSP has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction
A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement
This paper proposes a soft range limited K nearest neighbours (SRL-KNN)
localization fingerprinting algorithm. The conventional KNN determines the
neighbours of a user by calculating and ranking the fingerprint distance
measured at the unknown user location and the reference locations in the
database. Different from that method, SRL-KNN scales the fingerprint distance
by a range factor related to the physical distance between the user's previous
position and the reference location in the database to reduce the spatial
ambiguity in localization. Although utilizing the prior locations, SRL-KNN does
not require knowledge of the exact moving speed and direction of the user.
Moreover, to take into account of the temporal fluctuations of the received
signal strength indicator (RSSI), RSSI histogram is incorporated into the
distance calculation. Actual on-site experiments demonstrate that the new
algorithm achieves an average localization error of m with of the
errors under m, which outperforms conventional KNN algorithms by
under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
K-nearest neighbor (KNN), fingerprint-based localizatio
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