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With the enhanced computing capabilities and a wide variety of functions available on smartphones, critical and sensitive information, such as contact lists, messages, schedules, credit card numbers, is stored on smartphones, which makes preventing smartphone from being stolen of an unprecedented importance. Loss of smartphones not only causes economic loss but also jeopardizes the privacy of the...
In this paper, we address the problem of predicting wind turbine electrical subsystem fault using time series data obtained from multiple sensors on wind turbine. While considering this as a time series classification problem, we are facing with the challenge that there is no explicit label information regarding the temporal location and duration of symptoms of the fault. Besides, significant data...
Wearable devices such as smartwatches have become popular. The accelerometer embedded in smartwatch can record the movement of hand, so that it may lead to privacy compromise when performing sensitive inputting on keyboard with hand wearing smartwatch. The existing inference attack collects smartwatch's accelerometer readings which correspond to the movement of hand when inputting a numeric password,...
In real applications of one class classification, new features may be added due to some practical or technical reason. While lacking of representative samples for the new features, multi-task learning idea could be used to bring some information from the former learning model. Based on the above assumption, a new multi-task learning approach is proposed to deal with the training of the updated system...
On-line supervised spotting and classification of subsequences can be performed by comparing some distance between the stream and previously learnt time series. However, learning a few incorrect time series can trigger disproportionately many false alarms. In this paper, we propose a fast technique to prune bad instances away and automatically select appropriate distance thresholds. Our main contribution...
Multi-modal scene analysis is a growing field of importance as additional sensors, such as 3D LIDAR, is becoming a common complement to image capturing systems. However, while additional sensory data potentially can make the analysis more accurate, it also comes with a host of associated issues. For example, inconsistencies in the data between sensors resulting from, e.g., misalignment, moving objects,...
In this paper, we propose a multiclass classifier training method which reduces “fatal” misclassifications by cost-relaxation of “tolerable” misclassifications in one-against-all classifiers training, named misclassification tolerable learning. In a binary classifier in the one-against-all classifiers, we introduce a new class group “conceptually similar classes,” whose class labels are similar to...
We present a multi-user multiple-input multiple-output (MIMO) cognitive radio system consisting of a secondary receiver that deploys spatial multiplexing to decode signals from multiple secondary transmitters, under the presence of primary transmissions. The secondary receiver carries out minimum mean-squared error detection to decode the secondary data streams, while it performs spectrum sensing...
Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, Lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings....
For the problems that we can't monitor abnormal conditions of heart rate as well as carrying out scientific and efficient training plans based on knowledge from variation of them. A ZigBee and big data analysis based pulse monitoring system has been proposed. The system is composed of multiple ZigBee based pulse monitoring sensors, customized gateways and back-end system. Individuals' pulse information...
In this paper we introduce an automated mechanism for knowledge discovery from data streams. As a part of this work, we also present a new approach to the creation of classifiers ensemble based on a wide variety of models. Furthermore, we describe an innovative, highly scalable feature extraction and selection framework designed to work with the MapReduce programming model and the application of designed...
Prediction for deck-motion is a practical measure to improve the landing/taking off safety of carrier-based aircraft when those deck-motions in six-degree freedoms cannot be effectively controlled/restrained. Deck-motions excited by waves and winds own characteristics of randomness and nonlinearity. It is generally believed those classical feed-forward neural networks, such as back propagation networks...
Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions...
Water is the most valuable natural resource. To preserve this good, several water monitoring systems for pipe networks have been proposed. However, there is a lack of research on the development of high-frequency electromagnetic sensors for this task. This study proposes a non-destructive technique to locate carrying water polyvinyl chloride pipes using a microstrip patch antenna. An experimental...
In this paper we provide an intelligent and convenient shopping cart, Intelligent Shopping Assistant System (ISAS). With ISAS, customers can concentrate on what he/she has to buy, and do not be bothered where he/she has to buy or it is want to buy. In contrast to conventional shopping cart, two modes of autonomous functions are added to ISAS in order to reduce the labor of customers for pushing goods...
We examine a potential technique of performing a classification task based on compressively sensed (CS) data, skipping a computationally expensive reconstruction step. A deep Boltzmann machine is trained on a compressive representation of MNIST handwritten digit data, using a random orthoprojector sensing matrix. The network is first pre-trained on uncompressed data in order to learn the structure...
This paper proposes a GA-SVM classification method which is applied to the dynamic evaluation of taekwondo. For classifying a dynamic action, we converted a dynamic action signal to a frequency spectrum signal for analysis. However, the useful features were concentrated in a part of the frequency spectrum, and the useless features led to a decline in accuracy, operation speed, and efficiency of the...
Among most existing gait rehabilitation robots, it is difficult to find adequate devices for gait rehabilitation of chronic stroke patients who can already stand and move but still need to rehabilitate the affected lower limb through simple, compact, and easy-to use devices. This paper presents a novel haptic based gait rehabilitation system (HGRS) which has the potential to provide over-ground gait...
Indoor robot localization systems using wireless signal measurements have gained popularity in recent years, as wireless Local Area Networks can be found practically everywhere. In this field, a popular approach is the use of fingerprinting techniques, such as Gaussian Processes. In our approach, we improve Gaussian Processes based mapping using path loss models as priors. Path loss models encode...
The mathematical properties of high-dimensional spaces seem remarkably suited for describing behaviors produces by brains. Brain-inspired hyperdimensional computing (HDC) explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. Hypervectors are high-dimensional, holographic, and (pseudo)random with independent and identically distributed (i.i...
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