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Pervasive computing and social computing are two major computing paradigms of this decade, which have evolved more or less in isolation from each other. Integrating pervasive systems with social media can enhance the users' experience and enable them to form pervasive communities with others that share similar interests, habits, profile, behaviour, to communicate and interact with them, to socialise...
In human movement analysis accuracy of locating the hip joint center (HJC) becomes important in measurements of the hip muscle lengths and hip moment arms. Conventional gait analysis methods use regression and polynomial estimation techniques based on cadaver measurements to locate the HJC. Keeping in view the importance of Neural Networks (NN) in estimation, two Feedforward NN were constructed to...
This research focuses on the development of artificial neural network (ANN) model for estimation of daily global solar radiation on horizontal surface in Dhaka. In this analysis back-propagation algorithm is applied. Day of the year, daily mean air temperature, relative humidity and sunshine duration were used as input data, while the daily global solar radiation was the only output of the ANN. The...
In this paper an artificial neural network (ANN) based speed estimator is presented for vector-controlled squirrel cage induction motor (IM) drive. The drive is stable in all operating region and is independent of stator resistance variation. Stator currents, modified stator voltages (Reference values) with stator resistance adaption are used as input to the ANN and rotor speed is treated as the output...
Wireless Sensor Networks are dynamic in nature and the sensor nodes are self-organized. Due to the characteristics of the WSN and its nodes, knowing the location of the nodes is an important factor to be considered to improve the security in WSN. In this paper the location of a node [P] can be obtained using the Nearest Neighbor Reference Method [NNRM] and the reference node is the answer for the...
This contribution reviews some recent advances in the field of nearest-neighbor (NN) nonparametric estimation in sensor networks. Upon observing X0, the problem is to estimate the corresponding response variable Y0 by using the knowledge contained in a training set {(Xi, Yi)}in=1, made of n independent copies of (X0, Y0). In the distributed version of the problem, a network made of spatially distributed...
Photosynthetically active radiation (PAR) is a portion of solar radiation in the wavelength band of 400–700 nm providing energy for photosynthesis of plants. In this work, we proposed to estimate PAR from atmospheric parameters using an artificial neural network (ANN). The input data of the ANN are solar zenith angle (θz), cloud index derived from MTSAT-1R satellite together with precipitable water...
Different approaches based on various wireless have been proposed so far for indoor localization. Radio frequency Identification (RFID) indoor localization seems to be a promising way of research. The identification capability of this technology combined to localization methods improves the results obtained by other wireless technologies such as Wifi, GPS, Zigbee… This paper details some localization...
In the deregulated power systems, it is essential to know the value of Available Transfer Capability (ATC) for the smooth operation of the power system. ATC is generally calculated using repeated load-flow simulations of the interconnected transmission network. This paper presents an Artificial Neural Network based approach for online-ATC estimation for both bilateral and multilateral transactions...
The performance of wireless vehicular communications can depend on multiple context factors, such as the propagation conditions, the traffic density, or the location of communication infrastructure units. This paper proposes and evaluates two techniques that are able to identify and quantify such dependencies, and uses them to estimate the vehicular communications performance exploiting context information...
Battery performance degrades as the battery ages. For example, the battery capacity fades away after repeatedly cycling the battery. The degradation rate itself depends on many factors such as the depth-of-discharge (DOD), (dis)charge power, temperature, etc. In this paper, the application of artificial neural network (ANN) in estimating lithium-ion (Li-ion) battery capacity fade in electric vehicles...
In this paper, we present a no-reference, content-based Quality of Experience (QoE) estimation model for video streaming service over wireless networks. Since the impact of video quality impairments caused by both codec and network parameters is content-dependent, the cross-layer parameters, such as the bit rate, frame rate and resolution at the application layer, the packet loss rate at the network...
Software effort estimation is a crucial phase in software project management. Accuracy of estimation directly affects project success or failure. Managers try to estimate proper effort resources and this is a challenging issue for management. Having a set of tools and methodologies, estimation process can be made better. COCOMO is one of the most used model which has a parametric form. Also, artificial...
There is a relation, not always linear, between the blood pressure and the pulse duration, obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, in this paper the Artificial Neural Networks (ANNs) are used. Training data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care waveform database for better representation...
In the classic ANN-based approaches, the synchronous motor parameters mostly could be modeled with n-hidden layered networks. It is an important challenge in driver software development is to realize complex mathematical models in real time environments and circuits. This paper presents an Adaptive Artificial Neural Network-based (AANN) method to easily model excitation current of synchronous motors...
Load forecasting is the first phase of electric power system planning for economic power generation-distribution, effective control and operation conditions of the system, and also energy pricing. In this study, short-term load forecasting, as the main tool for economic operation conditions, is realized. 24-hour-ahead load forecasting without temperature data for Turkey is aimed and structures with...
We propose a neural network based approach for estimating the total wirelength of a digital circuit, mapped onto an FPGA, before circuit placement and routing. A 3-layer MLP neural network is trained to learn the behavior of a placement tool and then quickly predicts the wirelength of a circuit design with the accuracy similar to one obtained after placement. A priori knowledge about the wirelength...
In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable...
Accurate parametric identification of Linear Parameter-Varying (LPV) systems requires an optimal prior selection of model order and a set of functional dependencies for the parameterization of the model coefficients. In order to address this problem for linear regression models, a regressor shrinkage method, the Non-Negative Garrote (NNG) approach, has been proposed recently. This approach achieves...
In this paper, we have proposed a Kalman Filter (KF) - Recurrent Neural Network (RNN) based channel estimator block for Multiple-Input Multiple-Output (MIMO) multipath fading channels based on the IEEE 802.11n channel models for indoor wireless local area networks (WLAN). Two transmit antennas and two receive antennas are used here. The estimator block has been simulated in STBC coded MIMO multipath...
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