Conserving energy through neural prediction of sensed data

The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points mak- ing a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors’ idle periods and save over 65 percent of energ

Financial hazard assessment for electricity suppliers due to power outages: the revenue loss perspective

The electrical power infrastructure of the modern world is advanced, efficient, and robust, yet power outages still occur. In addition to affecting millions of people around the world, these outage events cost billions of dollars to the global economy. In this paper, the revenue loss borne by electricity-supplying companies in the United States due to power outage events is estimated and predicted. Various factors responsible for power outages are considered in order to present an exploratory data analysis at the U.S. level, followed by the top ten affected states, which bear over 85% of the total revenue loss. The loss is computed using historic observational data of electricity usage patterns and the tariff offered by the energy suppliers. The study is supplemented with reliable and publicly available records, including electricity usage patterns, the consumer category distribution, climatological annotations, population density, socio-economic indicators and land area. Machine learning techniques are used to predict the revenue loss for future outage events, as well as to characterize the key parameters for efficient prediction and their partial dependence. The results show that the revenue loss is a function of several parameters, including residential sales, percentage of industrial customer, time-period of the year, and economic indicators. This study may help energy suppliers make risk-informed decisions, while developing revenue generation strategies as well as identifying safer investment avenues for long-term returns

Feature Extraction, Pattern Recognition and Classification in X-ray Image Data

Excellence of food products highly depends on the quality checks at different stages while preparation and processing at industry. With the evolution of technology, traditional methods are being put back and state of the art equipment taking the position. Being fast, efficient and automatic, computers and machines are potentially replacing the human deployment in the food industry. One of the early stages of food preparation is the ingredient evaluation on feeding belt. This is still carried out mostly by humans; however efforts have been made for the development of such system which is capable to inspect the ingredient quality in an automatic way. The research work involves developing and estimating such an arrangement which provide the quality information of ingredient without human deployment. X-ray imaging was employed for internal analysis of ingredients: pine, pistachio and hazelnuts. A captured x-ray image containing few non-overlapping ingredients was analyzed using image processing techniques to develop a method for automatic detection and extraction of independent ingredient. Individual ingredient image samples were further analyzed to calculate the strong features on the global as well as local level. A number of features including statistical, texture and moment invariant properties were extracted from each image sample and were organized in diverse combinations to be utilized further. Different databases have different percentage of representation for healthy and unhealthy nuts so correspondingly several classification techniques were exercised including logistic regression, artificial neural network, anomaly detection and support vector machines. In addition to accuracy, the percentage of correct recognition unhealthy ingredients was observed which is vital. Concluding fine classification accuracy was observed with comparatively better false positive rate than related studies

Feature Extraction, Pattern Recognition and Classification in X-ray Image Data

Excellence of food products highly depends on the quality checks at different stages while preparation and processing at industry. With the evolution of technology, traditional methods are being put back and state of the art equipment taking the position. Being fast, efficient and automatic, computers and machines are potentially replacing the human deployment in the food industry. One of the early stages of food preparation is the ingredient evaluation on feeding belt. This is still carried out mostly by humans; however efforts have been made for the development of such system which is capable to inspect the ingredient quality in an automatic way. The research work involves developing and estimating such an arrangement which provide the quality information of ingredient without human deployment. X-ray imaging was employed for internal analysis of ingredients: pine, pistachio and hazelnuts. A captured x-ray image containing few non-overlapping ingredients was analyzed using image processing techniques to develop a method for automatic detection and extraction of independent ingredient. Individual ingredient image samples were further analyzed to calculate the strong features on the global as well as local level. A number of features including statistical, texture and moment invariant properties were extracted from each image sample and were organized in diverse combinations to be utilized further. Different databases have different percentage of representation for healthy and unhealthy nuts so correspondingly several classification techniques were exercised including logistic regression, artificial neural network, anomaly detection and support vector machines. In addition to accuracy, the percentage of correct recognition unhealthy ingredients was observed which is vital. Concluding fine classification accuracy was observed with comparatively better false positive rate than related studies

Conserving Energy Through Neural Prediction of Sensed Data

The constraint of energy consumption is a serious problem in wireless sensor networks to which many solutions have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy

Conserving Energy Through Neural Prediction of Sensed Data

The constraint of energy consumption is a serious problem in wireless sensor networks to which many solutions have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a nonlinear autoregressive network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviates the need for sensed data during sensors’ idle periods and saves over 65 percent of energy

Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients

Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient prediction of future blood glucose levels. An efficient diabetes management system demands the accurate estimation of blood glucose levels, which, apart from using an appropriate prediction algorithm, depends on discriminative data representation. In this research work, a transformation of event-based data into discriminative continuous features is proposed. Moreover, a multi-layered long short-term memory (LSTM)-based recurrent neural network is developed for the prediction of blood glucose levels in patients with type 1 diabetes. The proposed method is used to forecast the blood glucose level on a prediction horizon of 30 and 60 min. The results are evaluated for three patients using the Ohio T1DM dataset. The proposed scheme achieves the lowest RMSE score of 14.76 mg/dL and 25.48 mg/dL for prediction horizons of 30 min and 60 min, respectively. The suggested methodology can be utilized in closed-loop systems for precise insulin delivery to type 1 patients for better glycemic control

Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients

Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient prediction of future blood glucose levels. An efficient diabetes management system demands the accurate estimation of blood glucose levels, which, apart from using an appropriate prediction algorithm, depends on discriminative data representation. In this research work, a transformation of event-based data into discriminative continuous features is proposed. Moreover, a multi-layered long short-term memory (LSTM)-based recurrent neural network is developed for the prediction of blood glucose levels in patients with type 1 diabetes. The proposed method is used to forecast the blood glucose level on a prediction horizon of 30 and 60 min. The results are evaluated for three patients using the Ohio T1DM dataset. The proposed scheme achieves the lowest RMSE score of 14.76 mg/dL and 25.48 mg/dL for prediction horizons of 30 min and 60 min, respectively. The suggested methodology can be utilized in closed-loop systems for precise insulin delivery to type 1 patients for better glycemic control

Using time proportionate intensity images with non-linear classifiers for hand gesture recognition

Gestures are spatiotemporal signals that contain valuable information. Humans can understand gestures with ease, but for computers or robots it is a challenging task involving thousands of computations per video frame. Current state of the art gesture recognition systems treat gestures as Markov Chains. Then the task of gesture recognition is to match the incoming video sequence to these Markov Chains. Each Markov State is modeled with spatial features such as hand location and temporal features like the motion vectors. The main problem with this approach is the high order of computational complexity. In this paper we propose a novel gesture recognition technique based on projecting the temporal axis information onto the spatial plane. Then this spatial intensity image is fed to a machine learning classifier (SVM in our case) for recognition. We show that the proposed algorithm achieves an accuracy that is comparable to the current state of the art approaches, but with a (much) reduced computational burde

Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data

Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image–patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image–patch thermograms