Estimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods

The opacity of real-estate market involves some challenges in their agent-based simulation. While some real-estate Web sites provide the prices of a great amount of houses publicly, the prices of the rest are not available. The estimation of these prices is necessary for simulating their evolution from a complete initial set of houses. Additionally, this estimation could also be useful for other purposes such as appraising houses, letting buyers know which are the best offered prices (i.e., the lowest ones compared to the appraisals) and recommending the buyers to set an initial price. This work proposes combining dimensionality reduction methods with machine learning techniques to obtain the estimated prices. In particular, this work analyzes the use of nonnegative factorization, recursive feature elimination and feature selection with a variance threshold, as dimensionality reduction methods. It compares the application of linear regression, support vector regression, the k-nearest neighbors and a multilayer perceptron neural network, as machine learning techniques. This work has applied a tenfold cross-validation for comparing the estimations and errors and assessing the improvement over a basic estimator commonly used in the beginning of simulations. The developed software and the used dataset are freely available from a data research repository for the sake of reproducibility and the support to other researchers

Integral mathematical model of power quality disturbances

Power quality (PQ) disturbances lead to severe problems in industries and electrical grids. To mitigate PQ problems, the accurate detection and classification of the possible disturbances are essential. A large number of studies exists in this field. The first research step in these studies is to obtain several distorted signals to test the classification systems. In this regard, the most common trend is the generation of signals from mathematical models. In the literature, we can find several models with significant differences among them. However, to the best of our knowledge, there is no integral model that considers all types of distortions. This work presents an integral mathematical model based on the models found in the literature. The model also includes new types of combined disturbances. Twenty-nine disturbances are considered. Additionally, this work includes a software version of this integral model that is publicly available to be used by any interested researcher. In this way, PQ disturbances can be generated in a fast and automatic way. This software aims to facilitate future studies, supporting researchers in the modelling stage

Voltage-to-Frequency Converter for Low-Power Sensor Interfaces

This work presents a low-power rail-to-rail temperature compensated voltage-to-frequency converter (VFC) which constitutes the last stage of a sensor read-out interface targeting wireless sensor networks (WSN) applications. These quasi-digital converters are now receiving great interest, since they combine the simplicity of analog devices with the accuracy and noise immunity proper to digital signal processing; besides, frequency output is directly driven to the embedded node microcontroller C, which next performs the A/D conversion using its internal timers. A first read-out interface prototype using low-voltage low-power commercial components shows that the VFC means 99 % of the total interface consumption in read-out mode. Further, existing CMOS VFCs in the form of ASICs have a rather limited input range and an unsuitable output frequency span for typical C clock frequencies used in WSN. Hence, a novel full custom VFC solution is needed, fullfilling the main requirements of rail-to-rail operation, to take advantage of the full supply voltage range to optimize the output frequency resolution, and low-power low-voltage operation to have a power supply compatible with conventional WSN batteries while maximizing the operating life of the sensor node. Experimental results for a 0.18–μm 1.2–V CMOS VFC implementation show for an input range of (0–1.2 V) an output frequency range of (0.1–1.0 MHz), adequate to digitize the signal with the direct counting method in the sensor node μC achieving 13 bits resolution. It has a power consumption of 60 μW (35 nW in sleep mode) and it is temperature insensitive for a temperature range of (-40, 120 ºC)

Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones

Despite being a major public health problem, falls in the elderly cannot be detected efficiently yet. Many studies have used acceleration as the main input to discriminate between falls and activities of daily living (ADL). In recent years, there has been an increasing interest in using smartphones for fall detection. The most promising results have been obtained by supervised Machine Learning algorithms. However, a drawback of these approaches is that they rely on falls simulated by young or mature people, which might not represent every possible fall situation and might be different from older people's falls. Thus, we propose to tackle the problem of fall detection by applying a kind of novelty detection methods which rely only on true ADL. In this way, a fall is any abnormal movement with respect to ADL. A system based on these methods could easily adapt itself to new situations since new ADL could be recorded continuously and the system could be re-trained on the fly. The goal of this work is to explore the use of such novelty detectors by selecting one of them and by comparing it with a state-of-the-art traditional supervised method under different conditions. The data sets we have collected were recorded with smartphones. Ten volunteers simulated eight type of falls, whereas ADL were recorded while they carried the phone in their real life. Even though we have not collected data from the elderly, the data sets were suitable to check the adaptability of novelty detectors. They have been made publicly available to improve the reproducibility of our results. We have studied several novelty detection methods, selecting the nearest neighbour-based technique (NN) as the most suitable. Then, we have compared NN with the Support Vector Machine (SVM). In most situations a generic SVM outperformed an adapted NN

Fano Resonance and Incoherent Interlayer Excitons in Molecular van der Waals Heterostructures

Complex van der Waals heterostructures from layered molecular stacks are promising optoelectronic materials offering the means to efficient, modular charge separation and collection layers. The effect of stacking in the electrodynamics of such hybrid organic–inorganic two-dimensional materials remains largely unexplored, whereby molecular scale engineering could lead to advanced optical phenomena. For instance, tunable Fano engineering could make possible on-demand transparent conducting layers or photoactive elements, and passive cooling. We employ an adapted Gersten–Nitzan model and real time time-dependent density functional tight-binding to study the optoelectronics of self-assembled monolayers on graphene nanoribbons. We find Fano resonances that cause electromagnetic induced opacity and transparency and reveal an additional incoherent process leading to interlayer exciton formation with a characteristic charge transfer rate. These results showcase hybrid van der Waals heterostructures as paradigmatic 2D optoelectronic stacks, featuring tunable Fano optics and unconventional charge transfer channels

A smartphone-based system for detecting hand tremors in unconstrained environments

The detection of tremors can be crucial for the early diagnosis and proper treatment of some disorders such as Parkinson’s disease. A smartphone-based applica- tion has been developed for detecting hand tremors. This application runs in background and distinguishes hand tremors from common daily activities. This application can facilitate the continuous monitoring of patients or the early detection of this symptom. The evaluation analyzes 1770 accelerometer samples with cross-validation for assessing the ability of the system for processing unknown data, obtaining a sensitivity of 95.8 % and a specificity of 99.5 %. It also analyzes continuous data for some volun- teers for several days, which corroborated its high performance