Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms

The diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training

Exponential distribution of long heart beat intervals during atrial fibrillation and their relevance for white noise behaviour in power spectrum

The statistical properties of heart beat intervals of 130 long-term surface electrocardiogram recordings during atrial fibrillation (AF) are investigated. We find that the distribution of interbeat intervals exhibits a characteristic exponential tail, which is absent during sinus rhythm, as tested in a corresponding control study with 72 healthy persons. The rate of the exponential decay lies in the range 3-12 Hz and shows diurnal variations. It equals, up to statistical uncertainties, the level of the previously uncovered white noise part in the power spectrum, which is also characteristic for AF. The overall statistical features can be described by decomposing the intervals into two statistically independent times, where the first one is associated with a correlated process with 1/f noise characteristics, while the second one belongs to an uncorrelated process and is responsible for the exponential tail. It is suggested to use the rate of the exponential decay as a further parameter for a better classification of AF and for the medical diagnosis. The relevance of the findings with respect to a general understanding of AF is pointed out

A Fisher-Rao metric for paracatadioptric images of lines

In a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it diffcult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied. The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and based on the Hough transform

Caracterización en contenido de fenoles y azúcares totales en distintas variedades de arándanos cultivados en La Plata

El arándano es un frutal perteneciente al género Vaccinium, de la familia de las Ericáceas y constituye un grupo de especies ampliamente distribuidas en el Hemisferio Norte. En Argentina la plantación de arándanos comenzó a expandirse a partir de 1993, en gran medida por su adaptación a las condiciones agroecológicas, alta rentabilidad y elevada demanda externa. Argentina tiene la ventaja de producir en contraestación en relación a los principales países productores y consumidores, comenzando la cosecha a mediados de septiembre y extendiéndose hasta mediados del mes de diciembre. En cuanto a las propiedades nutracéuticas, tienen alta concentración de compuestos bioactivos, muy valorados por sus beneficios en la salud. Dentro de estos compuestos están los polifenoles, incluyendo antocianinas, ácidos fenólicos, taninos, carotenoides, vitaminas A, C, E, ácido fólico y minerales como el calcio, selenio y cinc. Los fenoles son metabolitos secundarios distribuidos en los vegetales, que cumplen diversas funciones en las plantas, como protectores frente a las radiaciones ultravioleta, y forman los pigmentos naturales de las plantas (p.ej., antocianinas, flavonas y flavonoles), intervienen como reguladores de interacciones plantamicroorganismos, están implicados en procesos defensivos, así como efectos alelopáticos y de atracción de polinizadores. Algunos fenoles también juegan un importante papel en la tolerancia al estrés. El contenido de azúcares de los frutos influye en gran medida en el sabor, y este parámetro de calidad puede variar según el material genético, época de cosecha, madurez a cosecha y manejo del cultivo entre otros. El objetivo de este trabajo fue determinar diferencias en la calidad general y nutracéutica de cuatro variedades de arándanos en condiciones de cultivo de la localidad de La Plata. Las muestras fueron tomadas de la Estación Experimental “Julio Hirschhorn” (34º54´ LS). El monte de arándanos está conducido en líneas cada 3m y 1m entre plantas, con mínimas prácticas de podas y controles de maleza, sin aplicaciones de productos fitosanitarios.Facultad de Ciencias Agrarias y Forestale

An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 male subjects, and we obtained satisfactory respiration signals that showed high correlation (r2 > 0.8) with the signal acquired from the chest-belt respiration sensor

TOpic: rare and special cases, the real "Strange cases"

Introduction: The bladder hernia represents approximately 1-3% of all inguinal hernias, where patients aged more than 50 years have a higher incidence (10%). Many factors contribute to the development of a bladder hernia, including the presence of a urinary outlet obstruction causing chronic bladder distention, the loss of bladder tone, pericystitis, the perivesical bladder fat protrusion and the obesity

A novel crosstalk between CCAR2 and AKT pathway in the regulation of cancer cell proliferation

Human CCAR2 has recently emerged as having a pivotal role in the DNA damage response, promoting apoptosis and repair of heterochromatic DNA breaks. However, less is known about the function of CCAR2 in tumor formation and cancer progression. Here, we demonstrate, for the first time, that CCAR2 loss inhibits the proliferation of cancer cells, but preserves the growth of normal cells. Investigating the mechanisms responsible for this differential effect, we found that CCAR2 depletion specifically impairs the activation of AKT pathway in cancer cells, but not in normal cells, by reducing AKT phosphorylation on Ser473. This effect is achieved through the transcriptional upregulation of TRB3 gene and accumulation of TRB3 protein, which then binds to and inhibits the phosphorylation and activation of AKT. The defective activation of AKT finally results in reduced GSK3\u3b2 phosphorylation, prevention of G1/S transition and inhibition of cancer cell growth. These results establish an important role for CCAR2 in cancer cells proliferation and could shed new light on novel therapeutic strategies against cancer, devoid of detrimental side effects

Rapidly detecting disorder in rhythmic biological signals: A spectral entropy measure to identify cardiac arrhythmias

We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s this becomes 89.5%, and with 60 s it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6 s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals.Comment: 11 page

Quality-sensitive foraging by a robot swarm through virtual pheromone trails

Large swarms of simple autonomous robots can be employed to find objects clustered at random locations, and transport them to a central depot. This solution offers system parallelisation through concurrent environment exploration and object collection by several robots, but it also introduces the challenge of robot coordination. Inspired by ants’ foraging behaviour, we successfully tackle robot swarm coordination through indirect stigmergic communication in the form of virtual pheromone trails. We design and implement a robot swarm composed of up to 100 Kilobots using the recent technology Augmented Reality for Kilobots (ARK). Using pheromone trails, our memoryless robots rediscover object sources that have been located previously. The emerging collective dynamics show a throughput inversely proportional to the source distance. We assume environments with multiple sources, each providing objects of different qualities, and we investigate how the robot swarm balances the quality-distance trade-off by using quality-sensitive pheromone trails. To our knowledge this work represents the largest robotic experiment in stigmergic foraging, and is the first complete demonstration of ARK, showcasing the set of unique functionalities it provides