Bayesian Robust PCA of Incomplete Data

We present a probabilistic model for robust factor analysis and principal component analysis in which the observation noise is modeled by Student-t distributions in order to reduce the negative effect of outliers. The Student-t distributions are modeled independently for each data dimensions, which is different from previous works using multivariate Student-t distributions. We compare methods using the proposed noise distribution, the multivariate Student-t and the Laplace distribution. Intractability of evaluating the posterior probability density is solved by using variational Bayesian approximation methods. We demonstrate that the assumed noise model can yield accurate reconstructions because corrupted elements of a bad quality sample can be reconstructed using the other elements of the same data vector. Experiments on an artificial dataset and a weather dataset show that the dimensional independency and the flexibility of the proposed Student-t noise model can make it superior in some applications.     

Muscular,cardiorespiratory and thermal strain of mast and pole workers

This field study evaluated the level of muscular, cardiorespiratory and thermal strain of mast and pole workers. We measured the muscular strain using electromyography (EMG), expressed as a percentage in relation to maximal EMG activity (%MEMG). Oxygen consumption (VO₂) was indirectly estimated from HR measured during work and expressed as a percentage of maximum VO₂ (%VO₂max). Skin and deep body temperatures were measured to quantify thermal strain. The highest average muscular strain was found in the wrist flexor (24 ± 1.5%MEMG) and extensor (21 ± 1.0%MEMG) muscles, exceeding the recommendation of 14%MEMG. Average cardiorespiratory strain was 48 ± 3%VO₂max. Nearly half (40%) of the participants exceeded the recommended 50%VO₂max level. The core body temperature varied between 36.8°C and 37.6°C and mean skin temperature between 28.6°C and 33.4°C indicating possible occasional superficial cooling. Both muscular and cardiorespiratory strain may pose a risk of local and systemic overloading and thus reduced work efficiency. Thermal strain remained at a tolerable level.     

Context-aware pervasive service composition and its implementation

Incorporating service composition and pervasive computing into managing users’ complex everyday activities calls for the Pervasive Service Composition paradigm for everyday life. In this paper, we propose the concept of Context-Aware Pervasive Service Composition (CAPSC), which aims at enabling a pervasive system to provide user service compositions that are relevant to the situation at hand. We investigate CAPSC requirements and design a CAPSC architecture by taking into account context-aware peer coordination, context-aware process service adaptation, and context-aware utility service adaptation. We present a proof of concept application prototype as well.     

Semiconductor and thermoluminescent dosimetry of pulsed soft X ray plasma sources

A multichannel detection system having a dynamic range of approximately 1 x 10(-9) Gy --20 Gy was developed with the use of commercially produced Si-photodiodes and TLDs for accurate measurement of X ray energy emitted from plasma-focus facility and from laser-produced plasmas. The proof of linearity of the employed detectors accomplished by a comparison of their responses to a broad band spectrum of X rays emitted from plasmas, is reported. It is demonstrated that TLDs irradiated with no protective filter show an incorrect response due to overloading in the sub-keV range and repopulation of dosimetric peaks induced by the UV radiation. The measurement of the power of undesirable secondary X ray sources driven by the primary plasma inside the interaction chamber was performed on the basis of analysis of space dependence of X ray intensity with respect to the assumed r(-2) decrease in the intensity far away from the plasma.     

Improving the integrity of IR based online moisture measurement used in the paper and board industry

The automatic control of product moisture content in the paper industry is based upon online moisture measurement. Furthermore, the automatic control of other quality variables such as coat weight may also rely on the moisture measurement. The integrity of infrared radiation (IR) based online moisture measurement systems was studied by performing basic calibrations at the mills with references sealed between two glass plates. The calibrations were performed according to the EN ISO/IEC 17025 standard which is a requirement for calibration laboratories. The observed measurement errors were 2.9% moisture in the first time calibrations at the 95% confidence level. It is demonstrated that these errors can be reduced to under 0.5% moisture by applying traceable methods in the basic calibration, thus improving the integrity of online moisture measurements. Also a standardised algorithm, based on the experience from the performed calibrations, is proposed to ease the adjustment of different moisture measurement systems.     

Estimation of drag forces caused by natural woody vegetation of different scales

To reliably estimate water levels and velocities in vegetated rivers and floodplains, flow resistance models based on physical plant properties are advantageous. The purpose of this study is （1） to assess the suitable parameterization of woody riparian vegetation in estimating the drag forces, （2） to address the effect of plant scale on the drag estimates and reconfiguration, and （3） to evaluate the applicability of three recently developed flow resistance models. Experiments on four tree species in a towing tank together with detailed characterization of tree properties were carried out to establish a novel dataset. Despite the variability in the tree height （0.9 m-3.4 m）, the stem, leaf and total areas proved to be suitable characteristic dimensions for estimating the flow resistance at different scales. Evaluations with independent data revealed that the tested models produced reasonable results. The performance of the models was controlled by the parameter values used rather than the model structure or the plant scale.     

Deep learning–based automatic bird identification system for offshore wind farms

Practical deterrent methods are needed to prevent collisions between birds and wind turbine blades for offshore wind farms. It is improbable that a single deterrent method would work for all bird species in a given area. An automatic bird identification system is required in order to develop bird species–level deterrent methods. This system is the first and necessary part of the entirety that is eventually able to automatically monitor bird movements, identify bird species, and launch deterrent measures. A prototype system has been built on Finnish west coast. In the proposed system, a separate radar system detects birds and provides WGS84 coordinates to a steering system of a camera. The steering system consists of a motorized video head and our software to control it. The steering system tracks flying birds in order to capture series of images by a digital single‐lens reflex camera. Classification is based on these images, and it is implemented by convolutional neural network trained with a deep learning algorithm. We applied to the images our data augmentation method in which images are rotated and converted into different color temperatures. The results indicate that the proposed system has good performance to identify bird species in the test area. Aiming accuracy for the video head was 88.91 %. Image classification performance as true positive rate was 0.8688.     

Strategic Vision on Convergence of Wired and Wireless Networks

In this paper we provide an overview of how convergence will play an important role in future generation networks. We discuss how the technology today will eventually converge towards an all-IP based platform, while the commonly used layered software structure, will eventually merge into a layer less structure within wireless communication. Furthermore we discuss some of the important aspects in personalisation of future network paradigms with focus on how networks and network services will be able to sense the user's environment and adapt to this. Finally we discuss one of the most important aspect, namely security and privacy issues within future generation networks.     

Jitter characteristics of an on-chip voltage reference-locked time-to-digital converter

The noise and jitter characteristics of an on-chip voltage reference-locked ring oscillator used in the time-to-digital converter (TDC) of the integrated receiver of a pulsed time-of-flight laser rangefinder are presented. The frequency of the ring oscillator, 683 MHz, was locked to the on-chip voltage reference by means of a frequency-to-voltage converter, resulting in better than 90 ppm/°C stability. The noise and jitter transfer characteristics of the loop were derived, and simulations were performed to see the effects of different noise types (white and 1/f noise) on the cumulative jitter of the locked ring oscillator. Finally, these results were verified by jitter measurements performed using an integrated time-to-digital converter (TDC) fabricated on the same die (0.18 μm CMOS process). The cumulative jitter of the on-chip reference-locked ring oscillator was less than 30 ps (sigma value) over a time range of 70 ns, which made it possible to use this oscillator as the heart of a TDC when aiming at centimetre-level precision (1 cm = 67 ps) in laser ranging.