Point cloud segmentation (PCS) aims to make per-point predictions, enabling robots and autonomous driving cars to understand their environments. The range image is a dense representation of a large-scale outdoor point cloud. Segmentation models built upon the range image commonly execute efficiently. However, the projection of the point cloud onto the range image inevitably leads to dropping points because multiple points could be projected onto the same image location, but only one point is kept. More importantly, it is challenging to assign correct predictions to the dropped points with different classes from the kept point class. Besides, existing post-processing methods, such as K -nearest neighbor ( K NN) search and kernel point convolution (KPConv), cannot be trained with the models in an end-to-end manner or cannot process varying-density outdoor point clouds well, thereby enabling the models to achieve sub-optimal performance. To alleviate this problem, we propose a trainable pointwise decoder module (PDM) as the post-processing approach, which gathers weighted features from the neighbors and then makes the final prediction for the query point. In addition, we introduce a virtual range image-guided copy-rotate-paste (VRCrop) strategy in data augmentation. VRCrop constrains the total number of points and eliminates undesirable artifacts in the augmented point cloud. Also, VRCrop is model-agnostic and can be easily employed in various PCS models. With PDM and VRCrop, existing range image-based segmentation models consistently surpass their counterparts on the SemanticKITTI, SemanticPOSS, and nuScenes datasets.

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

Point cloud segmentation (PCS) aims to classify each point in a point cloud. The task plays an important role in robotics and remote sensing. However, existing copy-paste and copy-rotate-paste augmentation techniques cause undesirable artifacts in the augmented point cloud and cannot effectively copy and paste interesting objects from the whole training dataset, bringing difficulty in training image-points fused models and leading to sub-optimal PCS performance. In this paper, we propose an improved virtual range image-guided copy-rotate-paste (VRCrop++) strategy and a global copy-rotate-paste (GCrop) technique. VRCrop++ and GCrop remove unwanted artifacts in the augmented point cloud by a simple but effective “point-to-patch” strategy in the pasting step. Besides, GCrop copies the objects globally with the reciprocal of the point distribution ratio and effectively pastes the objects considering the minimal overlapping region. Extensive experiments conducted on SemanticKITTI and SemanticPOSS datasets demonstrate that with VRCrop++ and GCrop, the existing range image-points fused models consistently surpass their counterparts.

Cyber Situational Awareness (CSA) is an important element in both cyber security and cyber defence to inform processes and activities on strategic, tactical, and operational level. Furthermore, CSA enables informed decision making. The ongoing digitization and interconnection of previously unconnected components and sectors equally affects the civilian and military sector. In defence, this means that the cyber domain is both a separate military domain as well as a cross-domain and connecting element for the other military domains comprising land, air, sea, and space. Therefore, CSA must support perception, comprehension, and projection of events in the cyber space for persons with different roles and expertise. This paper introduces NEWSROOM, a research initiative to improve technologies, methods, and processes specifically related to CSA in cyber defence. For this purpose, NEWSROOM aims to improve methods for attacker behavior classification, cyber threat intelligence (CTI) collection and interaction, secure information access and sharing, as well as human computer interfaces (HCI) and visualizations to provide persons with different roles and expertise with accurate and easy to comprehend mission- and situation-specific CSA. Eventually, NEWSROOM’s core objective is to enable informed and fast decision-making in stressful situations of military operations. The paper outlines the concept of NEWSROOM and explains how its components can be applied in relevant application scenarios.

Genome studies heavily rely on valuable sources of nucleic acids found in biological fluids. Sweat, an easily collectible and cost-effective biofluid, has emerged as a promising material for genomic research. However, the suitability of sweat DNA for genome sequencing compared to venous blood DNA requires further investigation. Sweat samples were collected from four individuals, and a statistical comparison was conducted between sweat DNA and healthy blood DNA reads. The study focused on four key aspects: genome coverage, fragment lengths, mapping quality, telomere sequences, and chaos game representation (CGR) analysis. The length, quality, and genome coverage of all DNA fragments were examined, and tandem repeats in the telomere sequences of the study samples were identified in this article. The statistical analysis revealed significant similarities in genomic study outcomes between sweat and blood samples. The results suggest that sweat has the potential to be an alternative substitute for genomic analysis, offering a cost-effective and easily accessible avenue for future investigations. Our study highlights the potential utility of sweat as a valuable source of nucleic acids for genomic research. While significant similarities were observed between sweat and blood samples regarding genomic outcomes, further studies are warranted to explore the full capabilities of sweat DNA in genome sequencing. Using sweat in genome research could lead to cost-effective and accessible approaches for future genomic investigations.

This study explores the effects of different data sizes and data imbalance on model performance and the stability of SHapley Additive ex-Planations (SHAP).The study utilizes a Type 2 diabetes (T2D) dataset to train three machine learning (ML) models: linear discriminant analysis, XGBoost, and a neural network.It shows that adjusting the background dataset size leads to variations in the SHAP values, with decreased variance observed in larger and balanced datasets.Furthermore, the study highlights that the data characteristics leading to high model performance may not always produce reliable and stable SHAP explanations.

Semantic segmentation (SS) aims to classify each pixel into one of the pre-defined classes. This task plays an important role in self-driving cars and autonomous drones. In SS, many works have shown that most misclassified pixels are commonly near object boundaries with high uncertainties. However, existing SS loss functions are not tailored to handle these uncertain pixels during training, as these pixels are usually treated equally as confidently classified pixels and cannot be embedded with arbitrary low distortion in Euclidean space, thereby degenerating the performance of SS. To overcome this problem, this paper designs a Hyperbolic Uncertainty Loss (HyperUL), which dynamically highlights the misclassified and high-uncertainty pixels in Hyperbolic space during training via the hyperbolic distances. The proposed HyperUL is model agnostic and can be easily applied to various neural architectures. After employing HyperUL to three recent SS models, the experimental results on Cityscapes, UAVid, and ACDC datasets reveal that the segmentation performance of existing SS models can be consistently improved. Additionally, reliable measurement of model uncertainty plays a key role in real-world applications such as autonomous controls of vehicles and drones. To meet this requirement, we propose the Hyperbolic Uncertainty Estimation method, which is easily implemented by only post-processing the generated Hyperbolic embeddings. By this approach, we can calculate the uncertainty values almost for free. Quantitative and qualitative results on Cityscapes, UAVid, and ACDC datasets verify that our proposed uncertainty estimation method usually outputs more meaningful results compared with popular MC-dropout and ensembling methods.

Mobile robots are used to disinfect surfaces with C-band ultraviolet light (UV-C). However, the coverage path planning strategy that delivers the right dose of energy, avoiding waste, is still a challenge. This article proposes a lighting sweep strategy for surfaces, based on robot speed, UV-C output power and distance from the surface, to ensure an adequate dose of UV-C light on the surface. In this work, the distance between the scan paths is mathematically defined and, through simulations, its effectiveness is demonstrated. The experimental results show that when using the ideal speed of sweeping, it is possible to disinfect a surface 2.9 times greater than the best case of the static lamp, in the same period of time. The proposed method in this paper is capable of performing surface coverage, delivering at least the target dose of UV-C while reducing energy and time waste.

In this study, chaos game representation (CGR) is introduced for investigating the pattern of genome sequences. It is an image representation of the genome for the overall visualization of the sequence. The CGR representation is a mapping technique that assigns each sequence base into the respective position in the two-dimension plane to portray the DNA sequence. Importantly, CGR provides one to one mapping to nucleotides as well as sequence. A coordinate of the CGR plane can tell the corresponding base and its location in the original genome. Therefore, the whole nucleotide sequence (until the current nucleotide) can be restored from the one point of the CGR. In this study, CGR coupled with artificial neural network (ANN) is introduced as a new way to represent the genome and to classify intra-coronavirus sequences. A hierarchy clustering study is done to validate the approach and found to be more than 90% accurate while comparing the result with the phylogenetic tree of the corresponding genomes. Interestingly, the method makes the genome sequence significantly shorter (more than 99% compressed) saving the data space while preserving the genome features.

This paper presents Kinetic Energy Difference (KED) as a metric for collision proximity. The calculation of KED for differentially driven robots is explained, along with an example obstacle avoidance algorithm that utilizes it. This example algorithm is computationally efficient and simulations show that it is capable of guiding robots with slow dynamics through narrow corridors.

Classifier calibration does not always go hand in hand with the classifier’s ability to separate the classes. There are applications where good classifier calibration, i.e., the ability to produce accurate probability estimates, is more important than class separation. When the amount of data for training is limited, the traditional approach to improve calibration starts to crumble. In this article, we show how generating more data for calibration is able to improve calibration algorithm performance in many cases where a classifier is not naturally producing well-calibrated outputs and the traditional approach fails. The proposed approach adds computational cost but considering that the main use case is with small datasets this extra computational cost stays insignificant and is comparable to other methods in prediction time. From the tested classifiers, the largest improvement was detected with the random forest and naive Bayes classifiers. Therefore, the proposed approach can be recommended at least for those classifiers when the amount of data available for training is limited and good calibration is essential.

Heart rate is a good measure for physical exercise as it accurately reflects exercise intensity and is easy to measure. If the heart rate response to a complete exercise session is predicted beforehand, information related to the exercise can be inferred, such as exercise intensity and calorie consumption. While most current heart rate prediction models are developed and tested for the scenarios of indoor running exercise or low running speed exercise, we adopt a nonlinear Ordinary Differential Equation (ODE) model for complete outdoor running exercise sessions to predict the heart rate response and identify the parameters of the model with machine learning algorithms. The proposed model enables us to predict a complete outdoor running exercise session instead of predicting the heart rate for a short duration. Model validation is carried out both on the training and testing sets. Our results show that the proposed model captures very stable prediction performance.

This paper presents a path following controller that is suitable for asymmetrical planar robots with significant mass and limited motor torques. the controller is resistant against environmental forces, and inaccurate estimates of robot's inertia, by estimating their effects with unscented kalman filter. the controller outputs wheel torque commands which take in account the motor torque limits and given relative priority of internal control elements. the method presented is thoroughly explained and the simulation results demonstrate the performance of the controller.

Pseudo-omnidirectional robots with individually steerable wheels ofïer a good balance between mobility, robustness and load-carrying capacity. However, accurate synchronization of the wheels' steering and rolling speeds is necessary to prevent energy-loss, mechanical stress and wheel slippage due to actuator infighting. This has proven to be a complex problem, especially when the wheels are not fixed to the robot body but are instead connected to it via actuated chains. This paper presents a mathematically simple method consisting of closed-form equations for path following and synchronizing the steering and rolling speeds of planar robots with a variable or fixed footprint, while respecting the velocity limits of each wheel's steering and rolling actuators. The presented method is thoroughly explained and simulation results are presented to show its performance.

In this article, we combine the Alias method with the concept of systematic sampling, a method commonly used in particle filters for efficient low-variance resampling. The proposed method allows very fast sampling from a discrete distribution: drawing k samples is up to an order of magnitude faster than binary search from the cumulative distribution function (cdf) or inversion methods used in many libraries. The produced empirical distribution function is evaluated using a modified Cramér-Von Mises goodness-of-fit statistic, showing that the method compares very favorably to multinomial sampling. As continuous distributions can often be approximated with discrete ones, the proposed method can be used as a very general way to efficiently produce random samples for particle filter proposal distributions, for example, for motion models in robotics.

Information security has become interdependent, global and critical - it has become cybersecurity. In this complex environment, legal consideration and economic incentives are as integral to ensuring the security of information systems as the technological realization. In this paper, we argue that comprehensive cybersecurity requires that these three disciplines are considered together. To this end, we propose a legal analysis framework, which can can be used to study legal and economic requirements for cybersecurity in relation to technological realities. The framework yields concrete recommendations, which complex system and critical infrastructure stakeholders can utilize to improve security within their networks. The analysis framework aims to offer key stakeholders a better understanding of the legal and economic requirements for cybersecurity and provide them with recommendations that are in line with modern cybersecurity strategies, including the enhancement of cooperation and collaboration capabilities and the implementation of other state-of-the-art security mechanisms.

The control of multi-agent systems, including multi-robot systems, requires some level of context and environment awareness as well as interaction among the interworked cognitive entities, whether they are artificial or natural. Proper specification of the cognitive functionalities and of the corresponding interfaces helps in achieving the capability to reach interoperability across different operational domains, and to reuse the system design across different application domains. The model for interworking cognitive entities presented in this article, which includes explicitly interworking capabilities, is applied to two major classes of interaction in multi-robot systems. Being the model inspired by both artificial and natural systems, makes it suitable for both machine-machine and human-machine interaction.

In this study, a novel user-independent method to recognize activities accurately in situations where traditional accelerometer based classification contains a lot of uncertainty is presented. The method uses two recognition models: one using only accelerometer data and other based on sensor fusion. However, as a sensor fusionbased method is known to consume more battery than an accelerometer-based, sensor fusion is only used when the classification result obtained using acceleration contains uncertainty and, therefore, is unreliable. This reliability is measured based on the posterior probabilities of the classification result and it is studied in the article how high the probability needs to be to consider it reliable. The method is tested using two data sets: daily activity data set collected using accelerometer and magnetometer, and tool recognition data set consisting of data from accelerometer and gyroscope measurements. The results show that by applying the presented method, the recognition rates can be improved compared to using only accelerometers. It was noted that all the classification results should not be trusted as posterior probabilities under 95% cannot be considered reliable, and by replacing these results with the results of sensor fusion -based model, the recognition accuracy improves from three to six percentage units.

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

This paper studies the Lego NXT platform's suitability for evolutionary robotics. It is shown that the low-cost Lego NXT educational set is indeed adequate for simple experiments in evolutionary robotics. This is demonstrated by an experiment, where an artificial neural network-based controller capable of behaving meaningfully in a Lego sumo wrestling context is evolved on physical Lego NXT robots without the aid of simulation. A detailed description of the experiment is provided, and the practical aspects of actually conducting evolutionary robotics on the platform are studied. Earlier research suggests that using evolutionary robotics in education could provide a good and concrete example of the principles and mechanisms of evolution. The research described here utilizes only standard Lego NXT Educational kits, making conducting the experiments possible for a very wide audience. To the authors' knowledge this is the first time the non-simulated Lego NXT is used to conduct artificial neural network-based evolutionary robotics.

Many governments and institutions have guidelines for health-enhancing physical activity. Additionally, according to recent studies, the amount of time spent on sitting is a highly important determinant of health and wellbeing. In fact, sedentary lifestyle can lead to many diseases and, what is more, it is even found to be associated with increased mortality.

With increasing mobile data demand there is a push towards heterogeneous networks. Small-scale operators (SSOs) of WLANs are becoming more prevalent, while Mobile Network Operators (MNOs) seek an outlet for their customers' data usage. These conditions prompt the need for an effective relationship between the two parties for the purpose of offloading cellular data traffic to WLANs in a way that is economically beneficial to all involved. This paper presents a model of such a relationship, in which the SSO sets a strategic offloading price per subscriber and the MNO chooses how many subscribers it wants to offload in order to minimize its costs. The application of this model is simulated in a real-world WLAN deployment in Oulu, Finland. Our findings can be used by both MNOs and SSOs to make informed network deployment decisions, even before engaging in an offloading relationship.

A wireless body sensor network for arterial pulse wave (PW) measurements is presented and tested with ten subjects. The system is capable of recording both mechanical PW contours with sensors made of a low-cost polypropylene-based material called electromechanical film (EMFi) and volume pulse signal with photoplethysmographic transducers. By using both types of sensors, the PW contours can be recorded from various locations. The system combined with automatic analysis methods enables to easily analyze the PW contours in order to obtain a more comprehensive view on the vascular health. To demonstrate this, two parameters used in literature, reflection index and radial augmentation index were calculated for the test subjects as a function of time. The results show that these parameter values may vary more than 20% in a period of 100 s, which suggests that a large number of PWs should be analyzed before making conclusions based on the calculated indices. In addition, the effects of the static bias force to the mechanical PW signal recorded with the EMFi sensors were studied. The PW signal with the maximum amplitude is obtained when the pressure caused by the static bias force corresponds to the contact pressure between typical systolic and diastolic blood pressures. The EMFi sensors used in the proposed system are a potential low-cost alternative for tonometric sensors in collecting data in the PW analysis for arterial screening.

In this study, every day activities are recognized from data collected using smartphones accelerometer sensors. Offline experiments are made to show that the presented method is user- and body position-independent. In addition, it is shown that the features used in the classification are not dependent on the calibration of the phone. The recognition models trained using the offline data are also tested online. A mobile application running these models is built for two operating systems: Symbian^3 and Android. Real-time experiments using these applications are made to show that the presented method can be implemented to any operating system and hardware variations do not affect recognition results. High recognition accuracies are obtained, in the offline study, the average recognition rate is almost 99% and, also, in the online study, the average recognition accuracy is over 90%.

A novel method to classify long-term human activities is presented in this study. The method consists of two parts: quick test and periodic classification. The quick test uses temporal information to improve recognition accuracy, while the periodic classification is based on the assumption that recognized activities are long-term. Periodic quick test (PQT) classification was tested using a data set consisting of six long-term sports exercises. The data were collected from six persons wearing a two-dimensional accelerometer on their wrist. The results show that the presented method is not only faster than a normal method, that does not use temporal information and does not assume that activities are long-term, but also more accurate. The results were compared with a normal sliding window technique which divides signal into smaller sequences and classifies each sequence into one of the six classes. The classification accuracy using a normal method was around 84% while using PQT the recognition rate was over 90%. In addition, the number of classified sequences using a normal method was over six times higher than using PQT.

The study concentrates on tracking swimming exercises based on the data of 3D accelerometer and shows that human activities can be tracked accurately using low sampling rates. The tracking of swimming exercise is done in three phases: first the swimming style and turns are recognized, secondly the number of strokes are counted and thirdly the intensity of swimming is estimated. Tracking is done using efficient methods because the methods presented in the study are designed for light applications which do not allow heavy computing. To keep tracking as light as possible it is studied what is the lowest sampling frequency that can be used and still obtain accurate results. Moreover, two different sensor placements (wrist and upper back) are compared. The results of the study show that tracking can be done with high accuracy using simple methods that are fast to calculate and with a really low sampling frequency. It is shown that an upper back-worn sensor is more accurate than a wrist-worn one when the swimming style is recognized, but when the number of strokes is counted and intensity estimated, the sensors give approximately equally accurate results.

Towards persuasive system for efficient use of water resource, we propose a method to detect "water waste" among water-related activities based on water sound analysis. We supposed two types of water-wastes: inter-activity water waste and intra-activity water waste. An evaluation with a variety of experimental conditions presents that the aggregate accuracies to identify the inter-activity water waste and the intra-activity waste are 96.3% and 92.6%, respectively.

In this paper, water tap usage is examined based on water sound analysis. We focus on detecting "water waste" to make persuasion of water savings effective, where two types of water waste are defined: inter-activity water waste and intra-activity water waste. Based on a preliminary user survey, four types of basin-related activities are identified that occur with water waste. We apply a spectrum subtraction method for feature selection and propose cascaded classifiers for activity recognition. The result of an evaluation presents that the aggregate accuracies to identify inter-activity water waste and intra-activity one are 100.0 % and 81.1%, respectively.

In this paper we present an improvement to the preimage attacks on Very Smooth Hash (VSH) function. VSH was proposed as a collision resistant hash function by Contini et al., but it has been found lacking in preimage resistance by Saarinen. With our method, we show how to find preimages of multiple passwords secured by VSH. We also demonstrate that our method is faster in finding preimages of multiple passwords than the methods proposed earlier. We tested the methods with five, ten and fifty randomised alphanumeric passwords. The results show that our method is many times faster than the original method of Saarinen and almost three times faster than the improved method proposed by Halunen et al. Furthermore, we argue that the methods presented previously and our method are essentially the only significantly different methods derivable from Saarinen's work.

The mining of meaningful shapes of time series is done widely in order to find shapes that can be used, for example, in classification problems or in summarizing signals. Normally, shapes that summarize periodic signals have to be mined visually, and in order to find a shape of high quality, several tests haves to be made. This makes visual mining slow and sometimes even frustrating. A method for summarizing a periodic time series automatically is presented in this study. The method is based on evolutionary computation and the results show that by using it, shapes can be found that summarize a time series better than shapes found using visual mining.

Automatic recognition of activities using time series data collected from exercise can facilitate development of applications that motivate people to exercise more frequently and actively. This article presents a method for recognizing nine different everyday sport activities, such as running, walking, aerobics and Nordic walking, using only two-dimensional wrist-worn accelerometer. The suggested method is based on clustering the data by first using an EM-algorithm to form homogeneous groups and then applying C4.5-based decision trees inside these groups. The features extracted for classification process are simple features, such as variance and mean, which are calculated from compressed signals that contain only such points of the original time series where the derivative is equal to zero. The data were collected by ten subjects and they contained nine different sports. Using the presented method, the data were classified with an accuracy of 85%, whereas the accuracy using an automatically generated decision tree was 80%. The purpose of this method is to recognize activities in order to form an activity diary.

This paper describes the development of Morri, a multi purpose robot platform. The design of this robot includes mechanical, electrical and software development. Key features of the robot are modularity for multi-purpose applications, affordable size for outdoor and indoor operation, low cost, high performance, and easy to use and repair in field conditions. The main focus on software architecture development has been on creating fully-functional real-time architecture, where several algorithms and methods can be easily integrated as part of the system. As a result, this robot took part on M-Elrob outdoor robot competition in July 2008 and won ldquoCamp Securityrdquo scenario.

This paper presents a new architecture, Reach4Cloud, for an existing system which allows the building of user friendly interfaces into interactive spaces. Our new system facilitates the controlling of local resources and services using physical user interfaces. In this paper we focus our attention on applying cloud computing architectures used in botnets and malware to our previous distributed computing system. We have identified the command-and-control message bus as the common denominator of botnets. We have also selected the IRC as the message bus and have applied this model as distributed system architecture to a previous version of the system, partially re-architecting it to communicate over the IRC. We have investigated botnets and compare the botnet-based Reach4Cloud system to the original REACHeS architecture while reporting our observations along the way.

In this paper we improve Saarinen's method for finding preimages of Very Smooth Hash (VSH) hash values and generalise it to some of the variants of VSH proposed by Contini et al. VSH is a new hash function that has been proved to be collision resistant under similar assumptions on the factoring of large integers as the RSA cipher. Saarinen has developed a method for finding preimages of VSH hash values and in his paper he gave an example of its application to 169-bit VSH and 4-character passwords consisting of lowercase alphabets. Because there were no results on the practical effectiveness of this approach with cryptographically significant security parameters, we give some results on the time and memory required to find the preimages of 8-character alphanumeric passwords secured by 1024-bit and 2048-bit VSH on quite modest hardware. In our study we implemented both the original VSH and the cubing variant of VSH. Our results show that both Saarinen's method and our method can find preimages of password hash values very quickly and that our method is faster in many cases. Our method also uses reusable tables that can be used to find the preimages of subsequent hash values faster than with the original method.

This article presents an approach to estimating exercise energy expenditure based on acceleration measurements from a wrist-worn biaxial sensor. The method uses the linear mixed model that makes it possible to model both between-subject and within-subject variation in energy expenditure. More precisely, a random-intercepts model is used. The variance and mean of the acceleration signals at 15-second intervals as well as subject demographics (height, weight, body mass index, age and VO2max) are used. Energy expenditure is modelled in four different activities: walking, running, Nordic walking and bicycling. This study introduces an effective backward model selection procedure for selecting the fixed-effect variables in the model. The procedure uses leave-one-out cross-validation to be able to effectively exploit the available data set and to ensure the robustness of the model. Estimation accuracy in test sets is used as a criterion of model performance. The model selection procedure proposed notably improves estimation accuracy. In walking, running, Nordic walking and bicycling, average estimation errors of 3.9, 3.6, 1.9 and13.5 percent are reached. The respective Pearson correlations for these activities are 0.91, 0.98, 0.97, and 0.81. These results are also compared to the performance of the general linear model. It is discovered that the linear mixed model outperforms the model that does not take the individual levels of energy expenditure of the subjects into account.

This article presents a method for the calculating similarity of two trajectories. The method is especially designed for a situation where the points of the trajectories are distributed sparsely and at non-equidistant intervals. The proposed method is based on giving different weights to different points: points that are close to each other get smaller weights than the points that do not have neighbors nearby. The effectiveness of the method was tested with 12 data sets generated from two benchmark data sets. The classifying accuracy of the proposed similarity measure was compared with three other methods, such as dynamic time warping, and it was noted that the new proposed method classifies instances mainly more accurately and faster than the other three methods.

This paper presents the design of an all-terrain wheeled robotic vehicle. The robot is tele-supervised containing several semi-autonomous and autonomous functionalities. The human operator's role is more like giving directions and targets for the robot rather that directly driving the robot. Navigating in an uncharted and non-structured environment presents a great challenge for a mobile autonomous robot in terms of obstacle detection and route navigation. The outdoor weather conditions also pose several challenges for the configuration of the robot's sensors and mechanics. The focus on this paper is on the design of the sensor system and the mechanics of the robot.

Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent selection of training data for model fitting have to be developed. In this article, training instances are selected to fit predictive regression models developed for optimization of the steel manufacturing process settings beforehand, and the selection is approached from a clustering point of view. Because basic k-means clustering was found to consume too much time and memory for the purpose, a new algorithm was developed to divide the data coarsely, after which k-means clustering could be performed. The instances were selected using the cluster structure by weighting more the observations from scattered and separated clusters. The study shows that by using this kind of approach to data set selection, the prediction accuracy of the models will get even better. It was noticed that only a quarter of the data, selected with our approach, could be used to achieve results comparable with a reference case, while the procedure can be easily developed for an actual industrial environment.

The purpose of this study was to develop a product design model for impact toughness estimation of low-alloy steel plates. Based on these estimates, the rejection probability of steel plates can be approximated. The target variable was formulated from three Charpy-V measurements with a LIB transformation, because the mean of the measurements would have lost valuable information.The method is suitable for all steel grades in production and it is not restricted to a few test temperatures. There were differences between the performances of different product groups, but overall performance was promising. Next the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost.

Detecting the root-cause of failures in modern, complex networks is tedious. Understanding the problem fully requires good instrumentation and thorough understanding of the information flows in the network. In this paper, we describe two techniques for understanding the information flows and pinpointing the problems in them: causal relationship extraction and communication pattern detection. We instrumented a network with probes. The probes collect all the data from the network into a ringbuffer and index it, making it possible to either quickly retrieve flows and packets associated to them for further analysis. Our software then extracts and visualizes causal relationships between the events. The causal relationship extraction and communication pattern detection proved to be an effective method for pinpointing the cause of network system failures, understanding security risks and managing complexity. Our research prototype demonstrates a method for making problem solving faster and more systematic. The methods can also be used to detect emerging problems proactively.

Nowadays mobile devices are used in many professional business and leisure-time services. Major information security threats related to mobile services are examined from the service developer's perspective in this study. These threats can be categorized as mobile network, mobile device, digital convergence, authentication and payment threats, and service development threats. The threat analysis is based on an interview study carried out in some Finnish industrial companies operating in the mobile service field.

Traditionally, an embedded system design process demands a considerable amount of expertise, time and money. This makes developing embedded systems difficult for many companies, and in research facilities it hinders the testing of new research results with real embedded systems. We have earlier presented an easy and fast embedded system development concept based on embedded objects. The embedded object concept (EOC) utilizes common object-oriented methods used in software by applying them in combined Lego-like software-hardware entities. This concept enables fast prototyping with target hardware, incremental device development and high-level device building for nonexperts. The EOC requires a modularly extendable architecture along with mechanical and technical definitions in order to enable physical and electrical interconnectivity with versatile signaling between embedded objects. This paper presents the Atomi II framework, which is our solution for this need. The framework has been tested and implemented with so-called Atomi objects

Traditionally, the embedded system design process demands a considerable amount of expertise, time and money. This makes developing embedded systems impossible for many companies, and in research facilities it hinders the testing of new research results with real embedded systems. We previously presented an easy and fast embedded system development concept based on embedded objects. The embedded object concept (EOC) utilizes common object oriented methods used in software by applying them to combined Lego-like software-hardware entities. This concept enables people without comprehensive knowledge in electronics design to create new embedded systems. In this paper we present a physical and logical architecture for this concept.

Combination of classifiers is usually a good strategy to improve accuracy in pattern recognition systems. In this paper, we present a new approach to footstep-based biometric identification by combining pattern classifiers with different feature sets. Footstep profiles are obtained from a pressure-sensitive floor. Our identification system consists of two different combination stages. At the first stage, three pattern classifiers, trained with feature sets presenting different characteristics of input signal, are combined. The feature sets include the spatial domain properties of the footstep profile as well as the frequency domain presentation of the signal and its derivative. At the second stage, multiple input samples are combined, using the posterior probability outputs from the first stage, to make the final decision. The building blocks of the classification system are examined, and the methodological justifications are analyzed. The experimental results show improvements in identification accuracies compared to previously reported work.

In swarm robotics research, instead of using large size robots, it is often desirable to have multiple small size robots for saving valuable work space and making the maintainance of the robots easier. Also, the implementation costs of a miniature robot is lower because of simpler mechanical design. In this paper, we present a novel modular miniature mobile robot designed for swarm robotics research. The sensor set of the robot includes a color stereo camera system with two CMOS cameras and DSP, allowing each robot to do sophisticated stereo image processing on-board. The modular design permits the addition of new modules into the system. The modules communicate using three serial buses (SPI, I2C, and UART), which enable flexible, adaptive, and fast inter-module data exchange. The robot is developed for swarm robotics research with the aim to provide a low-cost and low-power miniature mobile robot with capabilities typically found only in large size robots.

Implementation of data mining applications is a challenging and complicated task, and the applications are often built from scratch. In this paper, a component-based application framework, called smart archive (SA) designed for implementing data mining applications, is presented. SA provides functionality common to most data mining applications and components for utilizing history information. Using SA, it is possible to build high-quality applications with shorter development times by configuring the framework to process application-specific data. The architecture, the components, the implementation and the design principles of the framework are presented. The advantages of a framework-based implementation are demonstrated by presenting a case study which compares the framework approach to implementing a real-world application with the option of building an equivalent application from scratch. In conclusion, the paper presents a lucid framework for creating data mining applications and illustrates the importance and advantages of using the presented approach.

This paper presents a software architecture for the network-transparent control of distributed robotic systems. The system consists of two main components: a generic and easily extensible CORBA-based interface to distributed services, and a high-level XML-based description language for specifying the behavior of the robots. The architecture makes it possible to create dynamically modifiable, extensible control software with ease. It is successfully utilized in implementing a coffee serving system in which the co-operation of two very different robots and two other distributed services are needed.

In this paper we show how simple laterally interacting computational entities, i.e. neurons, can be guided by a selectionist process into spatial patterns that show interesting and purposeful dynamics with regard to a particular utility measure. In other words, if a suitable population of laterally interacting mobile entities exist, it is possible to gradually arrange the entities into a spatial pattern that exhibits the desired dynamics. In this paper, the selectionist process is implemented with the stochastic evolutionary neuron migration process (SENMP) and approach is used to evolve dynamic recurrent neural networks (DRNNs) for controlling complex dynamic systems such as autonomous mobile robots, for example. The feasibility and advantages of the approach are demonstrated by evolving neural controllers for solving a non-Markovian double pole balancing problem. In addition, we have earlier used SENMP to evolve navigation behaviors for mobile robots in complex simulated and real environments.

Mimicking of the growth and adaptation of a biological neural circuit in an artificial medium is a challenging task. In this paper, we propose a phenomenological developmental model based on a stochastic evolutionary neuron migration process (SENMP). Employing a spatial encoding scheme with lateral interaction of neurons for artificial neural networks representing candidate solutions within a neural ensemble, neurons of the ensemble form problem-specific geometrical structures as they migrate under selective pressure. The approach is applied to gain new insights into the development, adaptation and plasticity in artificial neural networks and to evolve purposeful behavior for autonomous robots. We demonstrate the feasibility and advantages of the approach by. using a simulator to evolve a robust navigation behavior for a mobile robot and by verifying the results in a real office environment. We also present some preliminary results regarding the behavior of the adapting neural ensemble and, particularly, a phenomenon exhibiting Hebbian dynamics.

Presents a general architectural solution for concurrent real-time color-based tracking of multiple objects. The presented architecture, called Cocoa, is suggested for a mobile robot interacting with humans. The main contributions of the architecture are markers and color judges. Markers are a situated representation of the robot's environment. Color judges separate color-based segmentation methods from the process of labeling images. Furthermore, methodology for coping with objects entering and leaving the robot's field of view is suggested. Tracking experiments performed with a system implemented on a Nomad XR4000 robot are presented as well.

A method for health monitoring is considered. Measured physical signals have been dynamically classified to low-, middle- or high-levels and a self-organizing map (SOM) has been utilized to combine the information. The data were collected during spring 1996 and consist of over eight weeks of physical measurements and diaries recorded in a home environment by four test subjects. The research shows that this method can be used to monitor the system of a human being. The system finds some daily structures as well as differences between weekdays and weekend. The physical activities have much stronger effect on the signals than mental stress states, which show no clear clustering on maps.

The concept augmented realie is used to describe a system that supplements reality by adding virtual objects into a real-world view.This paper describes a flexible mobile code approach that we can use to implement ubiquitous, active, and mobile augmented reality systems.We will concentrate primarily on solving the problem of how to acquire the data for the virtual objects in a way that will be flexible and expandable enough to be used in truly ubiquitous computer systems.To clarify the concept, we will present an example system that provides virtual user interfaces for various real-world objects.This paper suggests that the mobile code approach offers a relatively simple solution that is flexible, scales well, and does not require the computing equipment attached to the real-world objects to be excessively complicated.We can use the same mobile code approach in applications that provide only simple descriptions of the real-world objects and in complex applications that allow the user to manipulate real-world objects via virtual user interfaces that exist only in augmented reality.Our approach also supports active augmented reality, in which the virtual objects can react to the real-world events.

The only cure for malignant melanoma (skin cancer) is early detection. Surgical removal of a newly developed melanoma will result in complete cure. In this study, the first steps towards the development of a skin cancer detection system are reported. One possible way to detect early the occurrence of melanoma is to screen the body of a patient at regular intervals for changes or new lesions. To increase the accuracy of this laborious and painstaking task, a computer vision system could be used. One of the most important problems involved in a vision system of this kind is the need to determine which lesions in successive skin images taken over a given period, represent the same lesions. Repeated registrations of skin images also detect new lesions that do not have a counterpart on the previous image. After registration, the lesions in successive images are compared for alterations in size, shape, colour and so on, to detect changes that are suggestive of melanoma. In this paper, we introduce a new algorithm for the registration of lesions in successive skin images. The baseline algorithm requires two initial matches to register the other lesions in the images. The initial matches are provided by a physician or an algorithm that selects the most likely initial matches. The test suggests that the baseline algorithm determines 99% of the matches correctly, and this performance is largely independent of the number of lesions in the skin images.

Presents an approach to robot-tool calibration. It is a well-known fact that industrial robots are not very accurate. Two aspects of the accuracy should be taken into account: the ability of a robot manipulator to perform accurate positioning in the task space and the ability of a robot to follow straight-line and circular trajectories. Many of the calibration approaches deal with the accurate positioning which improves the robot's capability to follow straight or circular trajectories. However, there are drawbacks in these approaches: the need for expensive measurement devices and no guarantee that the calibration model will still be correct on the manufacturing floor. Our approach concentrates on the ability of a robot to follow the technological trajectories. The approach does not require any expensive measurement techniques. The criteria of equal distances between the points in the robot space and the task space is used. The approach is demonstrated by the results of the calibration of the GMFanuc S-10 robot. The error analysis and convergence rate for this robot are also presented. The calibration can be performed directly on the manufacturing floor where the robot is used. The advantages of the approach are simplicity of the measurement setup, fast data collection and high reliability of the kinematic parameters obtained.

In this paper we describe a behavior-based control architecture capable of reactive task execution; that is, it reasons out actions based on task constraints and reacts quickly to unexpected events in the environment. Reactive task execution requires versatile behavior coordination methods. We suggest that the required behavior coordination can be achieved by combining action maps. The main advantages of this method are that it enables executing several tasks in parallel and facilitates considering environment dynamics. These properties make this behavior coordination method suitable for reactive task execution.

The application of computer-integrated engineering to machine vision for the goal of inspecting manufactured parts is considered. One of the most practical vision methods for making measurements on a smooth manufactured part is single-image stereo, which makes use of a structured light source and a single camera. The problem of analyzing the light reflected from the work piece can be simplified by tailoring the light source to the workpiece. By specifying the reflective pattern to be parallel straight lines, it is possible to determine the resolution of such a system. Distortions in the straight, parallel lines reflected by the surface can be quantified in terms of depth of flaw in the part.>