Microbiome studies are attracting increasing interest, especially in human health applications, where their use for disease prognostics, diagnostics and treatment can have immense effects on life quality. The settings in the microbiome data preprocessing stage can lead to the great variability of the generated operational taxonomic unit (OTU) tables, reflected in the size and sparseness of this data matrix. As there are still no solid guidelines on the best practices, it is valuable to assess which machine learning algorithms provide higher stability of results under variable preprocessing settings. In this study, we have generated OTU tables using data from the Moving pictures of human microbiome study using two different reference databases (Greengenes and Silva) and four levels of the similarity threshold (ranging from 90 to 99%), processed in the QIIME bioinformatics package. The results of the two best-performing classification and clustering algorithms are presented in detail: Random Forest classifier (RF) and Spectral clustering (SC). The random forest classifier has outperformed spectral clustering in terms of accuracy. As the rate of data generation increases, while the cost of labeling remains high, further improvement of clustering performance and ensemble approaches should be explored.

Tandem-duplication-random-loss (TDRL) is an important genome rearrangement operation studied in evolutionary biology. This paper investigates some of the formal properties of TDRL operations on the symmetric group (the space of permutations over an n-set). In particular, the cardinality of "balls" of radius one in the TDRL metric, as well as the cardinality of the maximum intersection of two such balls, are determined. The corresponding problems for the so-called mirror (or palindromic) TDRL rearrangement operations are also solved. The results represent an initial step in the study of error correction and reconstruction problems in this context, and are of potential interest in DNA-based data storage applications.

Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.

We introduce a framework to study slotted Aloha with cooperative base stations. Assuming a geographic-proximity communication model, we propose several decoding algorithms with different degrees of base stations' cooperation (noncooperative, spatial, temporal, and spatio-temporal). With spatial cooperation, neighboring base stations inform each other whenever they collect a user within their coverage overlap; temporal cooperation corresponds to (temporal) successive interference cancellation done locally at each station. We analyze the four decoding algorithms and establish several fundamental results. With all algorithms, the peak throughput (average number of decoded users per slot, across all base stations) increases linearly with the number of base stations. Further, temporal and spatio-temporal cooperations exhibit a threshold behavior with respect to the normalized load (number of users per station, per slot). There exists a positive load G*, such that, below G*, the decoding probability is asymptotically maximal possible, equal the probability that a user is heard by at least one base station; with non-cooperative decoding and spatial cooperation, we show that G* is zero. Finally, with spatio-temporal cooperation, we optimize the degree distribution according to which users transmit their packet replicas; the optimum is in general very different from the corresponding optimal distribution of the single-base station system.

Recent developments in molecular biology and techniques for genome-wide data acquisition have resulted in abundance of data to profile genes and predict their function. These datasets may come from diverse sources and it is an open question how to commonly address them and fuse them into a joint prediction model. A prevailing technique to identify groups of related genes that exhibit similar profiles is profile-based clustering. Cluster inference may benefit from consensus across different clustering models. In this paper, we propose a technique that develops separate gene clusters from each of available data sources and then fuses them by means of nonnegative matrix factorization. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous datasets and yield high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering.

We consider framed slotted Aloha where m base stations cooperate to decode messages from n users. Users and base stations are placed uniformly at random over an area. At each frame, each user sends multiple replicas of its packet according to a prescribed distribution, and it is heard by all base stations within the communication radius r. Base stations employ a decoding algorithm that utilizes the successive interference cancellation mechanism, both in space-across neighboring base stations, and in time-across different slots, locally at each base station. We show that there exists a threshold on the normalized load G = n/(τm), where τ is the number of slots per frame, below which decoding probability converges asymptotically (as n, m, τ → ∞, r → 0) to the maximal possible value-the probability that a user is heard by at least one base station, and we find a lower bound on the threshold. Further, we give a heuristic evaluation of the decoding probability based on the and-or-tree analysis. Finally, we show that the peak throughput increases linearly in the number of base stations.

In this paper, we propose a novel method for denoising images corrupted by the mixture of the additive white Gaussian noise and the heavy tailed noise. The proposed method is based on robust statistical approach, i.e. application of M-estimators in the combination with l1sparse regularization technique. Thus, we perform the denoising by numerical solving of the convex optimization problem. Additionally, the developed method performs denoising adaptively in order to preserve the image details, by adjusting regularization coefficients according to the local variance. The proposed de-noising scheme produces excellent results, both objectively (in terms of PSNR and MSSIM) and subjectively and outperforms state-of-the-art filters for denoising heavy tailed noise in images.

In this paper we propose an end-to-end solution for urban infrastructure-to-vehicle traffic data delivery based on a class of unequal error protection (UEP) rateless codes called expanding window fountain (EWF) codes. The proposed solution relies on attractive features that rateless codes introduce to networks with unpredictable dynamics: the universal capacity approaching property which is well-matched to time-varying behavior of wireless links, and the innovative nature of each encoded packet which makes both time-consuming retransmission and content-reconciliation mechanisms unnecessary. Furthermore, usage of EWF codes allows separation of delivered data in importance classes with different error protection and recovery time guarantees, enabling mobile users to retrieve more important information more reliably and in shorter time span, thus making the proposed solution suitable for time-critical services. The addressed urban communication scenario consists of large number of sensors that sample and relay traffic flow information to network of Access Points (APs). APs use the existing underlying communication infrastructure, such as metropolitan area networks (MANs), to exchange traffic flow data, encode it using EWF coding principles, and finally disseminate it to roaming vehicles that join the network service in an ad-hoc manner in order to retrieve information regarding the surrounding environment. The proposed approach is suitable for real-time applications, such as frequent periodic reporting of urban traffic conditions, that could be used by on-board computers to provide improved navigation for end-users.

Design of algorithms that are able to estimate video quality as perceived by human observers is of interest for a number of applications. Depending on the video content, the artifacts introduced by the coding process can be more or less pronounced and diversely affect the quality of videos, as estimated by humans. While it is well understood that motion affects both human attention and coding quality, this relationship has only recently started gaining attention among the research community, when video quality assessment (VQA) is concerned. In this paper, the effect of calculating several objective measure features, related to video coding artifacts, separately for salient motion and other regions of the frames of the sequence is examined. In addition, we propose a new scheme for quality assessment of coded video streams, which takes into account salient motion. Standardized procedure has been used to calculate the Mean Opinion Score (MOS), based on experiments conducted with a group of non-expert observers viewing standard definition (SD) sequences. MOS measurements were taken for nine different SD sequences, coded using MPEG-2 at five different bit-rates. Eighteen different published approaches related to measuring the amount of coding artifacts objectively on a single-frame basis were implemented. Additional features describing the intensity of salient motion in the frames, as well as the intensity of coding artifacts in the salient motion regions were proposed. Automatic feature selection was performed to determine the subset of features most correlated to video quality. The results show that salient-motion-related features enhance prediction and indicate that the presence of blocking effect artifacts and blurring in the salient regions and variance and intensity of temporal changes in non-salient regions influence the perceived video quality.

This paper presents a data gathering scheme designed for an emergency monitoring Wireless Sensor Network (WSN), which employs distributed rateless encoding and dispersion of the encoded data toward the perimeter of the monitored area, where it is collected by a mobile collector (MC). Rate-less codes are low encoding/decoding complexity codes, suitable for resource constrained devices, such as sensor nodes, while the proposed distributed encoding and dispersion, performed using only local knowledge, are adapted to WSNs where the frequent changes in underlying topology and connectivity are expected. The proposed scheme allows for data persistence and efficient data gathering in the scenarios where decentralized WSNs are deployed in inaccessible locations with no infrastructure support, where nodes are prone to failures due to harsh operating conditions and where the access of the MC to the monitoring area is severely limited.

In this paper, we propose a novel approach for data gathering in wireless sensor networks (WSN) based on distributed rateless codes. Rateless codes are an efficient, lowcomplexity solution for coded data transmission over channels with packet erasures, which motivates their application in distributed network scenarios such as WSN. Recently proposed distributed rateless coding techniques for WSN are node-centric, i.e., collecting sufficient number of different sensor data packets and performing rateless encoding is the task of sensor nodes. In the proposed packet-centric approach, this task is assigned to encoded packets called rateless packets. While randomly moving through the network, rateless packets collect and encode into their content required number of uniformly sampled sensor data packets, completing their paths in randomly selected network nodes. Using this approach, any degree distribution of rateless codes can be exactly obtained. The problem of uniform combining of sensor data into rateless packets, and uniform dispersion throughout the network is addressed. The efficiency of the proposed scheme and comparison with the performance of centralized rateless codes are demonstrated by simulation results.

Although used extensively for object recognition, texture has for the most part been ignored as a feature used for background modelling and object segmentation. The complexity of working with texture descriptors for segmentation in videos is two-fold: the descriptive features cannot be calculated in real time and features extracted based on arbitrarily chosen regions or blocks in the frame are not stable enough to allow for building models sufficiently accurate, yet simple enough to be used for real-time segmentation. The paper proposes an approach that can be used to detect regions of texture, stable enough to be modelled using probabilistic models commonly used for foreground segmentation. Based on the evaluated stable texture regions, a discriminative texture descriptor is proposed that can be evaluated in real time. When video is captured from a stationary camera, the background is expected to be stationary to a degree and an adaptive model can be built to serve as basis for segmentation. For reasons of efficiency most adaptive models are learnt on a per pixel basis [4]. Joint domain-space modelling [3], as well as modelling of the sequence at two different scales, has been suggested as a way to enhance the segmentation and escape the limitations of single-pixel-based models[1]. The improved approaches still fail to consider the texture properties of the objects in the background and suffer from the noise introduced by neighboring objects being modelled as one. Texture of the background can be used to build models in a more informed way. Large stable regions in the texture of the background allow for larger scale modelling and more precise extraction of texture related features. Conversely, unstable regions in terms of texture correspond to transitions between different objects in the background, and the features should be extracted for each of the objects separately. An approach to background modelling and foreground segmentation derived form these principles is proposed in the paper. In each frame of the sequence stable texture regions are determined, texture descriptor (feature) values extracted using the stable regions information and used to model the background and segment the foreground using standard probabilistic approaches. The process is illustrated in Fig. 1, where the brighter patches in the stability map correspond to more stable regions, in terms of texture. Once the size of the stable-texture region at each location is determined, arbitrary descriptive features can be extracted from the frames of the sequence. In addition, one can choose any of a number of probabilistic methods [4][2] to learn the statistics of the features. The size of the stable texture regions can be determined online, using integral images [6]. Observe that:

This paper deals with the evaluation of the end-to-end packet delay for a lossy network where network coding is adopted to achieve better performance. Differently from previous published papers on this subject, connection oriented services are assumed, this means that packets have to be in-order received and wait at the receiver buffer till all the previous ones have been correctly delivered. The focus is on a network model where two source nodes broadcast packets to a group of two sink nodes over lossy wireless channels. Three different alternatives have been considered in order to assure a reliable data multicasting, namely: a classical random linear network coding scheme, a linear network coding combined with a basic ARQ or, alternatively, with a soft combined ARQ scheme. Performance comparisons provided by means of analytical and numerical results clearly highlight that the better solution is to adopt the latter alternative.

The paper presents an efficient and reliable approach to automatic people segmentation, tracking and counting, designed for a system with an overhead mounted (zenithal) camera. Upon the initial block-wise background subtraction, k-means clustering is used to enable the segmentation of single persons in the scene. The number of people in the scene is estimated as the maximal number of clusters with acceptable inter-cluster separation. Tracking of segmented people is addressed as a problem of dynamic cluster assignment between two consecutive frames and it is solved in a greedy fashion. Systems for people counting are applied to people surveillance and management and lately within the ambient intelligence solutions. Experimental results suggest that the proposed method is able to achieve very good results in terms of counting accuracy and execution speed.

In real-world surveillance systems, where variation of light and camera parameters can sometimes severely impair the normal operation of background subtraction algorithms, better results are obtained with differencing schemes. We have earlier demonstrated that differencing of detail images produced by wavelet transformation can lead to more stable detection results. In this paper, we considerably extend that framework, by introducing the modified z-scores calculated from wavelet coefficient differences. Foreground pixels are detected as outliers in normal distribution by modified z-score test. The threshold value used in the outlier test is optimized by maximizing the precision and recall measures on several training frames. Finally, the elimination of ghosts from motion detection is done by double modified z-score testing, that is similar in idea to double frame differencing. The resulting motion detection method shows considerable resilience to changes in illumination and camera parameters and also produces a lower amount of detection errors than other motion detection methods.

In this paper, we propose a novel approach for data gathering in wireless sensor networks (WSN) based on distributed rateless codes. Rateless codes are an efficient, low-complexity solution for coded data transmission over erasure channels, which motivates their application in distributed network scenarios such as WSN. Recently proposed distributed rateless coding techniques for WSN are node-centric, i.e., the task of collecting sufficient number of different sensor data packets and performing rateless encoding is responsibility of sensor nodes. In the proposed packet- centric approach, this task is assigned to encoded data packets called rateless packets. While randomly moving through the network, rateless packets collect required number of uniformly sampled sensor data packets, finishing their paths in randomly selected network nodes. Using this approach, any degree distribution of rateless codes can be exactly obtained. The problem of uniform combining of sensor data into rateless packets, and uniform dispersion throughout the network is addressed. The efficiency of the proposed scheme and its comparison with the performance of centralized rateless codes are demonstrated by simulation results.

In this paper, we present a novel method for impulse noise filter construction, based on the switching scheme with two cascaded detectors and two corresponding estimators. Genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics. The first detector identifies the majority of noisy pixels. The second detector searches for the remaining noise missed by the first detector, usually hidden in image details or with amplitudes close to its local neighborhood. Both detectors are based on the robust estimators of location and scale-median and MAD. The filter made by the proposed method is capable of effectively suppressing all kinds of impulse noise, in contrast to many existing filters which are specialized only for a particular noise model. In addition, we propose the usage of a new impulse noise model-the mixed impulse noise, which is more realistic and harder to treat than existing impulse noise models. The proposed model is the combination of commonly used noise models: salt-and-pepper and uniform impulse noise models. Simulation results show that the proposed two-stage GP filter produces excellent results and outperforms existing state-of-the-art filters.

A new method for impulse noise removal is presented, where a robust estimator of the variance, MAD (median of the absolute deviations from the median), is used for impulse detection. By using this method, image details are well preserved, while impulse noise is suppressed. The algorithm is straightforward, low in complexity, achieves high filtering performance and requires no previous training.

A new method for impulse noise removal is presented, where a robust estimator of the variance, MAD (median of the absolute deviations from the median), is modified and used to efficiently separate noisy pixels from the image details. The algorithm is free of varying parameters, requires no previous training or optimization, and successfully removes all types of impulse noise. The pixel-wise MAD concept is straightforward, low in complexity, and achieves high filtering performance.