There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

What Will 5G Be

The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices.

Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!

Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.

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Massive MIMO for next generation wireless systems

Impulsive noise can appear in communication links. In the literature, it was demonstrated that when the noise is impulsive, standard Gaussian receivers perform poorly because of the outliers in the noise. Therefore, appropriate receivers must be used when the noise is impulsive. In this paper, we compare two types of massive multiple- input multiple-output (MIMO) receivers, namely those based on a Gaussian-mixture assumption and those based on a Cauchy assumption, in terms of channel estimation quality, when the noise is impulsive. Symmetric α-stable (SαS) noises are used to model impulsive noises in the paper. In the numerical results, the Gaussian-mixture receiver outperforms the Cauchy-based receiver. 

Channel Estimation in Massive MIMO with Heavy-Tailed Noise: Gaussian-Mixture Versus Cauchy Models

Semiconductor gamma-camera systems based on cadmium zinc telluride (CZT) detectors present new challenges due to an energy-response that includes effects of low-energy tailing. In particular, such energy tails produce effects that need to be considered when imaging radionuclides with multiple emissions such as [Formula: see text]. Monte Carlo simulation can be used to investigate the behaviour of such systems and optimise their use, provided that the detector model closely reflects the real physical detector. The aim of this work is to develop a CZT model applicable for simulation of CZT-based gamma cameras.The equations describing the charge transport and signal induction are considered in three dimensions and are solved numerically, and the CZT model is then realised by coupling the detector-response to the photon-transport handled by the SIMIND Monte Carlo program. The CZT model is tuned to reproduce experimentally measured energy spectra of a hand-held gamma camera system for multiple radionuclides ([Formula: see text], [Formula: see text] and [Formula: see text]) and parallel-hole collimators (MEGP, LEHR) as well as an uncollimated system.Overall, the model results agree well with measurements across the range of experimental conditions. The applicability of the model is demonstrated by separating energy spectra into components to investigate the interference of high-energy photons on lower energy-windows, where pronounced effects of low-energy tailing for [Formula: see text] are observed.The developed model provides understanding of the specifics of the camera response and is expected to be helpful for future optimisation of gamma camera applications. 

/pdf/monte-carlo-modelling-of-a-compact-czt-based-gamma-camera-3i1qykw6.pdf

Monte Carlo modelling of a compact CZT-based gamma camera with application to 177Lu imaging

Due to increased susceptibility to soft errors in recent semiconductor technologies, techniques for detecting and recovering from errors are required. Roll-back Recovery with Checkpointing (RRC) is one well known technique that copes with soft errors by taking and storing checkpoints during execution of a job. Employing this technique, increases the average execution time (AET), i.e. the expected time for a job to complete, and thus impacts performance. To minimize the AET, the checkpointing frequency is to be optimized. However, it has been shown that optimal checkpointing frequency depends highly on error probability. Since error probability cannot be known in advance and can change during time, the optimal checkpointing frequency cannot be known at design time. In this paper we present techniques that are adjusting the checkpointing frequency on-line (during operation) with the goal to reduce the AET of a job. A set of experiments have been performed to demonstrate the benefits of the proposed techniques. The results have shown that these techniques adjust the checkpointing frequency so well that the resulting AET is close to the theoretical optimum.

/pdf/linkoping-university-post-print-on-line-techniques-to-adjust-20suxft3oc.pdf

Linköping University Post Print On-line Techniques to Adjust and Optimize Checkpointing Frequency

Space-Time Block Coding for Wireless Communications: RECEIVE DIVERSITY

The present invention relates to a method for controlling the operation of an electronic device for processing data, said device comprising at least one computational unit for receiving input data and processing said input data for generating output data, and further comprising a control unit for receiving at least a part of said input data and delivering at least one control signal to said at least one computational unit for controlling the operation of said at least one computational unit, characterised in said control unit using said input data to determine a computational effort and further using said control signal to control parameters of said at least one computational unit depending on said computational effort, wherein said parameters comprise a combination of: clock rate and/or supply voltage; and process complexity.

Erik G. Larsson

Papers

Channel Estimation in Massive MIMO with Heavy-Tailed Noise: Gaussian-Mixture Versus Cauchy Models

Monte Carlo modelling of a compact CZT-based gamma camera with application to 177Lu imaging

Linköping University Post Print On-line Techniques to Adjust and Optimize Checkpointing Frequency

Space-Time Block Coding for Wireless Communications: RECEIVE DIVERSITY

Method and system for controlling the operation of an electronic device