Search

This paper studies the processing principles, implementation challenges, and performance of OFDM-based radars, with particular focus on the fourth-generation Long-Term Evolution (LTE) and fifth-generation (5G) New Radio (NR) mobile networks' base stations and their utilization for radar/sensing purposes. First, we address the problem stemming from the unused subcarriers within the LTE and NR transmit signal passbands, and their impact on frequency-domain radar processing. Particularly, we formulate and adopt a computationally efficient interpolation approach to mitigate the effects of such empty subcarriers in the radar processing. We evaluate the target detection and the corresponding range and velocity estimation performance through computer simulations, and show that high-quality target detection as well as high-precision range and velocity estimation can be achieved. Especially 5G NR waveforms, through their impressive channel bandwidths and configurable subcarrier spacing, are shown to provide very good radar/sensing performance. Then, a fundamental implementation challenge of transmitter-receiver (TX-RX) isolation in OFDM radars is addressed, with specific emphasis on shared-antenna cases, where the TX-RX isolation challenges are the largest. It is confirmed that from the OFDM radar processing perspective, limited TX-RX isolation is primarily a concern in detection of static targets while moving targets are inherently more robust to transmitter self-interference. Properly tailored analog/RF and digital self-interference cancellation solutions for OFDM radars are also described and implemented, and shown through RF measurements to be key technical ingredients for practical deployments, particularly from static and slowly moving targets' point of view., Comment: Paper accepted by IEEE Transactions on Microwave Theory and Techniques

We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping compact superpixels that are used as basic data units for depth estimation and refinement. Superpixel representation provides a desirable reduction in the computational cost while preserving the image geometry with respect to the object contours. Each superpixel is modeled as a plane in the image space, allowing depth values to vary smoothly within the superpixel area. Initial depth maps, which are obtained by plane sweeping, are iteratively refined by propagating good correspondences within an image. To ensure the fast convergence of the iterative optimization process, we employ a highly parallel propagation scheme that operates on all the superpixels of all the images at once, making full use of the parallel graphics hardware. A few optimization iterations of the energy function incorporating superpixel-wise smoothness and geometric consistency constraints allows to recover depth with high accuracy in textured and textureless regions as well as areas with occlusions, producing dense globally consistent depth maps. We demonstrate that while the depth reconstruction takes about a second per full high-definition view, the accuracy of the obtained depth maps is comparable with the state-of-the-art results., Comment: 15 pages, 15 figures

There is recent interest in GPU architectures designed to accelerate ray tracing, especially on mobile systems with limited memory bandwidth. A promising recent approach is to store and traverse Bounding Volume Hierarchies (BVHs), used to accelerate ray tracing, in low arithmetic precision. However, so far there is no research on refitting or construction of such compressed BVHs, which is necessary for any scenes with dynamic content. We find that in a hardware-accelerated tree update, significant memory traffic and runtime savings are available from streaming, bottom-up compression. Novel algorithmic techniques of modulo encoding and treelet-based compression are proposed to reduce backtracking inherent in bottom-up compression. Together, these techniques reduce backtracking to a small fraction. Compared to a separate top-down compression pass, streaming bottom-up compression with the proposed optimizations saves on average 42% of memory accesses for LBVH construction and 56% for refitting of compressed BVHs, over 16 test scenes. In architectural simulation, the proposed streaming compression reduces LBVH runtime by 20% compared to a single-precision build, and 41% compared to a single-precision build followed by top-down compression. Since memory traffic dominates the energy cost of refitting and LBVH construction, energy consumption is expected to fall by a similar fraction. submittedVersion

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge. acceptedVersion

In this article, the prospects and enabling technologies for high-efficiency device positioning and location-aware communications in emerging 5G networks are reviewed. We will first describe some key technical enablers and demonstrate by means of realistic ray-tracing and map based evaluations that positioning accuracies below one meter can be achieved by properly fusing direction and delay related measurements on the network side, even when tracking moving devices. We will then discuss the possibilities and opportunities that such high-efficiency positioning capabilities can offer, not only for location-based services in general, but also for the radio access network itself. In particular, we will demonstrate that geometric location-based beamforming schemes become technically feasible, which can offer substantially reduced reference symbol overhead compared to classical full channel state information (CSI)-based beamforming. At the same time, substantial power savings can be realized in future wideband 5G networks where acquiring full CSI calls for wideband reference signals while location estimation and tracking can, in turn, be accomplished with narrowband pilots., Comment: Accepted for publication in IEEE Communications Magazine in March 2017. This is the revised version of the original article, and it is in press at the moment. 8 pages, 6 figures

We study optimized antenna reading area mappings for a radiofrequency identification- (RFID-) based access monitoring system, used in an underground nuclear waste storage facility. We shortly introduce the access monitoring system developed for the ONKALO tunnel in Finland and describe the antenna mounting points as well as the research area. Finally, we study the measurement results of the antenna reading areas and factors that affect the reading area size. Based on our results, in addition to antenna location and direction, absorption to obstacles, reflections, diffraction, scattering, and refraction affect the antenna reading area.

Usage of faster than Nyquist signaling (FTN) in practice is limited by the complexity of the formation and detection devices. Optimal FTN pulses may be formed via digital non-recursive filters. One of the important parameters that affect the performance of such devices is the number of quantization levels. In this paper, the influence of the number of quantization levels for optimal FTN signals with pulse-amplitude modulation on spectral characteristics, correlation properties and the probability of error detection is investigated. It is shown that at least 64 quantization levels should be chosen to provide almost potential bit error rate performance and additional spectral quantization noise about – 40 dB for volumes of channel alphabet upto 8. Decreasing the volume of channel alphabet allows reducing the requirements for the number of quantization levels.

This paper presents a novel approach to accelerate, distribute, and manage video encoding services in large-scale cloud systems. A proof-of-concept application is Kvazaar HEVC intra encoder, whose functionality is partitioned between FPGAs and processors. Typically, only 1-2 FPGA boards can be attached per cloud server, which severely limits the flexibility of the cloud systems. Our solution is based on Software Defined Networking (SDN), in which practically any number of FPGAs and servers can be deployed. The system features a resource manager that is responsible for allocation, deallocation, and load balancing of resources upon service requests or changes in network infrastructure. Our prototype cloud system is composed of three Intel Xeon servers, two HP SDN switches, and two Intel Arria 10 FPGAs. The servers and FPGAs have 20GbE and 40GbE connections to the SDN switches, respectively. The prototype system can encode two 4K HEVC streams at 60 fps and the performance is predicted to scale almost linearly with the number of servers and FPGAs. acceptedVersion

Application of Faster than Nyquist (FTN) signals allows achieving high spectral and energy characteristics of the communication system. Further approaching the Shannon limit that determines the channel throughput is related to increasing the size of the signal constellation. However, an increase in the size of the signal constellation results in sharp growth of energy losses for FTN signals. To reduce these energy losses optimal FTN pulses obtained as a result of solving the optimization problem with the constraint on signal constellation size M may be used. In this work, optimal pulse shapes for pulse-amplitude modulation (PAM) with M=4, 16, 64, 256 were found. It was shown that applying optimal FTN signals with PAM allows reducing energy losses by 4 dB regarding the FTN signals based on root-raised-cosine pulses for coherent symbol-by-symbol detection algorithm. In terms of approaching the Shannon limit, the closest results are provided by determining the bandwidth containing 99% of signal energy. acceptedVersion

The impedance of a Li-ion battery is an important parameter for the battery's state-of-charge (SOC) and state-of-health (SOH) estimation. Battery impedance is typically modeled by an equivalent-circuit-model (ECM) in which the variations in the specific model parameters can be used for estimating the SOC and the SOH. However, the fact that the battery impedance is highly non-linear complicates the parameterization of the model. The model is traditionally obtained by a complex non-linear least-squares fitting algorithm which comes with high complexity. This paper proposes a novel approach to extract all the ECM parameters of the battery impedance obtained with online-capable pseudo-random-sequence (PRS) measurements. Although the algorithm has low complexity, it still captures the desired variations in the ECM parameters as a function of SOC. The algorithm is validated for the impedance data from a lithium-iron-phosphate cell. acceptedVersion

Light fields representing Lambertian scenes are characterized by specific epipolar plane images, which support in Fourier domain is limited by the minimum and maximum scene depth. This feature of the light field has been taken into account when designing the shearlet-based light field reconstruction algorithm. However, this feature does not hold for the case of non-Lambertian scenes. Therefore, the original algorithm performs sub-optimally for such scenes. In this work, we extend the shearlet based reconstruction algorithm to enable light fields reconstruction for scenes composed of surfaces with non-Lambertian properties. Instead of assuming Lambertianity and the corresponding Fourier-domain support, we estimate the real Fourier-domain support of the given light field of a non-Lambertian scene. Based on the estimated support, we adapt the Shearlet frame to properly span that support. The results demonstrate that the new method yields a faithful reconstruction of densely sampled light fields of non-Lambertian scenes from a small number of captured views. acceptedVersion

High accuracy positioning enabled by 5G cellular networks will play a crucial role in the robot-based industrial applications, where the vertical accuracy will be as significant as the 3D accuracy. Aiming at target applications relying on flying robots in industrial environments, this paper presents and formulates two positioning algorithms when the location uncertainty of the access nodes (ANs) is taken into consideration. The first algorithm is a low-complexity geometry-based 3D positioning algorithm that utilizes both time-of-arrival and angle-of-arrival measurements. The second algorithm relies on extended Kalman Filter (EKF)-based positioning, by mapping the ANs' location uncertainty into the measurement noise statistics. The performance of the two proposed method is studied in terms of 3D and vertical positioning accuracy, sensitivity to location uncertainty of the ANs, and computational complexity in indoor scenarios. Based on the conducted complexity analysis, the proposed geometry-based algorithm is computationally more efficient than the EKF-based algorithm. In addition, the proposed geometry-based positioning method demonstrates a higher robustness against a high location uncertainty of ANs than the considered EKF-based method. acceptedVersion

This paper addresses high-efficiency radio positioning and tracking of high-speed objects in emerging fifth generation (5G) new radio (NR) networks. Methods and system concept are described, building on network side reference signal measurements and data fusion and tracking. Also realistic performance results are provided and analyzed, in the context of mmWave NR deployment for high-speed trains at 30 GHz carrier frequency. It is shown that below 3 m positioning accuracy can be achieved with 95% availability, which fits in the positioning performance requirements for traffic monitoring and control, as specified by the 3rd generation partnership project (3GPP). Moreover, the given positioning performance can be achieved without assuming clock synchronization between the baseband units (BBUs) at the network side. In fact, the proposed approach enables sub-nanosecond estimation accuracy of network clock offsets, which benefits various radio resource management (RRM) functionalities of the network for increased spectral efficiency. acceptedVersion

Calibration of multiple cameras is a critical step in most vision enhancement systems. Target-based calibration approaches are known to provide accurate and stable results. However, they require manually performed capture procedures. This paper presents a generalization of a widely used single-camera target-based calibration algorithm to the case of n cameras. In order to obtain fully repeatable results, we propose the elimination of the manual capture step using a programmable robotic arm. Furthermore, we investigate the use of the position feedback provided by the robot. This is done specifically for the case of calibrating cameras without assumptions on their positions and overlapping of their fields of view. Results show that automatically captured images provide more accurate calibration results than the classical approach. Additionally, calibration of fully non-overlapping setups is made possible through our approach. acceptedVersion

We propose simple and efficient method that produces content-adaptive superpixels, i.e. smaller segments in content-dense areas and larger segments in content-sparse areas. Previous adaptive methods distribute superpixels over the image according to image content. In contrast, we transform the image itself to redistribute the content density uniformly across the image area. This transformation is guided by a significance map, which characterizes the ‘importance’ of each pixel. Arbitrary superpixel algorithm can be utilized to segment the transformed image into regular superpixels, providing a suitable representation for subsequent tasks. Regular superpixels in the transformed image induce content-adaptive superpixels in the original image facilitating the improved segmentation accuracy. acceptedVersion

We present a learning-based optimization framework for depth of field extension, combining rigorous modeling of coded aperture imaging system and convolutional neural network based deblurring. The coded mask discretization is defined for desired depth range using wave optics based imaging model. Such approach significantly decreases the number of parameters to be optimized and increases the convergence speed of the network. We verify the proposed algorithm in different scenarios achieving superior or comparable performance with respect to existing methods. acceptedVersion

Collaborative filters perform denoising through transform-domain shrinkage of a group of similar blocks extracted from an image. Existing methods for collaborative filtering of stationary correlated noise have all used simple approximations of the transform noise power spectrum adopted from methods which do not employ block grouping. We note the inaccuracies of these approximations and introduce a method for the exact computation and effective approximations of the noise power spectrum. Unlike earlier methods, the calculated noise variances are exact even when noise in one block is correlated with noise in any of the other blocks. We discuss the adoption of the exact noise power spectrum within shrinkage, in similarity testing (block matching), and in aggregation. Extensive experiments support the proposed method over earlier crude approximations used by image denoising filters such as BM3D, demonstrating dramatic improvement in many challenging conditions. acceptedVersion

The software business is moving from product business to Soft-ware-as-a-Service (SaaS). In addition to business changes, this also changes how productization should be done. This paper explores the required levels of productization in different phases of SaaS business. The research is based on a case study in a company and in a context of enterprise software and B2B business. This study focuses on how to build an enterprise SaaS solution, with excellent viable product–market fit, and con-currently, before larger productization investments, must be sure that demand exists. As a result of the study a productization model including three productization levels is presented. In this study we refer to products as software products and services. acceptedVersion

Biaxial-stretching-induced morphology development and dielectric properties of hydrophobic fumed silica-BOPP nanocomposite films were investigated. The precursor (cast) film morphology, pre-heating as well as biaxial stretching temperature and stretch ratio were found to profoundly affect the silica-BOPP film bulk morphology, surface texture and the formation of particle/agglomerate-induced cavitation upon biaxial stretching. Isothermal high-field conductivity and thermally stimulated depolarization current measurements indicated decreasing high-field/high-temperature conductivity and modification of trap density of states upon incorporation of nanosilica in comparison to neat BOPP. Large-area DC dielectric strength is, however, sensitive to thermal/mechanical stresses upon biaxial stretching, hence necessitating careful optimization of the nanocomposite formulation and processing. publishedVersion

The measurement and interpretation of high-field thermally stimulated depolarization current (TSDC) of capacitor-grade biaxially oriented polypropylene (BOPP) films are investigated in order to facilitate the derivation of trap parameters. Two main relaxations associable with the glass transition and detrapping of space charge from deep traps are observed, and their dependence on the polarization/depolarization conditions are studied. The complex bipolar nature of the space charge relaxation makes the derivation of trap parameters challenging. Lastly, a brief attempt is made to correlate the TSDC features with morphology and other short-term dielectric properties. publishedVersion

Background. Companies commonly invest majorBackground. Companies commonly invest major effort into removing, respectively not introducing, technical debt issues detected by static analysis tools such as SonarQube, Cast, or Coverity. These tools classify technical debt issues into categories according to severity, and developers commonly pay attention to not introducing issues with a high level of severity that could generate bugs or make software maintenance more difficult. Objective. In this work, we aim to understand the diffuseness of Technical Debt (TD) issues and the speed with which developers remove them from the code if they introduced such an issue. The goal is to understand which type of TD is more diffused and how much attention is paid by the developers, as well as to investigate whether TD issues with a higher level of severity are resolved faster than those with a lower level of severity. We conducted a case study across 78K commits of 33 Java projects from the Apache Software Foundation Ecosystem to investigate the distribution of 1.4M TD items. Results. TD items introduced into the code are mostly related to code smells (issues that can increase the maintenance effort). Moreover, developers commonly remove the most severe issues faster than less severe ones. However, the time needed to resolve issues increases when the level of severity increases (minor issues are removed faster that blocker ones). Conclusion. One possible answer to the unexpected issue of resolution time might be that severity is not correctly defined by the tools. Another possible answer is that the rules at an intermediate severity level could be the ones that technically require more time to be removed. The classification of TD items, including their severity and type, require thorough investigation from a research point of view.effort into removing, respectively not introducing, technical debtissues detected by static analysis tools such as SonarQube, Cast, or Coverity. These tools classify technical debt issues intocategories according to severity, and developers commonly payattention to not introducing issues with a high level of severitythat could generate bugs or make software maintenance moredifficult. Objective. In this work, we aim to understand the diffuseness ofTechnical Debt (TD) issues and the speed with which developersremove them from the code if they introduced such an issue. The goal is to understand which type of TD is more diffusedand how much attention is paid by the developers, as well asto investigate whether TD issues with a higher level of severityare resolved faster than those with a lower level of severity. Weconducted a case study across 78K commits of 33 Java projectsfrom the Apache Software Foundation Ecosystem to investigatethe distribution of 1.4M TD items. Results. TD items introduced into the code are mostly relatedto code smells (issues that can increase the maintenance effort). Moreover, developers commonly remove the most severe issuesfaster than less severe ones. However, the time needed to resolveissues increases when the level of severity increases (minor issuesare removed faster that blocker ones). Conclusion. One possible answer to the unexpected issue ofresolution time might be that severity is not correctly definedby the tools. Another possible answer is that the rules at anintermediate severity level could be the ones that technicallyrequire more time to be removed. The classification of TD items, including their severity and type, require thorough investigationfrom a research point of view.

In the industrial environments of the future, robots, sensors, and other industrial devices will have to communicate autonomously and in a robust and efficient manner with each other, relying on a large extent on wireless communication links, which will expand and supplement the existing wired/Ethernet connections. The wireless communication links suffer from various channel impairments, such as attenuations due to path losses, random fluctuations due to shadowing and fading effects over the channel and the non line-of-sight (NLoS) due to obstacles on the communication path. Several channel models exist to model the industrial environments in indoor, urban, or rural areas, but a comprehensive comparison of their characteristics is still missing from the current literature. Moreover, several IoT technologies are already on the market, many competing with each other for future possible services and applications in Industrial IoT (IIoT) environments. This paper aims at giving a survey of existing wireless channel models applicable to the IIoT context and to compare them for the first time in terms of worst-case, median-case, and best-case predictive behaviors. Performance metrics, such as cell radius, spectral efficiency, and outage probability, are investigated with a focus on three long-range IoT technologies, one medium-range, and one short-range IoT technology as selected case studies. A summary of popular IoT technologies and their applicability to industrial scenarios is addressed as well. publishedVersion

Standard RGB-D trackers treat the target as an inherently 2D structure, which makes modelling appearance changes related even to simple out-of-plane rotation highly challenging. We address this limitation by proposing a novel long-term RGB-D tracker - Object Tracking by Reconstruction (OTR). The tracker performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction supports two performance-enhancing features: (i) generation of accurate spatial support for constrained DCF learning from its 2D projection and (ii) point cloud based estimation of 3D pose change for selection and storage of view-specific DCFs which are used to robustly localize the target after out-of-view rotation or heavy occlusion. Extensive evaluation of OTR on the challenging Princeton RGB-D tracking and STC Benchmarks shows it outperforms the state-of-the-art by a large margin.

Miniaturization of implantable devices is an important challenge for future brain-computer interface applications, and in particular for achieving precise neuron stimulation. For stimulation that utilizes light, i.e., optogenetics, the light propagation behavior and interaction at the nanoscale with elements within the neuron is an important factor that needs to be considered when designing the device. This paper analyzes the effect of light behavior for a single neuron stimulation and focuses on the impact from different cell shapes. Based on the Mie scattering theory, the paper analyzes how the shape of the soma and the nucleus contributes to the focusing effect resulting in an intensity increase, which ensures that neurons can assist in transferring light through the tissue toward the target cells. At the same time, this intensity increase can in turn also stimulate neighboring cells leading to interference within the neural circuits. This paper also analyzes the ideal placements of the device with respect to the angle and position within the cortex that can enable axonal biophoton communications, which can contain light within the cell to avoid the interference. acceptedVersion

The short-sample critical current is only an indicative property for the maximum current a magnet can be continuously operated with. This was especially visible in the experiments of one of the world's first Roebel-cable-based high temperature superconductors dipole magnet prototype built and tested at CERN in 2017 where the thermal runaway developed very slowly in many cases. Consequently, the maximum stable operation current could be overstepped and stable operation could be recovered by lowering the current below the maximum of the stable range again. It is non-trivial to quantitatively predict this behavior from the critical current measurements which are observed under specific cooling conditions and based on an arbitrarily selected electric field criterion for the critical current. To make more rigorous predictions on the maximum stable operation current, one needs to consider in detail the interplay of cooling over the magnet surface and heat generation in the winding. This paper presents a methodology to determine the maximum stable operation current for a given magnet, as well as studies its mathematical background. Insight to this problem comes from the Roebel-cable-based dipole magnet studied at CERN during 2017. acceptedVersion

We substantiate and extend recent research on the behavior of the out-of-band emissions in antenna array transmitters. Specifically, with multi-user precoding, we show that the emissions are always beamformed in the directions of the intended receivers, contrary to some claims in the recent literature. Moreover, while also other spurious directions exist, we show that the emissions are always strongest in the directions of the intended receivers. We also show that power amplifiers with mutually different nonlinear phase characteristics will reduce the beamforming gain of the unwanted emissions, with the gain approaching the noncoherent combining limit as the phase deviations are increased., Comment: Accepted for publication in IEEE Wireless Communications Letters

Programmable data plane is a key enabler of Software Defined Networking. By making networking devices programmable, novel networking services and functions could be realized by means of software running on these devices. In this paper, we present a lightweight packet processor that could process the packets on the fly as they arrive. As we will see, the area of this packet processor is smaller than a packet parser employing Ternary Content Addressable Memory. As an added benefit, the designed packet processor could also reduce the traffic to the lookup tables on the chip. Moreover, its use is not limited to switches and routers. It could also be used in the Network Interface Cards and offload packet processing tasks. Despite its packet processing capabilities, packet processor instances required for sustaining aggregate throughput of 640 Gbps have area equivalent to the packet parser instances in the Reconfigurable Match Tables Architecture. acceptedVersion

While the Internet of Things (IoT) has made significant progress along the lines of supporting individual machine-type applications, it is only recently that the importance of people as an integral component of the overall IoT infrastructure has started to be fully recognized. Several powerful concepts have emerged to facilitate this vision, whether involving the human context whenever required or directly impacting user behavior and decisions. As these become the stepping stones to develop the IoT into a novel people-centric utility, this paper outlines a path to materialize this decisive transformation. We begin by reviewing the latest progress in human-aware wireless networking, then classify the attractive human-machine applications and summarize the enabling IoT radio technologies. We continue with a unique system-level performance characterization of a representative urban IoT scenario and quantify the benefits of keeping people in the loop on various levels. Our comprehensive numerical results confirm the significant gains that have been made available with tighter user involvement, and also corroborate the development of efficient incentivization mechanisms, thereby opening the door to future commoditization of the global people-centric IoT utility., Comment: 8 pages, 5 figures. This work has been accepted to IEEE Communications Magazine, 2019

Due to the increased popularity of augmented (AR) and virtual (VR) reality experiences, the interest in representing the real world in an immersive fashion has never been higher. Distributing such representations enables users all over the world to freely navigate in never seen before media experiences. Unfortunately, such representations require a large amount of data, not feasible for transmission on today's networks. Thus, efficient compression technologies are in high demand. This paper proposes an approach to compress 3D video data utilizing 2D video coding technology. The proposed solution was developed to address the needs of "tele-immersive" applications, such as VR, AR, or mixed reality with "Six Degrees of Freedom" capabilities. Volumetric video data is projected on 2D image planes and compressed using standard 2D video coding solutions. A key benefit of this approach is its compatibility with readily available 2D video coding infrastructure. Furthermore, objective and subjective evaluation shows significant improvement in coding efficiency over reference technology. The proposed solution was contributed and evaluated in international standardization. Although it is was not selected as the winning proposal, as very similar solution has been selected developed since then. publishedVersion

Virtual reality (VR) applications make use of 360° omnidirectional video content for creating immersive experience to the user. In order to utilize current 2D video compression standards, such content must be projected onto a 2D image plane. However, the projection from spherical to 2D domain introduces deformations in the projected content due to the different sampling characteristics of the 2D plane. Such deformations are not favorable for the motion models of the current video coding standards. Consequently, omnidirectional video is not efficiently compressible with current codecs. In this work, a geometry-based motion vector scaling method is proposed in order to compress the motion information of omnidirectional content efficiently. The proposed method applies a scaling technique, based on the location in the 360° video, to the motion information of the neighboring blocks in order to provide a uniform motion behavior in a certain part of the content. The uniform motion behavior provides optimal candidates for efficiently predicting the motion vectors of the current block. The conducted experiments illustrated that the proposed method provides up to 2.2% bitrate reduction and on average around 1% bitrate reduction for the content with high motion characteristics in the VTM test model of Versatile Video Coding (H.266/VVC) standard. acceptedVersion

Omnidirectional video is widely used in virtual reality applications in order to create the immersive experience to the user. Such content is projected onto a 2D image plane in order to make it suitable for compression purposes by using current standard codecs. However, the resulted projected video contains deformations mainly due to the oversampling of the projection plane. These deformations are not favorable for the motion models that are used in the recent video compression standards. Hence, omnidirectional video is not efficiently compressible with the current codecs. In this work, an adaptive motion vector prediction method is proposed for efficiently coding the motion information of such content. The proposed method adaptively models the motion vectors of the coding block based on the motion information of the neighboring blocks and calculates a more optimal motion vector predictor for coding the motion information. The experimented results showed that the proposed motion vector prediction method provides up to 2.2% bitrate reduction in the content with high motion and on average 1.1% bitrate reduction for the tested sequences. acceptedVersion

Sensor components may markedly differ from typical silicon chips. Consequently, versatile attachment methods are required for their interconnections. Anisotropic conductive adhesives (ACA) are interesting materials for attachments of sensors due to their versatility. In this study reliability of ACA attached humidity sensors was studied in hygrothermal conditions. The reliability of the interconnections was found to be excellent even under prolonged exposure showing that high reliability can be achieved with ACA materials in sensor applications. publishedVersion

We present the fabrication and testing of graphene-based dipole antennas on cardboard which is a promising low-cost, recyclable, and flexible substrate for future wireless electronics. The article presents the details of the manufacturing, as well as results from the measurements and simulations. The measured sheet resistance of graphene antenna is 1.9 Ω/sq. Overall, a graphene-based planar dipole antenna with the length of 143 mm achieved the measured total efficiency of 40% and the realized gain of –2.18 dBi at 889 MHz. Moreover, a passive ultra-high-frequency radio-frequency tag based on a graphene dipole antenna on cardboard achieved the attainable read range of more than five meters at 950 MHz. acceptedVersion

Variable length encoding can considerably decrease code size in VLIW processors by reducing the number of bits wasted on encoding No Operations(NOPs). A processor may have different instruction templates where different execution slots are implicitly NOPs, but all combinations of NOPs may not be supported by the instruction templates. The efficiency of the NOP encoding can be improved by the compiler trying to place NOPs in such way that the usage of implicit NOPs is maximized. Two different methods of optimizing the use of the implicit NOP slots are evaluated: (a) prioritizing function units that have fewer implicit NOPs associated with them and (b) a post-pass to the instruction scheduler which utilizes the slack of the schedule by rescheduling operations with slack into different instruction words so that the available instruction templates are better utilized. Three different methods for selecting basic blocks to apply FU priorization on are also analyzed: always, always outside inner loops, and only outside inner loops only in basic blocks after testing where it helped to decrease code size. The post-pass optimizer alone saved an average of 2.4 % and a maximum of 10.5 % instruction memory, without performance loss. Prioritizing function units in only those basic blocks where it helped gave the best case instruction memory savings of 10.7 % and average savings of 3.0 % in exchange for an average 0.3 % slowdown. Applying both of the optimizations together gave the best case code size decrease of 12.2 % and an average of 5.4 %, while performance decreased on average by 0.1 %. acceptedVersion

In this systematic literature review, we study the role of user logging in virtual reality research. By categorizing literature according to data collection methods and identifying reasons for data collection, we aim to find out how popular user logging is in virtual reality research. In addition, we identify publications with detailed descriptions about logging solutions. Our results suggest that virtual reality logging solutions are relatively seldom described in detail despite that many studies gather data by body tracking. Most of the papers gather data to witness something about a novel functionality or to compare different technologies without discussing logging details. The results can be used for scoping future virtual reality research. acceptedVersion

Packet parsing is the first step in processing of packets in devices such as network switches and routers. The process of packet parsing has become more challenging due to the increase in line rates and emergence of Software Defined Networking which leads to new protocols being adopted. In this paper, we present a novel architecture for parsing of packets. The architecture is fully programmable and is not tied to any specific protocol. It can be programmed to parse any protocol making it suitable for Software Defined Networks. Compared with the parser used in the Reconfigurable Match Tables, our parser improves supported throughput by a factor of 3.2. Moreover, to achieve the target throughput of 640 Gbps, our parser needs only 2 percent of the number of gates used in the parsers of Reconfigurable Match Tables. acceptedVersion

The increase in the availability of low-cost components has made it possible to design and implement new and innovative devices for the Internet of Things (IoT). Today, cost-effective and open solutions can be created to replace previous expensive and proprietary measurement systems. This study presents the prototype of an ultrasound measurement system. The purpose of the system was to make the surveillance process of waste management containers more effective and reliable. The system was built using commonly available sensor components in combination with a microcontroller, and the collected data was stored and analyzed in a cloud service. The configuration of the ultrasound measurement system is introduced in this study. The measurement results obtained from the tests performed in the actual operating environment are also presented. Based on these tests, the accuracy of the system was observed to be well-suited for the use case. The average systematic error of the results was -2.0 mm and the average of random error was within 0.9 mm. publishedVersion

Customisation is one strategy to involve customers in the design process. However, qualitative empirical studies on customisation in different product contexts are scarce. This paper investigates the benefits customers perceive in craft customisation where they are actively involved in the customisation with the designer. The case of this study is a high involvement custom product, a surfboard. Findings derived from qualitative interviews with surfers (N=22) reveal the perceived benefits in the three distinct phases of customisation: pre-customisation, during which the decision to customise is made; customisation process, during which the product is customised; and product usage. The source of the found benefits are the product, process or customisation itself and they can be classified to functional, emotional, experiential, symbolic, aesthetic, personal, social, epistemic, creative, and hedonic. The benefits vary in occurrence during the different customisation phases. Based on the findings, we present a model for the benefits in the three phases of customisation. The model can be used when implementing craft customisation as a design strategy. Keywords: customisation, benefits of customisation, phases of customisation, high-involvement product, surfboard.

Today, the number of interconnected Internet of Things (IoT) devices is growing tremendously followed by an increase in the density of cellular base stations. This trend has an adverse effect on the power efficiency of communication, since each new infrastructure node requires a significant amount of energy. Numerous enablers are already in place to offload the scarce cellular spectrum, thus allowing utilization of more energy-efficient short-range radio technologies for user content dissemination, such as moving relay stations and network-assisted direct connectivity. In this work, we contribute a new mathematical framework aimed at analyzing the impact of network offloading on the probabilistic characteristics related to the quality of service and thus helping relieve the energy burden on infrastructure network deployments. publishedVersion

Single image super resolution (SISR) is an ill-posed problem aiming at estimating a plausible high resolution (HR) image from a single low resolution (LR) image. Current state-of-the-art SISR methods are patch-based. They use either external data or internal self-similarity to learn a prior for a HR image. External data based methods utilize large number of patches from the training data, while self-similarity based approaches leverage one or more similar patches from the input image. In this paper we propose a self-similarity based approach that is able to use large groups of similar patches extracted from the input image to solve the SISR problem. We introduce a novel prior leading to collaborative filtering of patch groups in 1D similarity domain and couple it with an iterative back-projection framework. The performance of the proposed algorithm is evaluated on a number of SISR benchmark datasets. Without using any external data, the proposed approach outperforms the current non-CNN based methods on the tested datasets for various scaling factors. On certain datasets, the gain is over 1 dB, when compared to the recent method A+. For high sampling rate (x4) the proposed method performs similarly to very recent state-of-the-art deep convolutional network based approaches., Paper accepted for publication on IEEE Transactions on Image Processing

The acceleration of mobile data traffic and the shortage of available spectral resources create new challenges for the next-generation (5G) networks. One of the potential solutions is network offloading that opens a possibility for unlicensed spectrum utilization. Heterogeneous networking between cellular and WLAN systems allows mobile users to adaptively utilize the licensed (LTE) and unlicensed (IEEE 802.11) radio technologies simultaneously. At the same time, softwarized frameworks can be employed not only inside the network controllers but also at the end nodes. To operate with the corresponding policies and interpret them efficiently, a signaling processor has to be developed and equipped with a fast packet parsing mechanism. In this scenario, the reaction time becomes a crucial factor, and this paper provides an overview of the existing parsing libraries (Scapy and dpkt) as well as proposes a flexible parsing tool that is capable of reducing the latency incurred by analyzing packets in a softwarized network. acceptedVersion

Recently, new opportunities for utilizing extremely high frequencies have become instrumental in developing fifth-generation(5G) mobile technology. The use of highly directional antennas in millimeter-wave (mmWave) bands poses an important question of whether two- dimensional modeling suffices to capture the resulting system performance. Accounting for the effects of human body blockage by mmWave transmissions, in this work we compare the performance of the conventional two-dimensional and the proposed three- dimensional modeling. With our stochastic geometry based approach, we consider the aggregate interference and signal-to- interference ratio (SIR) to be the main metrics of interest. Both counterpart models attempt to capture the inherent behavior of 5G mmWave systems by incorporating the effects of human body blockage and antenna directivity. We thus deliver a realistic numerical assessment by comparing the three-dimensional modeling with its two-dimensional projection to reveal the resulting discrepancy. acceptedVersion

Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users. Detecting outlier nodes and edges is important for data mining and graph analytics. However, previous research in the field has merely focused on detecting outlier nodes. In this article, we study the properties of edges and propose outlier edge detection algorithms using two random graph generation models. We found that the edge-ego-network, which can be defined as the induced graph that contains two end nodes of an edge, their neighboring nodes and the edges that link these nodes, contains critical information to detect outlier edges. We evaluated the proposed algorithms by injecting outlier edges into some real-world graph data. Experiment results show that the proposed algorithms can effectively detect outlier edges. In particular, the algorithm based on the Preferential Attachment Random Graph Generation model consistently gives good performance regardless of the test graph data. Further more, the proposed algorithms are not limited in the area of outlier edge detection. We demonstrate three different applications that benefit from the proposed algorithms: 1) a preprocessing tool that improves the performance of graph clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel noisy data clustering algorithm. These applications show the great potential of the proposed outlier edge detection techniques., Comment: 14 pages, 5 figures, journal paper

This paper presents an evaluation of a Heterogeneous Multicore Architecture (HMA) by implementing Orthogonal Frequency- Division Multiplexing (OFDM) receiver blocks as designs for the test of functionality. OFDM receiver consists of computationally intensive and general-purpose processing tasks that can provide maximum coverage to test and evaluate a massively-parallel as well as a general-purpose platform like the HMA. The blocks of the receiver are primarily designed by crafting template-based Coarse-Grained Reconfigurable Array (CGRA) devices and then arranging them in a sequence over a Network-on-Chip (NoC) structure along with a few RISC cores for complete OFDM processing. The OFDM blocks such as Fast Fourier Transform (FFT) and Time Synchronization are computationally intensive and require parallel processing. The OFDM receiver also contains tasks such as frequency offset estimation which require the processing of Taylor series and CORDIC algorithms that are serial in nature. Such a combination of serial and parallel algorithms can perform a thorough exploration and evaluation of almost all the design features of an HMA. The OFDM implementation has led to scale CGRAs to different dimensions, instantiate Processing Elements (PEs) as multiple arithmetic resources and to establish almost all possible ways of PE interconnections. It further explores time-multiplexed patterns for data placement in the CGRA memories. Nevertheless, the data can also be exchanged among different nodes over NoC structure simultaneously and independently by using direct memory access devices. In this experimental work, the performance of each CGRA, the collective performance of the whole platform and the NoC traffic are recorded in terms of the number of clock cycles and several high-level performance metrics. Today’s HMAs are generally over or under resourced for the applications that they are designed for and thus not an optimal choice for the end user. Apart from the interesting comparisons to the other state-of-the-art, our experimental setup has provided important insight and guidelines that the designers can use to implement near-optimal solutions for their target applications. acceptedVersion

Supporting end-users’ practical and emotional needs is an important goal in the design of novel smart services. We present a design research study with the experience-driven design approach to create concept ideas for smart city services. We present current challenges in the context of use and results of a two-phased user study of traveler experiences in the railway station area of the city of Tampere. Results show how experience-driven approach utilizing playful experience (PLEX) cards enables users to ideate experience-oriented concepts. As an outcome of the study, we describe one of the concepts selected for development, the smart community info wall concept. Our findings on the use of PLEX cards show that PLEX cards work with the amateur participants and support their idea generation. Furthermore, with the help of the PLEX cards, users were able to think on the subject more extensively and presented ideas on the experience-oriented categories.

Previous studies of dielectric properties of thermally sprayed insulating ceramic coatings are focused on linearly ramped dielectric breakdown strength as well as DC resistivity, relative permittivity and dielectric loss characterizations. However, reports of the effects of ramp rate or of any kind of long term stressing on the breakdown strength cannot be found in literature. The aim of this paper was to study the DC breakdown behavior of one type of HVOF sprayed alumina coating under different stresses. It can be concluded that the ramp rate of DC breakdown measurement has no remarkably influence on the breakdown strength. The breakdown behavior was also studied using step-by-step tests with two constant step voltages and step durations. The DC resistivity of the alumina coating showed strong dependence on the applied electric field. The resistivity behaved ohmicly below the field strength of ~0.5 V/µm and above ~8…12 V/µm, however, the resistivity decreased approximately three decades in the nonohmic region (0.5 V/µm). At electric field strengths above ~25 V/µm, the degradation started in the material leading to breakdown. However, when the step duration was longer (60 min), the degradation process started already slightly below the applied field of 25 V/µm. acceptedVersion

Fifth generation (5G) radio networks are expected to provide remarkable improvements in the user experience due to faster data rates and lower latencies, for example. These kind of enhancements are made possible by several technical advances when compared to the existing networks. In particular, densification of network, device-centric network topology and exploitation of smart antennas, among others, will play key roles in network developments. In addition to obvious benefits in solely communications type of services, the aforementioned technological advances allow for completely new kind of applications. In this paper, we focus on user equipment (UE) positioning, and additionally show how also successful network synchronization can be done in 5G networks in a highly sophisticated, but low-complex network-centric solution. We will extend the analysis available in the existing literature to fully unsynchronized networks where the UEs as well as the network remote radio heads (RRHs) have clock offsets and skews with respect to a given reference clock. Our numerical performance analysis, based on realistic network and propagation models, show that the proposed method can provide sub-meter range positioning accuracy and highly accurate synchronization of the network elements. acceptedVersion

Promoters in Escherichia coli include an ‘OFF’ state, during which transcription is halted. Here, we propose a novel empirical method for assessing the time-length spent by promoters in this state. It relies on direct measurements of RNA production kinetics at the single molecule level at different induction levels, followed by an estimation of the RNA production rate under infinite induction, which is then compared to this rate under real, maximum induction. We apply it to the LacO3O1 promoter and infer that, under full induction, on average, 15% of the time between successful transcription events is spent in the OFF state. We verify this result by comparing the kinetics of a mutant strain lacking repressor molecules with that of the inferred rate under infinite induction. We expect this strategy of dissecting the kinetics of transcription repression to be applicable to a wide number of promoters in E. coli. acceptedVersion

A valid model of the air surveillance system performance is highly valued when making decisions related to the optimal control of the system. We formulate a model for a multi-radar tracker system by combining a radar performance model with a tracker performance model. A tracker as a complex software system is hard to model mathematically and physically. Our novel approach is to utilize machine learning to create a tracker model based on measurement data from which the input and target output for the model are calculated. The measured data comprises the time series of 3D coordinates of cooperative aircraft flights, the corresponding target detection recordings from multiple radars, and the related multi-radar track recordings. The collected data is used to calculate performance measures for the radars and the tracker at specific locations in the air space. We apply genetic programming to learning such rules from radar performance measures that explain tracker performance. The easily interpretable rules are intended to reveal the real behavior of the system providing comprehension for its control and further development. The learned rules allow predicting tracker performance level for the system control in all radar geometries, modes, and conditions at any location. In the experiments, we show the feasibility of our approach to learning a tracker model and compare our rule learner with two tree classifiers, another rule learner, a neural network, and an instance-based classifier using the real air surveillance data. The tracker model created by our rule learner outperforms the models by the other methods except for the neural network whose prediction performance is equal. acceptedVersion

publishedVersion