In this paper, the potential of extending 5G New Radio physical layer solutions to support communications in sub-THz frequencies is studied. More specifically, we introduce the status of third generation partnership project studies related to operation on frequencies beyond 52.6 GHz and note also the recent proposal on spectrum horizons provided by federal communications commission (FCC) related to experimental licenses on 95 GHz-3 THz frequency band. Then, we review the power amplifier (PA) efficiency and output power challenge together with the increased phase noise (PN) distortion effect in terms of the supported waveforms. As a practical example on the waveform and numerology design from the perspective of the PN robustness, link performance results using 90 GHz carrier frequency are provided. The numerical results demonstrate that new, higher subcarrier spacings are required to support high throughput, which requires larger changes in the physical layer design. It is also observed that new phase-tracking reference signal designs are required to make the system robust against PN. The results illustrate that single-carrier frequency division multiple access is significantly more robust against PN and can provide clearly larger PA output power than cyclic-prefix orthogonal frequency division multiplexing, and is therefore a highly potential waveform for sub-THz communications.

Serverless, the new buzzword, has been gaining a lot of attention from the developers and industry. Cloud vendors such as AWS and Microsoft have hyped the architecture almost everywhere, from practitioners' conferences to local events, to blog posts. In this work, we introduce serverless functions (also known as Function-as-a-Service or FaaS), together with on bad practices experienced by practitioners, members of the Tampere Serverless Meetup group.

The popularity of tools for analyzing Technical Debt, and particularly the popularity of SonarQube, is increasing rapidly. SonarQube proposes a set of coding rules, which represent something wrong in the code that will soon be reflected in a fault or will increase maintenance effort. However, our local companies were not confident in the usefulness of the rules proposed by SonarQube and contracted us to investigate the fault-proneness of these rules. In this work we aim at understanding which SonarQube rules are actually fault-prone and to understand which machine learning models can be adopted to accurately identify fault-prone rules. We designed and conducted an empirical study on 21 well-known mature open-source projects. We applied the SZZ algorithm to label the fault-inducing commits. We analyzed the fault-proneness by comparing the classification power of seven machine learning models. Among the 202 rules defined for Java by SonarQube, only 25 can be considered to have relatively low fault-proneness. Moreover, violations considered as 'bugs' by SonarQube were generally not fault-prone and, consequently, the fault-prediction power of the model proposed by SonarQube is extremely low. The rules applied by SonarQube for calculating technical debt should be thoroughly investigated and their harmfulness needs to be further confirmed. Therefore, companies should carefully consider which rules they really need to apply, especially if their goal is to reduce fault-proneness.

This paper explores the activity of coding with smart toy robots Dash and Botley as a part of playful learning in the Finnish early education context. The findings of our study demonstrate how coding with the two toy robots was approached, conducted and played by Finnish preschoolers aged 5-6 years. The main conclusion of the study is that preschoolers used the toy robots with affordances related to coding mainly in developing gamified play around them by designing tracks for the toys, programming the toys to solve obstacle paths, and competing in player-generated contests of dexterity, speed and physically mobile play.

The limitations of state-of-the-art cellular modems prevent achieving low-power and low-latency Machine Type Communications (MTC) based on current power saving mechanisms alone. Recently, the concept of wake-up scheme has been proposed to enhance battery lifetime of 5G devices, while reducing the buffering delay. The existing wake-up algorithms use static operational parameters that are determined by the radio access network at the start of the userâ™s session. In this paper, the average power consumption of the wake-up enabled MTC UE is modeled by using a semi-Markov process and then optimized through a delay-constrained optimization problem, by which the optimal wake-up cycle is obtained in closed form. Numerical results show that the proposed solution reduces the power consumption of an optimized Discontinuous Reception (DRX) scheme by up to 40% for a given delay requirement.

Providing sufficient mobile coverage during mass public events or critical situations is a highly challenging task for the network operators. To fulfill the extreme capacity and coverage demands within a limited area, several augmenting solutions might be used. Among them, novel technologies like a fleet of compact base stations mounted on Unmanned Aerial Vehicles (UAVs) are gaining momentum because of their time- and cost- efficient deployment. Despite the fact that the concept of aerial wireless access networks has been investigated recently in many research studies, there are still numerous practical aspects that require further understanding and extensive evaluation. Taking this as a motivation, in this paper, we develop the concept of continuous wireless coverage provisioning by the means of UAVs and assess its usability in mass scenarios with thousands of users. With our system-level simulations as well as a measurement campaign, we take into account a set of important parameters including weather conditions, UAV speed, weight, power consumption, and millimeter- wave (mmWave) antenna configuration. As a result, we provide more realistic data about the performance of the access and backhaul links together with the practical lessons learned about the design and real-world applicability of the UAV-enabled wireless access networks.

In this paper, we introduce a new fading model which is capable of characterizing both the shadowing of the dominant component and composite shadowing which may exist in wireless channels. More precisely, this new model assumes a κ-μ envelope where the dominant component is fluctuated by a Nakagami-m random variable (RV) which is preceded (or succeeded) by a secondary round of shadowing brought about by an inverse Nakagami-m RV. We conveniently refer to this as the double shadowed κ-μ fading model. In this context, novel closed-form and analytical expressions are developed for a range of channel related statistics, such as the probability density function, cumulative distribution function, and moments. All of the derived expressions have been validated through Monte-Carlo simulations and reduction to a number of well-known special cases. It is worth highlighting that the proposed fading model offers remarkable flexibility as it includes the κ-μ, η-μ, Rician shadowed, double shadowed Rician, κ-μ shadowed, κ-μ/inverse gamma and η-μ/inverse gamma distributions as special cases.

Graphics Processing Units (GPU) have been widely used in various fields of scientific computing, such as in signal processing. GPUs have a hierarchical memory structure with memory layers that are shared between GPU processing elements. Partly due to the complex memory hierarchy, GPU programming is non-Trivial, and several aspects must be taken into account, one being memory access patterns. One of the fastest GPU memory layers, shared memory, is grouped into banks to enable fast, parallel access for processing elements. Unfortunately, it may happen that multiple threads of a GPU program may access the same shared memory bank simultaneously causing a bank conflict. If this happens, program execution slows down as memory accesses have to be rescheduled to determine which instruction to execute first. Bank conflicts are not taken into account automatically by the compiler, and hence the programmer must detect and deal with them prior to program execution. In this paper, we present an algebraic approach to detect bank conflicts and prove some theoretical results that can be used to predict when bank conflicts happen and how to avoid them. Also, our experimental results illustrate the savings in computation time.

Semiconductor devices based upon silicon have powered the modern electronics revolution through advanced manufacturing processes. However, the requirement of high temperatures to create crystalline silicon devices has restricted its use in a number of new applications, such as printed and flexible electronics. Thus, developments with high mobility solution-processable metal oxides, surpassing α-Si in many instances, is opening a new era for flexible and wearable electronics. However, high operating voltages and relatively high deposition temperatures required for metal oxides remain impediments for the flexible devices. Here, the fabrication of low operating voltage, flexible thin film transistors (TFT) using a solution processed indium oxide In₂ O₃) channel material with room temperature deposited anodized high-κ aluminum oxide (Al₂ O₃) for gate dielectrics are reported. The flexible TFTs operates at low voltage V_ds of 2 V, with threshold voltage V_th 0.42 V, on/off ratio 10³ and subthreshold swing (SS) 420 mV/dec. The electron mobility (μ), extracted from the saturation regime, is 2.85 cm²/V.s and transconductance, gm, is 38 μS.

High-level synthesis tools aim to produce hardware designs out of software descriptions with a goal to lower the bar in FPGA usage for software engineers. Despite their recent progress, however, HLS tools still require FPGA target specific pragmas and other modifications to the originally processor-targeting source code descriptions. Customized soft core based overlay architectures provide a software programmable layer on top of the FPGA fabric. The benefit of this approach is that a platform independent compiler target is presented to the programs, which lowers the porting burden, and online repurposing the same configuration is natural by just switching the executed program. The main drawback, like with any overlay architecture, are the additional implementation overheads the overlay imposes to the resource consumption and the maximum operating frequency. In this paper we show how by utilizing the efficient structure of Transport-Triggered Architectures (TTA), soft-cores can be customized automatically to benefit from the flexible FPGA fabric while still presenting a comfortable software layer to the users. The results compared to previously published non-specialized TTA soft cores indicate equal or better execution times, while the program image size is reduced by up to 49%, and overall resource utilization improved from 10% to 60%.

The blockchain technology is currently penetrating different sides of modern ICT community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect. At the same time, the use of wearable and mobile devices may also become a part of blockchain operation, especially during the charging time. The paper considers the possibility of using a large number of constrained devices supporting the operation of the blockchain. The utilization of such devices is expected to improve the efficiency of the system and also to attract a more substantial number of users. Authors propose a novel consensus algorithm based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS). The paper first overviews the existing strategies and further describes the developed cryptographic primitives used to build a blockchain involving mobile devices. A brief numerical evaluation of the designed system is also provided in the paper.

In this paper we discuss how does the input magnitude data setting influence the behavior of error-reduction algorithm in the case of the one-dimensional discrete phase retrieval problem. We present experimental results related to the convergence or stagnation of the algorithm. We also discuss the issue of the zeros distribution of the solution, when the solution of the problem exists.

Tracking the location of people and their mobile devices creates opportunities for new and exciting ways of interacting with public technology. For instance, users can transfer content from public displays to their mobile device without touching it, because location tracking allows automatic recognition of the target device. However, many uncertainties remain regarding how users feel about interactive displays that track them and their mobile devices, and whether their experiences vary based on the setting. To close this research gap, we conducted a 24-participant user study. Our results suggest that users are largely willing - even excited - to adopt novel location-tracking systems. However, users expect control over when and where they are tracked, and want the system to be transparent about its ownership and data collection. Moreover, the deployment setting plays a much bigger role on people's willingness to use interactive displays when location tracking is involved.

Coarse-grained reconfigurable architectures and other exposed datapath architectures such as transport-triggered architectures come with a high energy efficiency promise for accelerating data oriented workloads. Their main drawback results from the push of complexity from the architecture to the programmer; compiler techniques that allow starting from a higher-level programming language and generate code efficiently to such architectures robustly is still an open research area. In this article we survey the known main sources of challenges and outline a generic processor architecture template that covers the most common architecture variations along with a proposal for a common code generation framework for such challenging architectures.

Most applications and services rely on central authorities. This introduces a single point of failure to the system. The central authority must be trusted to have data stored by the application available at any given time. More importantly, the privacy of the user depends on the service provider capability to keep the data safe. A decentralized system could be a solution to remove the dependency from a central authority. Moreover, due to the rapid growth of mobile device usage, the availability of decentralization must not be limited only to desktop computers. In this work we aim at studying the possibility to use mobile devices as a decentralized file sharing platform without any central authorities. This was done by implementing Asterism, a peer-to-peer file-sharing mobile application based on the Inter-Planetary File System. We validate the results by deploying and measuring the application network usage and power consumption in multiple different devices. Results show that mobile devices can be used to implement a worldwide distributed file sharing network. However, the file sharing application generated large amounts of network traffic even when no files were shared. This was caused by the chattiness of the protocol of the underlying peer-to-peer network. Consequently, constant network traffic prevented the mobile devices from entering to deep sleep mode. Due to this the battery life of the devices was greatly degraded.

Artificial Intelligence (AI) is one of the current emerging technologies. In the history of computing AI has been in the similar role earlier - almost every decade since the 1950s, when the programming language Lisp was invented and used to implement self-modifying applications. The second time that AI was described as one of the frontier technologies was in the 1970s, when Expert Systems (ES) were developed. A decade later AI was again at the forefront when the Japanese government initiated its research and development effort to develop an AI-based computer architecture called the Fifth Generation Computer System (FGCS). Currently in the 2010s, AI is again on the frontier in the form of (self-)learning systems manifesting in robot applications, smart hubs, intelligent data analytics, etc. What is the reason for the cyclic reincarnation of AI? This paper gives a brief description of the history of AI and also answers the question above. The current AI “cycle” has the capability to change the world in many ways. In the context of the CE conference, it is important to understand the changes it will cause in education, the skills expected in different professions, and in society at large.

The present contribution analyzes the performance of non-orthogonal multiple access (NOMA)-based user cooperation with simultaneous wireless information and power transfer (SWIPT). In particular, we consider a two-user NOMA-based cooperative SWIPT scenario, in which the near user acts as a SWIPT-enabled relay that assists the farthest user. In this context, we derive analytic expressions for the pairwise error probability (PEP) of both users assuming the both amplify-and-forward (AF) and decode-and-forward (DF) relay protocols. The derived expressions are expressed in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. In addition to this, we derive a simple asymptotic closed-form expression for the PEP in the high signal-to-noise ratio (SNR) regime which provide useful insights on the impact of the involved parameters on the overall system performance. Capitalizing on this, we subsequently quantify the maximum achievable diversity order of both users. It is shown that numerical and simulation results corroborate the derived analytic expressions. Furthermore, the offered results provide interesting insights into the error rate performance of each user, which are expected to be useful in future designs and deployments of NOMA based SWIPT systems.

The agricultural sector in Finland has been lagging behind in digital development. Development has long been based on increasing production by investing in larger machines. Over the past decade, change has begun to take place in the direction of digitalization. One of the challenges is that different manufacturers are trying to get farmers' data on their own closed cloud services. In the worst case, farmers may lose an overall view of their farms and opportunities for deeper data analysis because their data is located in different services. The goals and previously studied challenges of the 'MIKÄ DATA' project are described in this research. This project will build an intelligent data service for farmers, which is based on the Oskari platform. In the 'Peltodata' service, farmers can see their own field data and many other data sources layer by layer. The project is focused on the study of machine learning techniques to develop harvest yield prediction and find out the correlation between many data sources. The 'Peltodata' service will be ready at the end of 2019.

Software Defined Networking (SDN) is a new networking paradigm in which the control plane and data plane are decoupled. Throughout the recent years, a number of architectures have emerged for protocol-independent packet processing. One such architecture is the Protocol Independent Switch Architecture (PISA). It is a programmable and protocol-independent architecture composed of a number of Match and Action stages. Inside each of these stages is a crossbar to generate the search key and another crossbar to provide the input to the Action Units. In this paper, we design and explore alternative interconnection schemes with the aim of finding the most area- and power-efficient interconnection structure. Moreover, we propose further modifications to the interconnection structure, as a result of which the on-chip area of both match and action crossbars will be reduced by more than 70 % and power dissipation will be reduced by 25.8 % and 23.1 % for match and action crossbars respectively.

Universities are still mainly preparing students for the world, where 'do something useful', i.e. 'do something with your hands' was the main principle and work was done during strictly regulated time. But world has changed and traditional areas of human activity (what also are the main target in University courses) are rapidly diminishing. More important have become virtual products - computer programs, mobile apps, social networks, new types of digital currencies, IOT (voice in your bathroom suggesting to buy the next model of Alexa), video games, interactive TV, virtual reality etc. Most of these new areas are not present in current curricula and there are problems with involving them in curricula - (working) students know (some aspects of) these areas better than many of university teachers, since corresponding knowledge is not yet present in textbooks - it is present only on Internet. The Internet strongly influences both what we teach and how we teach.

One of the biggest problems in managing devices for the Internet of Things (IoT) is the ability for a management server to independently discover and retrieve data models for vendor-specific devices. At the same time, several device management methods also lack methods for device vendors to share their data models in a consistent manner. This paper presents the design and implementation of a repository that can flexibly accommodate many needs with regards to these issues, and allows device vendors to publish semantically similar data models as well as attach meta-data to these models. A Machine-to-Machine (M2M) communication interface also allows a management server to communicate with the repository. We show how these techniques can be used with the Lightweight Machine-to-Machine (LWM2M) standard.

Terahertz (THz) band communications, capable of achieving the theoretical capacity of up to several terabits-per-second, are one of the attractive enablers for beyond 5G wireless networks. THz systems will use extremely directional narrow beams, allowing not only to extend the communication range but also to partially secure the data already at the physical layer. The reason is that, in most cases, the Attacker has to be located within the transmitter beam in order to eavesdrop the message. However, even the use of very narrow beams results in the considerably large area around the receiver, where the Attacker can capture all the data. In this paper, we study how to decrease the message eavesdropping probability by leveraging the inherent multi-path nature of the THz communications. We particularly propose sharing the data transmission over multiple THz propagation paths currently available between the communicating entities. We show that, at a cost of the slightly reduced link capacity, the message eavesdropping probability in the described scheme decreases significantly even when several Attackers operate in a cooperative manner. The proposed solution can be utilized for the transmission of the sensitive data, as well as to secure the key exchange in THz band networks beyond 5G.

Internet-of-things (IoT) objects are expected to exceed 75 billion objects by 2020, and a large part of the expansion is expected to be at a finer granularity than existing silicon-based IoT objects (i.e. tablets and cell phones) can deliver [1]. Currently, placing a room light or a thermostat on the internet for remote control is considered progressive. However, if printed electronics can achieve performance increases, then IoT objects could be affixed to almost anything, such as coffee creamer cartons, cereal boxes, or that missing sock. Each of these IoT objects could be driving a sensor, perhaps position, temperature or pressure, essentially a multitude of applications. In order for IoT objects to emulate a simple postage stamp, with self-powering from energy scavenging and local energy storage, all housed in a non-toxic flexible form factor, advances in solution processable devices need to occur.

Digital predistortion (DPD) has important applications in wireless communication for smart systems, such as, for example, in Internet of Things (IoT) applications for smart cities. DPD is used in wireless communication transmitters to counteract distortions that arise from nonlinearities, such as those related to amplifier characteristics and local oscillator leakage. In this paper, we propose an algorithm-architecture-integrated framework for design and implementation of adaptive DPD systems. The proposed framework provides energy-efficient, real-time DPD performance, and enables efficient reconfiguration of DPD architectures so that communication can be dynamically optimized based on time-varying communication requirements. Our adaptive DPD design framework applies Markov Decision Processes (MDPs) in novel ways to generate optimized runtime control policies for DPD systems. We present a GPU-based adaptive DPD system that is derived using our design framework, and demonstrate its efficiency through extensive experiments.

In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients. To address the vanishing gradient issue, we study a framework, where we insert an intermediate output branch after each layer in the computational graph and use the corresponding prediction loss for feeding the gradient to the early layers. The framework-which we name Elastic network-is tested with several well-known networks on CIFAR10 and CIFAR100 datasets, and the experimental results show that the proposed framework improves the accuracy on both shallow networks (e.g., MobileNet) and deep convolutional neural networks (e.g., DenseNet). We also identify the types of networks where the framework does not improve the performance and discuss the reasons. Finally, as a side product, the computational complexity of the resulting networks can be adjusted in an elastic manner by selecting the output branch according to current computational budget.

Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurrence Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.

The performance of physical-layer security of the classic Wyner's wiretap model over Fisher-Snedecor composite fading channels is considered in this work. Specifically, F the main channel (i.e., between the source and the legitimate destination) and the eavesdropper's channel (i.e., between the source and the illegitimate destination) are assumed to experience independent quasi-static Fisher-Snedecor fading conditions, which have been shown to be encountered in realistic wireless transmission scenarios in conventional and emerging communication systems. In this context, exact closed-form expressions for the average secrecy capacity (ASC) and the probability of non-zero secrecy capacity (PNSC) are derived. Additionally, an asymptotic analytical expression for the ASC is presented. The impact of shadowing and multipath fading on the secrecy performance is investigated. Our results show that increasing the fading parameter of the main channel and/or the shadowing parameter of the eavesdropper's channel improves the secrecy performance. The analytical results are compared with Monte-Carlo simulations to validate the analysis.

The Fisher-Snedecor F distribution was recently proposed as an accurate and tractable composite fading model in the context of device-to-device communications. The present work derives the product of the Fisher-Snedecor F composite fading model, which is useful in characterizing fading effects in numerous realistic communication scenarios. To this end, novel analytic expressions are first derived for the probability density function, the cumulative distribution function and the moment of the product of N statistically independent, but not necessarily identically distributed, Fisher-Snedecor F random variables. Capitalizing on these expressions, we derive tractable closed-form expressions for channel quality estimation of the proposed model as well as the corresponding outage probability and average bit error probability for binary modulations. The offered results are corroborated by extensive Monte-Carlo simulation results, which verify the validity of the derived expressions. It is shown that the number of cascaded channels affects considerably the corresponding performance, as a variation of over an order of magnitude is observed across all signal-to-noise ratio regimes.

Markov Decision Processes (MDPs) provide important capabilities for facilitating the dynamic adaptation of hardware and software configurations to the environments in which they operate. However, the use of MDPs in embedded signal processing systems is limited because of the large computational demands for solving this class of system models. This paper presents Sparse Parallel Value Iteration (SPVI), a new algorithm for solving large MDPs on resource-constrained embedded systems that are equipped with mobile GPUs. SPVI leverages recent advances in parallel solving of MDPs and adds sparse linear algebra techniques to significantly outperform the state-of-the-art. The method and its application are described in detail, and demonstrated with case studies that are implemented on an NVIDIA Tegra K1 System On Chip (SoC). The experimental results show execution time improvements in the range of 65 % -78% for several applications. SPVI also lifts restrictions required by other MDP solver approaches, making it more widely compatible with large classes of optimization problems.

Data compression is a common requirement for displaying large amounts of information. The goal is to reduce visual clutter. The approach given in this paper uses an analysis of a data set to construct a visual representation. The visualization is compressed using the address ranges of the memory structure. This method produces a compressed version of the initial visualization, retaining the same information as the original. The presented method has been implemented as a Memory Designer tool for ASIC, FPGA and embedded systems using IP-XACT. The Memory Designer is a user-friendly tool for model based embedded system design, providing access and adjustment of the memory layout from a single view, complementing the 'programmer's view' to the system.

The aim was to study if odors evaporated by an olfactory display prototype can be used to affect participants' cognitive and emotion-related responses to audio-visual stimuli, and whether the display can benefit from objective measurement of the odors. The results showed that odors and videos had significant effects on participants' responses. For instance, odors increased pleasantness ratings especially when the odor was authentic and the video was congurent with odors. The objective measurement of the odors was shown to be useful. The measurement data was classified with 100 % accuracy removing the need to speculate whether the odor presentation apparatus is working properly.

Data collection requires homogenization of the data prior to processing it. This can create a challenge to the companies since data have varicose formats and schemas. This paper discusses the implementation of data collection framework using the PLANTCockpit open source platform, which is an integration platform for business processes. The framework is extended to fetch data from heterogeneous sources and then, allow the user to select the relevant data that matches his/her needs. In addition to this, the extendable framework also makes it possible to select the output data structure based on the user's requirements. Defining such a framework can reduce company's efforts for reshaping and modifying their architectures to handle new challenges posed by the ever-changing data. The framework not only restricts one to collection and transformation, but also provides an option to perform available processing techniques on the transformed data structure. The proposed framework has been tested on a cloud-based platform provided by the Cloud Collaborative Manufacturing Networks (C2NET) project.

There is a trend about the adoption of Knowledge Representation and Reasoning formalisms, such as ontologies, for industrial automation. For example, semantic models are used as knowledge bases that encapsulate different type of information of manufacturing systems, e.g., statuses and capabilities of their cyber and physical resources. Moreover, these models can be updated and accessed during runtime. In this context, models are becoming a critical part of the system infrastructure for both controlling and monitoring activities. However, models tend to be designed for specific purposes and not standardized. This is an issue because the employed formalisms, such as ontologies, emerged in order to bring an engineering tool for commonly classifying, defining, and sharing information. This article proposes the development of modular ontologies based on different parts of the ISA-95 standard for describing the product, process, and resource information of manufacturing systems. In addition, this research work demonstrates a set of semantic rules that may be used for inferring implicit knowledge of the ontology that permits the automatic checking of the required machines to manufacture different product variants.

Industry 4.0 is about the interconnectivity and digitalisation of industrial systems that need to be integrated in order to improve the efficiency of resources and, in turn, processes. Both research and commercial sectors are working towards addressing specific challenges, such as data modelling, collection and processing. A correct manipulation and interpretation of data is critical and, now, more difficult than ever due to the dramatic increment of the amount of data generated at different levels of enterprises. Ultimately, this research work presents a solution, integrated with an existing cloud-based platform, for collecting and processing real-time factory shop floor streams of data. Such solution is an IoT-based development, which consist on both IoT hub and gateway that permit the consumption and communication of device information. The required message exchange is done within state of the art technologies and protocols e.g., MQTT protocol and REST-based interface. The implementation of the solution is demonstrated through an industrial-based scenario.

Cyber-physical Systems (CPS) in industrial manufacturing facilities demand a continuous interaction with different and a large amount of distributed and networked computing nodes, devices and human operators. These systems are critical to ensure the quality of production and the safety of persons working at the shop floor level. Furthermore, this situation is similar in other domains, such as logistics that, in turn, are connected and affect the overall production efficiency. In this context, this article presents some key steps for integrating three pillars of CPS (production line, logistics and facilities) into the current smart manufacturing environments in order to adopt an industrial Cyber-Physical Systems of Systems (CPSoS) paradigm. The approach is focused on the integration in several digital functionalities in a cloud-based platform to allow a real time multiple devices interaction, data analytics/sharing and machine learning-based global reconfiguration to increase the management and optimization capabilities for increasing the quality of facility services, safety and energy efficiency and industrial productivity. Conceptually, isolated systems may enhance their capabilities by accessing to information of other systems. The approach introduces particular vision, main components, potential and challenges of the envisioned CPSoS. In addition, the description of one scenario for realizing the CPSoS vision is presented. The results herein presented will pave the way for the adoption of CPSoS that can be used as a pilot for further research on this emerging topic.

How to measure and train for adaptability has emerged as a priority in military contexts in response to emergent threats and technologies associated with asymmetric warfare. While much research effort has attempted to characterize adaptability in terms of accuracy and response time using traditional executive function cognitive tests, it remains unclear and undefined how adaptability should be measured and thus how simulation-based training should be designed to instigate and modulate adaptable behavior and skills. Adaptable reasoning is well-exemplified in the rescue effort of Apollo 13 by NASA engineers who repurposed available materials available in the spacecraft to retrieve the astronauts safely back to earth. Military leaders have anecdotally referred to adaptability as 'improvised thinking' that repurposes 'blocks of knowledge' to device alternative solutions in response to changes in conditions affecting original tasks while maintaining end-state commander's intent. We review a previous feasibility study that explored the specification of Reusable Modeling Primitives for models and simulation systems building on Dimensional Analysis and Design Structure Matrix for Complexity Management formal methods. This Dimensional Analysis Conceptual Modeling (DACM) paradigm is rooted in science and engineering critical thinking and is consistent with the stated anecdotal premises as it facilitates the objective dimensional decomposition of a problem space to guide the corresponding dimensional composition of possible solutions. Arguably, adaptability also concerns the capability to overcome contradictions, detections, and reductions, which we present in an exemplar addressing the contradiction of increased drag due to increased velocity inherent to torpedoes. We propose that the DACM paradigm may be repurposed as a critical thinking framework for teaching the identification of relevant components in a theater of military operations and how the properties of those components may be repurposed to fashion alternative solutions to tasks involving navigation, call-for-fires, line-of-sight cover, weather and atmospheric effect responses, and others.

This paper describes the core functionality and a proof-of-concept demonstration setup for remote 360 degree stereo virtual reality (VR) gaming. In this end-to-end scheme, the execution of a VR game is off-loaded from an end user device to a cloud edge server in which the executed game is rendered based on user's field of view (FoV) and control actions. Headset and controller feedback is transmitted over the network to the server from which the rendered views of the game are streamed to a user in real-time as encoded HEVC video frames. This approach saves energy and computation load of the end terminals by making use of the latest advancements in network connection speed and quality. In the showcased demonstration, a VR game is run in Unity on a laptop powered by i7 7820HK processor and GTX 1070 GPU. The 360 degree spherical view of the game is rendered and converted to a rectangular frame using equirectangular projection (ERP). The ERP video is sliced vertically and only the FoV is encoded with Kvazaar HEVC encoder in real time and sent over the network in UDP packets. Another laptop is used for playback with a HTC Vive VR headset. Our system can reach an end-to-end latency of 30 ms and bit rate of 20 Mbps for stereo 1080p30 format.

Due to their unconstrained mobility and capability to carry goods or equipment, unmanned aerial vehicles (UAVs) or drones are considered as a part of the fifth-generation (5G) wireless networks and become attractive candidates to carry a base station (BS). As 5G requirements apply to a broad range of uses cases, it is of particular importance to satisfy those during spontaneous and temporary events, such as a marathon or a rural fair. To be able to support these scenarios, mobile operators need to deploy significant radio access resources quickly and on demand. Accordingly, by focusing on 5G cellular networks, we investigate the use of drone-assisted communication, where a drone is equipped with a millimeter-wave (mmWave) BS. Being a key technology for 5G, mmWave is able to facilitate the provisioning of the desired per-user data rates as drones arrive at the service area whenever needed. Therefore, in order to maximize the benefits of mmWave-drone-BS utilization, this paper proposes a methodology for its optimized deployment, which delivers the optimal height, coordinates, and coverage radius of the drone-BS by taking into account the human body blockage effects over a mmWave-specific channel model. Moreover, our methodology is able to maximize the number of offloaded users by satisfying the target signal quality at the cell edge and considering the maximum service capacity of the drone-BS. It was observed that the mmWave-specific features are extremely important to consider when targeting efficient drone-BS utilization and thus should be carefully incorporated into analysis.

This paper presents a novel digital self-interference canceller for inband full-duplex radio transceivers. The proposed digital canceller utilizes a Volterra series with sparse memory to model the residual SI signal, and it can thereby accurately reconstruct the self-interference even under a heavily nonlinear transmitter power amplifier. To the best of our knowledge, this is the first time such a sparse-memory Volterra series has been used to model the self-interference within an inband full-duplex device. The performance of the Volterra-based canceller is evaluated with real-life measurements that incorporate also an active analog canceller. The results show that the novel digital canceller suppresses the SI by 34 dB in the digital domain, outperforming the state- of-the-art memory polynomial-based solution by a margin of 5 dB. The total amount of cancellation is nearly 110 dB with a transmit power of +30 dBm, even though a shared transmit/receive antenna is used. To the best of our knowledge, this is the highest reported cancellation performance for a shared-antenna full-duplex device with such a high transmit power level.

To overcome the limited coverage in traditional wireless sensor networks, mobile crowd sensing (MCS) has emerged as a new sensing paradigm. To achieve longer battery lives of user devices and incentivize human involvement, this paper presents a novel approach that seamlessly integrates MCS with wireless power transfer, named wirelessly powered crowd sensing (WPCS), for supporting crowd sensing with energy consumption and offering rewards as incentives. An optimization problem is formulated to simultaneously maximize the data utility and minimize the energy consumption for service operator, by jointly controlling wireless-power allocation at the access point (AP) as well as sensing-data size, compression ratio, and sensor transmission duration at the mobile sensor (MS). Given the fixed compression ratios, the optimal power allocation policy is shown to have a threshold-based structure with respect to a defined crowd-sensing priority function for each MS. Given fixed sensing-data utilities, the compression policy achieves the optimal compression ratio. Extensive simulations are also presented to verify the efficiency of the contributed mechanisms.

By 2020, unmanned ships such as remotely controlled boats and autonomous vessels would become operational, marking a technological revolution for the maritime industry. Such ships are expected to serve needs ranging from coastal ferries to open sea cargo handling. In this paper we detail the security vulnerabilities of such unmanned ships. The attack surface as well as motivations for attack attempts also are discussed to provide a perspective of how and why attacks are undertaken. Finally defence strategies are proposed as countermeasures.

The evolution of the Internet to an ubiquitous computing environment where massive amounts of devices will be connected. Sharing, receiving and acting upon data has brought in a problem of security. There are as many firmware and software update procedures as there are manufacturers. Therefore it would be good if a common solution could be found. We looked for suitable mechanisms in the past three years, to be used in Internet of Things networks as well as an up and coming research and standardization work. Our findings show that there indeed are good options for firmware update mechanisms that use state-of-The-Art technologies to deliver updates in a secure manner. While not all the mechanisms were specifically targeting deployment scenarios found in the Internet of Things, we still believe the concept of such update mechanism is suitable also for IoT use and thus can be adapted trivially to IoT networks and devices. We also propose a generic four-element model for secure firmware updates.

Close to 100% employment of students and easy access to abundance of information on Internet has essentially changed student's learning practices and their earlier knowledge background, especially on rapidly progressing field of Software Engineering. On workplace they have to use technologies, which are used in practice of their employing enterprise, but often do not understand the scientific and/or technological principles on which these technologies are based. They seek explanations on Internet, but information on Internet is often low quality, one-sided and presented with business targets on mind - to get more users to technologies developed and sold by a business enterprise. Thus university has to explain basic principles of technologies what students already know and have used and correct some popular beliefs, which are supported by software vendors and based their business interests. Non-formal sources of knowledge - workplace training and Internet - do not reduce teacher's task, but force teachers constantly study all new which appears in this field, thus increase teachers workload. Students increasing use of non-formal sources of knowledge imply need for flipping the process - instead of teaching students are set to learn from provided detailed tutorials. Use of Internet and work has made self-study, seeking information from Internet sources very customary for current students, thus such flipping worked very well in a game programming course provided by the first author.