Future Access Enablers for Ubiquitous and Intelligent Infrastructures

Hazards in industry, wars and serious medical reasons determined the increase of the number of amputations and, thus, the need for designing prosthetics that replace the missing segment by imitating its natural movements. Research in prosthetics domain became, consequently, a primary activity both for engineers and physicians. Due to structural and functional acclimation to the complexity of human activities, one of the most difficult to approach limb of the human body is the hand. This paper is aimed to design an anthropomorphic prosthetic hand controlled based on surface electromyography sensors data acquired from two important muscles: flexor pollicis longus muscle and flexor digitorum profundus muscle. Another purpose of the paper consists in providing two main functions of the prosthetic hand, prehension and fingers flexion.

The healthcare system is one of the most important segments of the modern society. The support of cost effective and reliable digital solutions, can enhance the health of the patients and improve the healthcare system as a whole. The evolution of the eHealth systems shows immense benefits from implementation of modern technologies (e.g. smartphones, 3G and 4G networks, IoT sensor networks) improving quality of life and increasing comfort. One of the last technological breakthrough, the Blockchain technology, is promising even better improvements in eHealth systems by enhancing the privacy and security protection, easing the usability of the IoT devices, predicting potential hazardous illnesses and leveraging the comfort of living. This paper proposes a generic framework for a novel eHealth system based on the Blockchain technology that complements the development of the future 5G services.

Electrocardiogram (ECG) based affective computing is a new research field that aims to find correlates between human emotions and the registered ECG signals. Typically, emotion recognition systems are personalized, i.e. the discrimination models are subject-dependent. Building subject-independent models is a harder problem due to the high ECG variability between individuals. In this paper, we study the potential of two machine learning methods (Logistic Regression and Artificial Neural Network) to discriminate human emotional states across multiple subjects. The users were exposed to movies with different emotional content (neutral, fear, disgust) and their ECG activity was registered. Based on extracted features from the ECG recordings, the three emotional states were partially discriminated.

Monitoring of patient health data is an important part of the medical treatment of a patient. This paper studies how wireless smart technology for patient monitoring can be used and implemented in a hospital setup. The research focuses on the patients and the clinical perspective on how wireless monitoring of health data in the hospital can be utilized for support of mobility of the patients and for cost-efficiency of the patient pathway during hospitalization. Furthermore, is it investigated which strategic considerations should be made before developing and implementing the technology. It is proposed to design the wireless monitoring system in the hospital as a LPWAN network system with the DASH7 network protocol for data transmission as it would have the advantages of low cost, long range and low-energy consumption. The results indicate that patients will benefit from the implementation of a wireless monitoring system in terms of increased mobility at the hospital. Moreover, the clinical personnel could potentially achieve a decrease in workload and an improvement of the quality of treatments.

The trends in healthcare are continuously evolving towards a virtually rich personalized experience that involves human-to-human (H2H), human-to-machine (H2M) and machine-to-machine (M2M) interactions. This article proposes a platform that fosters an ecosystem of games and applies them to real-life situations to motivate an active lifestyle in elderly and health-impacted adults. The platform facilitates behavioral change through numerous games and applications that contribute to active living by introducing awards that can be earned upon reaching goals and can be redeemed in other applications of the GOAL ecosystem. The platform consists of core functionalities (account management, virtual reward system and activity recognition); tools for social inclusion (the social marketplace) and tools for healthy behavior (the goal setting service and the motivational agent). Multisensory technology has been proposed as means to enhance the evaluation on the achieved degree of user motivation. The platform applications are interactive games functioning as GOAL Coin Generators and/or Spenders.

Epilepsy is a chronic disease characterized by a deviation from the normal electrical activity of the brain leading to seizures caused by nerve impulses discharge. It is currently considered the fourth global neurological problem, being overcome only by diseases such as strokes. Moreover, according to the World Health Organization, nearly 50 million people suffer from epilepsy, with approximately 2.4 million patients annually diagnosed. It is worth mentioning that the elderly and children are the most exposed categories, but if the situation is considered, one of 26 people is likely to develop this condition at a point in life.Through three gates, the network can also be used for larger data sequences. Moreover, given that the EEG signals are significantly more dynamic and not linear, an LSTM-based approach has, by definition, an advantage given by the ability to isolate different characteristics of brain activity. In the United States, for example, this condition can be found at 48 people out of 100,000.

Utilising MEMS technology motion sensors and algorithms for motion data processing, a prototype device is proposed as a viable solution for movement diagnostics during sports or rehabilitation activities, based on the documented in the medical journals benefits of eccentric resistive training with full range of motion. The proposed device evaluates the quality of the movement by measuring the range of motion and both eccentric and concentric phases of the movement.

Modern air quality monitoring systems are characterised by high complexity and costs. The expensive embedded units such as sensor arrays, processors, power blocks, displays and communication units make them less appropriate for small indoor spaces.In this paper we demonstrate that two widely available, in private houses, sensors (for Humidity and Temperature) are promising alternative, to the expensive indoor air quality solutions, provided with intelligent data processing tools. Our findings suggest that neural network based data analytics system can learn to discriminate unusual indoor gases from normal home air components based only on temperature and humidity measurements.

In this paper we present several examples of video signal recovery from electromagnetic emissions generated by smartphones touchscreens as well as a number of measurements results performed in a specialized laboratory. We aimed the identification of the video signal parameters by using video images that were especially selected to facilitate this process. The measurements were performed by comparing two smartphones that have different display resolutions. In the final part we will also present a method to identify the emission frequencies for these compromising emanations.

Today the Internet of Things connects millions of devices around the world, offering access to new services and technology development capabilities. The use of multiple small-sized sensors makes it possible to control and manage different processes in a new, intelligent and flexible way. In this paper a survey of Low Power Wide Area Networks operating in the ISM band is conducted, examining future development trends, major challenges and applications. Using this type of network it becomes possible to transmit information over very long distances, minimize the energy used and deploy huge quantities of sensors over large geographical areas. This paper also presents an overview of RF Data Analytics as a modern technique to enhance the network performance of LPWANs. This can be achieved by examining raw RF data in order to predict the trends that characterise it and subsequently to implement a range of methods and algorithms for interference management and intelligent spectrum utilisation.

The current paper represents the portable system for monitoring of air quality by detecting the concentrations of an ammonia gas based on compact MOS sensor. The presence of the ammonia gas in the outdoor or indoor air is very important for the human health and safety because it may be dangerous in high concentrations. The continuous monitoring of the This portable system is designed to meet the requirements of the health protection not only in the indoor environments but also in the urban, industrial and rural locations by continuous measurement and transmission of the data to the remote server for real-time analysis of the working space air. The paper discusses the calibration and test of the metal-oxide sensors due to the very wide range of the initial sensitive layer resistance and calculation of the ammonia gas concentration in the air.

The paper present IoT Open Architecture Ground Control System by adaptive fusion intelligent interfaces to the robot vectors communications applied to network densification in 5G Era. Intelligent interfaces for optimization and decision-making using neural networks, neutrosophic logic and deep learning convolutional are analyzed. The proposed solution providing efficient information management and decision grounding at a tactical and operative level in a wide array of applications.

Nowadays, more than ever, agriculture area has to face difficult challenges due to numerous technological transformations used for increasing productivity and products quality. Due to the extended growth in agricultural product use, farmers and big companies operating in the “Big Data” area invest in precision agriculture by using sensor networks, drones, satellites and GPS tracking systems. Agricultural plants are extremely sensitive to climate change such as higher temperatures and changes in the precipitation area increase the chance of disease occurrence, leading to crop damage and even irreversible destruction of plants. Current advances in Internet of things (IoT) and Cloud Computing have led to the development of new applications based on highly innovative and scalable service platforms. IoT solutions have great potential in assuring the quality and safety of agricultural products. The design and operation of a telemonitoring system for precision farming is mainly based on the use of IoT platforms and therefore, this paper briefly presents the main IoT platforms used in precision agriculture, highlighting at the same time their main advantages and disadvantages. This overview can be used as a basic tool for choosing an IoT platform solution for future telemonitoring systems.

Music is often found in people’s lives, as a form of relaxation and inspiration. Small or medium concert venues use analog audio mixers to process the audio signals produced by each instrument and by the singer to deliver a pleasant sound to the audience. The problem is that the position of the mixer is, in most cases, on the stage or in a corner of the hall. This way the sound that the sound engineer hears will not be the same as what the audience hears. His place should be in public for a qualitative assessment of sound. Analog mixers cannot be controlled remotely, and the current alternative involves the replacement with a new and expensive digital audio mixer. The paper presents a cost-effective system that can be attached to any analog audio mixer, allowing to remote control main parameters like the attenuation of each signal. The remote control is a smartphone application, allowing easy further development and connectivity. The system was implemented, tested and the results and performances are presented in the paper, along with the details about the developed custom remote-to-mixer synchronous communication.

Modern buildings require different IT facilities, therefore integrated communication services have become a must. Although there are already commercial products on the market, most of them need well trained operators, while others require manual and time-consuming operations. The present paper introduces an intelligent displaying and alerting system (SICIAD), implemented over a communication infrastructure with support for wireless ePaper and iBeacon technologies to enhance displaying static and dynamic information, as well as to ease the indoor orientation of guests using smartphones. An Android mobile application is developed which enables indoor user location and guidance. Possible beneficiaries of such systems are educational and research institutions due to remote authentication support in research facilities through Eduroam technology. The paper gives functional validation and performance evaluation aspects are presented for the indoor positioning component of the proposed system.

Drone technologies have become integral component to a lot of civilian and military applications. Talking of wireless communication, Aerial Base Stations are being proposed to act as relay and/or to provide cellular communications to the ground users. Most of the work has been concentrated to enhance the coverage and capacity of the network by finding the optimal parameters like aerial BS height, power etc. using definite or statistical path loss models. However, no work has been done to analyze the path loss performance of aerial BS ad-hoc network in serving moving ground users aka Place Time Capacity (PTC). A concept of hovering base stations (HANET) has been proposed previously to serve the PTC problem and in this paper, we put forward the need for instantaneous path loss modeling for network situations where both user and BS are itinerant.

Facial expression recognition has been an active research topic for many years. In this paper a method for automatically recognizing pain intensity in images with facial expressions will be implemented. The method presented will contain a first step in which the face and the important points on the face will be located using the DLIB library. The second step consists of the calculation of HOG-type traits in order to describe the face found. The traits will be used to train a Random Forest (RF) regressor that will estimate the intensity of the pain. Training and testing will be done on the public UNBC-McMaster shoulder Pain Expression Archive database, using Python programming.

Modern day agriculture and civilization demand for increased production of food to feed fast increasing global population. New technologies and solutions are being adopted in agricultural sector to provide an optimal alternative to gather and process information while enhancing net productivity. At the same time, the alarming climate changes, increasing water crisis and natural disasters demand for an agricultural modernization with state-of-the-art technologies available in the market and improved methodologies for modern era agricultural and farming domains. Internet of things (IoT) has been broadly applied to every sector of agriculture and has become the most effective means & tools for booming agricultural productivity and for making use of full agricultural resources. The advent of Internet of Things (IoT) has shown a new way of innovative research in agricultural sector. The introduction of cloud computing and Internet of Things (IoT) into agricultural modernization will perhaps solve many issues. Based on significant characteristics of key techniques of IoT, visualization, Libelium and Adcon can build up data regarding agricultural production. It can accelerate fast development of agricultural modernization, integrate smart farming and efficiently solve the issues regarding agriculture. Our motive is to perform the research that would bring new solutions for the farmers to determine the most effective ways to manage and monitor the agricultural fields constantly.

Future 5G networks will demand high increases in capacity which are not acquirable by existing 4G implementations. The objective of this paper is to propose an open testbed solution in order to perform applied studies for 5G New Radio, using SDR, optimal parameter configuration, vendor equipment benchmarking, real life consistent tests for radio equipment behavior and create the possibility to extend the current platform to be able to accommodate future technical needs. GNU Radio, provides the opportunity to create software-defined radios based on virtual signal processing blocks using low-cost external RF hardware or simulation-like environment. This offers the opportunity to telecom mobile operators to set up the networks at the highest capabilities and also to have a clear vision before making strong investments in new equipment.

The increasing maturity of the concepts which would allow for the operation of a practical Cognitive Radio (CR) Network require functionalities derived through different methodologies from other fields. One such approach is Deep Learning (DL) which can be applied to diverse problems in CR to enhance its effectiveness by increasing the utilization of the unused radio spectrum. Using DL, the CR device can identify whether the signal comes from the Primary User (PU) transmitter or from an interferer. The method proposed in this paper is a hybrid DL architecture which aims at achieving high recognition rate at low signal-to-noise ratio (SNR) and various channel impairments including fading because such are the relevant conditions of operation of the CR. It consists of an autoencoder and a neural network structure due to the good denoising qualities of the former and the recognition accuracy of the latter. The autoencoder aims to restore the original signal from the corrupted samples which would increase the accuracy of the classifier. Afterwards its output is fed into the NN which learns the characteristics of each modulation type and classifies the restored signal correctly with certain probability. To determine the optimal classification DL model, several types of NN structures are examined and compared for input comprised of the IQ samples of the reconstructed signal. The performance of the proposed DL architecture in comparison to similar models for the relevant parameters in different channel impairments scenarios is also analyzed.

An important problem for massive multiple-input multiple-output (MIMO) systems operating with frequency-division duplexing (FDD) is to accurately estimate the channel response with low pilot signal overhead. Most existing algorithms for efficient channel estimation are based on compressive sensing (CS) and assume sparse structure of the channel vector. Relying on it, they try to minimize estimation error and reduce the number of required pilot signals. Utilizing real-world channel responses, we evaluate the performance of 11 state-of-the-art channel estimation algorithms for FDD massive MIMO systems. Results from simulation experiments with channel measurements for carrier frequency in the 2.4 GHz and 5 GHz bands for three environments and two levels of mobility are presented. Channel structures of theoretical and practically measured channels are compared and it is shown that the latter does not follow a specific sparse structure which leads to a significant increase in estimation errors according to our results. A comprehensive analysis of estimation quality and its dependence on signal-to-noise ratio (SNR) and number of pilot signals is provided. The results demonstrate that some algorithms perform well when applied to practical channels while others do not provide confident results. The effects of pilot matrix choice and angular domain channel representation are also studied and evaluated.

For the first time a queue, related to the shortage of network resources, is included in a model of overall telecommunication system with finite number of users and facilities which makes the model closer to the real system. The service in the queue depends on feedbacks of call attempts and of the state and duration of services in the overall system. The server of the queuing system has more than one exits. The results presented are a base for future development of tools for management, design, dimensioning and redimensioning of the system.

In this paper a simple test-bed to evaluate the effect of an unmodulated disrupting signal on a TETRA π/4-DQPSK (Differential Quadrature Phase-Shift Keying) signal has been developed and implemented, such that the transmitted signal does not affect other communication system existing in the same area. An unmodulated disrupting signal, with increasing amplitude, has been overlapped on the transmitted data and the parameters of the received signal has been evaluated with an Agilent Vector Signal Analyzer model 89600. Based on the results obtained, several interesting conclusions have been highlighted at the end.

Jamming as a form of denial-of-service is a commonly-used attack initiated against security at the physical layer of a wireless system. This paper starts with an overview of various types of jamming and measures for its detection. Then, a number of methods for jamming mitigation that can be used at the physical layer are discussed and compared according to their main advantages and drawbacks.

The main challenge of MNOs (Mobile Network Operators) is providing multimedia services with high performance. The 4G/LTE technology has been developed to meet user requirements and provide high network performance. In order to monitor and optimize the network performance, there is a need of using Key Performance Indicators (KPIs). The KPIs can control the quality of provided services and achieved resource utilization. These indicators are categorized into the following subcategories: accessibility, retainability, mobility, integrity and availability. The presented analysis is performed on real network implemented by Telecom of Kosovo (TK) that is the main mobile Operator in Kosovo. Measurements and analysis are focused on a 24-cell cluster of 4G/LTE TK.

Multi-Active Multi-Passive (MAMP) antenna arrays with reduced number of active elements are studied, for matching the patterns of all-active uniform linear arrays. Based on previous work on MAMP antenna arrays, we present a novel configuration, namely a circular one. By jointly calculating the PEs’ loads and baseband weights of the proposed MAMP array, we can produce a radiation pattern similar to that of a ULA with accuracy up to 97.5%, while the number of AEs is reduced by 33% and in some cases with suppressed side lobes. Moreover, a reduction in the width of the array by 3 times is achieved. Thus, the complexity, compactness and cost of the antenna array can be reduced without compromising the quality of the resulting beam.

The study aims to present virtual prototyping applicability for design and evaluation of complex system of automotive industry. It presents a new principle design solution and illustrates design development, based entirely on virtual prototyping. The design concept is to provide solution for radiators fan and its shroud for high speeds when the fan acts like resistance. Multiple design variants are examined using virtual prototype of radiators, fan, shroud and all engine components. Developed design variants are compared by their performance both at low and high speeds.

The high degree of automation in the use of electronic control in all industrial and mobile applications is most often done by hydraulic proportional control devices, where the regulating element changes remotely in proportion to the electrical signal. This provides the possibility to change the parameters of the hydraulic energy – flow rate and pressure – a realization of adaptive control by proportional electric control. The focus of this study is set on piezoelectric actuators that are electromechanical transducers, suitable for driving and controlling high-speed hydraulic actuators and relatively small insensitive zones. Detailed analysis of various existing designs is performed prior to development of conceptual model. Further, a design exploration is performed through virtual prototypes that helps studying in high level of detail various work parameters. It is of great importance for successive design development as some of the controlled parameters (as deformations) are very sensitive and has great influence over device performance.

This paper demonstrates implementation of virtual prototyping approach in early stage of design concept evaluation. Examined structure is of contemporary smart pole design with integrated telecommunication equipment. The combination of integrated design (inside poles) and contemporary electronic equipment thermal management leads to the need of careful examination of thermal behaviour of entire structure. Most important issue is connected to the problem how to transfer generated heat to the environment. Presented study is performed through multiphysics analyses – thermal CFD (Computational Fluid Dynamics) – using virtual prototyping techniques to assess several design variants performance parameters. Used virtual prototypes enable to view in detail heat transfer process and to reach a better solution for cooling components placement. Each design parameter is assessed and further recommendations are formed for design improvement. Final design uses fans placed on the top of the pole structure leading to allowable thermal loads over electronic equipment.

Personal implants for reconstruction of craniofacial harms become more and more important due to their better performance than modelling titanium mesh or alloplastic material during surgical operation. This is due to the good fit in the implant area, reduced surgical time and better cosmetic results. The creating of such implants is a challenging task. In this article structured process workflow with clearly defined steps was introduced. All of the steps were evaluated with solving of clinical case. In this first article the reconstruction from CT-data and 3D modelling of custom implants for the purpose of cranioplasty were reviewed in details.

Using of personal implants for reconstruction of craniofacial harms become more and more important due to the better performance, good fit in the implant area, reduced surgical time and better cosmetic results then traditional mesh. Although creating of such implants is a complex task, but in this article structured process workflow with clearly defined steps was introduced. All of the steps were evaluated with solving of clinical case. In this second article using innovative manufacturing methods based on 3D Reconstruction/Modelling and final result were explored in details.

This paper presents a novel modelling method for planar defected ground structure (DGS) square open loop resonator filters. The increased complexity of the coupling mechanism between the resonators and the impossibility to analytically calculate the coupling coefficients created the need of accurate modelling of the coupled resonators. Design process requires to calculate the filter dimension for the given coupling coefficient. A novel method based on artificial neural networks (ANNs) is proposed in this paper. ANNs are used to develop the filter forward and inverse models aimed to calculate the spacing between the resonators for predetermined coupling coefficients from the approximation. An example filter is designed, simulated and measured. A very good agreement between the measurements and the filter requirements is observed.

The paper describes a systematic approach for a precise short-time cloud coverage prediction based on an optical system. We present a distinct pre-processing stage that uses a model based clear sky simulation to enhance the cloud segmentation in the images. The images are based on a sky imager system with fish-eye lens optic to cover a maximum area. After a calibration step, the image is rectified to enable linear prediction of cloud movement. In a subsequent step, the clear sky model is estimated on actual high dynamic range images and combined with a threshold based approach to segment clouds from sky. In the final stage, a multi hypothesis linear tracking framework estimates cloud movement, velocity and possible coverage of a given photovoltaic power station. We employ a Kalman filter framework that efficiently operates on the rectified images. The evaluation on real world data suggests high coverage prediction accuracy above 75%.

We propose a stock market software architecture extended by a graphics processing unit, which employs parallel programming paradigm techniques to optimize long-running tasks like computing daily trends and performing statistical analysis of stock market data in real-time. The system uses the ability of Nvidia’s CUDA parallel computation application programming interface (API) to integrate with traditional web development frameworks. The web application offers extensive statistics and stocks’ information which is periodically recomputed through scheduled batch jobs or calculated in real-time. To illustrate the advantages of using many-core programming, we explore several use-cases and evaluate the improvement in performance and speedup obtained in comparison to the traditional approach of executing long-running jobs on a central processing unit (CPU).

Energy efficiency is a process under development and execution in all levels of society and economy, mostly driven by the environment protection interests. One of dilemmas in this process is the interest of the electricity provider companies, what kind of model to use in order to secure their profitability and benefits from energy efficiency projects deployment? This paper is presenting an ICT model for energy efficiency, model with scalable development, starting on a level of fundamental and currently available resources. The model is consumer centric and integrates communication tools. Using the approach of cooperative game theory, we are analyzing if this model is beneficial for all stakeholders in energy efficiency chain, the providers and the consumers. Having in mind the diversity of markets for electricity, in our case we deal with the simplest scenario, considering provider – consumer relation in two regimes of electricity network stage, peak and normal load, as the baseline from where the specific commercial cases could be further developed.

Information security is playing big role in the computer technologies. Its job is to detect unauthorized violation of the information integrity, secure it and also recover it, if the integrity was violated. One of the things that can alter an information are computer viruses. One of the task of the information security is also to detect these malicious applications and prevent their goal. This can be achieved in various techniques and one of them is signature based virus scanning. This technique uses a virus database (virus signatures) to detect if a file or application is infected with a specific virus. In this paper we are going to see in more details how is this implemented, which algorithm are mostly used and also try to improve its performance by parallelizing it on GPU by using CUDA. We are also going to see how CUDA utilizes large number of threads to solve a specific problem and use it to implement a parallel signature based virus scanner. Later we are going to see the performance benchmarks of the conducted experiments and discuss them and give a final conclusions for the usage of a GPU in signature based virus scanning.

Mass spectrometry is an optoelectronic method of determining organic substances by comparing their mass spectrum with mass spectra found in system libraries. In the case of biological products, substances of interest, biotic or xenobiotics, may be “hidden” from the background of the analyzed matrix noise, which alters the major aspect of the mass spectrum obtained and faces the impossibility of their identification. A gas chromatograph coupled with mass spectrometer (GC-MS) Varian was used, to develop a selected ion monitoring (SIM) method for increasing the signal-to-noise ratio for identifying the disulfoton in urine samples.

With the evolution of technology, we are witnessing the development of mobile terminals that are getting closer to a personal computer in terms of features and applications. At the same time, there is an increase in the number of mobile device users, which also leads to an increase in the use of online shopping or finance management applications. Hence, mobile terminals become a target for cyber criminals. Starting from the analysis of the current situation in the world regarding cyber security technology and solutions, we aim to build an integrating software platform for mobile malware analysis. The aim of the research is to develop a software platform that integrates the malware analysis procedures for most of the existing mobile terminals. So, the main objective of this article is to analyze the quality of cyber-protective solutions for mobile devices. We present our experiments which may bring solutions for some of the major vulnerabilities like active development of mobile malware, and hacking. Moreover, in this article we discuss the issue of security on Android, making use of security platform like Kali which permits to use different kinds of security analysis programs.

The latest technologies advancement in NLP (Natural Language Processing) solution allows developing innovative tools that enrich customer experience with products and services. Contact Center environments gradually adopted real-time analytics solutions, and latest research is focusing on how to integrate social media channels. Based on the work made in SoMeDi and Speech2Processes projects, we propose an innovative chatbot platform that integrates data mining and sentiment analysis technologies. The aim is to offer insight into customer preferences by using DII (Digital Interaction Intelligence) and assist in mitigating several know issues in Contact Center environments.