In this paper, we address the problem of energy efficient resource optimization for downlink transmission in user-centric ultra-dense networks enabled by wireless access via nonorthogonal multiple access and wireless backhaul via beamforming. Our objective is to maximize the system energy efficiency by optimizing user/access point scheduling, subchannel assignment, and power allocation jointly. The problem is formulated as a nonconvex mixed-integer nonlinear programming problem which is NP-hard. We then transform it into a convex subproblem using the sum-of-ratios decoupling and the iterative successive convex approximation method. An overall algorithm is further developed to solve the subproblem iteratively. Simulation results show that the proposed algorithm has improved the system-wide energy efficiency significantly when compared to the benchmark scheme.

The harmonic state-space, the dynamic phasor, and the generalized dq modeling are three methods developed for linearization of ac power electronic systems. This paper reveals explicitly mathematical relationships between the three modeling methods in both time and frequency domain. Representations of linearized models in different reference frames and from time domain to frequency domain, as well as their transformations are elaborated step by step. Case studies on a three-phase voltage-source converter that is connected to an unbalanced grid verify the theoretical findings.

Machine learning has had a significant impact on the value of spectroscopy-based characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to unintentional bias during classification. To address this, we present a deep learning-based signal preprocessing method capable of handling all the defects of raw Raman spectroscopy data without any need of human intervention. To achieve this, a novel deep convolutional neural network (CNN) architecture was trained on randomly generated spectra with various defects. We demonstrate that the proposed network results in faster training and that it can perform complete spectral preprocessing in a single step with more accuracy, speed, and defect tolerance than conventional methods. These improvements make it an ideal candidate for hyperspectral imaging applications in which tens of thousands of raw spectra may need to be processed rapidly. The superiority of this method is demonstrated for simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, classification of low resolution Raman spectra of human bladder cancer tissue, and finally classification of SERS spectra from human placental extracellular vesicles (EVs). These findings encourage the future use of deep learning as a rapid and unbiased method of preprocessing spectroscopy data and may be particularly useful in biomedical applications involving large data sets from highly heterogeneous samples and signal defects of complex nature.

Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this paper, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our k-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management.

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.

Manuscript draft of a privacy-preserving system for detecting and predicting occupancy in a building, deployed in a real application

This paper deals with cell ID estimation in narrowband-internet of things (NB-IoT) system. The cell ID value is carried by the narrowband secondary synchronization signal (NSSS). We suggest a low-complexity sub-optimal estimator, based on the auto- correlation of the received observations. It is up to thirty times less complex than the optimal maximum likelihood (ML) estimator based on cross-correlation. In addition, we present three methods allowing the receiver to take advantage of the different repetitions of the NSSS. They are based on a hard decision after every estimation, a soft combination of the different observations of the NSSS, and an hybrid mix between the two firsts, respectively. The advantages and drawbacks of the presented techniques are stated, and a performance analysis is proposed, which is further discussed through simulations results. It is shown the that different methods reach the performance of ML after several repetitions for a lower overall complexity.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction from limited-angle data acquisition is severely ill-posed due to the highly scattered photons in the medium and the relatively small number of collected projections. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Both quantitative and qualitative results on phantom and real data indicate the superiority of our multitask method in the reconstruction and localization of lesions in tissue compared to state-of-the-art methods. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

Today, a wide variety of IoT applications and services have appeared and one of them is the Smart Home. The objective of this article is to present basic electrical wiring using Arduino UNO board and ESP8266, writing in C programming Language in Arduino software Integrated Development Environment (IDE) to develop and build a prototype Smart Home Lighting System that displays the lighting of two fluorescent lamps when the switch button was turned on by the user from ESP8266 Web Server. The system installation has a real-time synchronization that is necessary and safe for immersive IoT applications.

This is the accepted version of our article in IEEE EMC Magazine: R. Asadi, H.  Aliakbarian, G. Khayambashi and P. Majdolashrafi, “A case-study on the  effect of averaging duration on RF dosimetry of general public  environments,” in IEEE Electromagnetic Compatibility Magazine, vol. 9, no. 3, pp. 45-54, 3rd Quarter 2020, doi: 10.1109/MEMC.2020.9241551.

Advances in design and development of light-weight and low power wearable and mobile devices open up the possibility of lifetime extension of these devices from ambient sources through energy harvesting devices as opposed to periodically recharge the batteries. The most commonly available ambient energy source for mobile devices is Kinetic energy harvesters (KEH). The major drawback of the energy harvesters is limited effectiveness of harvesting mechanism near a fixed resonant frequency. It is difficult to harvest a reliable amount of energy from every forms of device motions with different excitation frequencies. To overcome this drawback, in this paper we propose an adaptive electromagnetic energy harvester which utilises spring characteristics to adapt its resonant frequency to match the ambient excitation frequency. This paper presents a prototype design and analysis of an adaptive electromagnetic energy harvester both in simulation and real. The harvester has tested using a specially designed experimental setup and compared with numerical simulations. The proposed solution generates 3.5 times higher maximum power over the default power output and 2.4 times higher maximum frequency compared to a fixed resonant frequency electromagnetic energy harvester.

Java Spring is an application development framework for enterprise Java. It is an open source platform which is used to develop robust Java application easily. Spring can also be performed using MVC structure. The MVC architecture is based on Model View and Controller techniques, where the project structure or code is divided into three parts or sections which helps to categorize the code files and other files in an organized form. Model, View and Controller code are interrelated and often passes and fetches information from each other without having to put all code in a single file which can make testing the program easy. Testing the application while and after development is an integral part of the Software Development Life Cycle (SDLC). Different techniques have been used to test the web application which is developed using Java Spring MVC architecture. And compares the results among all the three different techniques used to test the web application.

The paper adopts an inverse problem approach to examine the role of some 2D geometries in the source reconstruction from far zone data. It aims at evaluating the number of independent pieces of information, i.e. the number of degrees of freedom (NDF), of the source and pointing out the set of far zone fields corresponding to stable solutions of the inverse problem. Some of the results are relevant to the synthesis problem of conformal antennas, since a general comparison of different source geometries in providing radiation pattern specifications is proposed.

This paper investigates three-phase, component-wise real and reactive distribution locational marginal pricing (DLMP). Through the use of case studies, mixtures of positive and negative, as well as real and reactive, DLMP components are explored in detail. A modified three-phase unbalanced 69-node PG&E system is used to demonstrate the validity of the three-phase real and reactive DLMP model. Results emphasize the importance of three-phase pricing schemes and reactive power pricing. The results also indicate the DLMP mechanism can potentially assist in balancing power across phases. DLMP can serve as an economic price signal to efficiently operate the system while minimizing losses, voltage violations, congestion, and imbalances across all phases.

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications.

Non-orthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand of higher SE in the NOMA based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this paper proposes a multiple unmanned aerial vehicles (UAVs) aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a part of ground users (GUs) and share the sub-channels with the left GUs that communicate with the base station (BS) directly. Furthermore, this paper proposes a K-means clustering based UAV deployment and location based user pairing scheme to optimize the transceiver association for the multiple UAVs aided NOMA uplinks. Finally, a sum power minimization based resource allocation problem is formulated with the lowest quality of service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive experiments are conducted to compare the performances of the proposed schemes with those of the single UAV aided NOMA uplinks, G2G based NOMA uplinks, and the proposed multiple UAVs aided uplinks with a random UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and user pairing based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of the UAVs.

Massive multiple-input multiple-output (MIMO) systems operating in the centimeter-wave (cmWave) and millimeter-wave (mmWave) region offer huge spectral efficiencies, which enable to satisfy the urgent need for higher data rates in mobile communication networks. However, the proper design of those massive MIMO systems first requires a deep understanding of the underlying wireless propagation channel. Therefore, we present a fully-digital MIMO measurement system operating around 28 GHz. The system enables to take fast subsequent snapshots of the complex MIMO channel matrix. Based on this method we statistically analyze the time-dependent channel behavior, the achievable signal quality and spectral efficiency, as well as the channel eigenvalue profile. Furthermore, the presented calibration approach for the receiver enables an estimation of the dominant absolute angle of arrival (AoA) and allows us to draw conclusions about the line-of-sight (LOS) dominance of the scenario. In total, 159 uplink communication measurements over 20 seconds are conducted in three different small cell site scenarios to investigate the wireless propagation behavior. The measurements reveal the existence of several spatial propagation paths between the mobile transmitter and the base station. Furthermore, an insight into their likelihood in different propagation scenarios is also given.

This paper presents a GPU implementation of two foreground object segmentation algorithms: Gaussian Mixture Model (GMM) and Pixel Based Adaptive Segmenter (PBAS) modified for RGB-D data support. The simultaneous use of colour (RGB) and depth (D) data allows to improve segmentation accuracy, especially in case of colour camouflage, illumination changes and occurrence of shadows. Three GPUs were used to accelerate calculations: embedded NVIDIA Jetson TX2 (Maxwell architecture), mobile NVIDIA GeForce GTX 1050m (Pascal architecture) and efficient NVIDIA RTX 2070 (Turing architecture). Segmentation accuracy comparable to previously published works was obtained. Moreover, the use of a GPU platform allowed to get real-time image processing. In addition, the system has been adapted to work with two RGB-D sensors: RealSense D415 and D435 from Intel.