The paper is dedicated to the multipath angle of arrival (AOA) estimation problem in millimeter wave (mmWave) 5G NR communication system. The case of a phased antenna array with a single digital port is considered. In this scenario, the conventional highly accurate subspace-based algorithms cannot be applied because of hardware restrictions. We proposed a novel subspace-based algorithm called power-based root minimal polynomial method (PR-MPM) that uses the spatial power spectrum to get an approximation of the signal correlation matrix. The power spectrum is measured via the conventional beam sweeping procedure over a finite number of directions. The efficiency of the proposed method is studied using the high-realistic ray-tracing-based channel model applied in mmWave IEEE 802.11ay standard. Simulation results show that AOAs can be precisely estimated using only single-port power measurement.

Non-homogeneous, bi-directional glass fabric reinforced thermoplastic (GFRT) composites are used in the automobile industry to produce economic, environmentally friendly and recyclable vehicles. Despite the fact that GFRT materials have superior mechanical characteristics and lower manufacturing cost compared with traditional materials, they are prone to significant deformation and defect emergence during the forming/manufacturing of parts from prefabricated laminates. Some defects, especially hidden ones like delamination and consolidation defects, can significantly reduce the mechanical properties of the final parts. It is essential to use 3D non-destructive testing (NDT) technology to detect and identify such hidden defects. This research investigates the ability of 3D THz imaging to detect hidden defects (i.e., delamination and consolidation) in GFRT composite materials. Frequency modulated continuous wave (FMCW) technique is used to generate 3D-THz cross-sectional images. Non-defective and defective samples with a thickness of 1.5 mm have been measured with two 3D THz imaging systems operating in the frequency range from 238 to 316 GHz and from 499 to 733 GHz. In addition, a simulation of the reflected FMCW spectrum is presented to analyze the measurements and intercompare the performance of the two imaging systems. A systematic analysis in a large sample set demonstrates that a delamination defect with a thickness of 125 µm as well as a consolidation defect with a difference in thickness less than 0.4 mm can be successfully detected by the 700-GHz imaging system.

This IRB approved study assessed the performance of Triage, an AI application, in determining the malignancy of lesions. The images were ran through the Triage application and the same images were assessed by primary care physicians and dermatologists. The top diagnosis given by the AI application was taken into account while the medical providers were asked to label the images as benign or malignant.

To appear in IEEE In recent years, Federated Edge Learning has gained interest from both industry and academia for deployment at the wireless network edge. However, some resource-restricted edge devices (EDs) bear more computation and communication loads due to the heterogeneity of data and resources. Several approaches have been proposed in the literature to reduce energy costs by scheduling only a few EDs to complete training tasks based on their energy budgets. Nevertheless, from a practical perspective, the incongruent data distribution cannot be captured, resulting in a biased model for EDs that are frequently selected. Furthermore, the frequently scheduled devices deplete their energy quickly, making them inaccessible. Thus, this paper proposes a novel scheduling policy based on the historical participation of each ED that ensures an unbiased model while balancing learning tasks so that all EDs consume equivalent energy at the end of the training. We formulate an optimization problem based on Jain’s fairness index, followed by tractable algorithms to solve this problem. Extensive experiments have been conducted, and the results show that the proposed algorithm balances the energy consumption among EDs and accelerates the convergence rate while achieving satisfactory performance.

The visions of 5G and Beyond (B5G) imply unprecedented expectations toward high-performing connectivity services in both public and private networks. Connectivity services that offer performance guarantees along multiple Quality of Service (QoS) dimensions are partially available today, but are confined to (virtual) private network services. However, open and equal access to public and Internet-scale Specialized Connectivity Services (SCS) delivered on-demand with interconnections across networks and support for mixed traffic modes that go beyond traditional best effort does not exist. In this paper, we argue that this is a huge industrial and societal problem that needs a solution. However, this problem is highly complex and multifaceted, and there are many reasons why we are more or less locked into the status quo. The paper identifies the stumbling blocks and proposes a set of solution elements to take us across these hurdles, alongside related research topics. This includes an approach to “Multi-Level Best-Effort (MLBE)” and evolved net neutrality. Models and simulations are provided showing how a mixed traffic mode approach provides anticipated benefits. Arguments are given why the context brought by B5G will put us into conditions for change, allowing public SCS eventually at a global scale.

This paper proposes synchronous grant-free non-orthogonal multiple access (GF-NOMA) frameworks that effectively integrate UE clustering and low-complexity power control to facilitate power reception disparity required by the power domain NOMA. While single-level GF-NOMA (SGF-NOMA) designates an identical transmit power for all user equipments (UEs),  multi-level GF-NOMA (MGF-NOMA) groups UEs into partitions based on the sounding reference signals strength and assign partitions with different identical power levels. Based on the objective of interest (e.g., max-sum or max-min rate), the proposed UE clustering scheme iteratively admits UEs to form clusters, whose size is dynamically determined based on the number of UEs and available resource blocks (RBs). Once the UEs are acknowledged with power levels and allocated RBs through random access response (RAR) messages, UEs can transmit anytime without grant acquisition. Numerical results show that the proposed GF-NOMA frameworks can compute clusters in the order of milliseconds for hundreds of UEs. The MGF-NOMA can reach up to 96-99% of the optimal benchmark max-sum rate, the SGF-NOMA reaches 87% of the optimal benchmark max-sum rate at the same power consumption. Since the MGF-NOMA and optimal benchmark enforces the strongest and weakest channel UEs to transmit at maximum and minimum transmit powers, respectively, the SGF-NOMA also offers a significantly higher energy consumption fairness and network lifetime as all UEs consume equal transmit powers. While the MGF-NOMA delivers an inferior max-min rate performance, the SGF-NOMA is shown to reach 3x106 MbpJ energy efficiency compared to 1x107 MbpJ

Smart homes are becoming increasingly more com- plex and difficult to defend. This paper explores how humans can interact with intelligent virtual agents to improve their awareness and detection of threats in smart homes. Mica Endsley’s situa- tional awareness (SA) model was adopted and used to assess how participants perceive device activity, comprehend this in the context of their environment, and project this knowledge to detect if a threat exists. A pretest-posttest study demonstrated a statistically significant improvement in accuracy and efficiency when detecting threats using the conversational agents. In addi- tion, a qualitative approach was also taken to examine usability in relation to a users preferred learning modality. Participants reported being more confident at detecting threats across the three elements of the SA model when using the conversational agents as part of a multi-modal approach.

In 2019 appeared a new Coronavirus Disease (COVID-19) in China, spreading rapidly globally and causing a pandemic with high infection and death numbers. To prevent a collapse of the health institutions, accurate decision making about assignments of intense care units (ICU) is required, depending on the probable outcome. The usage of machine learning (ML) for other medical fields had been successful before. So we applied ML techniques to a dataset of COVID-19 and influenza patients from Mexico to predict the severity of an individual’s infection regarding risk factors including, but not limited to, chronic obstructive pulmonary disease (COPD), cardiovascular disease, diabetes, asthma, immunosupression, and obesity. We conducted two experiments, one on hospitalised patients and the other one on a balanced dataset. The resulting applications should not be used as a diagnostic tool yet, due to a relatively short time period of data collection and 74.64% accuracy for the first experiment and 82.61% accuracy for the second one. Nonetheless it is a good starting point to continue research about predicting COVID-19 infection’s outcome based on risk factors.

Speech enhancement (SE) systems aim to improve the quality and intelligibility of degraded speech signals obtained from far-field microphones. Subjective evaluation of the intelligibility performance of these SE systems is uncommon. Instead, objective intelligibility measures (OIMs) are generally used to predict subjective performance increases. Many recent deep learning based SE systems, are expected to improve the intelligibility of degraded speech as measured by OIMs. However, validation of the OIMs for this purpose is lacking. Therefore, in this study, we evaluate the predictive performance of five popular OIMs. We compare the metrics’ predictions with subjective results. For this purpose, we recruited 50 human listeners, and subjectively tested both single channel and multi-channel Deep Complex Convolutional Recurrent Network (DCCRN) based speech systems. We find that none of the OIMs gave reliable predictions, and that all OIMs overestimated the intelligibility of ‘enhanced’ speech signals.

In this paper, a novel regular hexagonal is proposed to enhance the signal energy efficiency of QAM constellations. In the proposed constellation, the distribution of its constellation points are based on equilateral triangle patterns, therefore the Euclidean distance among its adjacent constellation points is equal, which can make the best use of the signal transmission energy.

Code smells are code structures that harm the software’s quality. An obstacle to developing automatic detectors is the available datasets’ limitations. Furthermore, researchers developed many solutions for Java while neglecting other programming languages. Recently, we created the code smell dataset for C# by following an annotation procedure inspired by the established annotation practices in Natural Language Processing. This paper evaluates Machine Learning (ML) code smell detection approaches on our novel dataset. We consider two feature representations to train ML models: (1) code metrics and (2) CodeT5 embeddings. This study is the first to consider the CodeT5 state-of-the-art neural source code embedding for code smell detection in C#. To prove the effectiveness of ML, we consider multiple metrics-based heuristics as alternatives. In our experiments, the best-performing approach was the ML classifier trained on code metrics (F-measure of 0.87 for Long Method and 0.91 for Large Class detection). However, the performance improvement over CodeT5 features is negligible if we consider the advantages of automatically inferring features. We showed that our model exceeds human performance and could be helpful to developers. To the best of our knowledge, this is the first study to compare the performance of automatic smell detectors against human performance.

Robotics is widely seen as a key enabling technology for the society of tomorrow. This work examines the role of robotics and intelligent medical devices in Intensive Care Medicine. Demographics predict that more elderly patients will need to be treated with fewer healthcare personnel, calling for innovations in ICU management.  Robotics is a key enabling technology for the 21st century and may help to smooth the foreseeable workload/manpower disparity in medicine. Studying the application of robotics in the ICU in a manner beneficial for patients and accepted by intensive care team is desirable. Financial sustainability is essential for the introduction of these new technologies to the ICU. This study therefore assesses the state of the art in robotics in intensive care medicine and identifies opportunities for progress, using an observational approach in a teaching hospital ICU in combination with an in-depth review of the literature and a survey of the market. Tasks amenable to robotics are identified, their acceptability to patients and caregivers are examined and their quantitative contribution to future management of an intensive care unit is assessed.

Surrogate-assisted evolutionary algorithms (SAEAs) have attracted considerable attention for reducing the computation time required by an EA on computationally expensive optimization problems. In such algorithms, a surrogate model estimates the solution evaluation with a low computing cost and is used to obtain promising solutions to which the accurate evaluation with an expensive computation cost is then applied. This study proposes a novel pairwise ranking surrogate model called the Extreme Learning-machine-based DirectRanker (ELDR). ELDR integrates two machine learning models: extreme learning machine (ELM) and DirectRanker (DR). ELM is a single-layer neural network capable of fast learning, whereas DR uses pairwise learning to rank using a neural network developed mainly for information retrieval. To investigate the effectiveness of the proposed surrogate model, this study first examined the estimation accuracy of ELDR. Subsequently, ELDR was incorporated into a state-of-the-art SAEA and compared with existing SAEAs on well-known real-valued optimization benchmark problems. The experimental results revealed that ELDR has a high estimation accuracy even on high-dimensional problems with a small amount of training data. In addition, the SAEA using ELDR exhibited the highest search performance compared with other existing SAEAs, especially on high-dimensional problems.

This survey paper aims to summarize and emphasize the security threats within Big Data framework, in addition, it is worth mentioning research work related to Big Data Analytics (BDA).

We experimentally demonstrate enhanced spectral broadening of femtosecond optical pulses after propagation through silicon-on-insulator (SOI) nanowire waveguides integrated with two-dimensional (2D) graphene oxide (GO) films. Owing to the strong mode overlap between the SOI nanowires and the GO films with a high Kerr nonlinearity, the self-phase modulation (SPM) process in the hybrid waveguides is significantly enhanced, resulting in greatly improved spectral broadening of the femtosecond optical pulses. A solution-based, transfer-free coating method is used to integrate GO films onto the SOI nanowires with precise control of the film thickness. Detailed SPM measurements using femtosecond optical pulses are carried out, achieving a broadening factor of up to ~4.3 for a device with 0.4-mm-long, 2 layers of GO. By fitting the experimental results with theory, we obtain an improvement in the waveguide nonlinear parameter by a factor of ~3.5 and the effective nonlinear figure of merit (FOM) by a factor of ~3.8, relative to the uncoated waveguide. Finally, we discuss the influence of GO film length on the spectral broadening and compare the nonlinear optical performance of different integrated waveguides coated with GO films. These results confirm the improved nonlinear optical performance for silicon devices integrated with 2D GO films.

Solving partial differential equations by separation of variables is historically thought to require equating the parts to a constant. Shown, with robust observational support, is that equating to a function of time yields a dimension of novel solutions to the wave equation. They transform known solutions in time, represent Doppler with acceleration, and correct wave idealizations that wrongly constrained fundamental physics, as well as communication and spectrum use. This is a draft version meant to time-stamp & share for comments mainly the following: discovery of incompleteness of Fourier’s method and the full separation of variables for the wave equation (Section 2, Appendices A, B); discovery of Green’s function wave solutions with intrinsic phase acceleration and expansion (Section 3); first explanation of the range data lags in the flyby anomalies (Sections 4, 5 and Appendix C); elegant proof of redundancy of quantization postulate (improving over arXiv:1602.04136, Appendix D); and possible redundancy of speed of light postulate over variable measuring rods and clocks (Section 7 conclusion). \tightlist An equivalence principle between true acceleration in Doppler and uplink transmitter frequency ramp appears already implied by the similarity of the anomalies with ramping (NEAR) and without (Rosetta 2005). This revision corrects the volume of tracking data that was involved in the NASA/ESA trajectory analyses (pp 10,11).

This paper deals with the orthogonal chirp division multiplexing (OCDM) modulation, and its capability to generate inherited waveforms, such as the orthogonal frequency division multiplexing (OFDM) or precoded OFDM, covering a large number of use cases. To this end, we first prove that the OCDM signal can be generated by a transmission chain composed of a discrete Fourier transform (DFT) matrix, followed by a multiplication by a chirp, and then an inverse DFT (IDFT). Then, we show that the OCDM transmission chain can be split into 6 sub-blocks from which we can generate numerous inherited waveforms. Thus, the OCDM waveform is an interesting candidate for flexible communication systems such as multiple radio access technology (multi-RAT).

We investigate the re-entrant undercut profile resulting from Au wet etching for patterning micron range thick films using an aqua regia-based solution in comparison with an iodine-iodide-based commercial etchant. Our work discriminates between two undercutting mechanisms: galvanic acceleration of etch rate at the Au adhesion or barrier layer, and delamination-based undercutting. We tracked etch outcomes of feature size reduction from photoresist size, undercut Au in cross-section and lift-off of small (5-10 μm) features. Results indicate that galvanic undercutting is well-mitigated by the aqua regia solution compared to commercial etchant results. Good Au adhesion eliminates undercut for 500 nm-thick Au and mitigates it by ~80% for 1 μm-thick Au. We discuss the electrochemical origin of this mitigated galvanic undercut.