The primary contribution of this paper is to develop a realistic yet mathematically tractable channel emulator for underwater acoustic communications (UWAC) channels. Our proposed emulator is based on the Wide-Sense Stationary Uncorrelated Scattering (WSSUS) assumption, where the channel samples are generated using finite sum-of-sinusoids (SOS) with uniformly distributed phases. The MATLAB-based Simulink platform and its associated block-set elements are used to generate numerical plots for amplitude distribution, Doppler frequency and channel impulse response. This Simulink-based design offers an added advantage over prevalent UWAC channel simulators, as it can be directly adapted to FPGA platform for hardware implementation.

This paper presents an extension of the models used to generate fractal traffic flows in high-speed computer networks by means of the formulation of a model that considers the use of one-dimensional chaotic maps. Based on the disaggregation of the temporal series generated, a valid explanation of behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

Advancements in mobile phones and technologies, have forced the mobile software community to develop smart applications which can help users in general purposes. During the pandemic, people have shifted their focus onto Video chatting and conferencing applications for conducting business, schools, and universities. The current software's available provide several useful functions like live transcripts, allow users to create notes and providing third party application support. But there are certain areas where, video conferencing applications can improve upon primarily in interns of language support. This application will provide a new video conferencing feature which is live translation of speech which could help conduct business and conducting lectures.

Reports on a reimplementation of the ENIAC programming demonstrated at its unveiling on Feb 15, 1946. The reconstructed demonstration is shown in the YouTube video: https://youtu.be/SGBT2Danh-g

Unit Commitment (UC) is an important problem in power system operations. It is traditionally scoped for 24 hours with one-hour time intervals. To improve system flexibility by accommodating the increasing net-load variability, sub-hourly UC has been suggested. Such a problem is larger and more complicated than hourly UC because of the increased number of periods and reduced unit ramping capabilities per period. The computational burden is further exacerbated for systems with large numbers of virtual transactions leading to dense transmission constraints matrices. Consequently, the state-of-the-art and practice method, branch-and-cut (B&C), suffers from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) is enhanced by embedding ordinal-optimization concepts for a drastic reduction in subproblem solving time. Rather than formally solving subproblems by using B&C, subproblem solutions that satisfy SAVLR’s convergence condition are obtained by modifying solutions from previous iterations or solving crude subproblems. All virtual transactions are included in each subproblem to reduce major changes in solutions across iterations. A parallel version is also developed to further reduce the computation time. Testing on MISO’s large cases demonstrates that our ordinal-optimization embedded approach obtains near-optimal solutions efficiently, is robust, and provides a new way on solving other MILP problems.

This paper presents a convex recovery method for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adapted for the signal of interest by minimizing the mixed l2/l1 norm over all possible block partitions. Then, by exploiting a variational representation of the l2 norm, we derive the proposed penalty function as a suitable convex relaxation of the nonconvex one. For a block-sparse recovery model designed with the proposed penalty, we develop an iterative algorithm which is guaranteed to converge to a globally optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method.

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a short woman with a pink t-shirt and white skirt carrying a black purse. She has brown hair. Such a description contains attributes like gender, height, type of clothing, colour of clothing, hair colour, and accessories. Such attributes are formally known as soft biometrics. They help bridge the semantic gap between a human description and a machine as a textual query contains the person’s soft biometric attributes. It is also not feasible to manually search through huge volumes of surveillance footage to retrieve a specific person. Hence, automatic person retrieval using vision and language-based algorithms is becoming popular. In comparison to other state-of-the-art reviews, the contribution of the paper is as follows: 1. Recommends most discriminative soft biometrics for specific challenging conditions. 2. Integrates benchmark datasets and retrieval methods for objective performance evaluation. 3. A complete snapshot of techniques based on features, classifiers, number of soft biometric attributes, type of the deep neural networks, and performance measures. 4. The comprehensive coverage of person retrieval from handcrafted features based methods to end-to-end approaches based on natural language description.

Classification of 3D objects – the selection of a category in which each object belongs – is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture and representation of the 3D shape used as an input. To investigate the effectiveness of their approaches, we conduct an extensive survey of existing methods and identify common ideas by which we categorize them into a taxonomy. Second, we evaluate 11 selected classification networks on three 3D object datasets, extending the evaluation to a larger dataset on which most of the selected approaches have not been tested yet. For this, we provide a framework for converting shapes from common 3D mesh formats into formats native to each network, and for training and evaluating different classification approaches on this data. Despite being generally unable to reach the accuracies reported in the original papers, we can compare the relative performance of the approaches as well as their performance when changing datasets as the only variable to provide valuable insights into performance on different kinds of data. We make our code available to simplify running training experiments with multiple neural networks with different prerequisites. The final version was published: https://doi.org/10.1109/TPAMI.2021.3102676

Energy efficient galvanic coupling (GC) technology is used in this work to send electromyography (EMG) data through intra-body communication links. Real EMG data are first acquired and recorded with needle electrodes inserted in the muscle of a person’s forearm. Then, the data are transferred via GC communication employing a GC sound card-based testbed. The experiments are conducted by transferring EMG data in both ex-vivo and in-vivo tissue with different electrodes placements. Almost error free performance is achieved with a robust and reliable communication, a valuable result in medical applications.

In our previous study, a concept of electromagnetic kurtosis for time-reversal source reconstruction was presented. From a two-step process, the electromagnetic kurtosis can first identify the excitation time instants then localize the sources locations with time-reversal technique. In this paper, we extend the concept of the electromagnetic kurtosis from two different perspectives. The proposed space kurtosis and time kurtosis in the electromagnetic time-reversal process can directly reconstruct excitation time instants and spatial locations of sources respectively. The newly proposed time kurtosis takes the advantages of time-domain computational electromagnetics methods. Its algorithm with the threshold technique simplifies the procedures of multiple sources localization. Numerical experiments are conducted to verify the effectiveness of the presented algorithm.

Fretting is a common problem for modular orthopaedic implants which may lead to mechanical failure of the implant or inflammatory tissue responses due excessive release of wear debris. Compressive residual stresses at the contacting surfaces may alleviate the problem. Here we investigate the potential of a surface enhancement method known as low plasticity burnishing (LPB) to increase the fretting resistance of bone-anchored implants for skeletal attachment of limb prostheses. Rotation bending fatigue tests performed on LPB treated and untreated test specimens demonstrate that the LPB treatment leads to statistically significantly increased resistance to fretting fatigue (LPB treated test specimens withstood on average 108 780 load cycles as compared with 37 845 load cycles for untreated test specimens, p = 0.004). LPB treated test specimens exhibited less wear at the modular interface as compared with untreated test specimens. This surface treatment may lead to reduced risk of fretting induced component failure with the consequence of a reduced need for implant revisions.

The paper describes an online deep learning algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-Learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA our algorithm shows 10% to 20% improvement of user throughput in full buffer case.

Transfer of bulk electric power required in the modern world can be realized only through EHV and UHV transmission lines. The scenario is dominated by overhead lines in which electromagnetic noise generated by corona is an important concern. Corona induced currents propagate along line conductors producing electromagnetic noise, which is essential to be quantified. In literature, large amount of work is based on experimental investigations which considers only limited frequency ranges and is not applicable to new line configurations. A set of semi-analytical methods have also been proposed, which employ the transmission line model for analysis. However, quasi-TEM mode of propagation inherently assumed by them has been questioned. Moreover, the corona current is modeled as shunt source without relating to the mechanism of induction due to corona. The present work aims to investigate the basic mechanism of current induction using an isolated avalanche developing under space charge modulated background field. The corresponding induced currents are quantified and the structure of the electric and magnetic fields is extracted. The basic issue with the long transmission line modelling is amply demonstrated. Even though single avalanche, being the basic process building corona is considered, general inferences can be drawn regarding corona on lines.

An analysis of many IoT deployments showed that most of them can address the Sustainable Development Goals (SDG) and the UN’s 2030 agenda. Interestingly, most of these projects concentrate on five SDGs: Industry, innovation, and infrastructure, Smart cities and communities, Affordable and clean energy, Good health and wellbeing, and Responsible production and consumption. Examples include a remote water-monitoring solution that ensures clean water in regions with an indigenous population, and smart lighting initiatives

Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44 %, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.

In data mining and machine learning, the class imbalance problem occurs when the number of samples in one class (minority) is obviously smaller than the other one (majority), ignoring the learning of minority class. To solve this problem, many over-sampling methods, that fill the minority area with synthetic samples, have been proposed. But no one pure over-sampling method has been specially designed to fit complex distributions (such as noise, class overlapping and disjuncts). To fill this gap, this study firstly proposes one searchlight-scanned over-sampling method (SCOS), which tactfully treats the data filling of minority area as the searchlight scanning of objective area in real life. By respectively regarding the minority and majority area as the objective area and the barrier area of buildings, a series of searchlight structures are computed to firstly pass through corresponding minority area and then be stopped by the majority area. Finally, synthetic samples are generated in those structures. Implement on real-world datasets demonstrates the capability of our method to complex distributions, and the outperforming performance to current state-of-art over-sampling methods.

Electrocardiogram (ECG) is the graphical portrayal of heart usefulness. The ECG signals holds its significance in the discovery of heart irregularities. These ECG signals are frequently tainted by antiques from various sources. It is basic to diminish these curios and improve the exactness just as dependability to show signs of improvement results identified with heart usefulness. The most commonly disturbed artifact in ECG signals is Motion Artifacts (MA). In this paper, we have proposed a new concept on how machine learning algorithms can be used for de-noising the ECG signals. Towards the goal, a unique combination of Recurrent Neural Network (RNN) and Deep Neural Network (DNN) is used to efficiently remove MA. The proposed algorithm is validated using ECG records obtained from the MIT-BIH Arrhythmia Database. To eliminate MA using the proposed method, we have used Adam optimization algorithm to train and fit the contaminated ECG data in RNN and DNN models. Performance evaluation results in terms of SNR and RRMSE show that the proposed algorithm outperforms other existing MA removal methods without significantly distorting the morphologies of ECG signals.