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Ensuring data quality in biomedical sciences is crucial for reliable research outcomes, particularly as precision medicine continues to gain prominence. Missing values compromise data quality and can make it difficult to perform data-based studies. The origins of missing values in biomedical datasets are diverse, including experimental errors, equipment malfunctions, and variations in data collection protocols tailored to individual patient conditions. To address the complex nature of missing values and the unique characteristics of biomedical data, a diverse spectrum of computational imputation techniques has emerged. These methods range from traditional statistical analysis to more modern approaches such as discriminative machine learning models and deep generative networks. This survey paper provides a comprehensive overview of the extensive literature on missing data imputation techniques, with a specific focus on applications in genomics, single-cell RNA sequencing, health records, and medical imaging. We outline the fundamental principles underlying each imputation technique and present a detailed analysis of their advantages and disadvantages, categorized by missing data patterns. To aid practitioners in method selection, we offer practical recommendations based on critical factors such as dataset size, data type, and missingness rate. By synthesizing insights from existing literature, we provide a holistic perspective on the effectiveness of various imputation methods under different biomedical contexts, thereby facilitating informed decision-making for researchers and practitioners in applying imputation techniques to biomedical data processing.

Despite decades of research and development, myoelectric prosthetic hands lack functionality and are often rejected by users. This lack in functionality can be partially attributed to the widely accepted anthropomorphic design ideology in the field; attempting to replicate human hand form and function despite severe limitations in control and sensing technology. Instead, prosthetic hands can be tailored to perform specific tasks without increasing complexity by shedding the constraints of anthropomorphism. In this paper, we develop and evaluate four open-source modular non-humanoid devices to perform the motion required to replicate human flicking motion and to twist a screwdriver, and the functionality required to pick and place flat objects and to cut paper. Experimental results from these devices demonstrate that, versus a humanoid prosthesis, non-humanoid prosthesis design dramatically improves task performance, reduces user compensatory movement, and reduces task load. Case studies with two end users demonstrate the translational benefits of this research. We found that special attention should be paid to monitoring end-user task load to ensure positive rehabilitation outcomes.

In the era of IoT and smart systems, an enormous amount of data will be generated from various IoT/smart devices in smart homes, smart cars, etc. Typically, this big data is collected and sent directly to the cloud infrastructure for processing, analyzing, and storing. However, traditional cloud infrastructure faces serious challenges when handling this massive amount of data, including insufficient bandwidth, high latency, unsatisfactory real-time response, high power consumption, and privacy protection issues. The edge-centric computing is emerging as a complementary solution to address the aforementioned issues of the cloud infrastructure. Furthermore, for many real-world IoT and smart systems, such as smart cars, real-time, in situ, and online data analysis and processing are crucial. With edge computing, data processing and analysis can be done closer to the source of the data (i.e., at the edge of the networks), which in turn enables real-time and in-situ data analytics and processing. As a result, edge computing will soon become the cornerstone of many IoT and smart applications. However, edge computing is still in its infancy; thus, requires novel models and techniques to support real-time and in-situ data processing and analysis. In this research work, we introduce novel and efficient computation models that are suitable for real-time processing and analysis on next-generation edge-computing platforms. Since most common edge-computing tasks are data analytics/mining, we focus on widely used data analytics techniques, including dimensionality reduction and classification techniques, specifically, principal component analysis (PCA) and support vectors machine (SVM), respectively. This is mainly because it is demonstrated that combination of PCA and SVM leads to high classification accuracy in many fields. In this paper, we introduce three different PCA+SVM models (i.e., Model 1, Model 2, and Model 3), for real-time processing and analysis (for online training and inference) on edge computing platforms. Model 1 and Model 2 are created utilizing the same SVM algorithm but with a different design/functional flows, whereas Model 3 is created with the same functional flow as Model 2 but utilizing a modified SVM algorithm. Our experimental results and analysis demonstrate that Model 3 utilizes dramatically lower number of iterations to produce the results, compared to that of other two models, while achieving acceptable performance results. Our results and analysis demonstrate that Model 3 is the most suitable computation model for real-time processing and analysis of edge computing platform.

Climate change pressure on the Arctic permafrost is rising alarmingly, creating a decisive need to produce Pan-Arctic scale permafrost landform and thaw disturbance information from remote sensing (RS) data. Very high spatial resolution (VHSR) satellite images can be utilized to detect ice-wedge polygons (IWPs)– the most important and widespread landform in the Arctic tundra region - across the Arctic without compromising spatial details. Automated analysis of peta-byte scale VHSR imagery covering millions of square kilometers is a computationally challenging task. Traditional semantic segmentation requires the use of task specific feature extraction with conventional classification techniques. Semantic complexity of VHSR images coupled with landscape heterogeneity makes it difficult to use conventional classification approaches to produce Pan-Arctic scale geospatial products. This leads to adapting deep convolutional neural network (DLCNN) approaches that have excelled in computer vision (CV) applications. Transitioning domains from everyday image understanding to remote sensing image analysis is challenging. This study aims to systematically investigate two main obstacles confronted when adapting DLCNNs in large-scale RS image analysis tasks; 1) the limited availability labeled data sets and 2) the prohibitive nature of hyperparameter tunning when designing DLCNNs that can capture the rich characteristics embedded in remotely-sensed images. With a case study on the production of the first pan-Arctic ice-wedge polygon map using thousands of VHSR images, we demonstrate the use of transfer learning and the impact of hyperparameter tuning with a 16% improvement of the Mean Average Precision (mAP50).

This paper presents the details of the development, evaluation process, and results of linear and nonlinear time-invariant models used to represent the frequency response of inductive high-voltage instrument transformers. The study encompasses both sinusoidal and distorted quasi-sinusoidal test waveform conditions, focusing on harmonic voltage amplitude and phase-angle measurements up to 10 kHz. A linear time-invariant model was derived using a higher-order transfer-function estimation technique that minimized the least-squares error between the measured frequency response of the transformer and the estimated transfer function across harmonic orders ranging from 50 Hz to 10 kHz. Acknowledging the limited validity of the linear time-invariant model under multi-tone distorted waveform conditions, a nonlinear time-invariant model employing a simplified Volterra series was introduced to represent the instrument transformer frequency response under multi-tone distorted waveform scenarios. The accuracy of both models was rigorously assessed using the experimental frequency responses obtained through sinusoidal low-voltage-frequency sweep signals and multi-tone-distorted high-voltage waveforms. This study contributes to a comprehensive understanding of the behaviour of instrument transformers at high frequencies under different input voltage conditions, providing valuable insights into the accuracy of high-frequency harmonic measurements up to 10 kHz.

The growing use of computer vision in many fields has seen an increasing need for visual data, both real and synthetic, to support the training and testing of computer vision models. To this end, the need for high quality datasets has also increased. That said, the quality of image data for the purpose of computer vision training remains a comparatively unexplored field. There is little information on how image parameters may affect data quality, whether such parameters affect real and synthetic data equally, and how such parameters may interact with each other. The goal of this study was to test the effect of basic image data parameters on real and synthetic data and the resultant effect such parameters have on training a human action recognition model. A human action dataset with two distinct actions was selected and individual image parameters were modified to produce a total of 247 data subsets. Testing a total of 247 human action recognition models, trained on the data subsets, it was found that image parameters affect real and synthetic data differently. The impact of parameters, such as resolution and colour space, varies not just on whether the image data is real or synthesised, but also on the human action being performed. Hybrid datasets created by mixing real and synthetic data, up to 70% synthetic data, are able to produce models with similar performance levels to models trained on real data, without the negative effects of some parameters that could be observed in synthetic only datasets. Lastly, the mislabelling of training data has a large impact on action prediction accuracy, even at low percentages of mislabelled data.

The rapid increase in mobile users, the IoT, and data-hungry applications have brought forth unprecedented demand on the spectrum, which is scarce; on top of that, the existing static spectrum allocation schemes have resulted in a heavily underutilized spectrum which can be mitigated with a Dynamic Spectrum Access (DSA) scheme with unlicensed users gaining access to the idle spectrum bands of licensed spectrum users opportunistically. Such a DSA and Dynamic Spectrum Management (DSM) scheme would significantly increase spectral efficiency while facilitating new services and applications beyond 5G (B5G) networks. Even with access to new spectrum bands like terahertz (THz) and Visible light communication and enabling technologies such as Software Defined Networks (SDN) and Cognitive Radio (CR), implementing a fully realized DSM requires rapid sensing, coordination, and management, and sharing of idle spectrum bands in a fair manner while preserving the security and privacy aspects, limiting interferences. With their decentralized, immutable nature, blockchains promise the execution of spectrum access and sharing in a fully transparent, fair manner while preserving privacy and security. Furthermore, blockchain-based Smart Contracts (SCs) allow automation of DSM, cryptocurrencies, and tokens to facilitate the trading of spectrum and related resources. In addition to that, blockchains act as an interface for integrating AI and Machine Learning (ML) techniques into DSM, which provides a certain level of intelligence to the underlying architecture. Although several attempts have been carried out to analyze the research gaps in DSM, a comprehensive analysis addressing the blockchains as the primary solution to address DSM has not been carried out. In this survey, we address the potential of a blockchain-based approach toward realizing a decentralized DSM while presenting future directives to improve the use of blockchains for DSM.

Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient during rehabilitation or when aiding essential daily tasks such as sit-to-walk (STW). STW itself can be generalized into strategies based on individual needs and movement patterns. In this study, three long short-term memory (LSTM) neural networks were empirically trained for hip and knee torque prediction considering these STW strategies and subject anthropometry. The hip and knee are the drivers of STW, while the network architectures were selected for recognizing temporal and spatial relationships. Performance of the LSTMs were compared and evaluated against the STW strategies to accurately generate strategy-specific and user-oriented torque. As such, train and test STW data were obtained from 65 subjects across three age groups: young, middle-aged, and older adults (19-73 years). Model inputs were hip and knee angles with horizontal center of mass velocity, while windowing allowed the LSTMs to dynamically adapt to real-time changes in STW transitions. The encoder-decoder LSTM showcased optimal performance with robust recognition of temporal features. It produced significantly ( ${P}\lt 0.05$ ) low hip and knee root mean square error ( $0.24~\pm ~0.07$ and $0.15~\pm ~0.02$ Nm/kg), strong Spearman’s correlation ( $93.43~\pm ~2.86$ and $84.83~\pm ~2.96$ %) and good intraclass correlation coefficients (greater than 0.75), demonstrating model reliability. Hence, this network predicts strategy and user oriented reference torques for personalized controllers in assistive devices, with more natural application of assistance.

Genre categorization of published titles is a common practice in publishing houses, libraries, and bookstores, as well as a fundamental element of editorial marketing. However, assigning subject codes to each title proves to be an arduous task for both publishers and data aggregators. The problem with automatic genre categorization is that some publishers use more than 200 categories, making it a highly complex task. Moreover, even though these publishers based their categorization on standards, they ofthen alter the names of these standards as they consider to be too technical. In this paper, we proposed Thema-based categorization as a tool to facilitate editors’ work by advancing the categorization process, allowing them to focus on finer category granularity. This categorization has four key features: first, it clusters the most important categories for Latin American publishers. Second, it stops grouping when the number of thematic categories remains practical for the purposes of the publishing business. Third, we assign names to these categories that resonate with Latin American stakeholders. Finally, the number of categories is optimized to provide reasonable classification performance. We worked on the description of books in Spanish of two publishers, and mapped them to this proposed categorization. This allowed us to created a database for train a model to automate categorization. After conducting our analysis, we determined that 26 thematic categories were an appropriate number that fulfilled the three features mentioned earlier. However, we recognized that classifying into 26 categories was still a complex task, so to overcome this challenge, we decided to augment data by back-translating it into Spanish using the translation function, $T_{l}^{S}\left ({T_{S}^{l}\left ({S }\right) }\right)$ , where $T_{S}^{l}\left ({S }\right)$ is the translation function from Spanish, $s$ , to language, $l$ ; $T_{l}^{s}\left ({l }\right)$ is the translation function from language, $l$ , to Spanish, $S$ , and $T_{S}^{l}\left ({S }\right)$ and $T_{l}^{S}\left ({l }\right)$ are not-invertible functions. Experimental results, obtained using 5-fold cross-validation, were approximately 57%, 57%, 63.38%, and 65.26% for the F1-score of Support Vector Machine (SVM), Logist Regression (LR), BERT, and RoBERTa models, respectively. We utilized the F1-score metric because our categories were not perfectly balanced. The results achieved by RoBERTa outperform those reported in the literature. Furthermore, these results are built upon the foundation of the Thema standard for categorizing book genres. Additionally, the categories have been specifically designed to align with the preferences and needs of Latin American publishers.

The prevalence of work-related musculoskeletal disorders is a common issue in many occupations involving manual handling activities. In order to aid manual workers in reducing the burden on the musculoskeletal system, various wearable robotic technologies have been developed over the years. An increase in research work on wearable technologies has been observed, particularly in the last decade. In that context, this article presents a comprehensive review and a bibliometric analysis of the recorded occupational exoskeletons for manual handling since 2010. The review is aimed at identifying the paradigm shifts of research in the recent past and associating the trends pertaining to the applications, mechanisms, and control systems in the development of wearable devices for manual handling. The scope of the review limits itself to active and passive exoskeletons designed to support the upper extremity, lower extremity, and spine for performing load lifting, load carrying, or static holding. The analysis of the results revealed the emerging trends with the aim of providing researchers with areas for improvement and suggestions for different clusters of devices.

Dysphagia, commonly referred to as abnormal swallowing, affects millions of people annually. If not diagnosed expeditiously, dysphagia can lead to more severe complications, such as pneumonia, nutritional deficiency, and dehydration. Bedside screening is the first step of dysphagia characterization and is usually based on pass/fail tests in which a nurse observes the patient performing water swallows to look for dysphagia overt signs such as coughing. Though quick and convenient, bedside screening only provides low-level judgment of impairment, lacks standardization, and suffers from subjectivity. Recently, high resolution cervical auscultation (HRCA) has been investigated as a less expensive and non-invasive method to diagnose dysphagia. It has shown strong preliminary evidence of its effectiveness in penetration-aspiration detection as well as multiple swallow kinematics. HRCA signals have traditionally been collected and investigated in conjunction with videofluoroscopy exams which are performed using barium boluses including thin liquid. An HRCA-based bedside screening is highly desirable to expedite the initial dysphagia diagnosis and overcome all the drawbacks of the current pass/fail screening tests. However, all research conducted for using HRCA in dysphagia is based on thin liquid barium boluses and thus not guaranteed to provide valid results for water boluses used in bedside screening. If HRCA signals show no significant differences between water and thin liquid barium boluses, then the same algorithms developed on thin liquid barium boluses used in diagnostic imaging studies, it can be then directly used with water boluses. This study investigates the similarities and differences between HRCA signals from thin liquid barium swallows compared to those signals from water swallows. Multiple features from the time, frequency, time-frequency, and information-theoretic domain were extracted from each type of swallow and a group of linear mixed models was tested to determine the significance of differences. Machine learning classifiers were fit to the data as well to determine if the swallowed material (thin liquid barium or water) can be correctly predicted from an unlabeled set of HRCA signals. The results demonstrated that there is no systematic difference between the HRCA signals of thin liquid barium swallows and water swallows. While no systematic difference was discovered, the evidence of complete conformity between HRCA signals of both materials was inconclusive. These results must be validated further to confirm conformity between the HRCA signals of thin liquid barium swallows and water swallows.

Delivery of core programming principles to novices is a challenging task and many introductory programming languages and platforms have been designed to support this process. Educational programming languages generally focus on alleviating the syntax overhead enforced on novice learners by designing languages with simple and concise keywords. Furthermore, only the most basic programming concepts and principles are incorporated and many languages follow unique methods to provide more simplified learning environments. However, considering the way programs are authored using these platforms, two common contrasting approaches to program representation are identified as text-based and block-based representations. Additionally, a hybrid approach of dual-modality interfaces, which combines the best of both techniques has gained traction as a current trend in the development of educational programming platforms. However, despite these extensive features, not all introductory programming languages can cater to the exact requirements of novice learners and a dearth of comprehensive studies and literature reviews have been conducted to investigate this context. This paper explores and presents a comprehensive review of how different elements of educational programming languages and platforms contribute towards learning by novices under the Technology Acceptance Model (TAM). The review is conducted under two main constructs of TAM as (1) Perceived Usefulness (PU) and (2) Perceived Ease of Use (PEOU) and external factors regarding the programming environment, language design, included programming concepts and supporting features such as the target audience group, language extensibility, and availability of learning materials are thoroughly investigated considering the typical behavioral patterns of novices concerning computer programming education.

Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity. In clinical practice, clinicians estimate the survival with their experience, which can be biased and optimistic. Over the past decades, diverse survival analysis approaches were proposed incorporating distinct data such as imaging and genetic information. The remarkable advancements in imaging and high throughput omics and sequencing technologies have enabled the acquisition of this information of glioma patients efficiently, providing novel insights for survival estimation in the present day. Besides, in the past years, machine learning techniques and deep learning have emerged into the field of survival analysis of glioma patients trading off the traditional statistical analysis-based survival analysis approaches. In this survey paper, we explore the prognostic parameters acquired, utilizing diagnostic imaging techniques and genomic platforms for survival or risk estimation of glioma patients. Further, we review the techniques, learning and statistical analysis algorithms, along with their benefits and limitations used for prognosis prediction. Consequently, we highlight the challenges of the existing state-of-the-art survival prediction studies and propose future directions in the field of research.

Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.

It is well known that three-phase induction motors have to be derated in the presence of supply voltage unbalance (negative sequence) exceeding a stipulated limit of 1% based on several widely used standards. Generally, voltage unbalance limits are decided based on the magnitude of negative sequence voltage unbalance factor which is quantified as the ratio of negative sequence voltage to positive sequence voltage. However, a specified voltage unbalance magnitude can arise as a result of numerous possibilities of the three phase supply voltages. Therefore, it is hypothesised that the current derating curve, which defines a derating factor that is dependent on the magnitude of the voltage unbalance, is not optimal and may not be economical and/or safe for some voltage unbalance conditions. To examine the validity of this hypothesis, modelling and experimental validation need to be carried out considering motor losses and temperature rise which are the main factors that help determine the derating factor of an induction motor. Realising these requirements, the emphasis of this study is to examine the dependency of losses, temperature rise and torque oscillations of a three-phase induction motor on the complex nature of voltage unbalance through calorimetric and finite element simulation-based studies. The outcomes are expected to assist in the development of suitable derating factors.

This paper presents factorizations of each discrete sine transform (DST) matrix of types I, II, III, and IV into a product of sparse, diagonal, bidiagonal, and scaled orthogonal matrices. Based on the proposed matrix factorization formulas, reduced multiplication complexity, recursive, and radix-2 DST I-IV algorithms are presented. We will present the lowest multiplication complexity DST-IV algorithm in the literature. The paper fills a gap in the self-recursive, exact, and radix-2 DST I-IIII algorithms executed via diagonal, bidiagonal, scaled orthogonal, and simple matrix factors for any input $n=2^{t} \; (t \geq 1)$ . The paper establishes a novel relationship between DST-II and DST-IV matrices using diagonal and bidiagonal matrices. Similarly, a novel relationship between DST-I and DST-III matrices is proposed using sparse and diagonal matrices. These interweaving relationships among DST matrices enable us to bridge the existing factorizations of the DST matrices with the proposed factorization formulas. We present signal flow graphs to provide a layout for realizing the proposed algorithms in DST-based integrated circuit designs. Additionally, we describe an implementation of algorithms based on the proposed DST-II and DST-III factorizations within a double random phase encoding (DRPE) image encryption scheme.

With the development of renewable energy and electric vehicle technologies, a need for multiport DC/DC/AC converters with high power density and reliability has arisen. A dual inverter based bidirectional multiport converter with two dissimilar voltage DC ports and an AC port is presented. The converter is capable of processing up to 1.67, 1.41, and 1 p.u. of power through its main DC, auxiliary DC and AC ports respectively, while only using the semiconductor and magnetics effort of a DC/AC converter rated for 1 p.u. Analysis reveals that this is more than thrice and twice the power limits of the main and auxiliary DC ports respectively, compared to using two independent inverters with a three-winding transformer. Furthermore, the proposed converter is more efficient than the latter option. These benefits are due to the multitasking of inverters and the existence of single stage DC/DC and DC/AC power transfer mechanisms enabled by the use of a center tapped transformer. A control strategy using these two mechanisms, capable of independently controlling the power flows between ports is also presented. Independent inverter control is also possible, allowing power flow between the main DC and AC ports during the failure of a single inverter. These claims are verified through thorough analysis, extensive simulation and experimental results using a 3 kW prototype.

Unmanned aerial vehicles (UAVs) is a promising technology for the next-generation communication systems. In this article, a fixed-wing UAV is considered to enhance the connectivity for far-users at the coverage region of an overcrowded base station (BS). In particular, a three dimensions (3D) UAV trajectory is optimized to improve the overall energy efficiency of the communication system by considering the system throughput and the UAV’s energy consumption for a given finite time horizon. The solutions for the proposed optimization problem are derived by applying Lagrangian optimization and using an algorithm based on successive convex iteration techniques. Numerical results demonstrate that by optimizing the UAV’s trajectory in the 3D space, the proposed system design achieves significantly higher energy efficiency with the gain reaching up to $20\,\,bitsJ^{-1}$ compared to the $14\,\,bitsJ^{-1}$ maximum gain achieved by the 2D space trajectory. Further, results reveal that the proposed algorithm converge earlier in 3D space trajectory compare to the 2D space trajectory.

In the era of IoT, embedded systems are becoming the cornerstone of many IoT related applications, such as smart cars and wearable devices. However, embedded devices have numerous constraints and requirements, including stringent area and power, reduced cost and time-to-market, and increased speedup. Furthermore, these applications are becoming increasingly compute/data-intensive requiring more processing power. Also, especially for IoT related applications, security is another major issue in resource-constrained embedded devices. Although cryptographic algorithms are widely used to ensure the security of these applications, commonly used ones, such as AES, are unsuitable for highly constrained embedded devices, due to their sheer complexity. Hence, several lightweight cryptographic algorithms were proposed in the literature that might be better suited for embedded devices. From these, SPECK and SIMON, introduced by NSA, are the two most popular ones. Another important challenge is how to incorporate the cryptographic algorithms in to embedded devices, efficiently and effectively, without compromising the integrity of the compute/data-intensive applications running on these small-footprint devices. Our previous analysis demonstrated that FPGAs are currently the best avenue to support compute/data-intensive applications running on resource-constrained embedded devices, due to FPGA's many attractive traits, including, post-fabrication reprogrammability, dynamic and partial reconfiguration capabilities, and reduced time-to-market. Also, FPGAs can be utilized to provide several advantages/features required for the embedded device's security, such as cryptographic algorithm agility, algorithm upload, algorithm modification, and resource efficiency. In this research work, we introduce novel, unique, and efficient dynamic and partial reconfigurable hardware architectures for the most popular SPECK and SIMON algorithms on embedded devices, considering the constraints associated with these devices and the requirements of the applications running on embedded devices. We also introduce unique system-level architectures for our proposed designs. To the best of our knowledge, no similar work exists in the literature that provides dynamic and partial reconfigurable hardware for SPECK and SIMON, and also provides system-level architecture. Our dynamic and partial reconfigurable hardware designs achieve 28% space saving compared to its static reconfigurable hardware, and 59 times speedup compared to its software counterpart.

With the proliferation of embedded computing, there has been a dramatic increase in utilization of FPGAs to accelerate real-time compute/data-intensive applications on embedded platforms. FPGA-based designs typically achieve high speed-performance by leveraging parallelism in computations, which require simultaneous multiple reads/writes from/to the on-chip memory. However, current FPGAs only comprise dual-port on-chip memories, which hinder simultaneous multiple read/write (R/W) operations needed for parallel computing. Although several multi-ported memories are proposed in the literature, these designs become complex due to the extra logic and routing used for techniques/architectures to provide an arbitrary number of R/W ports for simultaneous multiple R/W operations. Furthermore, most of the existing multi-ported memories only provide an arbitrary number of uni-directional R/W ports. There is only one existing multi-ported memory design in the literature that provides bi-directional R/W ports. Unlike uni-directional ones, bi-directional multi-ported memories give more flexibility to the designers, since the number of read and write transactions can be changed as needed on-the-fly at any time. Previously, we introduced unique and efficient uni-directional multi-ported memories, which were created in such a way to significantly reduce the design and routing complexity. In this research work, we introduce novel, unique, and efficient bi-directional multi-ported memory architectures to provide an arbitrary number of bi-directional R/W ports. Our proposed bi-directional multi-ported memories are designed in such a way to dramatically reduce the design and routing complexity by eliminating the circular paths that typically exists in the current multi-ported memories in the literature, while enhancing operating frequency and area-efficiency. To the best of our knowledge, no similar work exists in the literature that provide multi-ported memory designs without the circular paths. Experiments are performed to evaluate the feasibility and efficiency of our bi-directional multi-ported memory designs. These results and analyses illustrate that our bi-directional multi-ported memories are far more efficient compared to the existing ones in the literature. Due to lower design and routing complexity compared to the existing ones, our simplified memories would enable seamless integration to the next-generation FPGAs with minimal design cost.

This paper presents a self-contained factorization for the delay Vandermonde matrix (DVM), which is the super class of the discrete Fourier transform, using sparse and companion matrices. An efficient DVM algorithm is proposed to reduce the complexity of radio-frequency (RF) N-beam analog beamforming systems. There exist applications for wideband multi-beam beamformers in wireless communication networks such as 5G/6G systems, system capacity can be improved by exploiting the improvement of the signal to noise ratio (SNR) using coherent summation of propagating waves based on their directions of propagation. The presence of a multitude of RF beams allows multiple independent wireless links to be established at high SNR, or used in conjunction with multiple-input multiple-output (MIMO) wireless systems, with the overall goal of improving system SNR and therefore capacity. To realize such multi-beam beamformers at acceptable analog circuit complexities, we use sparse factorization of the DVM in order to derive a low arithmetic complexity DVM algorithm. The paper also establishes an error bound and stability analysis of the proposed DVM algorithm. The proposed efficient DVM algorithm is aimed at implementation using analog realizations. For purposes of evaluation, the algorithm can be realized using both digital hardware as well as software defined radio platforms.

In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the “non-sampled” parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle non-stationary data more effectively.

Millions of people across the globe suffer from swallowing difficulties, known as dysphagia, which can lead to malnutrition, pneumonia, and even death. Swallowing cervical auscultation, which has been suggested as a noninvasive screening method for dysphagia, has not been associated yet with any physical events. In this paper, we have compared the hyoid bone displacement extracted from the videofluoroscopy images of 31 swallows to the signal features extracted from the cervical auscultation recordings captured with a tri-axial accelerometer and a microphone. First, the vertical displacement of the anterior part of the hyoid bone is related to the entropy rate of the superior–inferior swallowing vibrations and to the kurtosis of the swallowing sounds. Second, the vertical displacement of the posterior part of the hyoid bone is related to the bandwidth of the medial–lateral swallowing vibrations. Third, the horizontal displacements of the posterior and anterior parts of the hyoid bone are related to the spectral centroid of the superior–inferior swallowing vibrations and to the peak frequency of the medial–lateral swallowing vibrations, respectively. At last, the airway protection scores and the command characteristics were associated with the vertical and horizontal displacements, respectively, of the posterior part of the hyoid bone. Additional associations between the patients’ characteristics and auscultations’ signals were also observed. The hyoid bone maximal displacement is a cause of swallowing vibrations and sounds. High-resolution cervical auscultation may offer a noninvasive alternative for dysphagia screening and additional diagnostic information.