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Abstract Background Coronary computed tomography angiography (CCTA) offers non-invasive diagnostics of the coronary arteries. Vessel evaluation requires the administration of intravenous contrast. The purpose of this study was to evaluate the utility of gadolinium-based contrast agent (GBCA) as an alternative to iodinated contrast for CCTA on a first-generation clinical dual-source photon-counting-detector (PCD)-CT system. Methods A dynamic circulating phantom containing a three-dimensional-printed model of the thoracic aorta and the coronary arteries were used to evaluate injection protocols using gadopentetate dimeglumine at 50%, 100%, 150%, and 200% of the maximum approved clinical dose (0.3 mmol/kg). Virtual monoenergetic image (VMI) reconstructions ranging from 40 keV to 100 keV with 5 keV increments were generated on a PCD-CT. Contrast-to-noise ratio (CNR) was calculated from attenuations measured in the aorta and coronary arteries and noise measured in the background tissue. Attenuation of at least 350 HU was deemed as diagnostic. Results The highest coronary attenuation (441 ± 23 HU, mean ± standard deviation) and CNR (29.5 ± 1.5) was achieved at 40 keV and at the highest GBCA dose (200%). There was a systematic decline of attenuation and CNR with higher keV reconstructions and lower GBCA doses. Only reconstructions at 40 and 45 keV at 200% and 40 keV at 150% GBCA dose demonstrated sufficient attenuation above 350 HU. Conclusion Current PCD-CT protocols and settings are unsuitable for the use of GBCA for CCTA at clinically approved doses. Future advances to the PCD-CT system including a 4-threshold mode, as well as multi-material decomposition may add new opportunities for k-edge imaging of GBCA. Relevance statement Patients allergic to iodine-based contrast media and the future of multicontrast CT examinations would benefit greatly from alternative contrast media, but the utility of GBCA for coronary photon-counting-dector-CT angiography remains limited without further optimization of protocols and scanner settings. Key Points GBCA-enhanced coronary PCD-CT angiography is not feasible at clinically approved doses. GBCAs have potential applications for the visualization of larger vessels, such as the aorta, on PCD-CT angiography. Higher GBCA doses and lower keV reconstructions achieved higher attenuation values and CNR. Graphical Abstract

Extracorporeal Carbon Dioxide Removal (ECCO2R) systems support patients with severe respiratory failure. Concurrent ambulation and physical therapy improve patient outcomes, but these procedures are limited by the complexity and size of the extracorporeal systems and rapid changes in patient metabolism and the acid–base balance. Here, we present the first prototype of a wearable ECCO2R system capable of adjusting to a patient’s changing metabolic needs. Exhaust gas CO2 (EGCO2) partial pressure is used as an analog for blood CO2 partial pressure (pCO2). Twin blowers modulate sweep gas through the AL to achieve a desired target EGCO2. The integrated system was tested in vitro for 24 h with water, under varying simulated metabolic conditions and target EGCO2 values, and in a single test with whole blood. When challenged with changing inlet water pCO2 levels in in vitro tests, the system adjusted the sweep gas to achieve target EGCO2 within 1 min. Control runs with a fixed sweep gas (without negative feedback) demonstrated higher EGCO2 levels when challenged with higher water flow rates. A single in vitro test with whole ovine blood confirmed functionality in blood. This is the first step toward wearable ECCO2R systems that automatically respond to changing metabolism. Such devices would facilitate physical therapy and grant greater autonomy to patients.

OBJECTIVES/GOALS: We report an automated manufacturing system, and a series of cylindrical multi-layer microfluidic artificial lungs manufactured with the system and tested for fluidic fidelity and function. METHODS/STUDY POPULATION: A Roll-to-Roll (R2R) system to engrave multiple-layer devices was assembled. A 100 um-thick silicone sheet passes through an embedded CO2 laser engraver, which creates patterns of any geometry on the surface. The sheet is plasma-activated to create an irreversible bond, and rerolled into a processed device. Unlike typical applications of R2R, this process is synchronized to achieve consistent radial positioning. This allows the fluidics in the device to be accessed without being unwrapped. The result is a cylindrical core surrounded by many layers of microfluidic channels that can be accessed through the side of the device or through fluidic vias. This core is cut to expose the microfluidic layers, and then installed into a housing which routes the fluids into their respective microfluidic flow paths. RESULTS/ANTICIPATED RESULTS: To demonstrate the capabilities of the R2R manufacturing system, this method was used to manufacture multi-layer microfluidic artificial lungs (µALs). Gas and blood flow channels are engraved in alternating layers and routed orthogonally. The close proximity of gas and blood separated by gas-permeable PDMS permits CO2 and O2 exchange. Three µALs were successfully manufactured. Their flow paths were visualized using dyed water and checked for leaks. Then they were evaluated using water for pressure drop and CO2 gas-exchange. The top performing device had 15 alternating blood and gas layers. Test with whole blood demonstrated oxygenation from venous (70%) saturation levels to arterial (95%) saturation levels at a flow rate of 3 ml/min. DISCUSSION/SIGNIFICANCE: The ability to cost-effectively produce high surface area microfluidic devices would bring many small-scale technologies from the realm of research to clinical and commercial applications. In particular, most microfluidic artificial lungs only have small rated flows due to a lack of manufacturing processes able to create high surface area devices.

Abstract Background Many factors can contribute to the exact makeup of the salivary microbiome. Differences in the oral microbiome occur with old age, which may be due to oral conditions and diseases associated with old age, such as edentulism, as well as other unknown causes. Methods The salivary microbiome was sampled in patients from a large urban clinic. For all subjects age, gender, periodontal status, caries status, presence of edentulism, medications, and tobacco usage were recorded. Multifactor analysis was used to study variation in salivary microbiome profiles linked to these factors. Results In the population sampled, there were significantly higher numbers of edentulous subjects, and increased levels of polypharmacy found with aging. Large differences in alpha diversity and beta diversity of the salivary microbiome in the old age group were largely linked to edentulism. However, multivariable analysis revealed, even after adjusting for differences in edentulism, polypharmacy, tobacco usage, periodontal disease, caries level, and gender, that old age itself was associated with lower levels of taxa Porphyromonas endodontalis , Alloprevotella tannerae, Filifactor alocis, Treponema, Lautropia Mirabilis and Pseudopropionibacterium sp._HMT_194. Surprisingly, of these taxa, most were ones known to reside on or near tooth surfaces. Conclusions Another factor or factors beyond edentulism, polypharmacy and periodontal disease play a role in the differences seen in oral microbiome with old age. The nature of this factor(s) is not known.

BackgroundCOVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensity the thrombotic burden in patients with COVID-19. However, venous thromboembolism, arterial thrombosis, and other vascular complications may go unnoticed in critical care settings. Early risk stratification is paramount in the COVID-19 patient population for proactive monitoring of thrombotic complications. ObjectiveThe aim of this exploratory research was to characterize thrombotic complication risk factors associated with COVID-19 using information from electronic health record (EHR) and insurance claims databases. The goal is to develop an approach for analysis using real-world data evidence that can be generalized to characterize thrombotic complications and additional conditions in other clinical settings as well, such as pneumonia or acute respiratory distress syndrome in COVID-19 patients or in the intensive care unit. MethodsWe extracted deidentified patient data from the insurance claims database IBM MarketScan, and formulated hypotheses on thrombotic complications in patients with COVID-19 with respect to patient demographic and clinical factors using logistic regression. The hypotheses were then verified with analysis of deidentified patient data from the Research Patient Data Registry (RPDR) Mass General Brigham (MGB) patient EHR database. Data were analyzed according to odds ratios, 95% CIs, and P values. ResultsThe analysis identified significant predictors (P1. The results from RPDR validated the IBM MarketScan findings, as they were largely consistent and afford mutual enrichment. ConclusionsThe analysis approach adopted in this study can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped to identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients represents only a case study; however, the same design can be used across other disease areas by extracting corresponding disease-specific patient data from available databases.

AbstractHere, we report the discovery of a novel Sediminibacterium

Artificial lung (AL) systems provide respiratory support to patients with severe lung disease, but none can adapt to the changing respiratory needs of the patients. Precisely, none can automatically adjust carbon dioxide (CO2) removal from the blood in response to changes in patient activity or disease status. Because of this, all current systems limit patient comfort, activity level, and rehabilitation. A portable servoregulation controller that automatically modulates CO2 removal in ALs to meet the real-time metabolic demands of the patient is described. The controller is based on a proportional-integral-derivative (PID) based closed-loop feedback control system that modulates sweep gas (air) flow through the AL to maintain a target exhaust gas CO2 partial pressure (target EGCO2 or tEGCO2). The presented work advances previous research by (1) using gas-side sensing that avoids complications and clotting associated with blood-based sensors, (2) incorporating all components into a portable, battery-powered package, and (3) integrating smart moisture removal from the AL to enable long term operation. The performance of the controller was tested in vitro for ∼12 h with anti-coagulated bovine blood and 5 days with distilled water. In tests with blood, the sweep gas flow was automatically adjusted by the controller rapidly (CO2 level when confronted with changes in inlet blood partial pressure of CO2 (pCO2) levels at various AL blood flows. Overall, the CO2 removal from the AL showed a strong correlation with blood flow rate and blood pCO2 levels. The controller successfully operated continuously for 5 days when tested with water. This study demonstrates an important step toward ambulatory AL systems that automatically modulate CO2 removal as required by lung disease patients, thereby allowing for physiotherapy, comfort, and activity.

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

Hepatocellular carcinoma (HCC) is the second most common cause of cancer death worldwide, with a majority of HCC patients not suitable for curative therapies. Approximately 70% of initially diagnosed patients cannot undergo surgical resection or transplantation due to locally advanced disease, poor liver function/underlying cirrhosis, or additional comorbidities. Local therapeutic options for patients with unresectable HCC, who are not suitable for thermal ablation, include transarterial embolization (bland, chemoembolization, radioembolization) and/or external beam radiation therapy (EBRT). Regarding EBRT specifically, technological advancements provide a means for safe and effective radiotherapy delivery in a wide spectrum of HCC patients. In multiple prospective studies, EBRT delivery in a variety of different fractionation schemes or in combination with transcatheter arterial chemoembolization (TACE) demonstrate improved outcomes, particularly with combination therapy. The Barcelona Clinic Liver Cancer classification provides a framework for treatment selection; however, given the growing complexity of treatment strategies, this classification system tends to simplify decision-making. In this review, we discuss the current literature regarding unresectable HCC and propose a modified treatment algorithm that emphasizes the role of radiation therapy for Child-Pugh score A or B patients with ≤3 nodules measuring >3 cm, multinodular disease or portal venous thrombosis.

Ribonucleotide reductase (RR) is a crucial enzyme in de novo DNA synthesis, where it catalyses the rate determining step of dNTP synthesis. RRs consist of a large subunit called RR1 (α), that contains two allosteric sites and one catalytic site, and a small subunit called RR2 (β), which houses a tyrosyl free radical essential for initiating catalysis. The active form of mammalian RR is an anbm hetero oligomer. RR inhibitors are cytotoxic to proliferating cancer cells. In this brief review we will discuss the three classes of RR, the catalytic mechanism of RR, the regulation of the dNTP pool, the substrate selection, the allosteric activation, inactivation by ATP and dATP, and the nucleoside drugs that target RR. We will also discuss possible strategies for developing a new class of drugs that disrupts the RR assembly.

Frequency estimation is a fundamental problem in many areas. The previously proposed q-shift estimator (QSE), which interpolates the discrete Fourier transform (DFT) coefficients by a factor of q, enables the estimation accuracy to approach the Cramér-Rao lower bound (CRLB). However, it becomes less effective when the number of samples is small. In this letter, we provide an in-depth analysis to unveil the impact of q on the convergence of QSE, and derive the bounds of a refined region of q that ensures the convergence of QSE to the CRLB even with a small number of samples. Simulations validate our analysis, showing that the refined interpolation factor is able to reduce the estimation mean squared error of QSE by up to 13.14 dB when the sample number is as small as 8. [ABSTRACT FROM AUTHOR]

Asynchronous switching differing from asynchronous consensus may hinder the system to reach a consensus. This receives very limited attention, especially when the multi-agent systems have a controller fault. In order to analyze the consensus in this situation, this paper studies the consensus of the second-order multi-agent systems under asynchronous switching with a controller fault. We convert the consensus problems under asynchronous switching into stability problems and obtain important results for consensus with the aid of linear matrix inequalities. An example is given to illustrate the effect of asynchronous switching on the consensus, and to validate the analytical results in this paper. [ABSTRACT FROM AUTHOR]

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.

Abstract Due to difficulties in manufacturing, data conversion devices with extremely high sampling rate are becoming the bottleneck in realising high‐speed communication systems with a large bandwidth. Dual pulse shaping (DPS) transmission allows half‐symbol‐rate conversion devices to be used for two parallel data streams to achieve full‐rate transmission, and is proved to be an effective solution. Here, two sets of ideal sinc‐function based complementary Nyquist pulses for DPS transmission are proposed. Theoretically, it is shown that the proposed pulses satisfy the inter‐symbol and cross‐symbol interference‐free conditions, and can achieve full‐Nyquist‐rate transmission with half of the sampling rate. With reference to commercially available D/As, two sets of practical dual spectral shaping pulses are further proposed, and the close relationship between the ideal and practical pulses are disclosed. Performance analysis for linear equalisation is provided in the presence of both timing offset between dual shaping pulses and carrier‐frequency offset. Two approaches are then proposed to improve the system robustness by adjusting the clock phase of the D/As and A/Ds. Simulation results are presented to provide a comparison between the proposed DPS transmission schemes and the state of the art, in terms of the performance metrics of peak‐to‐average power ratio and bit error rate.

The Mittelman–di Cicco–Walker (MDW) H α Sky Survey is an autonomously operated and ongoing all-sky imaging survey in the narrowband H α wavelength. The survey was founded by amateur astronomers and is presented here in its first stage of refinement for rigorous scientific use. Each field is exposed through an H α filter with a 3 nm bandwidth for a total of 4 hr, with a pixel scale of 3.″2. Here, we introduce the first Data Release of the MDW H α Survey (Data Release 0, or DR0), spanning 238 fields in the region of Orion (∼3100 deg ^2 ). DR0 includes: calibrated mean fields, star-removed mean fields, a point-source catalog matched to Data Release 1 of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS1) and the Isaac Newton Telescope Galactic Plane Survey (IGAPS), and mosaics. ^3

The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.

The Blockchain technology, featured with its decentralized tamper-resistance based on a Peer-to-Peer network, has been widely applied in financial applications, and even further been extended to industrial applications. However, the weak scalability of traditional Blockchain technology severely affects the wide adoption due to the well-known trillema of decentralization-security-scalability in Blockchains. In regards to this issue, a number of solutions have been proposed, targeting to boost the scalability while preserving the decentralization and security. They range from modifying the on-chain data structure and consensus algorithms to adding the off-chain technologies. Therein, one of the most practical methods to achieve horizontal scalability along with the increasing network size is sharding, by partitioning network into multiple shards so that the overhead of duplicating communication, storage, and computation in each full node can be avoided. This paper presents a survey focusing on sharding in Blockchains in a systematic and comprehensive way. We provide detailed comparison and quantitative evaluation of major sharding mechanisms, along with our insights analyzing the features and restrictions of the existing solutions. We also provide theoretical upper-bound of the throughput for each considered sharding mechanism. The remaining challenges and future research directions are also reviewed.

Multi-objective sparse reconstruction methods have shown strong potential in sparse reconstruction. However, most methods are computationally expensive due to the requirement of excessive functional evaluations. Most of these methods adopt arbitrary regularization values for iterative thresholding-based local search, which hardly produces high-precision solutions stably. In this article, we propose a multi-objective sparse reconstruction scheme with novel techniques of transfer learning and localized regularization. Firstly, we design a knowledge transfer operator to reuse the search experience from previously solved homogeneous or heterogeneous sparse reconstruction problems, which can significantly accelerate the convergence and improve the reconstruction quality. Secondly, we develop a localized regularization strategy for iterative thresholding-based local search, which uses systematically designed independent regularization values according to decomposed subproblems. The strategy can lead to improved reconstruction accuracy. Therefore, our proposed scheme is more computationally efficient and accurate, compared to existing multi-objective sparse reconstruction methods. This is validated by extensive experiments on simulated signals and benchmark problems.

This paper investigates the consensus of second-order multi-agent systems under switched topologies. Previous studies indicate that a consensus cannot be reached if the topology is fixed and has no spanning tree, but it is possible to reach a consensus for the multi-agent systems under switched topologies even if every topology has no spanning tree. However, in this paper, we show that some second-order multi-agent systems cannot reach a consensus even if the union of the directed interaction graphs has a spanning tree, and even if the union has a spanning tree frequently enough. It is often complex to judge whether the second-order multi-agent systems can reach a consensus or not under switched topologies if every topology has no spanning tree. This paper proposes a sequence-based topology-dependent method to determine whether a consensus can be reached in this circumstance. Our results are supported by examples and counterexamples.

The performance of millimeter wave (mmWave) line-of-sight multiple input multiple output (LOS MIMO) systems using hybrid arrays of planar subarrays was studied. We characterized the achievable maximum spatial multiplexing gain for such LOS MIMO systems by the measures of spectral efficiency and effective degree of freedom (EDoF). By proposing a joint plane-wave and spherical-wave-based general 3D channel model, we derived the optimal design parameters in the analog domain, i.e., the optimal subarray separation products, and analyzed their sensitivity on the system performance. We also gave analytical eigenvalue expressions of the equivalent LOS MIMO channel matrix, which are applicable to the case of a non-optimal design, as well as the upper and lower bounds of the EDoF for system performance evaluation. A piecewise uniform quantization codebook was further designed for quantizing phase shifter values in practical applications. The numerical and simulation results show that planar subarrays are superior to traditional arrays in terms of spectral efficiency and EDoF in Ricean fading channels because they are more robust to the change in the communication distance and the deviation from the optimal design. The use of hybrid arrays of planar subarrays effectively removes the limitation of mmWave LOS MIMO systems using traditional arrays, through which, the conventional Rayleigh distance criterion has to be satisfied to achieve the optimal performance.

Millimeter wave (mmWave) radar poses prosperous opportunities surrounding multiple-object tracking and sensing as a unified system. One of the most challenging aspects of exploiting sensing opportunities with mmWave radar is the labeling of mmWave data so that, in turn, a respective model can be designed to achieve the desired tracking and sensing goals. The labeling of mmWave datasets usually involves a domain expert manually associating radar frames with key events of interest. This is a laborious means of labeling mmWave data. This paper presents a framework for training a mmWave radar with a camera as a means of labeling the data and supervising the radar model. The methodology presented in this paper is compared and assessed against existing frameworks that aim to achieve a similar goal. The practicality of the proposed framework is demonstrated through experimentation in varying environmental conditions. The proposed framework is applied to design a mmWave multi-object tracking system that is additionally capable of classifying individual human motion patterns, such as running, walking, and falling. The experimental findings demonstrate a reliably trained radar model that uses a camera for labeling and supervision that can consistently produce high classification accuracy across environments beyond those in which the model was trained against. The research presented in this paper provides a foundation for future research in unified tracking and sensing systems by alleviating the labeling and training challenges associated with designing a mmWave classification model.

Employing a cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework was developed recently, enabling CP-OFDM sensing to surpass the conventional limits imposed by underlying communications. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveforms. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to remove false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution. In particular, our design can reduce the false alarm rate caused by false targets by over 50% compared with the prior art.

The millimeter wave (mmWave) channel is dominated by line-of-sight propagation. Therefore, the acquisition of angle-of-arrival (AoA) and polarization state of the wave is of great significance to the receiver. In this paper, we investigate AoA and polarization estimation in a mmWave system employing dual-polarized antenna arrays. We propose an enhanced AoA estimation method using a localized hybrid dual-polarized array for a polarized mmWave signal. The use of dual-polarized arrays greatly improves the calibration of differential signals and the signal-to-noise ratio (SNR) of the phase offset estimation between adjacent subarrays. Given the estimated phase offset, an initial AoA estimate can be obtained, and is then used to update the phase offset estimation. This leads to a recursive estimation with improved accuracy. We further propose an enhanced polarization estimation method, which uses the power of total received signals at dual-polarized antennas to compute the cross-correlation-to-power ratio instead of using only one axis dipole. Thus the accuracy of polarization parameter estimation is improved. We also derive a closed-form expression for mean square error lower bounds of AoA estimation and present an average SNR analysis for polarization estimation performance. Simulation results demonstrate the superiority of the enhanced AoA and polarization parameter estimation methods compared to the state of the art.

The proliferation of wireless applications, the ever-increasing spectrum crowdedness, as well as cell densification makes the issue of interference increasingly severe in many emerging wireless applications. Most interference management/mitigation methods in the literature are problem-specific and require some cooperation/coordination between different radio frequency systems. Aiming to seek a more versatile solution to counteracting strong interference, we resort to the hybrid array of analog subarrays and suppress interference in the analog domain so as to greatly reduce the required quantization bits of the analog-to-digital converters and their power consumption. To this end, we design a real-time algorithm to steer nulls towards the interference directions and maintain flat in non-interference directions, solely using constant-modulus phase shifters. To ensure sufficient null depth for interference suppression, we also develop a two-stage method for accurately estimating interference directions. The proposed solution can be applicable to most (if not all) wireless systems as neither training/reference signal nor cooperation/coordination is required. Extensive simulations show that more than 65 dB of suppression can be achieved for 3 spatially resolvable interference signals yet with random directions.

Joint communications and sensing (JCAS) has recently attracted extensive attention due to its potential in substantially improving the cost, energy and spectral efficiency of Internet of Things (IoT) systems that need both radio frequency functions. Given the wide applicability of orthogonal frequency division multiplexing (OFDM) in modern communications, OFDM sensing has become one of the major research topics of JCAS. To raise the awareness of some critical yet long-overlooked issues that restrict the OFDM sensing capability, a comprehensive overview of OFDM sensing is provided first in this paper, and then a tutorial on the issues is presented. Moreover, some recent research efforts for addressing the issues are reviewed, with interesting designs and results highlighted. In addition, the redundancy in OFDM sensing signals is unveiled, on which, a novel method is based and developed in order to remove the redundancy by introducing efficient signal decimation. Corroborated by analysis and simulation results, the new method further reduces the sensing complexity over one of the most efficient methods to date, with a minimal impact on the sensing performance.

This paper proposes an energy management scheme to maximize the use of solar energy in the smart grid. In this context, a shared facility controller (SFC) with a number of solar photovoltaic panels in a smart community is considered that has the capability to schedule the generated energy for consumption and trade to other entities. In particular, a mechanism is designed for the SFC to decide on the energy surplus, if there is any, that it can use to charge its battery and sell to the households and the grid based on the offered prices. In this regard, a hierarchical energy management scheme is proposed with a view to reduce the total operational cost to the SFC. The concept of a virtual cost is introduced that aids the SFC to estimate its future operational cost based on some available current information. The energy management is conducted for three different cases, and the optimal cost to the SFC is determined for each case by the theory of maxima and minima. A real-time algorithm is proposed to reach the optimal cost for all cases, and some numerical examples are provided to demonstrate the beneficial properties of the proposed scheme.

This paper proposes an adaptive array beamforming method by embedding antennas’ active pattern in the worst-case performance optimization algorithm. This method can significantly reduce the beamformer’s performance degradation caused by inconsistency between hypothesized ideal array models and practical ones. Simulation and measured results consistently demonstrate the robustness and effectiveness of the proposed method in dealing with array manifold mismatches.

We investigate reduced-rank shift-invariant technique and its application for synchronization and channel identification in UWB systems. Shift-invariant techniques, such as ESPRIT and the matrix pencil method, have high resolution ability, but the associated high complexity makes them less attractive in real-time implementations. Aiming at reducing the complexity, we developed novel reduced-rank identification of principal components (RIPC) algorithms. These RIPC algorithms can automatically track the principal components and reduce the computational complexity significantly by transforming the generalized eigen-problem in an original high-dimensional space to a lower-dimensional space depending on the number of desired principal signals. We then investigate the application of the proposed RIPC algorithms for joint synchronization and channel estimation in UWB systems, where general correlator-based algorithms confront many limitations. Technical details, including sampling and the capture of synchronization delay, are provided. Experimental results show that the performance of the RIPC algorithms is only slightly inferior to the general full-rank algorithms.

Quadrature orthogonal frequency division multiple access (Q-OFDMA) systems have been recently proposed to reduce the peak-to-average power ratio (PAPR) and complexity, and improve carrier frequency offset (CFO) robustness and frequency diversity for the conventional OFDMA systems. However, Q-OFDMA receiver obtains frequency diversity at the cost of noise enhancement, which results in Q-OFDMA systems achieving better performance than OFDMA only in the higher signal-to-noise ratio (SNR) range. In this paper, we investigate various detection techniques such as linear zero forcing (ZF) equalization, minimum mean square error (MMSE) equalization, decision feedback equalization (DFE), and turbo joint channel estimation and detection, for Q-OFDMA systems to mitigate the noise enhancement effect and improve the bit error ratio (BER) performance. It is shown that advanced detections, for example, DFE and turbo receiver, can significantly improve the performance of Q-OFDMA.

CFO and I/Q mismatch could cause significant performance degradation to OFDM systems. Their estimation and compensation are generally difficult as they are entangled in the received signal. In this paper, we propose some low-complexity estimation and compensation schemes in the receiver, which are robust to various CFO and I/Q mismatch values although the performance is slightly degraded for very small CFO. These schemes consist of three steps: forming a cosine estimator free of I/Q mismatch interference, estimating I/Q mismatch using the estimated cosine value, and forming a sine estimator using samples after I/Q mismatch compensation. These estimators are based on the perception that an estimate of cosine serves much better as the basis for I/Q mismatch estimation than the estimate of CFO derived from the cosine function. Simulation results show that the proposed schemes can improve system performance significantly, and they are robust to CFO and I/Q mismatch.