Search

Objective: Acute electrical stimulation of the common peroneal nerve (cPNS) has been shown to cause an immediate reduction in systolic blood pressure (SBP) in spontaneous hypertense rats (SHR), but the effect of this treatment in sub-chronic ambulatory SBP is unknown. Here we developed an implantable wireless WNClip neural stimulator to test the efficacy of 5-week cPNS as a treatment for hypertension. Results: Daily cPNS 2 Hz monophasic stimulation at threshold for 8 minutes every day for five weeks, reduced SBP in WKY animals by −4 mm Hg, and in SHR animals by −21 mmHg in week 5 (p < 0.01). Ambulatory SBP measured daily recorded approximately twenty-four hours after the cPNS treatment, showed a significant reduction from the first (176.6 ± 24.1 mm Hg; n = 5) to the last week of treatment (165.7± 42.7 mm Hg; n = 4), a −9 mm Hg reduction (p < 0.01). Evaluation of heart rate during the treatment showed no significant difference caused by the daily 8-minute cPNS. Conclusions: Electrical stimulation of the common peroneal nerve induced a reduction in SBP that is comparable to that reportedly achieved pharmacologically by ACE inhibitor Ramipril, or by renal denervation procedures. These results support the notion that neuromodulation of the common peroneal nerve can serve as an alternative treatment for drug resistant hypertension.

Accurately predicting grape yield in vineyards is essential for strategic decision-making in the wine industry. Current methods are labour-intensive, costly, and lack spatial coverage, reducing accuracy and cost-efficiency. Efforts to automate and enhance yield estimation focus on scaling fruit weight assessments. Machine learning, particularly deep learning, shows promise in improving accuracy through automatic feature extraction and hierarchical representation. However, most of these methods have been developed for analyses at a particular time point and solutions able to consider temporal information captured across sequential frames are currently poorly developed. This paper addresses this gap by introducing a system for yield estimation, utilising publicly available data repositories, such as Embrapa WGISD, alongside an in-house dataset collected by a Blackmagic camera at the pre-harvest stage. We introduce a system that utilises bunch weight regression to estimate grape yield. Bunch weight estimates are obtained by summing samples randomly drawn from the grape bunch weight distribution through empirical calibration. Grapevine bunches are identified and segmented using Mask R-CNN with Swin Transformer, and a SiamFC-based tracking mechanism is employed to estimate the number of unique bunches per panel or row. The number of berries for each tracked bunch is determined using a density approach known as multitask point supervision. In our experiments, we demonstrate the effectiveness of the proposed system for yield estimation, achieving harvested weight errors of less than 5% in two of the three vineyard panels. Larger harvest weight errors (around 15%) were observed due to inaccuracies in tracking certain bunches caused by dense concentration of bunches in one panel. However, these errors should be contrasted with the current practice error of up to 30%, highlighting the potential of machine vision for hands-off yield estimation at scale.

Regression testing is essential for maintaining software functionality in continuous integration (CI) systems, but it can become increasingly costly as software complexity grows. Machine learning-based Regression Test Prioritization (RTP) techniques have been developed to prioritize test cases based on their likelihood of failure, aiming to detect failures early and optimize resource use. However, the features used in the current state-of-the-art for training machine learning (ML) models often vary widely across different datasets, highlighting the need for further research to identify effective feature sets for RTP. Furthermore, the feature selection techniques are frequently biased toward specific features based on the dataset. Hence, we explored an ensemble technique to utilize three ML-based feature selection techniques in this study to identify and refine key features that enhance test case prioritization. These techniques were applied across four tree-based ML models using data from 15 large-scale open-source software projects. Our analysis identified the most compelling features for predicting failures and assessed their impact on RTP. The results showed that using a refined subset of features could achieve similar or up to a 10% increase in RTP performance, using only one-third of the original feature set. We also empirically evaluated the cost considerations when choosing the three methods and reported the ML models’ performance with the refined feature sets. This underscores the potential of integrating advanced feature selection methods into RTP processes.

The automation of High-Level Context (HLC) reasoning across intelligent systems at scale is imperative because of the unceasing accumulation of contextual data, the trend of the fusion of data from multiple sources (e.g., sensors, intelligent systems), and the intrinsic complexity and dynamism of context-based decision-making processes. To mitigate the challenges posed by these issues, we propose a novel Hierarchical Ontology-State Modeling (HOSM) framework CSM-H-R, which programmatically combines ontologies and states at the modeling phase and runtime phase for attaining the ability to recognize meaningful HLC. It builds on the model of our prior work on the Context State Machine (CSM) engine by incorporating the hierarchies, relationships, and state transitions to take care of the dynamic aspects of context. The design of the framework supports the sharing and interoperation of context among intelligent systems and the components for handling CSMs and the management of hierarchy, relationship, and transition. Case studies are developed for IntellElevator and IntellRestaurant, two intelligent applications in a smart campus setting. The prototype implementation of the framework experiments on translating the HLC reasoning into vector and matrix computing and presents the potential of using advanced probabilistic models to reach the next level of automation in integrating intelligent systems. The implementation of the framework is available at https://github.com/songhui01/CSM-H-R. Additionally, a built-in feature of privacy protection support is discussed in the application domain by anonymization through indexing and reducing information correlation.

Soil moisture (SM) is an important driver for forest ecosystems, creating a need for globally extensive SM information that can only be achieved with satellite-based sensors and/or process-based model. However, the reliability of remotely sensed or modeled SM data in forests is poorly understood due to a lack of suitable validation sites and interference with remote sensing caused by vegetation water content. Here, we examine three multiyear SM products: remotely sensed surface (0–5 cm) SM from combined soil moisture active passive (SMAP) and Sentinel-1 observations (SMAP/Sentinel); the SMAP Level-4 surface (0–5 cm) and root-zone (0–1 m) SM data assimilation product (SMAP-L4); and simulated surface (0–10 cm) and root-zone (0–1 m) SM from the North American land data assimilation system (NLDAS). These estimates were compared with in situ measurements from 39 National Ecological Observatory Network sites throughout the U.S. At 21 unforested sites, the performance of the three products was similar for surface SM, and all three were able to track temporal changes in surface SM. The performance of the three products declined at 18 forested sites; however, while the performance declined modestly for SMAP-L4 and NLDAS, SMAP/Sentinel performance declined so much that it was largely unable to track changes in surface SM. The SMAP-L4 and NLDAS products also reliably captured temporal changes in root-zone SM at both forested and unforested sites. Our findings indicate that both SMAP-L4 and NLDAS can be used to track surface and root-zone SM changes in forests (unbiased root-mean-square deviation: 0.03–0.06 m3 m−3).

Based on its unique sensitivity to Earth's temporal gravity, and since 2002, the gravity recovery and climate experiment (GRACE) twin satellites, and its successor, GRACE follow-on (GRACE-FO) missions have accumulated a two-decade-long and continuing Earth's mass change climate data record. Additionally, the Chinese gravimetry mission was launched as the last polar-pair satellite formation in 2021. With the opportunity of two existing polar-pair gravity satellite formations (EPGF) in operations, we explore the ideal configuration to launch a third-pair satellite formation to construct the triple-pair gravity satellite constellation (TGSC). Here, we examine the selection of initial orbit parameters of the third satellite formation based on subcycles and orbit parameters of EPGF to augment TGSC. The simulation study explores the effectiveness of the monthly temporal gravity field from the TGSC in potential contributions to geosciences. Our study reveals that TGSC improves continental hydrological signal recovery by approximately 24% and 38% in large and small basins (above/below 106 km2), as compared with GRACE-FO, which would be the polar-pair gravity satellite in operations. TGSCs effectiveness varies across drainage systems of the Greenland ice sheet (GrIS) due to different ground track coverage. Compared with GRACE-FO, TGSC enhances GrIS mass balance recovery by 37%–56%. Simulations for six mega earthquakes (above Mw 7.7) reveal that TGSC outperforms GRACE-FO by approximately 46%–58% in extracting coseismic signals. Our study reveals the importance of incorporating existing on-orbit gravity missions into the design of future gravity satellite constellations. This strategy aims to not only accomplish the predefined objectives of gravity satellite missions but also potentially provide additional benefits to the field of geosciences.

This paper explores two-phase immersion cooling using sealed enclosures of dielectric fluid as a technique to achieve compact, power dense converters on a single printed circuit board (PCB). The proposed approach employs passive circulation of the fluid and does not introduce system complexity beyond a heat exchanger required to condense the vapour. A test apparatus representing six 650 V, 150 A semiconductor switches in an inverter rejecting heat to a 65$\,^{\circ }$C water cooling loop is developed. Pool boiling experiments on a flat surface in Novec 7000 dielectric fluid demonstrate critical heat flux of 43 W cm$^{-2}$ at a saturation temperature of 94$\,^{\circ }$C and a corresponding pressure of 593 kPa. By augmenting the surface with pin fins (representative of a heat spreader attached to a switch) and grit blasting to improve the surface micro-geometry, the maximum heat transfer coefficient increased from 1.5 W cm$^{-2}$ K$^{-1}$ to 3.4 W cm$^{-2}$ K$^{-1}$ with a corresponding reduction in switch temperature from 125$\,^{\circ }$C to 107$\,^{\circ }$C at the total power dissipation of 186 W. A practical implementation with comparable thermal performance to the experimental apparatus but minimised volume of 0.12 L is presented. This yields a Cooling System Performance Index of 37 WL$^{-1}$ K$^{-1}$, including the heat exchanger and printed circuit board with switches, decoupling capacitors and gate drivers.

Dementia, a syndrome which is characterized by a decline in cognitive abilities such as memory, thinking, behavior, and the ability to perform daily living activities, is prevalent in people aged 60 and above. However, detecting it early enough can possibly slow its continuous degeneration and lessen the toll on families and caregivers alike. Due to its mortality within 10 years of onset as well as its enormous socioeconomic burden, there have been active efforts by researchers to find smart and innovative solutions for its early detection, prediction, monitoring, and management. These efforts are driven by the recent advancements in the Internet of Things (IoT), wearable technologies, and machine learning algorithms. The solutions are modeled around the modifiable risk factors of dementia. In this study, we conducted a survey of smart solutions developed or implemented to assist caregivers and clinicians in managing the health of these affected individuals. We then looked at the issues and limitations of these solutions, and argued that integrated solutions comprising wearable and non-wearable technologies modeled around multiple risk factors of dementia are necessary and should be the direction of future studies.

The advent of the Artificial Intelligent Internet of Things (AIoT) has sparked a revolution in the deployment of intelligent systems, driving the need for innovative data processing techniques. Due to escalating data privacy concerns and the immense volume of data produced by IoT devices, decentralized and distributed learning methods that are rapidly replacing traditional centralized learning play a pivotal role. As AIoT systems become increasingly ubiquitous, the accompanying computational and storage demands necessitate a departure from conventional paradigms towards more scalable, distributed, and decentralized architectures. This paper delves into the background of AIoT, with a particular focus on the evolution of distributed and decentralized learning mechanisms that operate without the need for centralized data collection, thus aligning with the General Data Protection Regulation (GDPR) for enhanced data privacy. The various distributed and decentralized learning strategies are the focus of this paper that facilitate collaborative model training across multiple AIoT nodes, thereby not only improving the performance of the AIoT system but also mitigating the risks of data concentration. The review further explores the adaptability of AI algorithms in these distributed settings, assessing their potential to optimize system performance and learning efficacy. The paper concludes with some use cases and lessons learned for decentralized and distributed learning in various AIoT areas.

The deployment of connected and automated vehicles (CAVs) may enhance operations and safety with little human feedback. Automation requires the use of communication and smart devices, thus introducing potential access points for adversaries. This paper develops a prototype real-time monitoring system for a vehicle to infrastructure (V2I) based CAV system that generates cyberattack data for CAV operations under realistic traffic conditions. The monitoring system detects any deviations from the normal operation of CAVs using a long-short term memory (LSTM) neural network proposed by the authors and reverts the system back to a safe state of operation using a set of countermeasures. The proposed algorithm was also compared to convolutional neural network (CNN) and other classical algorithms. The monitoring system detected three different emulated cyberattacks with high accuracy. The LSTM showed the highest accuracy of 98% and outperformed the other algorithms. Further, the performance of the monitoring systems was assessed in terms of the impact on traffic stream stability and safety. The results reveal that a fake basic safety message (BSM) attack on even a single CAV causes the traffic stream to become significantly unstable and increase safety risk without the monitoring system. The monitoring system, however, reverts the system to a safe state of operation and reduces the negative impacts of cyberattacks. The monitoring system improves flow stability by an average of 38% as quantified through acceleration variation and volatility. This is comparable to the base case without attacks. The findings have implications for the design of future resilient systems.

Situation Awareness (SA) is a process of sensing, understanding and predicting the environment and is an important component in complex systems. The reception of information from the environment tends to be continuous and of a multimodal nature. AI technologies provide a more efficient and robust support by subdividing the different stages of SA objectives into tasks such as data fusion, representation, classification, and prediction. This paper provides an overview of AI and multimodal methods used to build, enhance and evaluate SA in a variety of environments and applications. Emphasis is placed on enhancing perceptual integrity and persistence. Research indicates that the integration of artificial intelligence and multimodal approaches has significantly enhanced perception and comprehension in complex systems. However, there remains a research gap in projecting future situations and effectively fusing multimodal information. This paper summarizes some of the use cases and lessons learned where AI and multimodal techniques have been used to deliver SA. Future perspectives and challenges are proposed, including more comprehensive predictions, greater interpretability, and more advanced visual information.

This paper studies the coexistence between a downlink multiuser massive multi-input-multi-output (MIMO) communication system and MIMO radar. The performance of the massive MIMO system with maximum ratio (MR), zero-forcing (ZF), and protective ZF (PZF) precoding designs is characterized in terms of spectral efficiency (SE) and by taking the channel estimation errors and power control into account. The idea of PZF precoding relies on the projection of the information-bearing signal onto the null space of the radar channel to protect the radar against communication signals. We further derive closed-form expressions for the detection probability of the radar system for the considered precoding designs. By leveraging the closed-form expressions for the SE and detection probability, we formulate a power control problem at the radar and base station (BS) to maximize the detection probability while satisfying the per-user SE requirements. This optimization problem can be efficiently tackled via the bisection method by solving a linear feasibility problem. Our analysis and simulations show that the PZF design has the highest detection probability performance among all designs, with intermediate SE performance compared to the other two designs. Moreover, by optimally selecting the power control coefficients at the BS and radar, the detection probability improves significantly.

Cryogenic electric machines (CEMs) offer significant potential for highly power-dense and ultra-efficient net-zero aircraft. Aluminum (Al) displays superior conductivity to copper (Cu) at cryo-temperatures, making Al Litz wires an attractive option for CEM windings to minimize DC and eddy losses. However, accurately quantifying Al Litz wire losses remains challenging, particularly considering their unknown electromagnetic behavior at different cryo-temperatures and frequencies when embedded in iron cores. To address this, a specialized test setup was developed to measure the losses of two customized Al Litz coils, alongside a Cu Litz coil, under varying cryo-temperatures (25 to 77 K) and frequencies (50 to 1000 Hz). A numerical model was also developed using COMSOL, incorporating a temperature-dependent electrical conductivity and a homogenization model for Litz wires with rectangular cross-sections. The model was incorporated into an analytic design procedure to maximize the test-rig loss ratio within the tight space and maximum heat rejection constraints of the cryostat, to allow accurate loss separation. The experimental data aligns closely with the numerical simulations, enabling a comprehensive analysis of the loss characteristics of Al Litz wires. This study provides a detailed design methodology and serves as a valuable resource for developing CEMs for zero-emission electric aircraft.

We experimentally demonstrate the use of a multi-plane light converter (MPLC) as a non-mode selective (de)multiplexer for converting Hermite-Gaussian (HG) modes into a Hadamard matrix of fundamental Gaussian spots and vice versa. We interferometrically measure and characterize the complex output of the device and successfully demonstrate the conversion between HG modes and Hadamard matrices of Gaussian spots. The MPLC non-mode selective (de)multiplexers can be useful for versatile control of spatial properties of light beams with potential applications in beam shaping, coherent beam combining, and wavefront synthesis.

Markov Chain Monte Carlo (MCMC) is a robust statistical approach for estimating posterior distributions. However, the significant computational cost associated with MCMC presents a considerable challenge, complicating the selection of an appropriate algorithm tailored to the specific problem at hand. This study introduces a novel and comprehensive framework for evaluating the performance of MCMC algorithms, drawing inspiration from diagnostics used for multi-objective evolutionary algorithms. We employ visualizations to evaluate key algorithmic characteristics: Effectiveness (the ability to accurately find representative posterior modes, quantified by the Kullback-Leibler Divergence (KLD) and Wasserstein Distance (WD)), Efficiency (the speed of posterior characterization), Reliability (consistency across different random seeds), and Controllability (insensitivity to hyperparameter variation). Evaluating three prominent MCMC algorithms—Metropolis-Hastings (MH), Adaptive Metropolis (AM), and Differential Evolution Adaptive Metropolis (DREAM)—on high-dimensional and bimodal test problems, our analysis uncovers several insights. First, across algorithms, the number of function evaluations most controls performance on the high-dimensional problem, while the number of chains most controls performance on the bimodal problem. While this suggests similar controllability across algorithms, differences emerge on the other algorithmic characteristics. For high numbers of functions evaluations, AM performs best on the high-dimensional problem, while for low (< 5) and high (>15) chain counts, MH and AM perform best on the bimodal problem, as measured by KLD. However, outside these specific cases, DREAM consistently demonstrates superior efficiency and reliability, making it a robust choice for both high-dimensional and multimodal problems. These findings can inform MCMC algorithm selection for Bayesian inference applications, as well as hyperparameterization of the chosen algorithm. More importantly, the diagnostics represent a generalizable contribution to research on MCMC diagnostics that can be used to evaluate and inform the design of new algorithms.

Users of virtual reality (VR) technology, especially head-mounted displays (HMDs), often experience cybersickness, similar to motion sickness, with feelings of nausea, dizziness and sweatiness. Cybersickness typically increases with duration of wearing a HMD and is commonly evaluated in user studies with physiological measures, in-situ verbal reports and post session questionnaires. However, in addition to being time-consuming, user studies only provide insight into the specific configuration of the VR experience under study and can be limited to participant numbers, duration of VR exposure and the impact of cybersickness on VR experience dropout and completion rates. This paper presents a formal approach to modelling cybersickness. A Markov chain is used to define a general cybersickness model where probabilities represent changes in a user’s state of cybersickness. The Markov chain can be populated with historical user study data and interrogated to gain further awareness of the VR experience under evaluation. The approach is exemplified with a custom Markov chain model generated from a public VR experience dataset. The resulting model is shown to be representative of the ground truth user experience from the source material. Examples are presented to demonstrate how the model can be explored to gain insight for (i) scaled up parameters, such as exposure duration and participant numbers, and (ii) acceptance thresholds for minimum/maximum cybersickness. Limitations on the generation of the model and its utility across different user populations and environment types are considered and discussed in the context of future work.

This paper demonstrates a 120 Gbit/s 64-Quadrature Amplitude Modulation (64-QAM) transmission link using all-electronics sub-harmonic mixers for the transmitter and the receiver operating in the 300 GHz band. The wireless signal is propagated through a non-line-of-sight free space link where reflections from 2 mirrors provide a channel length of 3 m over-the-air transmission between the transmitter and the receiver modules. Here, we demonstrate that an accurate correction of different error sources such as channel impairments of the 300 GHz wireless link as well as the combined effects of hardware amplitude and phase impairments can successfully be achieved for the high spectral-efficiency 64-QAM modulation scheme. The measured bit error ratio of the calibrated testbed is under the soft-decision forward error correction (SD-FEC) threshold of $2.7\times 10^{-2}$ up to 120 Gbit/s with 20% overhead giving a net data rate of 100 Gbit/s post-FEC. To the best of our knowledge, this is the highest 64-QAM performance achieved using an electronics-based transmitter in the 300 GHz band.

We propose a novel user-centric clustering architecture for distributed massive multiple-input-multiple-output networks. We examine the uplink of a general multi-cell scenario in which a cluster of base stations (BSs) with large antenna arrays detect the signals of multiple users simultaneously. As opposed to traditional clustering schemes, where the channel coefficients of all the users within the cooperating cluster are learned at the BSs, in the proposed approach, BSs in the network learn the channels of only the strongest users based on the received signal-to-noise ratio. We consider two linear receivers, namely, zero forcing and linear minimum mean squared error receivers at the central processing unit. The two receivers operate with only partial knowledge of the user channels and the unknown terms are set to zero. We conduct a thorough analytical investigation and derive novel approximations for the instantaneous received signal-to-interference-and-noise ratio of an arbitrary user processed by a cluster of BSs. Furthermore, we develop simple closed-form expressions for the achievable rate and symbol error probability of an arbitrary user processed by a cluster of BSs. Numerical examples are used to illustrate the accuracy of the proposed approach and also to compare it to existing approaches. We show that our proposed architecture outperforms traditional clustering methods, particularly for cluster edge users.

While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the state-of-the-art metric used to gauge quantum program performance. The ESP suffers poor prediction since it fails to account for the unique combination of circuit structure, quantum state, and quantum computer properties specific to each program execution. Thus, an urgent need exists for a systematic approach that can elucidate various noise impacts and accurately and robustly predict quantum computer success rates, emphasizing application and device scaling. In this article, we propose quantum vulnerability analysis (QVA) to systematically quantify the error impact on quantum applications and address the gap between current success rate (SR) estimators and real quantum computer results. The QVA determines the cumulative quantum vulnerability (CQV) of the target quantum computation, which quantifies the quantum error impact based on the entire algorithm applied to the target quantum machine. By evaluating the CQV with well-known benchmarks on three 27-qubit quantum computers, the CQV success estimation outperforms the estimated probability of success state-of-the-art prediction technique by achieving on average six times less relative prediction error, with best cases at 30 times, for benchmarks with a real SR rate above 0.1%. Direct application of QVA has been provided that helps researchers choose a promising compiling strategy at compile time.

The Surface Water and Ocean Topography (SWOT) satellite and AirSWOT airborne instrument are the first imaging radar-altimeters designed with near-nadir and low incidence, 35.75 GHz Ka-band InSAR for mapping terrestrial water storage variability. Remotely sensed surface water extents are crucial for assessing such variability but are confounded by emergent and inundated vegetation along shorelines. However, because SWOT-like measurements are novel, there remains some uncertainty in the ability to detect certain land and water classes. This study examines the likelihood of misclassification between 15 land cover types and develops the Ka-band Phenomenology Scattering (KaPS) model to simulate changes to Ka-band backscatter as a result of changing surface water fraction and surface structure, including vegetation morphology and water surface waves. Using a separability metric, the study finds that water is five times more distinct compared with dry land classes, but has the potential to be confused with littoral zone and wet soil cover types. The KaPS scattering model simulates AirSWOT backscatter for incidence angles 1–27°, identifying the conditions under which open water is likely to be confused with littoral zone and wet soil cover types. KaPS characterization of the sensitivity of near-nadir and low incidence Ka-band SAR to small changes in both wet area fraction and surface structure enables a more nuanced classification of inundation area. These results provide additional confidence in the ability of SWOT to classify water inundation extent and open the door for novel hydrological and ecological applications of future Ka-band SAR missions.

Spatial resolution and image noise are two aspects of image quality of an X-ray computed tomography (CT) system and are determined by the X-ray source, the detector, and mathematical operations for image reconstruction. In CT scanners with flat panel detectors (FPDs), there is cross-talk (signal leakage) between detector pixels. The contribution of the cross-talk to spatial resolution and noise in reconstructed images has not been adequately modeled. Previously, we estimated cross-talk from autocovariance measurements in air, and modeled the impact of cross-talk on spatial resolution. We have extended that work to calculate the impact of cross-talk on signal-to-noise ratio in radiographs and to reconstructed image noise. We modeled the spatial resolution and noise of a CT scanner that uses a flat-panel detector with 0.2-mm pixels and a gadolinium oxysulfide scintillator, and a 450 kVp, dual-focus X-ray tube. Our noise model agrees with measurements from experimental data and simulations to within 10%. We show that cross-talk in FPDs can reduce resolution by over 30%, reduce noise by approximately a factor of two, and introduce correlation in the noise, and therefore, cannot be disregarded when assessing CT image quality.

This study demonstrates a measurement setup of an over-the-air (OTA) sub-THz link using a hybrid system of transmission comprising standard optical communications components and propagating the signal over 6 meters. The emitter was a p-i-n photodiode (PIN-PD), and the receiver was a sub-harmonic down-converter. The transmission testbed for the frequencies between 240 GHz to 300 GHz was characterised. Key parameters including the optical frequency, frequency hopping, the direct current (DC) bias of the emitter, the equalisation taps, and the digital signal processing stages were examined to optimise performance. An error-free 25.6 Gbit/s QPSK signal was achieved. A 16-QAM characterisation investigated the power combination from the two optical branches fed to the photodiode (PD) and its influence on the error vector magnitude (EVM) of the in-phase and quadrature (IQ) amplitude linearity and the probability density estimate. An optimum 16-QAM constellation was achieved using power combination of 5 mW and 10 mW for the optical modulator and optical carrier power, respectively. Finally, an image transmission of 24 data packets was performed and achieved error-free QPSK image reception at 25 Gbit/s.

Quasi-Delay Insensitive (QDI) asynchronous circuits, such as NULL Convention Logic (NCL), are being utilized more and more in industry to mitigate timing issues associated with process, voltage, and temperature (PVT) variations, which are making timing closure for synchronous circuits more problematic as transistor feature size continues to shrink. This paper combines aspects of relaxation optimization with utilizing the NCL_X architecture for select portions of the circuit, resulting in an integrated optimization method for NCL circuits that achieves an average speedup of 6.8% to 12.5%, using on average 24.6% fewer to 3.7% more transistors, compared to existing NCL synthesis and optimization methods.

Industrial and academic interest converge on scheduling flow shops with sequence- and time-dependent maintenance. We posit that anticipatory, integrated scheduling of operational and maintenance tasks leads to superior performance to purely ‘wait-then-fix’ handling of the maintenance tasks. Motivated by an industrial problem with (sequence dependent) setup times, maximum separation constraints, and a combination of sequence- and time- dependent maintenance tasks, this paper introduces an integer programming solution, a constraint programming solution and a heuristic solution based on list scheduling. The motivating use case provides a unique combination of concerns that is to the best of our knowledge, not yet studied in the literature. We build on existing work where we can by extending models for sequence-dependent maintenance scheduling to accommodate sequence- and time-dependent maintenance scheduling and also propose other new models. We show the relative performances of our methods through empirical evaluations and also show significant improvements – up to 25% reduction in makespan – when compared to a reactive scheduling approach that does not consider maintenance in its planning. Based on our evaluations on exact methods, constraint programming models scale better than mixed integer programming models for this problem.

We compare the switching losses of four equivalent silicon and wide-bandgap 650 V power transistors operated in a hard-switched half-bridge configuration, switching 400 V at 40 A. Each transistor is mounted on an identical PCB and driven by a gate drive circuit matched to its requirements. Switching speed is maximised by a PCB design featuring very low parasitic inductance and the use of zero external gate resistance (where possible). Switching losses are measured electrically using a Double Pulse Test (DPT) method. However, high-bandwidth electrical measurements are prone to error and so we assess the gain accuracy, offset accuracy, and the bandwidth requirements of the DPT measurements, and then verify the DPT results by calorimetry. The electrical and calorimetric measurements are shown to agree to within 5%. Comprehensive plots of gate-source voltage, drain-source voltage, and source current are provided for all transistors over a junction temperature range of 50–150°C. The ratio of the total half-bridge switching losses is 1:3.2:14:31 for the GaN HEMT, SiC MOSFET, Si IGBT, and Si superjunction MOSFET, respectively.

Falls have always been one of the major threats to the health and well-being of elderly people, particularly for those living alone. Both wearable and non-wearable fall detection systems have already been developed. However, the fall detection systems using WiFi channel state information (CSI) have attracted a significant interest from researchers due to their non-intrusive and low-cost nature. There are existing machine learning (ML) based fall detection systems using WiFi CSI; however, most systems trained with comprehensive datasets tend to achieve relatively lower accuracy compared to that of the systems trained with less inclusive datasets. To address these issues, we propose a novel, deep learning based fall detection technique. First, we implement different WiFi CSI collection tools and evaluate their potential for fall detection. To develop a highly accurate fall detection technique, we construct a comprehensive dataset, which consists of over 700 CSI samples including different types of falls and other daily activities, performed in four different indoor environments on and off the dominant paths. With this dataset, we then develop a deep learning based classifier using an image classification algorithm. The proposed technique, unlike the other fall detection systems, only requires down sampling and reshaping in pre-processing. The proposed fall detection system is evaluated with the constructed dataset, and it outperforms two other existing systems. It achieves over 96% accuracy for CSI collected in all four environments and 99% accuracy for CSI collected in certain combinations of the environments.

Resilience to sensor and actuator attacks is a major concern in the supervisory control of discrete events in cyber-physical systems (CPS). In this work, we propose a new framework to design supervisors for CPS under attacks using finite-state transducers (FSTs) to model the effects of the discrete events. FSTs can capture a general class of regular-rewriting attacks in which an attacker can nondeterministically rewrite sensing/actuation events according to a given regular relation. These include common insertion, deletion, event-wise replacement, and finite-memory replay attacks. We propose new theorems and algorithms with polynomial complexity to design resilient supervisors against these attacks. We also develop an open-source tool in Python based on the results and illustrate its applicability through a case study.

Mental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to explore an effective and accurate automated system for timely mental fatigue detection. Therefore, we present a systematic review of brain-computer interface (BCI) studies for mental fatigue detection using artificial intelligent (AI) techniques published in Scopus, IEEE Explore, PubMed and Web of Science (WOS) between 2011 and 2022. The Boolean search expression that comprised (((ELECTROENCEPHALOGRAM) AND (BCI)) AND (FATIGUE CLASSIFICATION)) AND (BRAIN-COMPUTER INTERFACE) has been used to select the articles. Through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, we selected 39 out of 562 articles. Our review identified the research gap in employing BCI for mental fatigue intervention through automated neurofeedback. The AI techniques employed to develop EEG-based mental fatigue detection are also discussed. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. The future direction includes data fusion, hybrid classification models, availability of public datasets, uncertainty, explainability, and hardware implementation strategies.

In this paper, an error-correction code (ECC)-enabled memory design with selective bit hardening is proposed for approximate data storage. Approximate memory is suitable for error-tolerant applications, and can achieve low-power design goals within targeted quality. Our design offers approximately 90% power savings compared to traditional memory, without compromising perceived quality. To the best of our knowledge, combining selective bit hardening with ECC is a novel approach to ensure the protection of MSBs and protect other faulty bits, respectively. Our proposed method requires no area overhead for MSB hardening, other than one additional input pin; and area overhead for implementing ECC is minimal.

For safe and effective robot-aided gait training, it is essential to incorporate the knowledge and expertise of physical therapists. Toward this goal, we directly learn from physical therapists’ demonstrations of manual gait assistance in stroke rehabilitation. Lower-limb kinematics of patients and assistive force applied by therapists to the patient’s leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist’s strategies in response to unique gait behaviors found within a patient’s gait. Preliminary analysis shows that knee extension and weight-shifting are the most important features that shape a therapist’s assistance strategies. These key features are then integrated into a virtual impedance model to predict the therapist’s assistive torque. This model benefits from a goal-directed attractor and representative features that allow intuitive characterization and estimation of a therapist’s assistance strategies. The resulting model is able to accurately capture high-level therapist behaviors over the course of a full training session (r2 = 0.92, RMSE = 0.23Nm) while still explaining some of the more nuanced behaviors contained in individual strides (r2 = 0.53, RMSE = 0.61Nm). This work provides a new approach to control wearable robotics in the sense of directly encoding the decision-making process of physical therapists into a safe human-robot interaction framework for gait rehabilitation.

With the emergence of the Internet-of-Things (IoT), artificial intelligence, and communication technologies, cyber-physical systems (CPS) have revolutionized the engineering paradigm with profound applications in many aspects of society including homes, energy, agriculture, health-care, transportation, business, and manufacturing. A CPS uses suitable computational techniques such as game theory to enable different entities to interact with one another for taking necessary actions to obtain selected objectives. Recent literature on CPS has extensively used game theory to approach a variety of technical challenges. In order to make these contributions more accessible to a broader audience, there is a need for studies that can provide readers with a comprehensive understanding of different types of CPS and their attributes, then clearly outline why game theory is relevant for modeling different aspects of CPS, and also discuss how game theory has been used in relevant literature to date. This paper bridges this gap by 1) providing a general discussion of different types of CPS and their characteristics; 2) giving an overview of different types of game-theoretic approaches; 3) explaining why game theory is appropriate for modeling different types of CPS; and 4) finally, studying how game theory has been used in different CPS types to address their challenges. Further, we also identify some key research challenges for future investigation where game theory could be applied as a potential solution.

Goal: Cerebrovascular impedance is modulated by a vasoactive autoregulative mechanism in response to changes in cerebral perfusion pressure. Characterization of impedance and the limits of autoregulation are important biomarkers of cerebral health. We developed a method to quantify impedance based on the spectral content of cerebral blood flow and volume at the cardiac frequency, measured with diffuse optical methods. Methods: In three non-human primates, we modulated cerebral perfusion pressure beyond the limits of autoregulation. Cerebral blood flow and volume were measured with diffuse correlation spectroscopy and near-infrared spectroscopy, respectively. Results: We show that impedance can be used to identify the lower and upper limits of autoregulation. Conclusions: This impedance method may be an alternative method to measure autoregulation and a way of assessing cerebral health non-invasively at the clinical bedside.

According to the Environmental Protection Agency, the power and transport systems contribute over 50% of greenhouse gas emissions in the United States. The combination of renewable energy resources and electrified transportation play a crucial role in helping countries meet their carbon neutrality goals. Coordinated planning of the power and transportation systems is necessary to ensure reliable, efficient, and economic operation of both systems. This paper surveys applications of large-scale transportation electrification, vehicle-to-grid charging, prosumer behavior, sector coupling, route optimization, and charging demand and estimation. The co-simulation frameworks available for coupling the power and transport systems to further study these applications are also surveyed. Most of the existing literature utilizes stochastic transportation models or ad-hoc co-simulation approaches which do not promote scalability, extensibility, and interoperability. It is proposed to extend the Hierarchical Engine for Large-Scale Infrastructure Co-simulation to communicate with an agent based transportation simulator for examining these interconnected applications.

This article presents a phase-shift control method for a converter containing a multi-winding transformer. The control strategy is developed from a power flow analysis of the converter and uses the derived power flow equations to supply constant power at a load port while the second load port experiences large load changes. This control method prevents the constant load port from experiencing transient behavior caused by magnetic coupling with the other load port. The approach is demonstrated with an experiment using a controller within a control hardware-in-the-loop set-up and then a full experimental setup within a lower power test bed. By using control to mitigate the effects of mutual flux within a multi-winding transformer, the load ports of the converter are effectively decoupled. The analytical procedure described in this article can also be applied to other converter topologies containing multi-winding transformers to achieve similar decoupled load performance, as demonstrated in the triple active bridge converter test bed.

This paper focuses on the combined radar and communications problem and conducts a thorough analytical investigation on the effect of phase and frequency change on the communication and sensing functionality. First, we consider the classical stepped frequency radar waveform and modulate data using $M$ -ary phase shift keying (MPSK). Two important analytical tools in radar waveform design, namely the ambiguity function (AF) and the Fisher information matrix (FIM) are derived, based on which, we make the important conclusion that MPSK modulation has a negligible effect on radar local accuracy. Next, we extend the analysis to incorporate frequency permutations and propose a new signalling scheme in which the mapping between incoming data and waveforms is performed based on an efficient combinatorial transform called the Lehmer code. We also provide an efficient communications receiver based on a modified Hungarian algorithm. From the communications perspective, we consider the optimal maximum likelihood (ML) detector and derive the union bound and nearest neighbour approximation on the block error probability. From the radar sensing perspective, we discuss the broader structure of the waveform based on the AF derivation and quantify the radar local accuracy based on the FIM. Extensive numerical examples are provided to illustrate the accuracy of our results.

In viticulture, yield estimation is a key activity, which is important throughout the wine industry value chain. The earlier that an accurate yield estimation can be made the greater its value, increasing management options for grape growers and commercial options for winemakers. For the yield estimate based on in- field measurements at scale, the number of inflorescences emerging after bud-burst offers the earliest practical signal, allowing a yield potential to be determined months before harvest. This paper presents an approach to automatically count the inflorescence number at the phenological stage E-L 12 using RGB video data and demonstrates its use for estimating yield. A dataset consisting of RGB videos was collected shortly after bud-burst from multiple vineyards, in conjunction with hand counts to produce a manual ground-truth for the inflorescence counting task. The video frames were annotated using bounding-boxes around the inflorescences to produce a digital ground-truth. A deep learning architecture was developed to learn features from the video frames during training and detect the inflorescences at the later inference stage. The detection results were fed to a tracking pipeline built using computer vision and deep learning techniques to generate numbers of inflorescences present in test videos. The visual and quantitative results are presented and evaluated for the inflorescence detection and counting tasks. The developed inflorescence detector achieves an average precision of 80.00%, a recall of 83.92%, and an F1-score of 80.48%, through a five-fold cross-validation on the annotated dataset. For the test videos, the developed automatic inflorescence counting model reports an absolute error of 11.03 inflorescences per panel, a normalized mean absolute error of 10.80%, and an $R^{2}$ of 0.86, when the predicted per-panel counts were compared to the corresponding manual ground-truth. Based on the counting results, we estimate an early yield that is within 4% to 11% error when compared to the actual yield after harvest. Based on these results and a separate analysis of the relationship between hand counts of inflorescences and harvest yields in three vineyards over three growing seasons, we conclude that computer vision and machine learning based methods have the potential to provide early yield estimation in viticulture with a commercially viable accuracy.

This study addresses the human pose estimation problem on thermal images using Convolutional Neural Networks and Vision Transformer architectures. To do this, eight human pose estimation methods designed for visible images were extended to be applied in the thermal domain. Due to the lack of large, representative datasets containing labeled thermal images, this extension requires transfer learning between the visible and the thermal domain, and a database for fine-tuning the networks in the thermal domain. Thus, it is proposed to train the networks using a grayscale version of the COCO dataset, and then fine-tune them in the thermal domain. Fine-tuning is carried out using the new UCH-Thermal-Pose database presented in this work. This database includes 600 thermal images for training, 200 for validation, and 104 for testing, all of them fully labeled. Moreover, in the paper, a comparative study of the eight extended deep-based methods for human pose detection is carried out. The UCH-Thermal-Pose database and the source code of all the methods are available at https://github.com/jsmithdlc/Thermal-Human-Pose-Estimation.

This paper presents polychronous oscillatory cellular neural networks, designed for solving graph coloring problems. We propose to apply the Potts model to the four-coloring problem, using a network of locally connected oscillators under superharmonic injection locking. Based on our mapping of the Potts model to injection-locked oscillators, we utilize oscillators under divide-by-4 injection locking. Four possible states per oscillator are encoded in a polychronous fashion, where the steady state oscillator phases are analogous to the time-locked neuronal firing patterns of polychronous neurons. We apply impulse sensitivity function (ISF) theory to model and optimize the high-order injection locking of the oscillators. CMOS circuit design of a polychronous oscillatory neural network is presented, and coloring of a geographic map is demonstrated, with simulation results and design guidelines. There is good agreement between theory and Spectre simulation.

We propose and numerically validate a patch reflectarray modeling approach suitable for small patches that describes each patch as a pair of polarizable magnetic dipoles. We introduce an extraction technique to obtain the effective polarizability of the patch dipoles via full-wave simulations on individual patches, followed by a beamforming design routine valid under weakly scattering configurations. This dipole framework serves as an alternative to the ray tracing model often used in reflectarray designs, in which rays are drawn from the feed point and scattered off of the patch elements. Whereas the ray tracing method solves the design problem in terms of phase delays, the dipole framework presented here has the potential to accurately design and predict beam patterns using a fully dipolar treatment of empirically characterized patches. We illustrate this technique by applying it to two modulation strategies: a variable patch size reflectarray in which the phase can be continuously tuned (grayscale patch response), and a fixed patch size (binary patch response) in which on/off modulation is achieved through selective patch electrical shorting. Methods for incorporating these cases into the dipole design framework are discussed and the results compared to those from full wave simulation.

Outdoor navigation remains a challenging activity for People with Visual Impairments (PVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PVI undertake a journey in an outdoor environment and what are their main challenges and obstacles. This is a critical step towards developing robust solutions that meet the requirements of this user group. We undertook a questionnaire-based study with the National Council for the Blind of Ireland (NCBI) and 49 PVI. According to the feedback from our questionnaire, current journey navigation apps do not provide PVI with sufficient information about traffic lights, crossroads, and physical obstacles to support a satisfactory interaction. Our study reveals key aspects of how PVI interact with outdoor navigation applications. Critical gaps exist, for example, over 63% of respondents indicated they had suffered an injury on at least one previous occasion when navigating outdoors. Based on the questionnaire feedback, we present a solution covering the main aspects of outdoor navigation for PVI. Our work aims to contribute to the improvement of outdoor navigation applications for PVI in the future.

Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. Device candidates for this application need to produce highly random “coinflips” while also having tunable biasing of the coin. The magnetic tunnel junction (MTJ) has been studied as an RNG due to its thermally-driven magnetization dynamics, often using spin transfer torque (STT) current amplitude to control the random switching of the MTJ free layer (FL) magnetization, here called the stochastic write method. There are additional knobs to control the MTJ-RNG, including voltage-controlled magnetic anisotropy (VCMA) and spin orbit torque (SOT), and there is a need to systematically study and compare these methods. We build an analytical model of the MTJ to characterize using VCMA and SOT to generate random bit streams. The results show that both methods produce high-quality, uniformly distributed bitstreams. Biasing the bitstreams using either STT current or an applied magnetic field shows a sigmoidal distribution versus bias amplitude for both VCMA and SOT, compared to less sigmoidal for stochastic write. The energy consumption per sample is calculated to be 0.1 pJ (SOT), 1 pJ (stochastic write), and 20 pJ (VCMA), revealing the potential energy benefit of using SOT and showing using VCMA may require higher damping materials. The generated bitstreams are then applied to two tasks: generating an arbitrary probability distribution and using the MTJ-RNGs as stochastic neurons to perform simulated annealing, where both VCMA and SOT methods show the ability to effectively minimize the system energy with a small delay and low energy. These results show the flexibility of the MTJ as a true RNG and elucidate design parameters for optimizing the device operation for applications.

Dental implants have become increasingly important in daily dental offices. The degree of pain and discomfort experienced during a surgical procedure varies from one patient to another. Using advanced machine learning algorithms to predict pain, the dentist and the patient would make more informed decisions about the treatment. This study aims at Predicting postoperative discomfort using an AI-based multi-linear regression model. The functional parametric association between the eight parameters (age, sex, and operating technique) and the patient’s postoperative pain was established following implant surgery. The output was normalized information regarding both incidence and severity of immediate discomfort post-implant surgery. To enhance the generalization ability of the multiple linear regression (MLR) model and avoid overfitting, 825 cases were provided as the training set, while 207 cases were given for data authentication. In addition, 45 samples were used as controls to determine the model’s prediction accuracy. Evaluation of the given model reveals a Root Mean Squared Error of 0.1085. This prototype predicted AI model postoperative pain following implant surgery with 89.6 % accuracy. Finally, this AI model exhibited clinical viability and utility in predicting postoperative pain after surgery.

Ransomware has been one of the biggest cyber threats against consumers in recent years. It can leverage various attack vectors while it also evolves in terms of finding more innovative ways to invade different cyber security systems. There have been many efforts to detect ransomware within the workforce and academia leveraging machine learning algorithms, which has shown promising results. Accordingly, there is a considerably large body of literature addressing various solutions on how ransomware threats can be detected and mitigated. Such large and rapidly growing scientific and technical materials start to make it difficult in knowing the actual ML algorithm(s) being used. Hence, the aim of this paper is to give insight about ransomware detection frameworks and those ML algorithms which are typically being used to extract ever-evolving characteristics of ransomware. In addition, this study will provide the cyber security community with a detailed analysis of those frameworks. This will be augmented with information such as datasets being used along with the challenges that each framework may be faced with in detecting a wide variety of ransomware accurately. To summarize, this paper delivers a comparative study which can be used by peers as a reference for future work in ransomware detection.

Objective: To identify radiomic and clinical features associated with post-ablation recurrence of AF, given that cardiac morphologic changes are associated with persistent atrial fibrillation (AF), and initiating triggers of AF often arise from the pulmonary veins which are targeted in ablation. Methods: Subjects with pre-ablation contrast CT scans prior to first-time catheter ablation for AF between 2014–2016 were retrospectively identified. A training dataset (D1) was constructed from left atrial and pulmonary vein morphometric features extracted from equal numbers of consecutively included subjects with and without AF recurrence determined at 1 year. The top-performing combination of feature selection and classifier methods based on C-statistic was evaluated on a validation dataset (D2), composed of subjects retrospectively identified between 2005–2010. Clinical models ( $\text{M}_{\mathrm {C}}$ ) were similarly evaluated and compared to radiomic ( $\text{M}_{\mathrm {R}}$ ) and radiomic-clinical models ( $\text{M}_{\mathrm {RC}}$ ), each independently validated on D2. Results: Of 150 subjects in D1, 108 received radiofrequency ablation and 42 received cryoballoon. Radiomic features of recurrence included greater right carina angle, reduced anterior-posterior atrial diameter, greater atrial volume normalized to height, and steeper right inferior pulmonary vein angle. Clinical features predicting recurrence included older age, greater BMI, hypertension, and warfarin use; apixaban use was associated with reduced recurrence. AF recurrence was predicted with radio-frequency ablation models on D2 subjects with C-statistics of 0.68, 0.63, and 0.70 for radiomic, clinical, and combined feature models, though these were not prognostic in patients treated with cryoballoon. Conclusions: Pulmonary vein morphology associated with increased likelihood of AF recurrence within 1 year of catheter ablation was identified on cardiac CT. Significance: Radiomic and clinical features-based predictive models may assist in identifying atrial fibrillation ablation candidates with greatest likelihood of successful outcome.

Large scale sensing systems increasingly operate in short-range geometries, providing three-dimensional imaging capabilities that leverage the large number of degrees of freedom of distributed arrays. The two most powerful modes of operation include active, multiple-input multiple-output arrays, in which an image is processed from signals measured using different pairs of transmitting and receiving antennas; and passive, synthetic aperture interferometric radiometer arrays, which can image using ambient, non-cooperative radiation by processing signals measured at different receiving antenna pairs. In this paper, we explore the analogy between these two imaging modes, and outline a generalized framework for analyzing these systems in the array near field. Focusing on the similarities between the formalisms describing the operation of generic active and passive systems, it is proposed, to our knowledge for the first time in the scientific literature, to adapt a Fourier-based imaging technique optimized for short-range active MIMO systems to passive technologies, interrogating the emissivity of a spatially distributed target. The systematic method presented in this paper provides a framework for characterizing and optimizing short range coherent or incoherent systems used in threat detection and non-destructive testing applications.

Conversion of a visible face image into a thermal face image (V2T), or one thermal face image into another one given a different target temperature (T2T), is required in applications such as thermography, human body thermal pattern analysis, and surveillance using cross-spectral imaging. In this work, we propose to use conditional generative adversarial networks (cGAN) with cGAN loss, perceptual loss, and temperature loss to solve the conversion tasks. In our experiment, we used Carl and SpeakingFaces Databases. Frèchet Inception Distance (FID) is used to evaluate the generated images. As well, face recognition was applied to assess the performance of our models. For the V2T task, the FID of the generated thermal images reached a low value of 57.3. For the T2T task, we achieved a rank-1 face recognition rate of 91.0% which indicates that the generated thermal images preserve the majority of the identity information.

In this article, we introduce a novel algorithm for efficient near-field synthetic aperture radar (SAR) imaging for irregular scanning geometries. With the emergence of fifth-generation (5G) millimeter-wave (mmWave) devices, near-field SAR imaging is no longer confined to laboratory environments. Recent advances in positioning technology have attracted significant interest for a diverse set of new applications in mmWave imaging. However, many use cases, such as automotive-mounted SAR imaging, unmanned aerial vehicle (UAV) imaging, and freehand imaging with smartphones, are constrained to irregular scanning geometries. Whereas traditional near-field SAR imaging systems and quick personnel security (QPS) scanners employ highly precise motion controllers to create ideal synthetic arrays, emerging applications, mentioned previously, inherently cannot achieve such ideal positioning. In addition, many Internet of Things (IoT) and 5G applications impose strict size and computational complexity limitations that must be considered for edge mmWave imaging technology. In this study, we propose a novel algorithm to leverage the advantages of non-cooperative SAR scanning patterns, small form-factor multiple-input multiple-output (MIMO) radars, and efficient monostatic planar image reconstruction algorithms. We propose a framework to mathematically decompose arbitrary and irregular sampling geometries and a joint solution to mitigate multistatic array imaging artifacts. The proposed algorithm is validated through simulations and an empirical study of arbitrary scanning scenarios. Our algorithm achieves high-resolution and high-efficiency near-field MIMO-SAR imaging, and is an elegant solution to computationally constrained irregularly sampled imaging problems.

Convoying or platooning with a fleet of autonomous vehicles, which is denoted as autonomous convoying in this paper, has attracted increasing attention from the research communities, governments, and private sectors in recent years. Autonomous convoying offers immense opportunities due to its potential in enhancing logistical efficiency as well as reducing road incidents/accidents by eliminating human errors due to stress and fatigue. While humans can make complex decisions, involving humans in decision-making processes often causes delays as compared with those of automated machines. Indeed, human errors cause approximately 90% of road accidents and fatalities. Efficient platooning techniques can also reduce fuel consumption and carbon footprints. This paper presents a concise survey on current research and development initiatives in autonomous convoying while critically discussing the underlying techniques and technologies developed in this domain. Implications of autonomous convoying toward different industries are also analyzed and discussed.

2D images can be used to capture food intake data in nutrition studies. Estimates of food volume from these images are required for nutrient analysis. Although 3D image capture is possible, it is not commonplace. Additionally, nutrition studies often require multiple food images taken by non-expert users, typically collected using mobile phones, due to their convenience. Current 2D image to 3D volume approaches are restricted by the need for prescribed camera placement, image metadata analysis and/or significant computational resources. A new method is presented combining 2D image capture and automated 3D scene projection with manual placement and resizing of wire mesh objects. 2D images, with a reference object, are taken on low specification mobile phones. 3D scene projection is calculated by twinning a cuboid in 3D space to the reference object in the 2D image. A manually selected 3D wire mesh object is then positioned over the target food item and manually transformed to improve accuracy. The virtual wire mesh object is then projected into the 3D scene and the volume of the target food item calculated. The whole process is computationally light and runs in real-time as an app on a standard Apple iPad. Based on a user study with 60 participants, experimental evaluations of volume estimates over regular shape and ground truth food items demonstrate that this approach provides acceptable accuracy. We demonstrate that the accuracy of estimates can be increased by combining multiple independent estimates.

The hydrophilic nature of cellulose compromises the dielectric properties of kraft paper insulation when exposed to water and moisture. The effort to address this setback is an ongoing research enquiry. In the present work, surface-modified rutile-titanium dioxide nanoparticles (rutile-TiO2 NPs) are reinforced into cellulose pulp to fabricate a nanocomposite kraft paper insulation. Surface modification of the nanofiller particles is intended to reduce the hydrophilicity of the resultant nanocomposite kraft paper insulation. The nanoparticles (rutile-TiO2 NPs) improve other dielectric properties of the insulation. The chemicals used for the surface modification of the rutile-TiO2 NPs were the alkyl ketene dimer (AKD) and alkenyl succinic anhydride (ASA). The nanofiller retention, dispersion and hydrophobic properties of the resultant reinforced kraft paper were experimentally analyzed. The results show that more than 50% of the nanofillers were retained within the reinforced kraft paper and translated to 1.5% by weight loading of the nanoparticles in the kraft paper. Scanning electron microscopy (SEM) images showed evenly distributed nanofillers, with some traces of agglomeration. The moisture absorption property of the kraft paper specimen modified with rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA improved by 74% compared to the control (reference) specimen. Water vapor transmission rate of the surface-modified nanoparticles reinforced kraft paper insulation decreased by 30%. Compared to the unfilled paper, the contact angle of water droplets on the surface of the reinforced kraft paper improved by 12%. Water absorption rate improved by being 4 times slower in paper specimens containing rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA. Dielectric dissipation factor measurement results showed that the specimen modified with rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA had 40% lower dielectric losses than the reference samples. This study, therefore, has successfully improved the hydrophobic properties of kraft paper by filling it with surface-modified rutile TiO2 using 5% ASA as the nanoparticle surfactant.