Real-time Programming Research Articles

Compiler-assisted energy reduction of java real-time programs

Optimizations in energy consumption are critical for battery-powered hard real-time embedded systems. Energy management is often accomplished by dynamic frequency scaling which requires compilers to insert special instructions in the code at appropriate places, which are then used by the operating system and the processor to calculate and change the frequency at these places in the code. The addition of code snippets for frequency change may affect the worst-case execution time of the program, which is a major concern for hard real-time systems. In addition to the above problem, run time calculation of the optimal frequency prohibits a compile-time estimate of the battery life by the system designers. In this paper, we present compiler optimizations for hard real-time Java applications executing on a time predictable Java processor, which reduces the energy consumption of the program while retaining the original worst-case execution time for the program intact. We also generate a compile-time frequency change schedule for the program resulting in compile-time estimates for worst-case energy consumption. We found that on average, reduction in energy consumption is better in run-time frequency schedule based mechanism but the compile-time frequency schedule allows us to get bounds on energy consumption at the compile-time.

Driver Drowsiness Detection in Real-time

In modern life, drowsiness is one of the major causes of road accidents, many of which are fatal. Analyzing statistics, it can be assumed that most road accidents occur as a result of drowsiness leading to serious injury and death. For this reason, various studies have been done on designing programs that can detect driver fatigue and alert them before a serious error occurs. This prevents them from falling asleep and having an accident. Some of the most common methods use automotive-based methods to design their own system. But these traditional measures were strongly influenced by other factors such as road structure, vehicle type and driver-wheel driveability. Some methods use psychological methods of their system that often provide the most accurate and consistent results in the driver's drowsiness monitoring. However, such techniques are very tedious as the electrodes need to be placed on the head and body. In addition, few studies are available where independent measurements are used as system installation, but such methods can confuse the driver and lead to unintended consequences. In this paper, we have proposed a non-disruptive and real-time program. Our proposed system classifies it as sleep deprivation. The model is fed with a large database of closed eyes and open eyes to produce results. The driver is notified by Buzz every time he is found drowsy. In our model, we use a standard forward-looking smartphone camera and use the information we have gained to produce results on our website. This can be more economical than using additional hardware.

Monitoring of Pressure Transients in Great Lakes Water Authority Water Transmission System

Great Lakes Water Authority (GLWA) operates one of the largest water systems in the United States and, like most other water utilities, is facing the problem of aging water infrastructure. Internal pressure transient events can be a major contributing factor in the deterioration and failure of aging water pipes. To evaluate the impact of pressure transients on water main deterioration, for over three years GLWA has maintained a real-time pressure transient monitoring program within its water transmission system. The Trimble Unity Remote Monitoring suite is used; it includes high speed pressure sensors and data loggers. Approximately 6000 transient events have been recorded by the 30 transient monitoring sensors installed within the transmission system. A quantitative approach to evaluating the relative impact of pressure transients on the deterioration of water pipes has been used in analyzing the pressure transient events. The approach is based on the frequencies and pressure ranges of transient events. This paper presents the development of the transient monitoring program and analytical results of the pressure transient data. These analytical results, plus the ongoing transient monitoring data, are being used in updating GLWA’s system risk assessment.

Real-time GIS Programming and Geocomputation

Real-time GIS Programming and Geocomputation

Use of real-time multimodal sensory feedback home program improved backward stride and retention for people with Parkinson Disease: A pilot study

IntroductionParkinson disease (PD) impairs sensory integration, contributes to motor dysfunction, loss of gait automaticity, and increased fall risk. Employing multimodal sensory feedback (MMSF) has the potential to improve proprioceptive integration and gait safety while reducing exercise burden especially for backward gait. MethodsThis single-blinded, randomized controlled pilot study used a home program with or without real-time visual, proprioceptive, and auditory feedback with stepping exercises which progressed in speed and distance. Both groups completed a six-week intervention followed by 6 weeks without exercise to assess long-term retention. Six additional weeks of exercises were completed to assess recovery of potential losses after the washout session.Eleven people with PD exercised with real-time MMSF and 7 exercised without MMSF. Outcome measures included backward stride length, velocity, cadence, and double support time. The Dual Timed Up and Go measured automaticity. Self-perceived improvements in gait, activities of daily living, participation, and quality of life were registered by a questionnaire. ResultsAnalysis was by repeated measures ANOVA. Using MMSF significantly improved backward stride length at 12 and 18 weeks, p = .007, η2 = 0.239. Both groups improved in all outcome measures after the initial 6-week exercise program, supporting efficacy of stepping exercises. The MMSF + ex group's significant improvements after a 6-week washout supported automaticity development. Questionnaire items received higher agreement percentages from MMSF + ex participants. ConclusionUsing real-time MMSF in a home program for pwPD provided significant and lasting improvements in backward stride, and potentially decreased fall risk and exercise burden compared to the same program without MMSF.

Deep Reinforcement Learning Acceleration for Real-Time Edge Computing Mixed Integer Programming Problems

In this work, we present the design and implementation of an ultra-low latency Deep Reinforcement Learning (DRL) FPGA based accelerator for addressing hard real-time Mixed Integer Programming problems. The accelerator exhibits ultra-low latency performance for both training and inference operations, enabled by training-inference parallelism, pipelined training, on-chip weights and replay memory, multi-level replication-based parallelism and DRL algorithmic modifications such as distribution of training over time. The design principles can be extended to support hardware acceleration for other relevant DRL algorithms (embedding the experience replay technique) with hard real time constraints. We evaluate the accuracy of the accelerator in a task offloading and resource allocation problem stemming from a Mobile Edge Computing (MEC/5G) scenario. The design has been implemented on a Xilinx Zynq Ultrascale+ MPSoC ZCU104 evaluation kit using High Level Synthesis. The accelerator achieves near optimal performance and exhibits a 10-fold decrease in training-inference execution latency when compared to a high-end CPU-based implementation.

Assessment of the utility of measuring Thyroglobulin mRNA levels by Quantitative Real-Time PCR in the follow-up of differentiated thyroid cancer patients

IntroductionThe determination of thyroglobulin levels by immunoassay and imaging studies is subject to interference by antithyroglobulin antibodies in up to 30% of cases, suggesting a need to find alternative methods for the follow-up of a significant number of thyroid cancer patients. ObjectivesAssess the sensitivity, specificity, and predictive values of thyroglobulin messenger RNA levels measured by quantitative Real Time-PCR (qRT-PCR) in the blood of patients followed for differentiated thyroid cancer. MethodsThis is a prospective study of Tg-mRNA levels measured with qRT-PCR. A peripheral blood sample was taken in patients with excellent response (69) and with structural incomplete response to treatment (23). Results were analysed using the Unity Real-Time program and expressed as fg/μg RNA. A Receiver Operating Characteristic curve was constructed to assess Tg-mRNA cut-off values. ResultsTg-mRNA levels were not significantly different between the group with excellent response [0.10 fg/μg RNA (0.08−0.17)] and the group with incomplete structural response [0.133 fg/μg RNA (0.07−0.33)] (P < .06). Test sensitivity was 69.6%, specificity was 59.4%, negative predictive value was 85.4% and positive predictive value 36.4% ConclusionsOur experience shows that this technique could be useful as a rule-out test in selected cases, but its low sensitivity and specificity preclude its usefulness as a first-line test.

Abstracts from the Spectra 2018 Symposium Part 2: Visualization

Abstracts from the Spectra 2018 Symposium Part 2: <i>Visualization</i>

Teleprogramming Service Provides Safe and Remote Stimulation Options for Patients with DRG-S and SCS Implants.

BackgroundChronic pain patients implanted with a neurostimulation device typically require follow-up and device programming visits to address changes in symptoms or treatment. Follow-up visits require access to specialty care and necessitate patients to take time off work, commute long distances, arrange for travel, and/or work with a caregiver’s schedule. Telemedicine was adopted for some patient management as a result of the Sars-Cov-2 pandemic; however, remote optimization for neuromodulation still required an in-person visit to adjust device parameters. An FDA-approved digital platform enables remote programming of an implanted neuromodulation device using a real-time audio-video link from the clinical programmer to the patient controller. The Remote Optimization, Adjustment, and Measurement for Chronic Pain Therapy (ROAM-CPT) is a multi-center, prospective study that is currently underway to access the effectiveness of the teleprogramming system in fulfilling patients’ clinical demands.MethodsThis pilot study surveyed 16 patients to determine the ability of the teleprogramming platform to provide a rapid solution safely and effectively for patient’s chronic pain. Data were collected using a questionnaire that asked 6 clinician-centric questions and 5 patient-centric questions.Results4/4 surveyed physicians were able to address patients’ needs. 16/16 surveyed patients reported a quick resolution to pain and 15/16 did not require additional follow-up. Data curated from this pilot study show that the teleprogramming application greatly improves patient care, is preferred by both clinicians and patients with minimal disruptions to patients’ everyday lives.ConclusionTeleprogramming provides real-time virtual programming capabilities and optimizes patients’ therapy.PerspectiveThis article describes remote device programming and analysis as an alternative to in-person programming/treatment sessions for neuromodulation patients. This remote option gives patients access to timely and clinically appropriate device management when in-person care may not be available.

Practical Challenges in Real-Time Demand Response

We report on a real-time demand response experiment with 100 controllable devices. The experiment reveals several key challenges in the deployment of a real-time demand response program, including time delays, uncertainties, characterization errors, multiple timescales, and nonlinearity, which have been largely ignored in previous studies. To resolve these practical issues, we develop and implement a two-level multi-loop control structure integrating feed-forward proportional-integral controllers and optimization solvers in closed loops, which eliminates steady-state errors and improves the dynamical performance of the overall building response. The proposed methods are validated by Hardware-in-the-Loop (HiL) tests.

The impact of community-level prevention strategies on high-dose opioid dispensing rates: 2014–2019

BackgroundPrescription opioids played a major role in the current opioid overdose epidemic. High rates of opioid prescribing and dispensing exposed many people to opioids, and high-dose opioid prescriptions (e.g., 90 morphine milligram equivalents [MME] per day) contributed to increases in opioid overdoses. The Centers for Disease Control and Prevention (CDC) Prevention for States (PfS) program provided funding to jurisdictions (“PfS recipients”) with a high burden of opioid-involved overdoses. This paper examines associations between strategies addressing high-dose opioid prescribing and changes in high-dose opioid dispensing. MethodsMonthly opioid dispensing data (2014–2019) from IQVIA Xponent were analyzed using longitudinal growth models (LGM) to compare high-dose opioid dispensing rates in the 29 jurisdictions that participated in PfS with rates in non-PfS jurisdictions. Additional models examined associations between specific PfS activities and changes in high-dose dispensing among PfS recipients. ResultsHigh-dose dispensing rates decreased significantly in both PfS and non-PfS jurisdictions from 2014 to 2019. Rates of high-dose opioid dispensing rates in PfS jurisdictions were not significantly different than those in non-PfS jurisdictions (p = 0.07). Among PfS recipients, multiple activities were associated with decreases in high-dose dispensing rates over time, including moving towards real-time prescription drug monitoring program (PDMP) reporting (p < 0.001) and implementation of opioid dispensing interventions for insurers/ health systems (p < 0.05). ConclusionsHigh-dose opioid dispensing rates decreased throughout the United States from 2014−2019. As the drug epidemic continues to evolve, implementation of prevention activities by state and local partners is important. These findings highlight two potential prevention strategies and activities that jurisdictions can utilize.

Real-time interactive digital healthcare system for post-operative breast cancer patients: study protocol for a randomized controlled trial

BackgroundAfter receiving breast cancer surgery or reconstruction, shoulder dysfunctions including weakness, post-operative pain, shoulder joint instability, and limited range of motion (ROM) often occur. Due to limited ROM, patients may suffer difficulty in activities of daily living, and quality of life may be reduced. The objective of this study is to compare the effects on shoulder ROM of a real-time interactive digital healthcare system and brochure-based home rehabilitation program in post-operative breast cancer patients.MethodsThis study is a prospective, multi-center, assessor-blinded randomized controlled trial. The study aims to recruit 100 breast cancer patients exhibiting limited shoulder ROM after undergoing axillary lymph node dissection or breast reconstruction following mastectomy. Patients will be randomly assigned to two groups for 8 weeks of rehabilitation: a digital healthcare system rehabilitation (intervention) group and a brochure-based rehabilitation (control) group. The primary outcome is the change in ROM of the affected shoulder between baseline and 12 weeks after enrollment. Secondary outcomes include pain in the affected shoulder, as measured using a numerical rating scale, functional outcomes (QuickDASH scores), and quality of life (FACT-B and EQ-5D-5L scores), all of which will be measured on enrollment and 4, 8, and 12 weeks thereafter.DiscussionThis study will compare the effectiveness of a newly developed, augmented reality-based real-time interactive digital healthcare system with that of brochure-based home rehabilitation for improving the shoulder ROM, pain, functional outcomes, and quality of life of post-operative breast cancer patients.Trial registrationClinicalTrials.govNCT04316156. Registered on 20 March 2020.

Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior

Real-time coaching programs are designed to give feedback on driving behavior to usage-based motor insurance users; they are often general purpose programs that aim to promote smooth driving. Here, we investigated the effect of different on-board real-time coaching programs on the driving behavior on highway deceleration lanes with a driving simulator experiment. The experiment was organized into two trials. The first was a baseline trial, in which participants drove without receiving any feedback; a cluster analysis was then performed to divide participants into two groups, based on their observed driving style. One month later, a second trial was carried out, with participants driving on the same path as the first trial, this time receiving contingent feedback related to their braking/acceleration behavior. Four feedback systems were tested; overall, there were eight experimental groups, depending on the clustered driving style (aggressive and defensive), feedback modality (visual and auditory), and feedback valence (positive and negative). Speed, deceleration, trajectory, and lateral control variables, collected before and onto the deceleration lane, were investigated with mixed ANOVAs, which showed that the real-time coaching programs significantly reduced speeds and maximum deceleration values, while improving lateral control. A change toward a safer exit strategy (i.e., entering the lane before starting to decelerate) was also observed in defensive drivers.

SUPPRESSION OF WHITE NOISE FROM THE MIXTURE OF SPEECH AND IMAGE FOR QUALITY ENHANCEMENT

This study proposed a correlation analysis of two recent approaches. The FAST ICA technique is used for the separation of the multimodal data (i.e, mixture of audio, noise and image signal) and the minimum mean-square error (MMSE) is used for the removal of white noise from the audio signal. Initially, multimodal data will be formed by combining all the three signals (i.e. a mixture of audio, noise and image signals). For creating an ideal situation and for SNR comparisons, separation of the signals will be performed using the Fast ICA technique. ICA, Independent element analysis is a recently developed technique in which the goal is to seek a linear interpretation of non-Gaussian knowledge for the elements to be as statistically free as possible. Such representations record the key structure of the data in several applications, including signal quality and signal separation. ICA learns a linear decay of the data. ICA can find the basic elements and sources included in the data found where traditional methods fail. After the separation of the mixed data, denoising will be performed using the MMSE technique. The main purpose of the MMSE technique is to remove White Noise from the unmixed audio signal which will be further used for overall and segmental SNR comparisons for quality enhancement. Based on the designed algorithms, both of these techniques are real-time data-driven programs. These techniques are explored with standard De-noising methods using several different estimation methods like signal-to-noise ratio (SNR). Experimental results prove that the proposed MMSE technique works well for both noise segmentation and overall consideration of noise distortion signals. These statistical techniques can be used in many applications, such as in different communication systems to eliminate background noise and in channels to reduce channel interference between different applications in speech communications

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

Integrated Hardware Garbage Collection

Garbage collected programming languages, such as Python and C#, have accelerated software development. These modern languages increase productivity and software reliability as they provide high-level data representation and control structures. Modern languages are widely used in software development for mobile, desktop, and server devices, but their adoption is limited in real-time embedded systems. There is clear interest in supporting modern languages in embedded devices as emerging markets, like the Internet of Things, demand ever smarter and more reliable products. Multiple commercial and open-source projects, such as Zerynth and MicroPython, are attempting to provide support. But these projects rely on software garbage collectors that impose high overheads and introduce unpredictable pauses, preventing their use in many embedded applications. These limitations arise from the unsuitability of conventional processors for performing efficient, predictable garbage collection. We propose the Integrated Hardware Garbage Collector (IHGC); a garbage collector tightly coupled with the processor that runs continuously in the background. Further, we introduce a static analysis technique to guarantee that real-time programs are never paused by the collector. Our design allocates a memory cycle to the collector when the processor is not using the memory. The IHGC achieves this by careful division of collection work into single-memory-access steps that are interleaved with the processor’s memory accesses. As a result, our collector eliminates run-time overheads and enables real-time program analysis. The principles behind the IHGC can be used in conjunction with existing architectures. For example, we simulated the IHGC alongside the ARMv6-M architecture. Compared to a conventional processor, our experiments indicate that the IHGC offers 1.5–7 times better performance for programs that rely on garbage collection. The IHGC delivers the benefits of garbage-collected languages with real-time performance but without the complexity and overheads inherent in software collectors.

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

Evaluación de la utilidad de medir los niveles de ARN mensajero de tiroglobulina por PCR cuantitativa en tiempo real para el seguimiento de pacientes con cáncer diferenciado de tiroides

IntroductionThe determination of thyroglobulin (Tg) levels by immunoassay is subject to interference by antithyroglobulin antibodies in up to 30% of cases, suggesting a need to find alternative methods for the follow-up of a significant number of thyroid cancer patients. ObjectivesAssess the sensitivity, specificity, and predictive values of thyroglobulin messenger RNA (Tg-mRNA) levels measured by quantitative Real Time-PCR (qRT-PCR) in the blood of patients followed for differentiated thyroid cancer. MethodsThis is a prospective study of Tg-mRNA levels measured with qRT-PCR. A peripheral blood sample was taken in patients with excellent response (n=69) and with structural incomplete response to treatment (n=23). Results were analysed using the Unity Real-Time program and expressed as fg/μg RNA. A Receiver Operating Characteristic curve was constructed to establish Tg-mRNA cut-off values. ResultsTg-mRNA levels were not significantly different between the group with excellent response [0.10fg/μg RNA (0.08-0.17)] and the group with incomplete structural response [0.133fg/μg RNA (0.07-0.33)] (p<0.06). Test sensitivity was 69.6%, specificity was 59.4%, negative predictive value was 85.4% and positive predictive value was 36.4%. ConclusionsOur experience shows that this technique could be useful as a rule-out test in selected cases, but its low sensitivity and specificity preclude its usefulness as a first-line test.

The Veterans Health Administration (VHA) Innovators Network: Evaluation design, methods and lessons learned through an embedded research approach

BackgroundCollaboration between researchers, implementers and policymakers improves uptake of health systems research. In 2018, researchers and VHA Innovators Network (iNET) leadership used an embedded research model to conduct an evaluation of iNET. We describe our evaluation design, early results, and lessons learned. MethodsThis mixed-methods evaluation incorporated primary data collection via electronic survey, descriptive analysis using existing VA datasets (examining associations between facility characteristics and iNET participation), and qualitative interviews to support real-time program implementation and to probe perceived impacts, benefits and challenges of participation. ResultsWe developed reporting tools and collected data regarding site participation, providing iNET leadership rapid access to needed information on projects (e.g., target populations reached, milestones achieved, and barriers encountered). Secondary data analyses indicated iNET membership was greater among larger, more complex VA facilities. Of the 37 iNET member sites, over half (n = 22) did not have any of the six major types of VA research centers; thus iNET is supporting VA sites not traditionally served by research innovation pathways. Qualitative findings highlighted enhanced engagement and perceived value of social and informational networks. ConclusionsWorking alongside our iNET partners, we supported and influenced iNET's development through our embedded evaluation's preliminary findings. We also provided training and guidance aimed at building capacity among iNET participants. ImplicationsEmbedded research can yield successful collaborative efforts between researchers and partners. An embedded research team can help programs pivot to ensure effective use of limited resources. Such models inform program development and expansion, supporting strategic planning and demonstrating value.