Real-time Software Research Articles

Qualitative visualization of butane and helium tracers in air using a digital background oriented schlieren method

Fluid flow around turbine blades, inside burners or on aerofoil is responsible for operation and overall design of the fluid energy machinery or airplane. Stall effects and turbulence behind blades are responsible for losses in efficiency. Because of this, exact knowledge of separation behaviour and vortex development of fluid flow around profile geometries or inside burners is mandatory. For this reason, aerodynamic research focuses on investigations and qualitative visualization of turbulence.The aim of this work is to develop a digital camera filter to visualize turbulence and stall effects directly around the object. One possibility is to use a digitally generated background pattern together with a filtered video image to visualize differences in diffraction coefficient of the gas. This so-called Background Oriented Schlieren (BOS) imaging can be used to visualize strains for comparison against Computational Fluid Dynamics (CFD) [1]. The BOS is based on the index of refraction of different gas phase species and temperatures. A qualitative evaluation of the flow field was also carried out, as an adjustment experiment for further turbulence models. It allows an evaluation of the flow resulting from different Reynolds numbers [2]. The 2-dimensional, dynamic image of the flow field allows to develop further approaches for mathematical models which are needed for validation. Different background patterns are generated and tested for their suitability for BOS imaging. Also, the experimental setup is explained, and different real time software filter methods are tested to get better quality of the visual results. Several images of different blade geometries are presented, and streams of hot air, butane and helium have been visualized. A filter from a Particle Image Velocity (PIV) method (here: PIVlab) was used to calculate velocities from the flow fields [3,4]. This enables further evaluation and visual representation i.e., of Reynolds numbers or a vector field [2].

Protection scheme for multi-terminal HVDC system with superconducting cables based on artificial intelligence algorithms

This paper presents the development of a novel data-driven fault detection and classification scheme for DC faults in multi-terminal HVDC transmission system which incorporates superconducting cables and modular multi-level converters. As the deployment of superconducting cables for bulk power transmission from remote renewable generation is progressively increasing in the future energy grids, many fault-related challenges have been raised (i.e., fault detection, protection sensitivity/stability). In this context, the applications of Artificial Intelligence techniques have started to be considered as a powerful tool for the development of robust fault management solutions. The proposed artificial intelligence-based method utilizes local current and voltage measurements to detect and classify all types of faults on the DC cables and DC buses, without the requirement of measurements exchange among different DC substations. The performance of the proposed scheme has been assessed through detailed transient simulation analysis and the results confirmed its effectiveness against a wide range of fault conditions (i.e., various fault types, fault locations and fault resistances). Furthermore, the feasibility of the developed scheme for real-time implementation has been validated using real-time software in the loop testing. The results revealed that the proposed algorithm can correctly, and within a very short period of time (i.e. less than 2 ms) detect and classify the faults within the protected zone and concurrently remain stable during external faults. Additionally, the generalization capability of the algorithm has been verified against influencing factors such as the addition of noise, highlighting the robustness of the presented scheme.

Design and Experimental Study of a Novel Semi-Physical Unmanned-Aerial-Vehicle Simulation Platform for Optical-Flow-Based Navigation

In the process of unmanned aerial vehicle (UAV) visual-navigation-algorithm design and accuracy verification, the question of how to develop a high-precision and high-reliability semi-physical simulation platform has become a significant engineering problem. In this study, a new UAV semi-physical-simulation-platform architecture is proposed, which includes a six-degree-of-freedom mechanical structure, a real-time control system and real-time animation-simulation software. The mechanical structure can realistically simulate the flight attitude of a UAV in a three-dimensional space of 4 × 2 × 1.4 m. Based on the designed mechanical structure and its dynamics, the control system and the UAV real-time flight-animation simulation were designed. Compared with the conventional simulation system, this system enables real-time flight-attitude simulation in a real physical environment and simultaneous flight-attitude simulation in virtual-animation space. The test results show that the repeated positioning accuracy of the three-axis rotary table reaches 0.006°, the repeated positioning accuracy of the three-axis translation table reaches 0.033 mm, and the dynamic-positioning accuracy reaches 0.04° and 0.4 mm, which meets the simulation requirements of high-precision visual UAV navigation.

Assessment of Accuracy in Unmanned Aerial Vehicle (UAV) Pose Estimation with the REAL-Time Kinematic (RTK) Method on the Example of DJI Matrice 300 RTK.

The growing possibilities offered by unmanned aerial vehicles (UAV) in many areas of life, in particular in automatic data acquisition, spur the search for new methods to improve the accuracy and effectiveness of the acquired information. This study was undertaken on the assumption that modern navigation receivers equipped with real-time kinematic positioning software and integrated with UAVs can considerably improve the accuracy of photogrammetric measurements. The research hypothesis was verified during field measurements with the use of a popular Enterprise series drone. The problems associated with accurate UAV pose estimation were identified. The main aim of the study was to perform a qualitative assessment of the pose estimation accuracy of a UAV equipped with a GNSS RTK receiver. A test procedure comprising three field experiments was designed to achieve the above research goal: an analysis of the stability of absolute pose estimation when the UAV is hovering over a point, and analyses of UAV pose estimation during flight along a predefined trajectory and during continuous flight without waypoints. The tests were conducted in a designated research area. The results were verified based on direct tachometric measurements. The qualitative assessment was performed with the use of statistical methods. The study demonstrated that in a state of apparent stability, horizontal deviations of around 0.02 m occurred at low altitudes and increased with a rise in altitude. Mission type significantly influences pose estimation accuracy over waypoints. The results were used to verify the accuracy of the UAV's pose estimation and to identify factors that affect the pose estimation accuracy of an UAV equipped with a GNSS RTK receiver. The present findings provide valuable input for developing a new method to improve the accuracy of measurements performed with the use of UAVs.

Continuous organ perfusion monitoring using indocyanine green in a piglet model.

Unrecognized organ hypoperfusion may cause major postoperative complications with detrimental effects for the patient. The use of Indocyanine Green (ICG) to detect organ hypoperfusion is emerging but the optimal methodology is still uncertain. The purpose of this study was to determine the feasibility of real-time continuous quantitative perfusion assessment with Indocyanine Green (ICG) to monitor organ perfusion during minimally invasive surgery using a novel ICG dosing regimen and quantification software. In this experimental porcine study, twelve subjects were administered a priming dose of ICG, followed by a regimen of high-frequency (1 dose per minute), low-dose bolus injections with weight-adjusted (0.008mg/kg) ICG allowing for continuous perfusion monitoring. In each pig, one randomly assigned organ of interest [stomach (n = 3), ascending colon (n = 3), rectum (n = 3) and spleen (n = 3)] was investigated with varying camera conditions. Video recording was performed with the 1588 AIM Stryker camera platform and subsequent quantitative analysis of the ICG signal were performed using a research version of a commercially available surgical real-time analysis software. Using a high-frequency, low-dose bolus ICG regimen, fluorescence visualization and quantification in abdominal organs were successful in the stomach (3/3), ascending colon (1/3), rectum (2/3), and the spleen (3/3). ICG accumulation in the tissue over time did not affect the quantification process. Considerable variation in fluorescence signal was observed between organs and between the same organ in different subjects. Of the different camera conditions investigated, the highest signal was achieved when the camera was placed 7.5cm from the target organ. This proof-of-concept study finds that real-time continuous perfusion monitoring in different abdominal organs using ICG is feasible. However, the study also finds a large variation in fluorescence intensity between organs and between the same organ in different subjects while using a fixed weight-adjusted dosing regimen using the same camera setting and placement.

Research on key technology of semiconductor robot controller

In order to solve the nationalization of Chinese semiconductor robot controllers, this paper studies the critical technology of semiconductor robot controllers. Firstly, the article describes the industry background and primary application of semiconductor robots and analyzes the core components' main problems. Secondly, the paper introduces the semiconductor robot's software architecture and technical index and designs a real-time controller function software based on x86 architecture. Third, the article studies the key software technologies of semiconductor robot controllers, and critical technologies such as high-precision trajectory planning, active wafer center, safety protection, and fast teaching are introduced. Finally, the robot controller software designed in this paper adopts layered architecture to ensure system reliability and meet the needs of the semiconductor manufacturing industry. The robot controller software supports non-standard requirements flexibly through modular design in the advanced technology and platform reliability to reach the domestic leading level.

How to Provide Fault-Tolerance Capabilities to Energy-Autonomous Sensor Systems with Real-Time Constraints?

A classical real-time applicative software consists of periodic tasks which have regular arrival times and strict deadlines by which they must always follow through on execution. Nonetheless, there is a possibility of faults in the software, the electrical power supply may run out and the processor may experience temporary overload. All these situations result in deadline missing for the tasks, which is non acceptable in hard real-time applications. The Deadline Mechanism has been introduced to cope with this issue through software redundancy. A task has two software versions, namely the primary which produces the best quality result and the backup which guarantees a result with a just acceptable precision. The aim of the paper is to show how to optimally implement the Deadline Mechanism in an autonomous sensor that should adopt an energy neutral mode for perpetual operation. In other terms, the sensor has to satisfy all deadlines despite fluctuations of energy availability and presence of software faults even if under a degraded operational mode.

Video Conference System in Mixed Reality Using a Hololens

The mixed reality conference system proposed in this paper is a robust, real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference, which realizes the entire process of holographic video conference from client to cloud to the client. This paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed reality. Several mixed reality conference system components are discussed, including data collection, data transmission, processing, and mixed reality presentation. The data layer is mainly used for data collection, integration, and video and audio codecs. The network layer uses Web-RTC to realize peer-to-peer data communication. The data processing layer is the core part of the system, mainly for human video matting and human-computer interaction, which is the key to realizing mixed reality conferences and improving the interactive experience. The presentation layer explicitly includes the login interface of the mixed reality conference system, the presentation of real-time matting of human subjects, and the presentation objects. With the mixed reality conference system, conference participants in different places can see each other in real-time in their mixed reality scene and share presentation content and 3D models based on mixed reality technology to have a more interactive and immersive experience.

Volumetric evaluation of the sphenoid sinus among different races in the Southeast Asian (SEA) population: a computerized tomography study.

Introduction: The fundament of forensic science lies in identifying a body. The morphological complexity of the paranasal sinus (PNS), which varies greatly amongst individual, possess a discriminatory value that potentially contributes to the radiological identification. The sphenoid bone represents the keystone of the skull and forms part of the cranial vault. It is intimately associated with vital neurovascular structures. The sphenoid sinus, located within the body of the sphenoid bone, has variable morphology. The sphenoid septum's inconsistent position and the degree, as well as the direction disparities of sinus pneumatization, have indeed accorded it a unique structure in providing invaluable information in forensic personnel identification. Additionally, the sphenoid sinus is situated deep within the sphenoid bone. Therefore, it is well protected from traumatic degradation from external causes and can be potentially utilized in forensic studies. The authors aim to study the possibility of variation among the race, and gender in the Southeast Asian (SEA) population, using volumetric measurements of the sphenoid sinus. Materials and methods: This is a retrospective cross-sectional analysis of computerized tomographic (CT) imaging of the PNS of 304 patients (167 males, 137 females) in a single centre. The volume of the sphenoid sinus was reconstructed and measured using commercial real-time segmentation software. Result: The total volume of sphenoid sinus of male gender had shown to be larger, 12.22 (4.93 - 21.09) cm3 compared to the counterpart of 10.19 (3.75 - 18.72) cm3 (p = .0090). The Chinese possessed a larger total sphenoid sinus volume, 12.96 (4.62 - 22.21) cm3) than the Malays, 10.68 (4.13 - 19.25) cm3 (p = .0057). No correlation was identified between the age and volume of the sinus (cc= -.026, p = .6559). Conclusion: The sphenoid sinus volume in males was found to be larger than those of females. It was also shown that race influences sinus volume. Volumetric analysis of the sphenoid sinus can potentially be utilized in gender and race determination. The current study provided normative data on the sphenoid sinus volume in the SEA region, which can be helpful for future studies.

Maximizing the Security Level of Real-Time Software While Preserving Temporal Constraints

Embedded computing systems are becoming increasingly relevant in the Internet of Things (IoT) and edge computing domains, where they are often employed as the control entity of a cyber-physical system. When operating in such interconnected domains, a software system is susceptible to cyber-attacks from external agents, which can compromise the correct behavior of the system. In addition, the software executing in these systems is typically characterized by stringent timing constraints, which must be satisfied during system execution. Enabling software protections to enhance the security level of the embedded software comes at the cost of increasing the computation times of the tasks, introducing the risk of deadline misses that could also jeopardize the system behavior. This paper presents a methodology to optimize the security level of real-time software while preserving system-wide schedulability by leveraging timing analysis. The proposed approach is based on a mixed-integer linear programming (MILP) formulation that maximizes the security level of the tasks and integrates a response-time analysis technique to assess the schedulability of the system whenever additional protections are activated to shield the software against cyber-attacks targeting specific classes of vulnerabilities. An experimental evaluation is presented to assess the performance of the proposed approach on a representative set of tasks included in standard benchmarking suites for embedded software.

Shaped Offset Quadrature Phase Shift Keying Based Waveform for Fifth Generation Communication

Fifth generation (5G) wireless networks must meet the needs of emerging technologies like the Internet of Things (IoT), Vehicle-to-everything (V2X), Video on Demand (VoD) services, Device to Device communication (D2D) and many other bandwidth-hungry multimedia applications that connect a huge number of devices. 5G wireless networks demand better bandwidth efficiency, high data rates, low latency, and reduced spectral leakage. To meet these requirements, a suitable 5G waveform must be designed. In this work, a waveform namely Shaped Offset Quadrature Phase Shift Keying based Orthogonal Frequency Division Multiplexing (SOQPSK-OFDM) is proposed for 5G to provide bandwidth efficiency, reduced spectral leakage, and Bit Error Rate (BER). The proposed work is evaluated using a real-time Software Defined Radio (SDR) testbed-Wireless open Access Research Platform (WARP). Experimental and simulation results show that the proposed 5G waveform exhibits better BER performance and reduced Out of Band (OOB) radiation when compared with other waveforms like Offset Quadrature Phase Shift Keying (OQPSK) and Quadrature Phase Shift Keying (QPSK) based OFDM and a 5G waveform candidate Generalized Frequency Division Multiplexing (GFDM). BER analysis shows that the proposed SOQPSK-OFDM waveform attains a Signal to Noise Ratio (SNR) gain of 7.2 dB at a BER of , when compared with GFDM in a real-time indoor environment. An SNR gain of 8 and 6 dB is achieved by the proposed work for a BER of when compared with QPSK-OFDM and OQPSK-OFDM signals, respectively. A significant reduction in OOB of nearly 15 dB is achieved by the proposed work SOQPSK-OFDM when compared to 16 Quadrature Amplitude Modulation (QAM) mapped OFDM.

An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features

It is possible to detect visual surface defects with software in industrial tile production and increase productivity by automating the quality control process. In this process, low error rate and low cost are important indicators. In order to eliminate this negativity and the effect of the human factor, error detection software has been developed in an artificial intelligence-based industrial artificial vision environment. Spots, scratches, cracks, pore defects, which are the most common surface defects, are classified according to 6 different geometric and wavelet transform attributes. Firstly, an industrial artificial vision environment was created. In this environment, a total of 150 tile images, equal numbers from each class, were obtained on the real-time production line. The resulting images were converted into binary images by preprocessing and filtering. For classification, the support vector machines method, which performs high in two-class classifications, is used with the one versus all approach. In classifications made using RBF kernel function using wavelet features as classification performance, a higher success was achieved in all defect classes than geometric features. Real-time application software for all these processes has been developed with the Python language on Ubuntu operating system.

Real-Time Frequency Stabilization of Standalone PV System with SMC

This paper aims to obtain suitable control for the Standalone Photo Voltaic (SAPV) system to meet consumer demands in weather conditions. A SAPV system is developed with a solar array with Maximum Power Point Tracking (MPPT), a Boost converter, and an inverter. This system uses controllers for maximum power output tracking, constant DC output of the Boost converter, and constant output voltage and frequency with Pulse Width Modulation (PWM) control mode. MPPT, boost converter, and inverter controller parameters are designed and tuned, respectively. Proportional gain (Kp) and Integral gain (Ki) values and the Sliding Mode Method (SMC) tune the controller parameters. The innovative aspect of this work is to propose a standalone PV system with controllers based only on the sliding mode control approach. Moreover, the current controller provides an output current of high quality with a THD of 1.6 %. Effectiveness and robustness of the proposed scheme modeled and simulated under OPAL-RT real-time Software in Loop (SIL) platform with MATLAB Simulink under fast variations of irradiance and temperature. Various analyses have been carried out for examining the proposed Sliding Mode Controller-based standalone photovoltaic system.

916. Increased efficiency and impact of implementing ILUM insight within an antimicrobial stewardship program (ASP) at an academic medical center

Abstract Background An ASP is mandated for all hospitals and requires extensive resources with multidisciplinary collaboration. We measured the impact of implementing real-time decision support software (ILUM Insight) within our ASP. Methods Our ASP has relied on prior authorization since 2002 and focused audit and feedback since 2015. In August 2021 we implemented to bring actionable data to front-line stewards. ILUM provides real-time notifications, organizes communications, and tracks patient-and provider-level data. We hypothesized that ILUM would increase the efficiency of ASP workflow and result in decreased antimicrobial utilization. We compared data 6 months before (8/20 – 1/21) and after (8/21 – 1/22) implementation. There were no significant staffing changes during either period. Results Existing notifications within ILUM were tailored to local practices, including alerts with intervention for positive blood cultures, antibiotic de-escalation, and bug-drug mismatches. New notifications were built for restricted antimicrobials, antibiotic timeouts, and MRSA screening. ASP pharmacists and physicians received training in July and November, respectively. A breakdown of all notifications received during the post-implementation period is provided in Fig 1. With increased ILUM usage, the number of interventions made by our ASP increased while missed opportunities decreased (Fig 2.). During the same time period, ASP communications rose from 205 to 1200 per month. Comparing pre- and post-implementation periods, antimicrobial days of therapy (DOT) per 1,000 patient days (PD) decreased by 14.5% from a median of 969 to 846 per month (Fig 3;P=0.002). Antimicrobial expenditures were decreased by a median 21% per month during the post-intervention period compared to baseline. Among patients prescribed antimicrobials during an index admission, 30-day re-admissions decreased from 330 to 262 and re-admissions associated with re-ordering of antimicrobials decreased from 235 to 182 (Fig 4). Conclusion Custom-designed, task-specific software improves the efficiency of daily ASP workflow and significantly decreased antimicrobial utilization without the need for additional ASP team members. Disclosures Ryan K. Shields, PharmD, MS, Infectious Disease Connect: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Roche: Grant/Research Support J Ryan Bariola, MD, Infectious Disease Connect: Salary support|Merck: Grant/Research Support Caley Yakemowicz, n/a, Infectious Disease Connect: Employee Courtney Simonick, n/a, Infectious Disease Connect: Stocks/Bonds Riaan Erwee, na, Infectious Disease Connect: Employee Erin K. McCreary, PharmD, Infectious Disease Connect: Advisor/Consultant Rima Abdel-Massih, MD, Infectious Disease Connect: Co founder and Chief Medical Officer|Infectious Disease Connect: Ownership Interest.

PLUME Dashboard: A free and open-source mobile air quality monitoring dashboard

The deployment of a mobile air quality monitoring laboratory requires advanced real-time instrument monitoring and data management software, which can be prohibitively expensive. In this work we present the PLUME Dashboard: a software package built in Python designed specifically for mobile air quality monitoring purposes. It aims to provide a free and open-source alternative to comparable commercial packages, thus reducing the barrier to entry of conducting such research. This paper outlines the development of the PLUME Dashboard and justifies the design choices that were made while also providing thorough documentation and explanation for how the software works. Functionality includes real-time data display, real-time peak identification, baseline subtraction, real-time air quality and self-sampling alerts (based on wind direction and vehicle speed), and post-processing tools such as peak identification and map merging with GPS data. The functionality of PLUME Dashboard is tested using real-world data collected in Toronto and Vancouver Canada.

Optimal Deep Belief Network Enabled Vulnerability Detection on Smart Environment

Abstract Cyborgs invoke visions of super-humans intertwined with innovative technologies able to surpass the restrictions of the human body. Conventional vulnerability detection models sustain maximum false positive rates and depend upon manual participation. Machine learning (ML) and Artificial intelligence (AI) technologies are exploited in several real-time applications, like vulnerability, malware, and software function detection, for high-quality feature representation learning. In this aspect, this study introduces a hyperparameter-optimized deep belief network-enabled vulnerability and classification (HOSDBN-VC) technique on cyborgs. The presented HOSDBN-VC model aims to detect and classify the existence of vulnerabilities. The presented HOSDBN-VC model involves a Z-score normalization approach to transform the input data into a useful format. In addition, the Hypercube Optimization Search Algorithm-based feature selection (HOS-FS) method is employed for selecting feature subsets. Moreover, a flower pollination algorithm (FPA) with a deep belief network (DBN) paradigm is applied for vulnerability and classification. The metaheuristics-based FPA is exploited to choose the hyperparameters related to the DBN paradigm appropriately. A wide-ranging experiment has been conducted to investigate the results of the HOSDBN-VD model under two databases, FFmpeg and LibPNG. The experimental outcomes implied the betterment of the HOSDBN-VD algorithm over recent approaches.

Real-Time Scheduling of Parallel Task Graphs With Critical Sections Across Different Vertices

All existing work on real-time scheduling of parallel task graph models with shared resources assumes that a critical section must be contained inside a single vertex. However, this assumption does not hold in many realistic parallel real-time software. In this work, we conduct the first study on real-time scheduling and analysis of parallel task graphs where critical sections are allowed to cross different vertices. We show that allowing this may potentially lead to deadlocks and the so-called resource unrelated blocking time problem. We formalize the conditions for the deadlocks and resource unrelated blocking time to happen, and propose two different solutions to address them and develop corresponding schedulability analysis techniques. We conduct comprehensive experiments to evaluate our method. The results indicate that there is a significant impact to the system schedulability when tasks incur deadlock and resource unrelated blocking. Moreover, the schedulability can benefit from the execution of workload in parallel with critical sections if tasks can be carefully designed so that all deadlocks and resource unrelated blocking time can be avoided, and our methods are efficient to determine the schedulability of systems where critical sections across different vertices exist.

Machine-learning algorithm in acute stroke: real-world experience

To assess the clinical performance of a commercially available machine learning (ML) algorithm in acute stroke. CT and CT angiography (CTA) studies of 104 consecutive patients (43 females, age range 19-93, median age 62) performed for suspected acute stroke at a single tertiary institutionwith real-time ML software analysis (RAPID™ ASPECTS and CTA) were included. Studies were retrospectively reviewed independently by two neuroradiologists in a blinded manner. The cohort included 24 acute infarcts and 16 large vessel occlusions (LVO). RAPID™ ASPECTS interpretation demonstrated high sensitivity (87.5%) and NPV (87.5%) but very poor specificity (30.9%) and PPV (30.9%) for detection of acute ischaemic parenchymal changes. There was a high percentage of false positives (51.1%).In cases of proven LVO, RAPID™ ASPECTS showed good correlation with neuroradiologists' blinded independent interpretation, Pearson correlation coefficient = 0.96 (both readers), 0.63 (RAPID™ vs reader 1), 0.69 (RAPID™ vs reader 2). RAPID™ CTA interpretation demonstrated high sensitivity (92.3%), specificity (85.3%), and negative predictive (NPV) (98.5%) with moderate positive predictive value (PPV) (52.2%) for detection of LVO (N=13). False positives accounted for 12.5% of cases, of which 27.3% were attributed to arterial stenosis. RAPID™ CTA was robust and reliable in detection of LVO. Although demonstrating high sensitivity and NPV, RAPID™ ASPECTS interpretation was associated with a high number of false positives, which decreased clinicians' confidence in the algorithm. However, in cases of proven LVO, RAPID™ ASPECTS performed well and had good correlation with neuroradiologists' blinded interpretation.

CPR Quality Assessment in Schoolchildren Training.

Whilst CPR training is widely recommended, quality of performance is infrequently explored. We evaluated whether a checklist can be an adequate tool for chest compression quality assessment in schoolchildren, compared with a real-time software. This observational study (March 2019-2020) included 104 schoolchildren with no previous CPR training (11-17 years old, 66 girls, 84 primary schoolchildren, 20 high schoolchildren). Simultaneous evaluations of CPR quality were performed using an observational checklist and real-time software. High-quality CPR was determined as a combination of 70% correct maneuvers in compression rate (100-120/min), depth (5-6 cm), and complete release, using a real-time software and three positive performance in skills using a checklist. We adjusted a multivariate logistic regression model for age, sex, and BMI. We found moderate to high agreement percentages in quality of CPR performance (rate: 68.3%, depth: 79.8%, and complete release: 91.3%) between a checklist and real-time software. Only 38.5% of schoolchildren (~14 years-old, ~54.4 kg, and ~22.1 kg/m2) showed high-quality CPR. High-quality CPR was more often performed by older schoolchildren (OR = 1.43, 95%IC:1.09-1.86), and sex was not an independent factor (OR = 1.26, 95%IC:0.52-3.07). For high-quality CPR in schoolchildren, a checklist showed moderate to high agreement with real-time software. Better performance was associated with age regardless of sex and BMI.

NIMG-64. INTRAOPERATIVE LANGUAGE MAPPING USING GAMMA-BAND MODULATIONS OF ELECTROCORTICOGRAM (ECOG) INDUCED BY WORD/SOUND CATEGORIZATION TASK: VALIDATION WITH REPRODUCIBLE SPEECH ARRESTS DURING LINGUISTIC TASKS

Abstract OBJECTIVE Determination of the correlation between gamma-band modulations of electrocorticogram (ECoG) induced by linguistic tasks and reproducible speech arrests caused by bipolar direct cortical stimulations (DCS). METHODS 3 subjects (age 39, 56, 64 years) with left temporal lobe glioma underwent surgery involving awake craniotomy. A 4x8 ECoG electrode grid (2.3mm contact exposure, 1cm contact spacing) was placed above the respective tumor area. A MATLAB-Simulink based real-time software system running on a portable laptop computer was used to map gamma-band modulations as a 2D heat map while the subjects engaged in different linguistic tasks: word/sound categorization by pressing a button, object naming, action naming, written descriptive naming, and auditory descriptive naming. Auditory stimulus was applied during word/sound categorization task (duration 300 – 500ms), auditory descriptive naming ( > 1s); other tasks involved visual stimulus only. The subjects repeated the four naming tasks while bipolar DCS (2/4/6 mA, 60Hz, 2s) was applied at different electrode pairs. RESULTS The electrodes having stronger gamma-band modulations were distinct for different tasks. Reproducible speech arrests occurred during object, action, auditory naming tasks while stimulating specific electrode pairs, even though not all these electrodes had strong activations during these tasks. Across all subjects these electrodes had strong activations consistently during word/sound categorization tasks, starting as early as 250ms and lasting even after the auditory stimuli were terminated (~ 650ms). The longer activations can be associated with word recognition process. The subjects self-reported about having difficulty in comprehension rather than speech production during speech arrests. 3D brain rendering using MRI images showed that the speech arrest electrodes were identically located on the superior temporal gyrus, inferior to central sulcus for all 3 subjects. CONCLUSION Intraoperative language mapping guided by gamma-band ECoG modulations induced by word/sound categorization tasks can be utilized to localize eloquent cortex associated with auditory processing.