Idle State Research Articles

CoreNap: Energy Efficient Core Allocation for Latency-Critical Workloads

In data-center servers, the dynamic core allocation for Latency-Critical (LC) applications can play a crucial role in improving energy efficiency under Service Level Objective (SLO) constraints, allowing cores to enter idle states (i.e., C-states) that consume less power by turning off a part of hardware components of a processor. However, prior studies focus on the core allocation for application threads while not considering cores involved in network packet processing, even though packet processing affects not only response latency but also energy consumption considerably. In this paper, we first investigate the impacts of the explicit core allocation for network packet processing on the tail response latency and energy consumption while running LC applications. We observe that co-adjusting the number of cores for network packet processing along with the number of cores for LC application threads can improve energy efficiency substantially, compared with adjusting the number of cores only for application threads, as prior studies do. In addition, we propose a dynamic core allocation, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> , which allocates/de-allocates cores for both LC application threads and packet processing. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> measures the CPU-utilization by application threads and packet processing individually, and predicts response latency and power consumption when the combination of core allocation is enforced via a lightweight prediction model. Based on the prediction, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> chooses/enforces the energy-efficient combination of core allocation. Our experimental results show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> reduces energy consumption by up to 18.6% compared with state-of-the-art study that adjusts cores only for LC application in parallel packet processing environments.

Analysis of Power Management for Hard Real-Time Embedded System between Adaptive Power Management Method and Idle Energy Reduction Method

Static and dynamic power management have become crucial in real-world applications of embedded systems development. While many technologies and algorithms can reduce power consumption efficiently, like dynamic voltage and frequency scaling, varying voltage and frequency make overall power consumption less efficient. The comprehensive embedded system spends time and energy on the transition between states, voltage, and frequency. To better understand how these problems can be addressed, this paper will explore two methods, the adaptive power management method and the idle state energy reduction method, to mitigate issues on the algorithm side. Furthermore, this paper is intended to give researchers and real-time embedded systems developers an overview of power management methods and to help them better develop the embedded systems.

Legal Policy in Regulating the Sale of State Houses Class III (Idle State Property) Without Auction to Civil Servants Reviewed from the Perspective of “The Theory of Fairness” From John Rawls

Auction is universally recognized as the most accountable and fair-trading system. This is also recognized by the Republic of Indonesia, as stated in Article 48 of the Law of the Republic of Indonesia Number 1 of 2004 concerning the State Treasury junto Article 61 Government Regulation Article 61 Number 27 of 2014 concerning Management of State Property, where it is stated that all sales of State Property must be done through auction. The problem is, in the same Article there are exceptions in certain matters without going through an auction. This particular matter relates to discrimination against civil servants in the arrangement for the sale of State Houses Class III (Idle State Property) without having to go through an auction and even with a price of 50% of the valuation. Of course, the legal policy that apply these exceptions in addition to causing legal problems, also philosophical, theoretical, and sociological issues. The legal issue that arises is a conflict of norms between what should be (das sollen) in the constitution and the norms written in laws and regulations (das sein). In this case there has been a conflict of norm between Article 27 paragraph (1) of the 1945 Constitution of the Republic of Indonesia which contradicts the regulations related to the regulation of State Houses as State Property in the form of land and buildings. From the juridical problems mentioned above. The legal issues mentioned above, then there are relevant questions to be studied. The question is: how is the legal policy that gives exceptions or differential treatment (“discrimination”) to civil servants viewed from the perspective of social justice from John Rawls in accordance with the principles and goals of the Indonesian nation, namely to realize Social Justice for All Indonesian People. The question is the research problem referred in this paper

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications.

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)- based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability

A Hybrid Asynchronous Brain–Computer Interface Based on SSVEP and Eye-Tracking for Threatening Pedestrian Identification in Driving

A brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has achieved remarkable performance in the field of automatic driving. Prolonged SSVEP stimuli can cause driver fatigue and reduce the efficiency of interaction. In this paper, a multi-modal hybrid asynchronous BCI system combining eye-tracking and EEG signals is proposed for dynamic threatening pedestrian identification in driving. Stimuli arrows of different frequencies and directions are randomly superimposed on pedestrian targets. Subjects scan the stimuli according to the direction of arrows until the threatening pedestrian is selected. The thresholds determined by offline experiments are used to distinguish between working and idle states of the asynchronous online experiments. Subjects need to judge and select potentially threatening pedestrians in online experiments according to their own subjective experience. The three proposed decisions filter out the results with low confidence and effectively improve the selection accuracy of hybrid BCI. The experimental results of six subjects show that the proposed hybrid asynchronous BCI system achieves better performance compared with a single SSVEP-BCI, with an average selection time of 1.33 s, an average selection accuracy of 95.83%, and an average information transfer rate (ITR) of 67.50 bits/min. These results indicate that our hybrid asynchronous BCI has great application potential in dynamic threatening pedestrian identification in driving.

Analytic and Computational Analysis of GI/Ma,b/c Queueing System

Bulk-service queueing systems have been widely applied in many areas in real life. While single-server queueing systems work in some cases, multi-servers can efficiently handle most complex applications. Bulk-service, multi-server queueing systems (compared to well-developed single-server queueing systems) are more complex and harder to deal with, especially when the inter-arrival time distributions are arbitrary. This paper deals with analytic and computational analyses of queue-length distributions for a complex bulk-service, multi-server queueing system GI/Ma,b/c, wherein inter-arrival times follow an arbitrary distribution, a is the quorum, and b is the capacity of each server; service times follow exponential distributions. The introduction of quorum a further increases the complexity of the model. In view of this, a two-dimensional Markov chain has to be involved. Currently, it appears that this system has not been addressed so far. An elegant analytic closed-form solution and an efficient algorithm to obtain the queue-length distributions at three different epochs, i.e., pre-arrival epoch (p.a.e.), random epoch (r.e.), and post-departure epoch (p.d.e.) are presented, when the servers are in busy and idle states, respectively.

Optimal Entry and Exit Decisions Under Uncertainty and the Impact of Mean Reversion

This paper derives an entrepreneur’s optimal switching between an idle and an active state under stochastic mean reverting output prices. The paper suggests a new categorisation of the effects of mean reversion. Mean reversion affects valuation and optimal entry and exit thresholds via the variance of output prices and expected future cashflows. High variance increases the value of optionality and enhances hysteresis effects. Changes to the expected cashflow path affect the attractiveness of the active relative to the idle state. In addition, changes to the moments affect the implicit risk discounting rate and thereby valuation and the optimal switching strategy.

Telematics Collaborative Resource Allocation Algorithm Based on Cloud Sidecar

This paper provides an in-depth study and analysis of a distributed allocation algorithm for collaborative resources for cloud-edge-vehicle-based Telematics. The approach starts from the emerging application of urban environmental monitoring based on vehicular networking, with an integrated design of data sensing detection and transmission, and collaborative monitoring of vehicle swarm intelligence based on urban air quality collection to avoid redundancy of information and communication overload. A hybrid routing method with minimal delay for reliable data transmission is proposed. The power adjustment algorithm divides the channel into 3 states. When the CBR is less than 0.5, the channel is in an idle state, and when the CBR is greater than 0.5 and less than 0.8, the channel is in an active state. The algorithm designs redundancy strategies based on coding mechanisms to improve the reliability of data transmission, combines coding mechanisms with routing design, incorporates routing switching ideas, and performs probability-based routing decisions to minimize the delay. In straight-line road sections, a fuzzy logic prediction-based vehicle adaptive connectivity clustering routing algorithm is proposed to reduce the communication overhead during vehicle collaboration and ensure high network connectivity; at intersections, a probability-based minimum delay routing decision algorithm is proposed to reduce the information transmission delay. Experiments show that the proposed method effectively improves the efficiency of data-aware collection and transmission, and increases the reliability of transmission. With the explosive growth of video services, the problem of intelligent transmission of DASH-based video streams has become another research hotspot in mobile edge networks. Based on the edge container cloud architecture of vehicular networking, the resource constraints of microservices when deployed in the edge cloud platform were analyzed, and a multi-objective optimization model for microservice resource scheduling was established with the comprehensive performance objectives of shortest microservice invocation distance, highest resource utilization of physical machine clusters, and ensuring load balancing as much as possible.

Outage analysis of cognitive communication system using opportunistic relay selection and secondary users as relays

ABSTRACT Efficient bandwidth utilisation is significant in new communication systems where secondary users can be used besides primary users considering interference issues and idle state of primary users. Using secondary users as relays to transmit their own signals in addition to the primary signals can be applied for more reliability of the system where opportunistic relay selection can significantly enhance the performance of the system. The best-condition secondary user is selected as the optimum relay for retransmission of primary/secondary signal. Outage probability is analysed in this paper based on decode and forward technique in secondary users while the closed-form statement for outage probability is provided and verified by numerical evaluations.

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.

Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.

The virtual machines scheduling strategy based on M/M/[formula omitted] queueing model with vacation

To enhance the energy savings of cloud systems and ensure the quality of service, energy saving measures was proposed for virtual machines (VMs) in cloud data centers. Considering the disadvantages of synchronous vacation and asynchronous vacation and the energy consumption caused by frequent VM state switching. In this study, a policy combining synchronous vacation and asynchronous vacation is proposed. The scheduling policy is modeled as a two-dimensional Markov stochastic model with multiple servers. Then, the analytical expressions of the equations are obtained using the quasi-birth-and-death process and the matrix-geometric solution, such as the average delay of tasks, the average number of busy state VMs and the average number of idle state VMs. Eventually, numerical experiments demonstrate that the strategy can decrease the energy consumption of the system effectively.

Experimental studies and modeling of a 250-kW alkaline water electrolyzer for hydrogen production

Well-matured alkaline water electrolysis is considered as the most favorable method for cost-effective production of green hydrogen using renewable energy. In this study, a 250-kW pressurized alkaline water electrolyzer has been operated and observed, through modified procedures, as part of a wind-to-hydrogen demonstration project. Cold start from an idle state to rated hydrogen production state requires more than 6 h. A semi-empirical model is proposed to describe the behavior of the real-scale alkaline electrolyzer system. The cell voltage and gas purity of the electrolyzer are considered as functions of operating pressure, temperature, and current density. The model parameters have been adjusted using a non-linear regression, based on experimental data taken from varying operating conditions. The simulated and measured cell voltages are compatible with each other, with a maximum error of 30 mV, and the relative error evaluated for the hydrogen impurities in oxygen is lower than 3.9%. Simulation results reveals that efficiency and safety are significantly affected by the operating conditions. Meanwhile, the model is further experimentally validated with wind-to-hydrogen demonstration test results. The proposed model is a helpful tool in designing and optimizing control strategies of alkaline electrolyzers, coupled with renewable energy sources.

Secure Spectrum-Energy Efficiency Tradeoff Based on Stackelberg Game in a Two-Way Relay Cognitive Satellite Terrestrial Network

In this letter, we consider a two-way relay <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cognitive satellite terrestrial network</i> (CSTN) in the presence of a friendly jammer and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eavesdropper</i> (Eve). In this system, we investigate the tradeoff between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure spectrum efficiency</i> (SSE) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure energy efficiency</i> (SEE). To be specific, firstly, two frames corresponding to the busy and idle working states of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">primary users</i> (PUs) are designed to flexibly analyze the system performance. Secondly, the SSEs, SEEs, and utility functions for both the cognitive satellite network and the terrestrial primary network are deduced, respectively, considering the different working states of the PUs and the Eve. Finally, based on the Stackelberg Game model, the mathematically intractable problem on the spectrum-energy exchange is solved, thus we maximize the SSE and the SEE jointly. Numerical results are given to validate that there exists an SSE-SEE tradeoff. Also, we show that the proposed spectrum-energy exchange scheme based on the Stackelberg Game outperforms the fixed parameter allocation scheme remarkably.

FPGA-Based High-Throughput CNN Hardware Accelerator With High Computing Resource Utilization Ratio.

The field-programmable gate array (FPGA)-based CNN hardware accelerator adopting single-computing-engine (CE) architecture or multi-CE architecture has attracted great attention in recent years. The actual throughput of the accelerator is also getting higher and higher but is still far below the theoretical throughput due to the inefficient computing resource mapping mechanism and data supply problem, and so on. To solve these problems, a novel composite hardware CNN accelerator architecture is proposed in this article. To perform the convolution layer (CL) efficiently, a novel multiCE architecture based on a row-level pipelined streaming strategy is proposed. For each CE, an optimized mapping mechanism is proposed to improve its computing resource utilization ratio and an efficient data system with continuous data supply is designed to avoid the idle state of the CE. Besides, to relieve the off-chip bandwidth stress, a weight data allocation strategy is proposed. To perform the fully connected layer (FCL), a single-CE architecture based on a batch-based computing method is proposed. Based on these design methods and strategies, visual geometry group network-16 (VGG-16) and ResNet-101 are both implemented on the XC7VX980T FPGA platform. The VGG-16 accelerator consumed 3395 multipliers and got the throughput of 1 TOPS at 150 MHz, that is, about 98.15% of the theoretical throughput ( 2 ×3395 ×150 MOPS). Similarly, the ResNet-101 accelerator achieved 600 GOPS at 100 MHz, about 96.12% of the theoretical throughput ( 2 ×3121 ×100 MOPS).

An efficient sparse stiffness matrix vector multiplication using compressed sparse row storage format on AMD GPU

SummaryThe performance of sparse stiffness matrix‐vector multiplication is essential for large‐scale structural mechanics numerical simulation. Compressed sparse row (CSR) is the most common format for storing sparse stiffness matrices. However, the high sparsity of the sparse stiffness matrix makes the number of nonzero elements per row very small. Therefore, the CSR‐scalar algorithm, light algorithm, and HOLA algorithm in the calculation will cause some threads in the GPU to be in idle state, which will not only affect the computing performance but also waste computing resources. In this article, a new algorithm, CSR‐vector row, is proposed for fine‐grained computing optimization based on the AMD GPU architecture on heterogeneous supercomputers. This algorithm can set a vector to calculate a row based on the number of nonzero elements of the stiffness matrix. CSR‐vector row has efficient reduce operations, deep memory access optimization, better memory access, and calculation overlapping kernel function configuration scheme. The access bandwidth of the algorithm on AMD GPU is more than 700 GB/s. Compared with CSR‐scalar algorithm, the parallel efficiency of CSR‐vector row is improved by 7.2 times. And floating‐point computing performance is 41%–95% higher than that of light algorithm and HOLA algorithm. In addition, CSR‐vector row is used to calculate the examples from CFD, electromagnetics, quantum chemistry, power network, and semiconductor process, the memory access bandwidth and double floating‐point performance are also improved compared with rocSPARSE‐CSR‐vector.

CloudSatNet-1: FPGA-Based Hardware-Accelerated Quantized CNN for Satellite On-Board Cloud Coverage Classification

CubeSats, the nanosatellites and microsatellites with a wet mass up to 60 kg, accompanied by the cost decrease of accessing the space, amplified the rapid development of the Earth Observation industry. Acquired image data serve as an essential source of information in various disciplines like environmental protection, geosciences, or the military. As the quantity of remote sensing data grows, the bandwidth resources for the data transmission (downlink) are exhausted. Therefore, new techniques that reduce the downlink utilization of the satellites must be investigated and developed. For that reason, we are presenting CloudSatNet-1: an FPGA-based hardware-accelerated quantized convolutional neural network (CNN) for satellite on-board cloud coverage classification. We aim to explore the effects of the quantization process on the proposed CNN architecture. Additionally, the performance of cloud coverage classification by biomes diversity is investigated, and the hardware architecture design space is explored to identify the optimal FPGA resource utilization. Results of this study showed that the weights and activations quantization adds a minor effect on the model performance. Nevertheless, the memory footprint reduction allows the model deployment on low-cost FPGA Xilinx Zynq-7020. Using the RGB bands only, up to 90% of accuracy was achieved, and when omitting the tiles with snow and ice, the performance increased up to 94.4% of accuracy with a low false-positive rate of 2.23% for the 4-bit width model. With the maximum parallelization settings, the hardware accelerator achieved 15 FPS with 2.5 W of average power consumption (0.2 W increase over the idle state).

Thermal Environment and Thermal Comfort in University Classrooms during the Heating Season

In recent years, there has been increasing concern about the effects of indoor thermal environments on human physical and mental health. This paper aimed to study the current status of the thermal environment and thermal comfort in the classrooms of Northeastern University during the heating season. The indoor thermal environment was analyzed with the use of field measurements, a subjective questionnaire, regression statistics, and the entropy weight method. The results show that personnel population density is an important factor affecting the temperature and relative humidity variations in classrooms. The results also show that the temperature and relative humidity in a lecture state are respectively 4.2 °C and 11.4% higher than those in an idle state. In addition, in university classrooms in Shenyang, the actual thermal neutral temperature is 2.5 °C lower than the predicted value of the Predicted Mean Vote. It was found that increasing indoor relative humidity can effectively improve the overall thermal comfort of subjects. Furthermore, the temperature preference of women was higher than that of men. Therefore, when setting the initial heating temperature, the personnel population density and sufficient indoor relative humidity have been identified as the key factors for improving the thermal environment of the classroom.

Multi-local search-based general variable neighborhood search for distributed flow shop scheduling in heterogeneous multi-factories

Heterogeneous multi-factories in different regions bring a challenge to managers in distributed production scheduling. This paper studies a distributed flow shop scheduling problem in heterogeneous multi-factories (DHFSP) with different processing capabilities of machines and electricity prices in multi-factories. The mixed-integer linear programming model (MILP) of the DHFSP is established, where different time-of-use electricity prices and states of machines (running and idle) are integrated. The proposed MILP model is based on position and time-indexed variables where the position of a job in the sequence is modeled and the time horizon is discretized into time slots. Every job on a machine corresponds to a series of position and time-indexed decision variables A multi-local search-based general variable neighborhood search (MGVNS) is presented to address the DHFSP. The earliest due date rule and a distributed version of NEH (Nawaz–Enscore–Ham) are combined to generate a promising initial solution. Four efficient greedy local search methods that move jobs between heterogeneous multi-factories and within the critical factory are presented. The properties of DHFSP are used to reduce the computing time of objectives. A right-shifting procedure is presented to save the electricity cost by considering the total electricity cost consumed by the processing of the jobs and the idle state of the machine between two adjacent jobs. The experiments and investigation are provided on large-sized instances to demonstrate the effectiveness and efficiency of MGVNS. The effectiveness of initialization, accelerated methods, right-shifting procedure, and local search methods are also significant. From the managerial insights, the proposed model can be extended to many real scenarios, such as semiconductor or automobile production, by integrating specific production constraints. The proposed model and scheduling methods provide an economical way to generate efficient schedules to balance the production efficiency and cost.

A warning system for urolithiasis via retrograde intrarenal surgery using machine learning: an experimental study

BackgroundTo develop a warning system that can prevent or minimize laser exposure resulting in kidney and ureter damage during retrograde intrarenal surgery (RIRS) for urolithiasis. Our study builds on the hypothesis that shock waves of different degrees are delivered to the hand of the surgeon depending on whether the laser hits the stone or tissue.MethodsA surgical environment was simulated for RIRS by filling the body of a raw whole chicken with water and stones from the human body. We developed an acceleration measurement system that recorded the power signal data for a number of hours, yielding distinguishable characteristics among three different states (idle state, stones, and tissue–laser interface) by conducting fast Fourier transform (FFT) analysis. A discrete wavelet transform (DWT) was used for feature extraction, and a random forest classification algorithm was applied to classify the current state of the laser-tissue interface.ResultsThe result of the FFT showed that the magnitude spectrum is different within the frequency range of < 2500 Hz, indicating that the different states are distinguishable. Each recorded signal was cut in only 0.5-s increments and transformed using the DWT. The transformed data were entered into a random forest classifier to train the model. The test result was only measured with the dataset that was isolated from the training dataset. The maximum average test accuracy was > 95%. The procedure was repeated with random signal dummy data, resulting in an average accuracy of 33.33% and proving that the proposed method caused no bias.ConclusionsOur monitoring system receives the shockwave signals generated from the RIRS urolithiasis treatment procedure and generates the laser irradiance status by rapidly recognizing (in 0.5 s) the current laser exposure state with high accuracy (95%). We postulate that this can significantly minimize surgeon error during RIRS.