Jetson TX2 Research Articles

RA-MOSAIC : Resource Adaptive Edge AI Optimization over Spatially Multiplexed Video Streams

Sustaining real-time, high fidelity AI-based vision perception on edge devices is challenging due to both the high computational overhead of increasingly “deeper” Deep Neural Networks (DNNs) and the increasing resolution/quality of camera sensors. Such high-throughput vision perception is even more challenging in multi-tenancy systems, where video streams from multiple such high-quality cameras need to share the same GPU resource on a single edge device. Criticality-aware canvas-based processing is a promising paradigm that decomposes multiple concurrent video streams into Regions of Interest (RoI) and spatially channels the limited computational resources to selected RoI with higher “resolution”, thereby moderating the trade-off between computational load, task fidelity, and processing throughput. RA-MOSAIC (Resource Adaptive MOSAIC) employs such canvas-based processing, while further tuning the incoming video streams and available resources on-demand to allow the system to adapt to dynamic changes in workload (often arising from variations in the number or size of relevant objects observed by individual cameras). RA-MOSAIC utilizes two distinct and synergistic concepts. First, at the camera sensor, a bandwidth-adaptive and lightweight Bandwidth Aware Camera Transmission (BACT) method applies differential down-sampling to create mixed-resolution individual frames that preferentially preserve resolution for critical ROIs, before being transmitted to the edge node. Second, at the edge, BACT video streams received from multiple cameras are decomposed into multi-scale RoI tiles and spatially packed using a novel workload-adaptive bin-packing strategy into a single ‘canvas frame’. Notably, the canvas frame itself is dynamically sized such that the edge device can opportunistically provide higher processing throughput for selected high-priority tiles during periods of lower aggregate workloads. To demonstrate RA-MOSAIC’s gains in processing throughput and perception fidelity, we evaluate RA-MOSAIC on a single NVIDIA Jetson TX2 edge device for two benchmark tasks: Drone-based Pedestrian Detection and Automatic License Plate Recognition. In a bandwidth-constrained wireless environment, RA-MOSAIC employs a batch size of 1 to pack up to 6 concurrent video streams on a dynamically sized canvas frame to provide (i) 14.3% gain in object detection accuracy and (ii) 11.11% gain in throughput on average (up to 20 FPS per camera, cumulatively 120 FPS), over our previous work MOSAIC, a naïve canvas-based baseline. Compared to prior state of the art baselines such as batched inference over extracted RoI, RA-MOSAIC provides a very-significant, 29.6% gain in accuracy for a comparable throughput. Similarly, RA-MOSAIC dramatically outperforms bandwidth adaptive baselines, such as FCFS ( \(\leq 1\%\) accuracy gain but \(5.6\) x or 566.67% throughput gain) and uniform grid packing (17% accuracy improvement and 5% throughput gain).

Image Acquisition of Critical Bridge Components Using Vision-guided Autonomous Vehicle

This research proposes a vision-guided autonomous navigation framework for unmanned vehicles performing image acquisition for bridge inspection. The proposed framework integrates visual SLAM with RGB-D image input with semantic segmentation to detect and localize critical structural components like columns. The detected components are converted to the parametric map to generate navigation goals for image collection. The proposed approach is first validated in the synthetic bridge inspection environment using an unmanned ground vehicle. The feasibility of the framework is further studied by the laboratory-scale prototyping and validation using TurtleBot3 equipped with Jetson TX2 onboard computer. In the simulation environment, the proposed framework can achieve autonomous navigation to up to 6 columns and acquisition of image data with 90% success rate for 3 columns. Furthermore, the performance evaluation in the real-world environment shows that the developed hardware-software prototype can navigate and collect image data of up to 2 columns, with more than 60% success rate navigating to the first column. The results indicate the significant potential of achieving autonomous navigation and image acquisition with limited onboard computational resources, contributing to the enhanced efficiency and reliability of bridge management.

Field Pest Detection via Pyramid Vision Transformer and Prime Sample Attention

Background: Pest detection plays a crucial role in smart agriculture; it is one of the primary factors that significantly impact crop yield and quality. Objective: In actual field environments, pests often appear as dense and small objects, which pose a great challenge to field pest detection. Therefore, this paper addresses the problem of dense small pest detection. Methods: We combine a pyramid vision transformer and prime sample attention (named PVTPSA) to design an effective pest detection model. Firstly, a pyramid vision transformer is adopted to extract pest feature information. Pyramid vision transformer fuses multi-scale pest features through pyramid structure and can capture context information of small pests, which is conducive to the feature expression of small pests. Then, we design prime sample attention to guide the selection of pest samples in the model training process to alleviate the occlusion effect between dense pests and enhance the overall pest detection accuracy. Results: The effectiveness of each module is verified by the ablation experiment. According to the comparison experiment, the detection and inference performance of the PVT-PSA is better than the other eleven detectors in field pest detection. Finally, we deploy the PVT- PSA model on a terrestrial robot based on the Jetson TX2 motherboard for field pest detection. Conclusion: The pyramid vision transformer is utilized to extract relevant features of pests. Additionally, prime sample attention is employed to identify key samples that aid in effectively training the pest detection models. The model deployment further demonstrates the practicality and effectiveness of our proposed approach in smart agriculture applications.

Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge

While state-of-the-art permissioned blockchains can achieve thousands of transactions per second on commodity hardware with x86/64 architecture, their performance when running on different architectures, such as ARM, is not clear. The goal of this work is to characterize the performance and cost of permissioned blockchains on different hardware systems, which is important as diverse application domains are adopting blockchains. To this end, we conduct extensive cost and performance evaluation of two permissioned blockchains, namely Hyperledger Fabric and ConsenSys Quorum, on five different types of hardware covering both x86/64 and ARM architecture, as well as both cloud and edge computing. The hardware nodes include servers with Intel Xeon CPU, servers with ARM-based Amazon Graviton CPU, and edge devices with ARM-based CPU such as Nvidia Jetson TX2 and Raspberry Pi 4. Our results reveal a diverse profile of the two blockchains across different settings, demonstrating the impact of hardware choices on the overall performance and cost. We find that Graviton servers outperform Xeon servers in many settings due to their powerful CPU and high memory bandwidth. Edge devices with ARM architecture, on the other hand, exhibit low performance. When comparing the cloud with the edge, we show that the cost of the latter is much smaller in the long run if manpower cost is not considered.

Malaria Cell Image Classification Using Compact Deep Learning Architectures on Jetson TX2

Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. Traditional diagnostic methods, like blood smear microscopy, are time-intensive and prone to error. This study introduces a deep learning approach for classifying malaria-infected cells in blood smear images using convolutional neural networks (CNNs); Six CNN models were designed and trained using a large labeled dataset of malaria cell images, both infected and uninfected, and were implemented on the Jetson TX2 board to evaluate them. The model was optimized for feature extraction and classification accuracy, achieving 97.72% accuracy, and evaluated using precision, recall, and F1-score metrics and execution time. Results indicate deep learning significantly improves diagnostic time efficiency on embedded systems. This scalable, automated solution is particularly useful in resource-limited areas without access to expert microscopic analysis. Future work will focus on clinical validation.

Enhanced YOLOv8 Ship Detection Empower Unmanned Surface Vehicles for Advanced Maritime Surveillance.

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency. Demonstrating superior detection accuracy with a mean Average Precision (mAP) of 0.99 and achieving an operational speed of 17.99 FPS, all while maintaining energy consumption at just 5.61 joules. The remarkable balance between accuracy, processing speed, and energy efficiency underscores the potential of this system to significantly advance maritime safety, security, and environmental monitoring.

An automatic rebar spacing measuring method based on the YOLOv8-GB model

In engineering construction projects, rebar spacing measurement requires significant manual labor with low efficiency. This paper proposes a new intelligent rebar spacing measurement method based on the YOLOv8-GB model to save the workforce and improve efficiency. This method collects images of rebars to be measured using a binocular camera, utilizes the proposed YOLOv8-GB model to extract rebars from the scene, and achieves spacing measurement. The system is deployed on the NVIDIA Jetson TX2 NX for on-site portable measurement and can run in real-time at 24 frames per second. Experimental results show that the improved YOLOv8-GB network, compared with the YOLOv8n network, increased Recall, Precision, mAP@0.5, and mAP50-95 by 0.6 %, 5.5 %, 2.3 %, and 7.6 %, respectively. The measurement system built with YOLOv8-GB achieved an average absolute error of ± 1.7 mm, ±2.1 mm, and ± 2.7 mm for rebar spacing measurements on three different ground textures, with average relative errors of 0.85 %, 0.93 %, and 1.32 %, meeting engineering requirements. Compared to the measurement system built with YOLOv8n, the average absolute error decreased by 37.0 %, 8.0 %, and 25.0 % under the three different ground textures, while the average relative error decreased by 36.1 %, 8.8 %, and 23.7 %, respectively.

Electric trucks pantograph-catenary interaction condition monitoring method based on semantic segmentation network and linear fitting

Abstract The pantograph-catenary system (PCS) in dual-source electric trucks is crucial for maintaining a stable connection to the power grid, which directly impacts power quality. Ensuring reliable contact between the pantograph and catenary requires accurate detection of the contact point (CPT). However, existing CPT detection methods designed for trains are not well-suited for electric trucks due to differences in structural design. To address this challenge, this paper proposes a novel CPT detection method that integrates semantic segmentation and linear fitting techniques. Firstly, we introduce a lightweight Pantograph and Contact Line Segmentation Network (PCSN), which accurately extracts the regions of the pantograph and contact line. Secondly, a position correction algorithm combined with a least-squares linear fitting technique is employed to detect the CPT of electric trucks. Experimental results demonstrate that the proposed method achieves a detection error within ±5 pixels. In terms of processing speed, it reaches 76.9 FPS on an RTX 3080 GPU, 47.13 FPS on an Intel I9-12900 CPU, and 11.63 FPS on an embedded Jetson TX2 device.

CUAHN-VIO: Content-and-uncertainty-aware homography network for visual-inertial odometry

Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped with a downward-facing camera. The vision frontend is a content-and-uncertainty-aware homography network (CUAHN). Content awareness measures the robustness of the network toward non-homography image content, e.g. 3-dimensional objects lying on a planar surface. Uncertainty awareness refers that the network not only predicts the homography transformation but also estimates the prediction uncertainty. The training requires no ground truth that is often difficult to obtain. The network has good generalization that enables “plug-and-play” deployment in new environments without fine-tuning. A lightweight extended Kalman filter (EKF) serves as the VIO backend and utilizes the mean prediction and variance estimation from the network for visual measurement updates. CUAHN-VIO is evaluated on a high-speed public dataset and shows rivaling accuracy to state-of-the-art (SOTA) VIO approaches. Thanks to the robustness to motion blur, low network inference time (∼23 ms), and stable processing latency (∼26 ms), CUAHN-VIO successfully runs onboard an Nvidia Jetson TX2 embedded processor to navigate a fast autonomous MAV.

Removing nonrigid refractive distortions for underwater images using an attention-based deep neural network

Refractive distortions in underwater images usually occur when these images are captured through a dynamic refractive water surface, such as unmanned aerial vehicles capturing shallow underwater scenes from the surface of water or autonomous underwater vehicles observing floating platforms in the air. We propose an end-to-end deep neural network for learning to restore real scene images for removing refractive distortions. This network adopts an encoder-decoder architecture with a specially designed attention module. The use of the attention image and the distortion field generated by the proposed deep neural network can restore the exact distorted areas in more detail. Qualitative and quantitative experimental results show that the proposed framework effectively eliminates refractive distortions and refines image details. We also test the proposed framework in practical applications by embedding it into an NVIDIA JETSON TX2 platform, and the results demonstrate the practical value of the proposed framework.

A Lightweight Insulator Defect Detection Model Based on Drone Images

With the continuous development and construction of new power systems, using drones to inspect the condition of transmission line insulators has become an inevitable trend. To facilitate the deployment of drone hardware equipment, this paper proposes IDD-YOLO (Insulator Defect Detection-YOLO), a lightweight insulator defect detection model. Initially, the backbone network of IDD-YOLO employs GhostNet for feature extraction. However, due to the limited feature extraction capability of GhostNet, we designed a lightweight attention mechanism called LCSA (Lightweight Channel-Spatial Attention), which is combined with GhostNet to capture features more comprehensively. Secondly, the neck network of IDD-YOLO utilizes PANet for feature transformation and introduces GSConv and C3Ghost convolution modules to reduce redundant parameters and lighten the network. The head network employs the YOLO detection head, incorporating the EIOU loss function and Mish activation function to optimize the speed and accuracy of insulator defect detection. Finally, the model is optimized using TensorRT and deployed on the NVIDIA Jetson TX2 NX mobile platform to test the actual inference speed of the model. The experimental results demonstrate that the model exhibits outstanding performance on both the proprietary ID-2024 insulator defect dataset and the public SFID insulator dataset. After optimization with TensorRT, the actual inference speed of the IDD-YOLO model reached 20.83 frames per second (FPS), meeting the demands for accurate and real-time inspection of insulator defects by drones.

An Intelligent Bait Delivery Control Method for Flight Vehicle Evasion Based on Reinforcement Learning

During aerial combat, when an aircraft is facing an infrared air-to-air missile strike, infrared baiting technology is an important means of penetration, and the strategy of effective delivery of infrared bait is critical. To address this issue, this study proposes an improved deep deterministic policy gradient (DDPG) algorithm-based intelligent bait-dropping control method. Firstly, by modeling the relative motion between aircraft, bait, and incoming missiles, the Markov decision process of aircraft-bait-missile infrared effect was constructed with visual distance and line of sight angle as states. Then, the DDPG algorithm was improved by means of pre-training and classification sampling. Significantly, the infrared bait-dropping decision network was trained through interaction with the environment and iterative learning, which led to the development of the bait-dropping strategy. Finally, the corresponding environment was transferred to the Nvidia Jetson TX2 embedded platform for comparative testing. The simulation results showed that the convergence speed of this method was 46.3% faster than the traditional DDPG algorithm. More importantly, it was able to generate an effective bait-throwing strategy, enabling the aircraft to successfully evade the attack of the incoming missile. The strategy instruction generation time is only about 2.5 ms, giving it the ability to make online decisions.

A lightweight and accurate recognition algorithm of pointer meter based on improved Deeplabv3+ for inspection robots

Pointer meter is widely utilized in the fields of modern industry. Nowadays, intelligent inspection robots are gradually employed in place of labors for inspection task. Pointer meter recognition is one of the most important tasks of inspection robots. This article presents a lightweight pointer meter recognition algorithm, which is suitable for deployment on inspection robots. First, pointer meter is identified by pruned YOLOv5. Afterwards, the dial and the pointer are segmented through improved Deeplabv3+ in which the JPU and depthwise separable convolutions are utilized in lieu of dilated convolutions. After perspective transform and central-line extraction of pointer, the reading of the pointer meter can be determined using the angle method. Experimental results verify the FPS of pruned YOLOv5 improves 13.18% compared to original YOLOv5 and the FPS of improved Deeplabv3+ improves 45.74% compared to original Deeplabv3+. Additionally, the reading accuracy of the algorithm is 98.40% and average fiducial error is 0.32%, which indicate good accuracy. The relative standard deviation of reading is less than 1.4%, which indicates good stability of proposed algorithm. This study proposes a lightweight and accurate pointer meter recognition algorithm based on improved Deeplabv3+, the algorithm is ported to NVIDIA Jetson TX2 NX to verify its stability and accuracy.

AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application

To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests.

Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

With the rapid development of science and technology and industry, robot technology has become a key pillar in many fields, especially in industrial manufacturing, robots have been widely used in automated operations. However, in the face of the complex and changeable demands of modern industry, simple mechanized operation can no longer be satisfied, and intelligence has become the inevitable trend of robot technology development. By combining computer vision technology, this study provides robots with powerful environmental awareness and target recognition capabilities, thus achieving higher-level autonomous operation and decision-making. Aiming at the intelligent sorting system, this research adopts XYZ mechanical arm as the execution platform, equipped with TCS34725 color sensor to identify the color of articles, and integrates Hikvision industrial camera and NVIDIA Jetson TX2 image processor to capture and process image data, so as to achieve accurate positioning of articles. In the aspect of software design, this research realizes the function of accurately sorting different color items from the conveyor belt by combining color recognition algorithm and image processing technology with mechanical engineering control. The test results of the system show that the system has high accuracy and stability in color recognition and object positioning, and the trajectory accuracy of the manipulator meets the requirements, which verifies the realization of the research goal. The intelligent robot control system designed in this paper not only improves the level of industrial automation and reduces labor costs, but also broadens the application fields of robots in complex environments.

L1RR: Model Pruning Using Dynamic and Self-Adaptive Sparsity for Remote-Sensing Target Detection to Prevent Target Feature Loss

Limited resources for edge computing platforms in airborne and spaceborne imaging payloads prevent using complex image processing models. Model pruning can eliminate redundant parameters and reduce the computational load, enhancing processing efficiency on edge computing platforms. Current challenges in model pruning for remote-sensing object detection include the risk of losing target features, particularly during sparse training and pruning, and difficulties in maintaining channel correspondence for residual structures, often resulting in retaining redundant features that compromise the balance between model size and accuracy. To address these challenges, we propose the L1 reweighted regularization (L1RR) pruning method. Leveraging dynamic and self-adaptive sparse modules, we optimize L1 sparsity regularization, preserving the model’s target feature information using a feature attention loss mechanism to determine appropriate pruning ratios. Additionally, we propose a residual reconstruction procedure, which removes redundant feature channels from residual structures while maintaining the residual inference structure through output channel recombination and input channel recombination, achieving a balance between model size and accuracy. Validation on two remote-sensing datasets demonstrates significant reductions in parameters and floating point operations (FLOPs) of 77.54% and 65%, respectively, and a 48.5% increase in the inference speed on the Jetson TX2 platform. This framework optimally maintains target features and effectively distinguishes feature channel importance compared to other methods, significantly enhancing feature channel robustness for difficult targets and expanding pruning applicability to less difficult targets.

Clairvoyance: Vision-impaired friendly assistive mobile device

Our design is a wearable device that assists the visually impaired to move. Our goal is to enable visually impaired people to travel alone after wearing our designs, helping them reduce the risks they may face when walking out alone. After several iterations of design ideas, our final design mainly relies on two webcams, Jetson TX2 Development board, and six vibrators. These components are installed on a sports vest and a belt. We decided to use visual systems and GPS to predict the trajectory of people and objects, and help users identify the direction of obstacles and achieve the purpose of obstacle avoidance through vibrators at different positions.

Investigating visual navigation using spiking neural network models of the insect mushroom bodies.

Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN-a library for building GPU accelerated spiking neural networks-we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model's Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

A photogrammetric approach for real‐time visual SLAM applied to an omnidirectional system

AbstractThe problem of sequential estimation of the exterior orientation of imaging sensors and the three‐dimensional environment reconstruction in real time is commonly known as visual simultaneous localisation and mapping (vSLAM). Omnidirectional optical sensors have been increasingly used in vSLAM solutions, mainly for providing a wider view of the scene, allowing the extraction of more features. However, dealing with unmodelled points in the hyperhemispherical field poses challenges, mainly due to the complex lens geometry entailed in the image formation process. To address these challenges, the use of rigorous photogrammetric models that appropriately handle the geometry of fisheye lens cameras can overcome these challenges. Thus, this study presents a real‐time vSLAM approach for omnidirectional systems adapting ORB‐SLAM with a rigorous projection model (equisolid‐angle). The implementation was conducted on the Nvidia Jetson TX2 board, and the approach was evaluated using hyperhemispherical images captured by a dual‐fisheye camera (Ricoh Theta S) embedded into a mobile backpack platform. The trajectory covered a distance of 140 m, with the approach demonstrating accuracy better than 0.12 m at the beginning and achieving metre‐level accuracy at the end of the trajectory. Additionally, we compared the performance of our proposed approach with a generic model for fisheye lens cameras.

Convolution technique for focusing of ISAR images

ABSTRACT Defocusing is an undesirable phenomenon in remote sensing. Defocusing arises due to the platform motion irregularity or the target motion. The focusing techniques in the literature either need the imaging parameters to compensate for the motion aberrations or a large dataset of defocused, focused images to model the phase errors. This paper proposes a focusing technique called convolution technique that uses only the blurred image data to focus it. The experimental results on the inverse synthetic aperture (ISAR) dataset show that the proposed technique achieved better entropy and contrast than state-of-the-art methods. The execution time of the proposed technique on NVIDIA Jetson Tx2 device is 182.9 msec. Hence, the convolution technique can be used for real-time applications.