Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
11,002
result(s) for
"Intel"
Sort by:
Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping
Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of four different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields.
Journal Article
An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515
RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated in both hardware and software. Models D415, SR305, and L515 are examples of successful cameras launched by Intel® RealSense™ between 2018 and 2020. These three cameras are different since they have distinct operating principles. Then, their behavior concerning depth estimation while in the presence of many error sources will also be specific. For instance, semi-transparent and scattering media are expected error sources for an RGB-D sensor. The main new contribution of this paper is a full evaluation and comparison between the three Intel RealSense cameras in scenarios with transparency and translucency. We propose an experimental setup involving an aquarium and liquids. The evaluation, based on repeatability/precision and statistical distribution of the acquired depth, allows us to compare the three cameras and conclude that Intel RealSense D415 has overall the best behavior namely in what concerns the statistical variability (also known as precision or repeatability) and also in what concerns valid measurements.
Journal Article
Acceleration of Image Classification and Object Tracking by the Intel Neural Compute Stick 2 with Power Efficiency Evaluation on Raspberry Pi 4B
This work investigates the efficiency and power consumption of using the Intel® (Santa Clara, CA, USA) Neural Compute Stick 2 (NCS2) on the Raspberry Pi 4B platform to accelerate image classification and object tracking. The motivation behind this study is to enable the real-time operation of complex neural networks in embedded systems, potentially reducing the cost of deep learning neural network deployment and expanding industrial applications. This study also supplements the OpenVINO™ 2022.3.2 documentation by recording the application of the Raspberry Pi 4B combined with the NCS2 in the latest European software repositories. Supported by OpenVINO™ 2022.3.2 and the Deep SORT algorithm, this study consists of two distinct tests: image recognition and real-time object tracking. A single model is used for image recognition, while two models are deployed for object tracking. These test cases evaluate the performance of the execution hardware by varying the different number of models in different application scenarios and evaluating the impact of NCS2 acceleration under various conditions. The results indicate that, for the specific models used in this experiment, the NCS2 increases image recognition performance by approximately 400% and real-time object tracking by around 1400% to 1200%. The results presented in this work indicate that the NCS2 can achieve more than 50 FPS (frames per second) in image recognition and more than 20 FPS in object tracking. The power efficiency obtained by using the NCS2 can vary from 200% to 400%. These findings highlight the significant performance gains NCS2 offers in constrained hardware environments.
Journal Article
IoT-based traffic prediction and traffic signal control system for smart city
Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of ‘5’ phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods.
Journal Article
Moore's law : the life of Gordon Moore, Silicon Valley's quiet revolutionary
\"A chemist and founder of Intel, Gordon Moore played a major role in revolutionizing technology and shaping the growth and reach of Silicon Valley. The story of the man--an inventor and businessman whose influence on the world is at least as great as Thomas Edison's, Henry Ford's, or Bill Gates's--has never before been told ... [In this book], Arnold Thackray sheds light on Gordon Moore, gives context to the technologies and world of high-tech power he helped to develop, and provides [an] ... introduction to the history and science of the silicon transistor, the technological building block that has transformed commercial business, defense strategies, and the everyday lives of individuals around the globe\"-- Provided by publisher.
Book
Revisiting the performance optimization of QR factorization on Intel KNL and SKL multiprocessors
This study focused on the optimization of double-precision general matrix–matrix multiplication (DGEMM) routine to improve the QR factorization performance. By replacing the MKL DGEMM with our previously developed blocked matrix–matrix multiplication routine, we found that the QR factorization performance was suboptimal due to a bottleneck in the
A
T
·
B
matrix–panel multiplication operation. We present an investigation of the limitations of our matrix–matrix multiplication routine. It was found that the performance of the matrix multiplication routine depends on the shape and size of the matrices. Therefore, we recommend different kernels tailored to matrix shapes involved in QR factorization and developed a new routine for the
A
T
·
B
matrix–panel multiplication operation. We demonstrated the performance of the proposed kernels on the ScaLAPACK QR factorization routine by comparing them with the MKL, OPENBLAS, and BLIS libraries. Our proposed optimization demonstrates significant performance improvements in the multinode cluster environments of the Intel Xeon Phi Processor 7250 codenamed Knights Landing (KNL) and Intel Xeon Gold 6148 Scalable Skylake Processor (SKL).
Journal Article