PROFILING NVIDIA JETSON EMBEDDED GPU DEVICES FOR AUTONOMOUS MACHINES
Yazhou Li1and Yahong Rosa Zheng2
1Beihang University, Beijing, China
2Lehigh University, Bethlehem, PA, 18015, USA
This paper presents two methods, tegrastats GUI version jtop and Nsight Systems, to profile NVIDIA Jetson embedded GPU devices on a model race car which is a great platform for prototyping and field testing autonomous driving algorithms. The two profilers analyze the power consumption, CPU/GPU utilization, and the run time of CUDA C threads of Jetson TX2 in five different working modes. The performance differences among the five modes are demonstrated using three example programs: vector add in C and CUDA C, a simple ROS (Robot Operating System) package of the wall follow algorithm in Python, and a complex ROS package of the particle filter algorithm for SLAM (Simultaneous Localization and Mapping). The results show that the tools are effective means for selecting operating mode of the embedded GPU devices.
Nvidia Jetson, embedded GPU, CUDA, Automous Driving. Robotic Operating Systems (ROS).
For More Details :
https://aircconline.com/csit/papers/vol10/csit101811.pdf
Volume Link :
http://airccse.org/csit/V10N18.html
A GRID-POINT DETECTION METHOD BASED ON U-NET FOR A STRUCTURED LIGHT SYSTEM
Dieuthuy Pham1,2, Minhtuan Ha1,2and Changyan Xiao1
1Hunan University, Changsha 410208, China
2Saodo University, Haiduong 170000, Vietnam
Accurate detection of the feature points of the projected pattern plays an extremely important role in one-shot 3D reconstruction systems, especially for the ones using a grid pattern. To solve this problem, this paper proposes a grid-point detection method based on U-net. A specific dataset is designed that includes the images captured with the two-shot imaging method and the ones acquired with the one-shot imaging method. Among them, the images in the first group after labeled as the ground truth images and the images captured at the same pose with the oneshot method are cut into small patches with the size of 64x64 pixels then feed to the training set. The remaining of the images in the second group is the test set. The experimental results show that our method can achieve a better detecting performance with higher accuracy in comparison with the previous methods.
Feature point detection, U-net architecture, Structured light system & Grid pattern
For More Details :
https://aircconline.com/csit/papers/vol10/csit101603.pdf
Volume Link :
http://airccse.org/csit/V10N16.html
ARTIST, STYLE AND YEAR CLASSIFICATION USING FACE RECOGNITION AND CLUSTERING WITH CONVOLUTIONAL NEURAL NETWORKS
Doruk Pancaroglu STM A.S., Ankara, Turkey
Artist, year and style classification of fine-art paintings are generally achieved using standard image classification methods, image segmentation, or more recently, convolutional neural networks (CNNs). This works aims to use newly developed face recognition methods such as FaceNet that use CNNs to cluster fine-art paintings using the extracted faces in the paintings, which are found abundantly. A dataset consisting of over 80,000 paintings from over 1000 artists is chosen, and three separate face recognition and clustering tasks are performed. The produced clusters are analyzed by the file names of the paintings and the clusters are named by their majority artist, year range, and style. The clusters are further analyzed and their performance metrics are calculated. The study shows promising results as the artist, year, and styles are clustered with an accuracy of 58.8, 63.7, and 81.3 percent, while the clusters have an average purity of 63.1, 72.4, and 85.9 percent.
Face Recognition, Clustering, Convolutional Neural Networks, Art Identification
For More Details :
https://aircconline.com/csit/papers/vol10/csit101604.pdf
Volume Link :
http://airccse.org/csit/V10N16.html
ENERGY AWARE ROUTING WITH COMPUTATIONAL OFFLOADING FOR WIRELESS SENSOR NETWORKS
Adam Barker and Martin Swany
Indiana University, Bloomington, Indiana, USA
Wireless sensor networks (WSN) are characterized by a network of small, battery powered devices, operating remotely with no pre-existing infrastructure. The unique structure of WSN allow for novel approaches to data reduction and energy preservation. This paper presents a modification to the existing Q-routing protocol by providing an alternate action of performing sensor data reduction in place thereby reducing energy consumption, bandwidth usage, and message transmission time. The algorithm is further modified to include an energy factor which increases the cost of forwarding as energy reserves deplete. This encourages the network to conserve energy in favor of network preservation when energy reserves are low. Our experimental results show that this approach can, in periods of high network traffic, simultaneously reduce bandwidth, conserve energy, and maintain low message transition times.
Ad Hoc Network Routing, Q-routing, Wireless Sensor Network, Computational Offloading, Energy Aware.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101418.pdf
Volume Link :
http://airccse.org/csit/V10N14.html
FUSION OF MULTI-FOCUS IMAGES WITH NEIGHBOUR LOCAL DISTANCE
Ias Sri Wahyuni1and Rachid Sabre2
1Universitas Gunadarma, Indonesia
2University of Burgundy/Agrosup Dijon, France
The aim of multi-focus image fusion is to integrate images with different objects in focus so that we obtained a single image with all objects in focus. In this paper, we present a novel multifocus image fusion method based on neighbour local variability (NLV). This method takes into consideration the information in the surrounding region of pixels. Indeed, at each pixel, the method exploits the local variability calculated from quadratic difference between the value of pixel and the value of all pixels that belong to its neighbourhood. It expresses the behaviour of pixel relative to all pixels belonging to its neighbourhood. The variability preserves edge feature because it detects the abrupt image intensity. The fusion of each pixel is performed by weighting each pixel by the exponential of the local variability. The precision of this fusion depends on the largenumberof the neighbourhood where the largenumber depends on the blurring characterized by the variance and its size of blurring filter. We constructed a model that gives the value of the large….. from the variance and the size of blurring filter. Comparing our method with other methods, it shows the best result.
Neighbour Local Variability; Multi-focus image fusion; Root Mean Square Error (RMSE)
For More Details :
https://aircconline.com/csit/papers/vol10/csit101220.pdf
Volume Link :
http://airccse.org/csit/V10N12.html
A NOVEL MOBILE ECG SENSOR WITH WIRELESS POWER TRANSMISSION FOR REMOTE HEALTH MONITORING
Jin-Chul Heo, Eun-Bin Park, Chan-Il Kim, Hee-Joon Park and Jong-Ha Lee
Keimyung University, Daegu, Korea
For electromagnetic induction wireless power transmission using an elliptical receiving coil, we investigated changes in magnetic field distribution and power transmission efficiency due to changes in the position of the transmitting and receiving coils. The simulation results using the high-frequency structure simulator were compared with the actual measurement results. It has been shown that even if the alignment between the transmitting coil and the receiving coil is changed to some extent, the transmission efficiency on the simulator can be maintained relatively stable. The transmission efficiency showed the maximum when the center of the receiving coil was perfectly aligned with the center of the transmitting coil. Although the reduction in efficiency was small when the center of the receiving coil was within ± 10 mm from the center of the transmitting coil, it was found that the efficiency was greatly reduced when the receiving coil deviated by more than 10 mm. Accordingly, it has been found that even if the perfect alignment is not maintained, the performance of the wireless power transmission system is not significantly reduced. When the center of the receiving coil is perfectly aligned with the center of the transmitting coil, the transmission efficiency is maximum, and even if the alignment is slightly changed, the performance of wireless power transmission maintains a certain level. This result proposes a standardized wireless transmission application method in the use of wireless power for implantable sensors.
ECG, Implantable sensors, Simulation, Power transmission efficiency, Wireless power transmission
For More Details :
https://aircconline.com/csit/papers/vol10/csit101102.pdf
Volume Link :
http://airccse.org/csit/V10N11.html
USABILITY EVALUATION TO IMPROVE OPERATION INTERFACE OF WIRELESS DEVICE: PRESSURE RANGE OF TOUCH SENSOR
Sangwoo Cho1and Jong-Ha Lee2
1,2Keimyung University, Daegu, South Korea
Usability evaluation of wireless device can find improvement about user convenience. This study investigated natural finger pressure range when presses touch sensor. Fifteen adults (Male: 10, Female: 5, Age: 26.13 ± 3.98 years) were recruited in this experiment. Subjects carried out a usability evaluation about wireless device operation. The usability evaluation measured finger pressure on touch sensor operation of wireless device using finger pressure sensor. Subjects performed 1.76±0.95 times until pressing the touch sensor to complete task (t = 3.091, p = 0.008). In comparisons between natural movement and the movement to complete task, more finger pressure value was decreased in natural movement than the movement to complete task (t = -2.277, p = 0.039). This study found a finger pressure values to improve effectiveness of wireless device operation interface. Finger pressure value was presented to induce natural movement for the use of touch sensor.
Usability Evaluation, Operation Interface, Finger Pressure Range, Wireless Device
For More Details :
https://aircconline.com/csit/papers/vol10/csit101104.pdf
Volume Link :
http://airccse.org/csit/V10N11.html
USING SDR PLATFORM TO EXTRACT THE RF FINGERPRINT OF THE WIRELESS DEVICES FOR DEVICE IDENTIFICATION
Ting-Yu Lin, Chia-Min Lai and Chi-Wei Chen
Institute for Information Industry, Taipei, R.O.C, Taiwan
Due to the advent of the Internet of Things era, the number of related wireless devices is increasing, making the abundant and complex information networks formed by communication between devices. Therefore, security and trust between devices a huge challenge. In the traditional identification method, there are identifiers such as hash-based message authentication code, key, and so on, often used to mark a message that the receiving end can verify it. However, this kind of identifiers is easy to tamper. Therefore, recently researchers address the idea that using RF fingerprint, also called radio frequency fingerprint, for identification. Our paper demonstrates a method that extracts properties and identifies each device. We achieved a high identification rate, 99.9% accuracy in our experiments where the devices communicate with Wi-Fi protocol. The proposed method can be used as a stand-alone identification feature, or for two-factor authentication.
Internet-of-Things (IoT), Authentication, RF fingerprint, Machine Learning (ML), Device Identification.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101013.pdf
Volume Link :
http://airccse.org/csit/V10N10.html
WIRELESS SENSOR NETWORKS SIMULATORS AND TESTBEDS
Souhila Silmi1,2, Zouina Doukha1, Rebiha Kemcha2,3and Samira Moussaoui1
1USTHB University, RIMAA Lab, Algiers, Algeria
2Higher Normal School Elbachir Elibrahimi-Kouba, Algiers, Algeria
2University of Boumerdes, LIMOSE Laboratory, Boumerdes, Algeria
Wireless sensor networks (WSNs) have emerged as one of the most promising technologies for the current era. Researchers have studied them for several years ago, but more work still needed to be made since open opportunities to integrate new technologies are added to this field. One challenging task is WSN deployment. Yet, this is done by real deployment with testbeds platforms or by simulation tools when real deployment could be costly and timeconsuming. In this paper, we review the implementation and evaluation process in WSNs. We then describe relevant testbeds and simulation tools, and their features. Lastly, we conduct an experimentation study using these testbeds and simulations to highlight their pro and cons. As a use case, we implement a localization protocol. This work gives clarity to future-work for better implementation in order to improve reliability, accuracy and time consumed.
Wireless Sensor Network, Testbeds, Simulation Tools, Localization Protocol
For More Details :
https://aircconline.com/csit/papers/vol10/csit100912.pdf
Volume Link :
http://airccse.org/csit/V10N09.html
CONTEXTUAL FACTORS INFLUENCING THE DESIGN AND MANAGEMENT OF HEALTH INFORMATION SYSTEMS’ INTEROPERABILITY
Grace Kobusinge1,2
1Gothenburg University, Gothenburg, Sweden
1Makerere University, P.O. Box 7062 Kampala, Uganda
Due to their renowned great information processing and dissemination power, Health information systems (HIS) can readily avail past patient medical information across the continuum of care in order to facilitate ongoing treatment. However, a number of existing HIS are designed as vertical silos with no interoperability onuses and therefore, cannot exchange patient information. At the same time, there is limited knowledge about the intricacies and factors that surround HIS’ interoperability implementations. This study therefore, employs an institutional lens perspective to investigate contextual factors influencing HIS’ interoperability designing. Through this perspective, seven contextual factors were arrived at institutional autonomism, intended system goals, existing health-information-systems, national HIS implementation guidelines, interoperability standards, policy and resources in terms of money and labour. A further study implication is the use of institutional lens in making sense of the institutions’ context of integration in order to discover salient factors that might influence health-information-systems’ interoperability designing.
Health Information Systems’ Interoperability, Design and Management, Contextual Factors.
For More Details :
https://aircconline.com/csit/papers/vol10/csit100502.pdf
Volume Link :
http://airccse.org/csit/V10N05.html
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