70,293 research outputs found
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
An intelligent, free-flying robot
The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image preprocessing, model initialization and architecture choice. We present a
study investigating these different effects.
In detail, our work compares four popular neural network architectures,
studies the effect of pretraining, evaluates the robustness of the considered
alignment preprocessings via cross-method test set swapping and intuitively
visualizes the model's prediction strategies in given preprocessing conditions
using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
evaluations on the challenging Adience benchmark show that suitable parameter
initialization leads to a holistic perception of the input, compensating
artefactual data representations. With a combination of simple preprocessing
steps, we reach state of the art performance in gender recognition.Comment: 8 pages, 5 figures, 5 tables. Presented at ICCV 2017 Workshop: 7th
IEEE International Workshop on Analysis and Modeling of Faces and Gesture
Scaling Virtualized Smartphone Images in the Cloud
Ăks selle Bakalaureuse töö eesmĂ€rkidest oli Android-x86 nutitelefoni platvormi juurutamine
pilvekeskkonda ja vÀlja selgitamine, kas valitud instance on piisav virtualiseeritud nutitelefoni
platvormi juurutamiseks ning kui palju koormust see talub. Töös kasutati Amazoni instance'i
M1 Small, mis oli piisav, et juurutada Androidi virtualiseeritud platvormi, kuid jÀi kesisemaks
kui mobiiltelefon, millel teste lĂ€bi viidi. M1 Medium instance'i tĂŒĂŒp oli sobivam ja nĂ€itas
paremaid tulemusi vÔrreldes telefoniga.
Teostati koormusteste selleks vastava tööriistaga Tsung, et nĂ€ha, kui palju ĂŒheaegseid
kasutajaid instance talub. Testi lÀbiviimiseks paigaldasime Dalviku instance'ile Tomcat
serveri.
PĂ€rast teste ĂŒhe eksemplariga, juurutasime kĂŒlge Elastic Load Balancing ja
automaatse skaleerimise Amazon Auto Scaling tööriista. Esimene neist jaotas koormust
instance'ide
vahel.
Automaatse
skaleerimise
tööriista
kasutasime,
et
rakendada
horisontaalset skaleerimist meie Android-x86 instance'le. Kui CPU tĂ”usis ĂŒle 60% kauemaks
kui ĂŒks minut, siis tehti eelmisele identne instance ja koormust saadeti edaspidi sinna. Seda
protseduuri vajadusel korrati maksimum kĂŒmne instance'ini. Meie teostusel olid tagasilöögid,
sest Elastic Load Balancer aegus 60 sekundi pÀrast ning me ei saanud kÔikide vÀlja
saadetud pÀringutele vastuseid. Serverisse saadetud faili kirjutamine ja kompileerimine olid
kulukad tegevused ja seega ei lÔppenud kÔik 60 sekundi jooksul. Me ei saanud koos Load
Balancer'iga lÀbiviidud testidest piisavalt andmeid, et teha jÀreldusi, kas virtualiseeritud
nutitelefoni platvorm Android on hÀsti vÔi halvasti skaleeruv.In this thesis we deployed a smartphone image in an Amazon EC2 instance and ran stress tests on them to know how much users can one instance bear and how scalable it is. We tested how much time would a method run in a physical Android device and in a cloud instance. We deployed CyanogenMod and Dalvik for a single instance. We used Tsung for stress testing. For those tests we also made a Tomcat server on Dalvik instance that would take the incoming file, the file would be compiled with java and its class file would be wrapped into dex, a Dalvik executable file, that is later executed with Dalvik. Three instances made a Tsung cluster that sent load to a Dalvik Virtual Machine instance. For scaling we used Amazon Auto Scaling tool and Elastic Load Balancer that divided incoming load between the instances
The assignment brief
This article is available open access under a creative commons license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Copyright @ 2009 Elsevier Ltd.This paper reports on the results of a pilot study conducted in The School of Engineering and Design at Brunel University, which considered how easily students could extract âmeaningfulâ information from an assignment brief. The study used two documents, a âstandardâ module specific assignment brief (PB1), which used a proforma document issued by the Taught Programmes Office (TPO) and a âredesignedâ assessment brief (RB2), which also used the âbasicâ proforma document as the design template. Both documents used the same data but the redesigned version used principles of Information Architecture to structure the data. The study used a designed questionnaire to elicit responses from students at Level 2, which compared and contrasted the two documents
Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors
The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone
In the Vernacular: Photography of the Everyday
This is the catalogue of the exhibition "In the Vernacular" at Boston University Art Gallery
- âŠ