Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze

Unsupervised segmentation of action segments in egocentric videos is a desirable feature in tasks such as activity recognition and content-based video retrieval. Reducing the search space into a finite set of action segments facilitates a faster and less noisy matching. However, there exist a substantial gap in machine understanding of natural temporal cuts during a continuous human activity. This work reports on a novel gaze-based approach for segmenting action segments in videos captured using an egocentric camera. Gaze is used to locate the region-of-interest inside a frame. By tracking two simple motion-based parameters inside successive regions-of-interest, we discover a finite set of temporal cuts. We present several results using combinations (of the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains egocentric videos depicting several daily-living activities. The quality of the temporal cuts is further improved by implementing two entropy measures.Comment: To appear in 2017 IEEE International Conference On Signal and Image Processing Application

On the thermal impact during drilling operations in guided dental surgery: An experimental and numerical investigation

In recent years, a major development in dental implantology has been the introduction of patient-specific 3D-printed surgical guides. The utilization of dental guides offers advantages such as enhanced accuracy in locating the implant sites, greater simplicity, and reliability in performing bone drilling operations. However, it is important to note that the presence of such guides may contribute to a rise in cutting temperature, hence increasing the potential hazards of thermal injury to the patient's bone. The aim of this study is to examine the drilling temperature evolution in two distinct methods for 3D-printed surgical dental guides, one utilizing an internal metal bushing system and the other using external metal reducers. Cutting tests are done on synthetic polyurethane bone jaw models using a lab-scale automated Computer Numeric Control (CNC) machine to find out the temperature reached by different drilling techniques and compare them to traditional free cutting configurations. Thermal imaging and thermocouples, as well as the development of numerical simulations using finite element modeling, are used for the aim. The temperature of the tools' shanks experienced an average rise of 2.4 °C and 4.8 °C, but the tooltips exhibited an average increase of around 17 °C and 24 °C during traditional and guided dental surgery, respectively. This finding provides confirmation that both guided technologies have the capability to maintain temperatures below the critical limit for potential harm to bone and tissue. Numerical models were employed to validate and corroborate the findings, which exhibited identical outcomes when applied to genuine bone samples with distinct thermal characteristics

Adaptation of Deeplab V3+ for Damage Detection on Port Infrastructure Imagery

Regular inspection and maintenance of infrastructure facilities are crucial to ensure their functionality and safety for users. However, current inspection methods are labor-intensive and can vary depending on the inspector. To improve this process, modern sensor systems and machine learning algorithms can be deployed to detect defects based on rapidly acquired data, resulting in lower downtime. A quality-controlled processing chain allows to provide hence informed uncertainty assessments to inspection operators. In this study, we present several Deeplab V3+ models optimized to predict corroded segments of the quay wall at JadeWeserPort, Germany, which is a dataset from the 3D HydroMapper research project. Our models achieve generally high accuracy in detecting this damage type. Therefore, we examine the use of a Region Growing-based weakly supervised approach to efficiently extend our model to other common types in the future. This approach achieves about 90 % of the results compared to corresponding fully supervised networks, of which a ResNet-50 variant peaks at 55.6 % Intersection-over-Union regarding the test set's corrosion class

Pulsed Beam Tests at the SANAEM RFQ Beamline

A proton beamline consisting of an inductively coupled plasma (ICP) source, two solenoid magnets, two steerer magnets and a radio frequency quadrupole (RFQ) is developed at the Turkish Atomic Energy Authority's (TAEA) Saraykoy Nuclear Research and Training Center (SNRTC-SANAEM) in Ankara. In Q4 of 2016, the RFQ was installed in the beamline. The high power tests of the RF power supply and the RF transmission line were done successfully. The high power RF conditioning of the RFQ was performed recently. The 13.56 MHz ICP source was tested in two different conditions, CW and pulsed. The characterization of the proton beam was done with ACCTs, Faraday cups and a pepper-pot emittance meter. Beam transverse emittance was measured in between the two solenoids of the LEBT. The measured beam is then reconstructed at the entrance of the RFQ by using computer simulations to determine the optimum solenoid currents for acceptance matching of the beam. This paper will introduce the pulsed beam test results at the SANAEM RFQ beamline. In addition, the high power RF conditioning of the RFQ will be discussed.Comment: 6 pages, 6 figures. Proceedings of the International Particle Accelerator Conference 2017 (IPAC'17), May 14-19, 2017, TUPAB015, p. 134

Reflective prism display using pepper’s ghost technique software toolkit plugin for unity 3d

Reflective prism display is a display technology that has potentials in displaying images with fascinating effects. However, the process of creating the display is quite challenging considering the lack of specialized software and bulky hardware setup. In this project, we propose a software toolkit plugin for Unity 3D, called Prismatic, to simplify the process as an alternative over the conventional method of creating a reflective prism display. Adopting the idea from Pepper’s ghost technique, a combination of four cameras facing an object were setup within Unity to produce four viewport renderings of the object, easily projected from a device as small as a smartphone to the size of widescreen TVs. This software toolkit combined with Unity offer simple and centralized control over camera, facets, and object. Prismatic has the potential in assisting apps developer in creating the display such as in showcasing models for education and business purposes

CYCLE-GAN BASED FEATURE TRANSLATION FOR OPTICAL-SAR DATA IN BURNED AREA MAPPING

For the management of the forest and the assessment of impacts on ecosystems, post-fire burned area mapping is crucial for sustainable environment and forestry. While optical remote sensing data has been extensively used for monitoring forest fires due to its spatial and temporal resolutions, it is susceptible to limitations imposed by poor weather conditions. To overcome this challenge, the complementary use of optical and Synthetic Aperture Radar (SAR) data is beneficial, as SAR can penetrate clouds and capture images in all-weather conditions. However, SAR lacks the necessary spectral features for comprehensive forest fire monitoring and burned area mapping. To overcome these limitations, this study proposes a Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) based deep feature translation method for burned area mapping by combining optical and SAR data. This approach allows for the retrieval of precise information of interest with a level of precision that cannot be achieved by either optical or SAR data alone. The Cycle-GAN uses a cyclic structure to transfer data from one domain (optical) to another domain (SAR) into the same feature space. As a result, it can maintain its spectral characteristics while providing ongoing and current information for monitoring forest fires. For this purpose, Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalised Burn Ratio (NBR) were determined using optical data and image translation was performed with Cycle-GAN on SAR data. The accuracy of the fake BAI, MIRBI and NBR spectral burn indices determined from the SAR was established by correlating the original spectral burn indices determined from the optical data. The results demonstrate a significant correlation between the real and generated fake burn indices, particularly with a noteworthy correlation coefficient of 0.93 observed for the NBR index. In addition, the findings validate the effectiveness of the generated indices in accurately representing and quantifying the extent of burned areas

Emotion Interaction With Virtual Reality Using Hybrid Emotion Classification Technique Toward Brain Signals

Human computer interaction (HCI) considered main aspect in virtual reality (VR) especially in the context of emotion, where users can interact with virtual reality through their emotions and it could be expressed in virtual reality. Last decade many researchers focused on emotion classification in order to employ emotion in interaction with virtual reality, the classification will be done based on Electroencephalogram (EEG) brain signals. This paper provides a new hybrid emotion classification method by combining self- assessment, arousal valence dimension and variance of brain hemisphere activity to classify users’ emotions. Self-assessment considered a standard technique used for assessing emotion, arousal valence emotion dimension model is an emotion classifier with regards to aroused emotions and brain hemisphere activity that classifies emotion with regards to right and left hemisphere. This method can classify human emotions, two basic emotions is highlighted i.e. happy and sad. EEG brain signals are used to interpret the users’ emotional. Emotion interaction is expressed by 3D model walking expression in VR. The results show that the hybrid method classifies the highlighted emotions in different circumstances, and how the 3D model changes its walking style according to the classified users’ emotions. Finally, the outcome is believed to afford new technique on classifying emotions with feedback through 3D virtual model walking expression

A Physical Approach for Stochastic Modeling of TERO-based TRNG

International audienceSecurity in random number generation for cryptography is closely related to the entropy rate at the generator output. This rate has to be evaluated using an appropriate stochastic model. The stochastic model proposed in this paper is dedicated to the transition effect ring oscillator (TERO) based true random number generator (TRNG) proposed by Varchola and Drutarovsky in 2010. The advantage and originality of this model is that it is derived from a physical model based on a detailed study and on the precise electrical description of the noisy physical phenomena that contribute to the generation of random numbers. We compare the proposed electrical description with data generated in a 28 nm CMOS ASIC implementation. Our experimental results are in very good agreement with those obtained with both the physical model of TERO's noisy behavior and with the stochastic model of the TERO TRNG, which we also confirmed using the AIS 31 test suites

A Very High Speed True Random Number Generator with Entropy Assessment

International audienceThe proposed true random number generator (TRNG) exploits the jitter of events propagating in a self-timed ring (STR) to generate random bit sequences at a very high bit rate. It takes advantage of a special feature of STRs that allows the time elapsed between successive events to be set as short as needed, even in the order of picoseconds. If the time interval between the events is set in concordance with the clock jitter magnitude, a simple entropy extraction scheme can be applied to generate random numbers. The proposed STR-based TRNG (STRNG) follows AIS31 recommendations: by using the proposed stochastic model, designers can compute a lower entropy bound as a function of the STR characteristics (number of stages, oscillation period and jitter magnitude). Using the resulting entropy assessment, they can then set the compression rate in the arithmetic post-processing block to reach the required security level determined by the entropy per output bit. Implementation of the generator in two FPGA families confirmed its feasibility in digital technologies and also confirmed it can provide high quality random bit sequences that pass the statistical tests required by AIS31 at rates as high as 200 Mbit/s

Revealing the molecular signatures of host-pathogen interactions.

Advances in sequencing technology and genome-wide association studies are now revealing the complex interactions between hosts and pathogen through genomic variation signatures, which arise from evolutionary co-existence