23 research outputs found
Deep Learning for Object Recognition in picking tasks
Treball de Final de Màster Universitari Erasmus Mundus en Robòtica Avançada. Curs acadèmic 2016-2017In the light of current advancement in deep learning, robot vision is not an exception. Many popular
machine learning algorithms has already been proposed and implemented to solve intricate computer
vision problems. The same has not been in the case of robot vision. Due to real time constraints and
dynamic nature of environment such as illumination and processing power, very few algorithms are
able to solve the object recognition problem at large.
The primary objective of the thesis project is to converge into an accurate working algorithm for
object recognition in a cluttered scene and subsequently helping the BAXTER robot to pick up
the correct object among the clutter. Feature matching algorithms usually fail to identify most of
the object having no texture, hence deep learning has been employed for better performance. The
next step is to look for the object and localize it within the image frame. Although basic shallow
Convolutional Neural Network easily identifies the presence of an object within a frame, it is very
difficult to localize the object location within the frame. This work primarily focuses on finding
a solution for an accurate localization. The first solution which comes to mind is to produce a
bounding box surrounding the object. In literature, YOLO is found to be providing a very robust
result on existing datasets. But this was not the case when it was tried on new objects belonging
to the current thesis project work. Due to high inaccuracy and presence of a huge redundant area
within the bounding box, an algorithm was needed which will segment the object accurately and
make the picking task easier. This was done through semantic segmentation using deep CNNs.
Although time consuming, RESNET has been found to be very efficient as its post processed output
helps to identify items in a significantly difficult task environment. This work has been done in light
of upcoming AMAZON robotic challenge where the robot successfully classified and distinguished
everyday items from a cluttered scenario. In addition to this, a performance analysis study has also
been done comparing YOLO and RESNET justifying the usage of the later algorithm with the help
of performance metrics such IOU and ViG
Lasagna: Layered Score Distillation for Disentangled Object Relighting
Professional artists, photographers, and other visual content creators use
object relighting to establish their photo's desired effect. Unfortunately,
manual tools that allow relighting have a steep learning curve and are
difficult to master. Although generative editing methods now enable some forms
of image editing, relighting is still beyond today's capabilities; existing
methods struggle to keep other aspects of the image -- colors, shapes, and
textures -- consistent after the edit. We propose Lasagna, a method that
enables intuitive text-guided relighting control. Lasagna learns a lighting
prior by using score distillation sampling to distill the prior of a diffusion
model, which has been finetuned on synthetic relighting data. To train Lasagna,
we curate a new synthetic dataset ReLiT, which contains 3D object assets re-lit
from multiple light source locations. Despite training on synthetic images,
quantitative results show that Lasagna relights real-world images while
preserving other aspects of the input image, outperforming state-of-the-art
text-guided image editing methods. Lasagna enables realistic and controlled
results on natural images and digital art pieces and is preferred by humans
over other methods in over 91% of cases. Finally, we demonstrate the
versatility of our learning objective by extending it to allow colorization,
another form of image editing
Accurate diagnosis of liver diseases through the application of deep convolutional neural network on biopsy images
Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the algorithms. To detect prominent characteristics in the liver from a collection of Biopsy pictures, we employed the YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and SSD models. A highlighting point of this paper is comparing the state-of-the-art Instance Segmentation models, including Mask R-CNN, U-Net, YOLOv5 Instance Segmentation, YOLOv7 Instance Segmentation, and YOLOv8 Instance Segmentation. The extent of severity of NAFLD and non-alcoholic steatohepatitis was examined for liver cell ballooning, steatosis, lobular, and periportal inflammation, and fibrosis. Metrics used to evaluate the algorithms' effectiveness include accuracy, precision, specificity, and recall. Improved metrics are achieved by optimizing the hyperparameters of the associated models. Additionally, the liver is scored in order to analyse the information gleaned from biopsy images. Statistical analyses are performed to establish the statistical relevance in evaluating the score for different zones
Probing magnetic anisotropy and spin-reorientation transition in 3D antiferromagnet, HoDyFeOPt using spin Hall magnetoresistance
Orthoferrites (FeO) containing rare-earth () elements are 3D
antiferromagnets (AFM) that exhibit characteristic weak ferromagnetism
originating due to slight canting of the spin moments and display a rich
variety of spin reorientation transitions in the magnetic field
()-temperature () parameter space. We present spin Hall magnetoresistance
(SMR) studies on a -plate (-plane) of crystalline
HoDyFeOPt (HDFOPt) hybrid at various in the
range, 11 to 300 K. In the room temperature phase,
the switching between two degenerate domains, and
occurs at fields above a critical value, Oe. Under , the angular dependence of SMR
(-scan) in the phase yielded a highly skewed
curve with a sharp change (sign-reversal) along with a rotational hysteresis
around -axis. This hysteresis decreases with an increase in . Notably, at
, the -scan measurements on the single domain,
exhibited an anomalous sinusoidal signal of
periodicity 360 deg. Low- SMR curves ( = 2.4 kOe), showed a systematic
narrowing of the hysteresis (down to 150 K) and a gradual reduction in the
skewness (150 to 52 K), suggesting weakening of the anisotropy possibly due to
the -evolution of Fe- exchange coupling. Below 25 K, the SMR modulation
showed an abrupt change around the -axis, marking the presence of
phase. We have employed a simple Hamiltonian and
computed SMR to examine the observed skewed SMR modulation. In summary, SMR is
found to be an effective tool to probe magnetic anisotropy as well as a spin
reorientation in HDFO. Our spin-transport study highlights the potential of
HDFO for future AFM spintronic devices.Comment: 12 pages, 7 figure
The role of life events in obsessive-compulsive disorders
Background: A small number of studies are available to assess the role of stressful life events (SLEs) in obsessive-compulsive disorder (OCD). The previous studies have reported contradictory results and they have methodological limitations.
Aims and Objectives: The objectives of our study are (i) to find out the frequency of life events in patients with OCD in comparison to their matched healthy controls and (ii) to find out the impact of life events on the severity of the disorder.
Materials and Methods: Sixty patients fulfilling Diagnostic and Statistical Manual of Mental Disorder, 5th edition (DSM-V) criteria of OCD were rated with Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Hamilton Rating Scale for Anxiety (HAM-A), Hamilton Rating Scale for Depression (HAM-D), and Presumptive Stressful Life Events Scale (PSLES). A group of 60 normal controls were also rated on PSLES. Finally, both groups were compared in terms of life events.
Results: The frequency of life events, past 1 year (t=5.307, P=0.006) and lifetime (t=11.527, P<0.001), were significantly higher in the patient group in comparison to controls. PSLES scores showed a
significant correlation with Y-BOCS total scores, Y-BOCS obsession scores, and HAM-A scores. There was a positive correlation between past 1 year PSLES score and HAM-D scores. Step-wise linear regression analysis showed PSLES scores significantly positively predicted Y-BOCS total score, Y-BOCS obsession score, and Y-BOCS compulsion score.
Conclusion: Life events were significantly more frequent in OCD patients both past 1 year and lifetime, as compared to healthy controls. The severity of obsessive compulsive symptoms was found to be directly proportional to the number of SLEs experienced in the past 1 year and lifetime
Fine-tuning the balance between crystallization and gelation and enhancement of CO2 uptake on functionalized calcium based MOFs and metallogels
The synthesis, structure, gas adsorption and catalytic properties of a new 3D porous, crystalline metal–organic framework (Ca-5TIA-MOF) as well as stable viscoelastic metallogels (Ca-5TIA-Gel) are reported. Remarkably, the preparation of both types of materials can be carried out starting from the same organic ligand (i.e. 5-(1,2,4-triazoleyl)isophthalic acid (5TIA)), divalent metal ion (i.e. Ca(II)) and organic solvent (i.e. DMF). In this particular case, the presence of water in the solvent system favors the formation of a crystalline MOF, whereas a pure organic solvent induces gelation. The characterization of the materials was carried out using a series of techniques including XRD, FT-IR, TGA, TEM, SEM, SAXS and dynamic rheology. Experimental PXRD peaks of both Ca-5TIA-xerogel and Ca-5TIA-MOF matched reasonably well with simulated PXRD, suggesting the presence of, at least, some common structural elements in the 3D networks of both xerogel and crystalline phases. Moreover, the nature of the metal counteranion was found to have a critical influence on the gelation phenomenon. To the best of our knowledge, this report describes unprecedented Ca-based LMW-metallogels, as well as the first porous Ca-based MOF, which shows adsorption capacity for CO2 at 1 atm pressure. Interestingly, Ca-5TIA-xerogel presented 20% higher CO2-uptake than the crystalline Ca-5TIA-MOF at 1 atm and 298 K. Both Ca-5TIA-MOF and Ca-5TIA-Gel also displayed a modest catalytic activity towards the hydrosilylation of benzaldehyde, with slightly better performance for the gel phase material
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Efficient and stereoselective nitration of mono- and disubstituted olefins with AgNO2 and TEMPO.
Nitroolefin is a common and versatile reagent. Its synthesis from olefin is generally limited by the formation of mixture of cis and trans compounds. Here we report that silver nitrite (AgNO2) along with TEMPO can promote the regio- and stereoselective nitration of a broad range of olefins. This work discloses a new and efficient approach wherein starting from olefin, nitroalkane radical formation and subsequent transformations lead to the desired nitroolefin in a stereoselective manner
Synthesis, Structure, and H<sub>2</sub>/CO<sub>2</sub> Adsorption in a Three-Dimensional 4‑Connected Triorganotin Coordination Polymer with a sqc Topology
A 4-connected triorganotin 3D coordination
polymer in a sqc topology has been shown to possess 1D microchannels
along its crystallographic <i>a</i> axis. This main-group-element-containing
framework structure shows selective gas adsorption, preferring CO<sub>2</sub> and H<sub>2</sub> over N<sub>2</sub>