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

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, Ho0.5_{0.5}Dy0.5_{0.5}FeO3∣_{3}\vertPt using spin Hall magnetoresistance

Orthoferrites ($RE$FeO$_{3}$) containing rare-earth ($RE$) 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 ($H$)-temperature ($T$) parameter space. We present spin Hall magnetoresistance (SMR) studies on a $b$-plate ($ac$-plane) of crystalline Ho$_{0.5}$Dy$_{0.5}$FeO$_{3}|$Pt (HDFO$|$Pt) hybrid at various $T$ in the range, 11 to 300 K. In the room temperature $\Gamma_4(G_x, A_y, F_z)$ phase, the switching between two degenerate domains, $\Gamma_4(+G_x, +F_z)$ and $\Gamma_4(-G_x, -F_z)$ occurs at fields above a critical value, $H_{\text{c}} \approx 713$ Oe. Under $H > H_{\text{c}}$, the angular dependence of SMR ($\alpha$-scan) in the $\Gamma_4(G_x, A_y, F_z)$ phase yielded a highly skewed curve with a sharp change (sign-reversal) along with a rotational hysteresis around $a$-axis. This hysteresis decreases with an increase in $H$. Notably, at $H < H_{\text{c}}$, the $\alpha$-scan measurements on the single domain, $\Gamma_4(\pm G_x, \pm F_z)$ exhibited an anomalous sinusoidal signal of periodicity 360 deg. Low-$T$ SMR curves ($H$ = 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 $T$-evolution of Fe-$RE$ exchange coupling. Below 25 K, the SMR modulation showed an abrupt change around the $c$-axis, marking the presence of $\Gamma_2(F_x,C_y,G_z)$ 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

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 H₂/CO₂ 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 CO₂ and H₂ over N₂

An electron rich porous extended framework as a heterogeneous catalyst for Diels-Alder reactions

An electron rich porous metal-organic framework (MOF) has been synthesized, which acts as an effective heterogeneous catalyst for Diels-Alder reactions through encapsulation of the reactants in confined nano-channels of the framework