Improved photoluminescence and monodisperse performance of colloidal CdTe quantum dots with Cannula method

Erdem Elibol,Pınar Sevim Elibol,Musa Çadırcı,Nedim Tutkun 한국화학공학회 2019 Korean Journal of Chemical Engineering Vol.36 No.4

Colloidal quantum dots are nano semiconductor materials that have been found in many applications, producing multiple exciton generation, unique optical and electronic properties, adjustable in size and bandwidth. Synthesized QDs are expected to exhibit high photoluminescence quantum yield and monodisperse properties according to their application area. Cannula method was adapted together with the organometallic synthesis method for the first time in the literature to increase the photoluminescence quantum yield of organometallic CdTe QD and minimize the full width at half maximum value of the photoluminescence band. Injection of precursors by the Cannula method is much faster than the injecting with the conventional method of using a glass syringe, which limits the size distribution in the solution during synthesis. In addition, the fastest injection method using Cannula method yields the shortest full width half maximum value of 27.20 nm for CdTe QDs in the literature. The photoluminescence quantum yield value of the CdTe QDs synthesized by the classical method was 8.12±2.1%, while the photoluminescence quantum yield of the CdTe QDs synthesized by the Cannula method was increased to 25.66±2.1%.

Fast Underwater Image Mosaicing through Submapping

Elibol, A.,Kim, J.,Gracias, N.,Garcia, R. Springer Science + Business Media 2017 JOURNAL OF INTELLIGENT AND ROBOTIC SYSTEMS Vol.85 No.1

<P>One of the most important features of mobile robots is their capability to gather data from areas beyond human reach. This capability has increased the demand for the use of robots undertaking exploration tasks, which has naturally led to the need for efficient methods to process the obtained data. Image mosaicing is a useful tool for obtaining a high-resolution visual representation of a large area that has been explored using optical sensors. In this paper, we present an efficient image mosaicing approach that utilizes submapping methods to obtain a map of a surveyed area with reduced computational effort. The approach uses a modified agglomerative hierarchical clustering method to form submaps according to similarity information obtained through feature descriptor matching, and takes advantage of this submapping to reduce the computation and time costs. Comparative results on real challenging underwater datasets are presented.</P>

Graph theory approach for match reduction in image mosaicing

Elibol, A.,Gracias, N.,Garcia, R.,Kim, J. OPTICAL SOC- AMERICA 2014 Journal of the Optical Society of America. A, Opti Vol.31 No.4

Online underwater optical mapping for trajectories with gaps

Elibol, A.,Shim, H.,Hong, S.,Kim, J.,Gracias, N.,Garcia, R. SPRINGER SCIENCE + BUSINESS MEDIA 2016 INTELLIGENT SERVICE ROBOTICS Vol.9 No.3

<P>This paper proposes a vision-only online mosaicing method for underwater surveys. Our method tackles a common problem in low-cost imaging platforms, where complementary navigation sensors produce imprecise or even missing measurements. Under these circumstances, the success of the optical mapping depends on the continuity of the acquired video stream. However, this continuity cannot be always guaranteed due to the motion blurs or lack of texture, common in underwater scenarios. Such temporal gaps hinder the extraction of reliable motion estimates from visual odometry, and compromise the ability to infer the presence of loops for producing an adequate optical map. Unlike traditional underwater mosaicing methods, our proposal can handle camera trajectories with gaps between time-consecutive images. This is achieved by constructing minimum spanning tree which verifies whether the current topology is connected or not. To do so, we embed a trajectory estimate correction step based on graph theory algorithms. The proposed method was tested with several different underwater image sequences and results were presented to illustrate the performance.</P>

Radiological noninvasive assessment of ureteral stone impaction into the ureteric wall: A critical evaluation with objective radiological parameters

Ozlem Elibol,Kadihan Yalcin Safak,Ayse Buz,Bilal Eryildirim,Kutluhan Erdem,Kemal Sarica 대한비뇨의학회 2017 Investigative and Clinical Urology Vol.58 No.5

Purpose: To determine the predictive value of certain radiological parameters for an objective asssessment of the presence of ureteral stone impaction. Materials and Methods: Seventy-nine patients with a single proximal ureteral stones were retrieved from the departmental database. Both clinical and particularly radiological data of all cases were well evaluated on this aspect. In addition to the time period between the first colic attack and definitive management; diameter of proximal ureter and renal pelvis, longitudinal and transverse stone size, Hounsfied unit (HU) of the stone and lastly ureteral wall thickness at the impacted stone site were all carefully evaluated and noted. Results: Patients had a single proximal ureteral stone. While mean age of the cases was ranged 20 to 78 years; mean stone size was 15.62±4.26 mm. Evaluation of our data demonstrated that although there was a statistically significant correlation between ureteral wall thickness and patients age, transverse diameter of the stone, ureteral diameter just proximal to the stone, renal pelvic diameter and the duration of renal colic attacks; no correlation could be demonstrated between patients sex and the HU of the stone. Conclusions: Prediction of the presence and degree of proximal ureteral stone impaction is a challenging issue and our data indicated a highly significant correlation between ureteral wall thickness and the some certain radiological as well as clinical parameters evaluated which will give an objective information about the presence of impaction which may in turn be helpful in the follow-up and also management plans of such calculi.

Model Diet: A Simple yet Effective Model Compression for Vision Tasks

Jongmin Lee,Armagan Elibol,Nak Young Chong 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10

Computer vision coupled with machine learning algorithms has greatly helped mobile robotic platforms become more intelligent and capable of performing in the real world. Specifically, Convolutional Neural Networks (CNNs) have achieved a high accuracy on a range of visual perception tasks (e.g., object detection, classification, segmentation, and similar others). One of the bottlenecks in CNNs is their high computational requirement. This makes most of them not easily deployable on robotic platforms, since their on-board computational power is limited. Recently, Involution successfully reduced the number of parameters of CNNs by replacing all the 3 × 3 convolution kernels with involution kernels, which use 1 × 1 convolution for the kernel generation. Filter pruning methods have also successively reduced the number of parameters in CNNs. Notably, however, Involution has reshaping layers and the kernel size is unknown when loading the pre-trained model. In this paper, we propose a pruning method named Model Diet that can be applied to Involution and other CNNs. We present experimental results showing that it has better results compared with randomly initialized weights.

A Novel Room Categorization Approach to Semantic Localization for Domestic Service Robots

Felix Yustian Setiono,Armagan Elibol,Nak Young Chong 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10

Recently, room categorization as part of indoor robot localization has become a vital topic for semantic mapping. One approach is implemented via scene understanding by integrating available object information in the scene. In this paper, a novel room association approach is proposed based on the prior knowledge of the object appearance frequency in the specific room category inside the house. The front interface of the proposed technique employs a state-of-the-art YOLOv2-based object detection framework. Detected objects and their prior appearance frequency information form the input to the proposed room association through a novel scoring approach. This scoring function avoids any limit on the number of detected objects and is capable of operating with a low object detection confidence level. The experimental results of the novel proposed technique show significant improvement over the previously developed room categorization approach. On average, the correctness score increased up to 0:8387 while the indecisiveness level of the object detection framework decreases.

Performance Enhancement Step for Motion Estimation via Feature-based Image Matching

Keita Miyaura,Armagan Elibol,Nak Young Chong 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11

Most of the complicated and sophisticated tasks in visual robotics applications usually build upon the image matching step as matching images of the same scene can provide important information (e.g., camera motion). Image matching is generally done via extracting and matching some distinctive points via their feature vectors. This procedure generates some mismatched points due to imperfections. Mismatched points are called outliers and identified via probabilistic methods. Since the probabilistic methods work iteratively, they generally occupy a large portion of the computational cost of the whole image matching pipeline. In this paper, we present a simple yet efficient algorithm that is employed for eliminating the outliers aiming at reducing the total number of iterations needed in the probabilistic methods. Our method is motivated by the common way of visualizing the established matches among images. We tile images together and search for parallel lines connecting correspondences. We present extensive computational and comparative experiments using both simulated data involving along with real images and using a real dataset.

Leveraging Extended Chat History through Sentence Embedding in Multi-turn Dialogue toward Increasing User Engagement

Zeyu Ding,Armagan Elibol,Nak Young Chong 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11

Multi-turn dialogue is the major manifestation of a conversation. Compared with single-turn dialogue, response selection is more complex as the context varies. We stress the importance of dialogue history and apply the pre-trained model BERT to assign proper weight to each utterance of a dialogue. Previous works take all the dialogue history as context to measure the matching degree of a context-response pair, causing the quadratic computational cost and truncation of longer sequences exceeding the length limitation of BERT. We propose a sentence-based method to deal with the aforementioned problems, obtaining the sentence embedding of a single unit utterance of dialogue and forming a classification token of a context-response pair. We discuss how to obtain a sentence embedding with high quality and to design the input representations in response selection. The results show that the average of the first-last layer output exhibits the best performance for obtaining a sentence representation. The proposed method, concatenating the sentence embeddings of context with the token embeddings of response candidates, is nearly on a par with the token embedding based SOTA method. Notably, the processable length of dialogue history is enlarged about ten times with a low computational cost, potentially reducing chatbot response time and inspiring user engagement.

Persona-based Dialogue Generation with Sentence Embedding for Prolonged Human-Robot Communication

Chen Yue,Armagan Elibol,Nak Young Chong 제어로봇시스템학회 2023 제어로봇시스템학회 국제학술대회 논문집 Vol.2023 No.10