Development of a SCARA robot arm for palletizing applications based on computer vision

This paper develops a computer vision system integrated with a SCARA robot arm to pick and place objects. A novel method to calculate the 3D coordinates of the objects from a camera is proposed. This method helps simplify the camera calibration process. It requires no knowledge of camera modeling and mathematical knowledge of coordinate transformations. The least square method will predate the Equation describing the relationship between pixel coordinates and 3D coordinates. An image processing algorithm is presented to detect objects by color or pixel intensity (thresholding method). The pixel coordinates of the objects are then converted to 3D coordinates. The inverse kinematic Equation is applied to find the joint angles of the SCARA robot. A palletizing application is implemented to test the accuracy of the proposed method. The kinematic Equation of the robot arm is presented to convert the 3D position of the objects to the robot joint angles. So, the robot moves exactly to the required positions by providing suitable rotational movements for each robot joint. The experiment results show that the robot can pick and place 27 boxes on the conveyor to the pallet with an average time of 2.8s per box. The positions of the boxes were determined with an average error of 0.5112mm and 0.6838mm in the X and Y directions, respectively

A Hybrid Approach to Word Segmentation of Vietnamese Texts

The original publication is available at www.springerlink.comInternational audienceWe present in this article a hybrid approach to automatically tokenize Vietnamese text. The approach combines both finite-state automata technique, regular expression parsing and the maximal-matching strategy which is augmented by statistical methods to resolve ambiguities of segmentation. The Vietnamese lexicon in use is compactly represented by a minimal finite-state automaton. A text to be tokenized is first parsed into lexical phrases and other patterns using pre-defined regular expressions. The automaton is then deployed to build linear graphs corresponding to the phrases to be segmented. The application of a maximal- matching strategy on a graph results in all candidate segmentations of a phrase. It is the responsibility of an ambiguity resolver, which uses a smoothed bigram language model, to choose the most probable segmentation of the phrase. The hybrid approach is implemented to create vnTokenizer, a highly accurate tokenizer for Vietnamese texts

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

In vitro antioxidant activity and bioactive compounds from Calocybe indica

Nowadays, the use of mushrooms in medicine is ubiquitous and has achieved particular success. The antioxidants in mushrooms can deactivate free radicals. This study assesses the antioxidant potential of mushroom Calocybe indica with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) radical scavenging methods and the total antioxidant capacity. The mushroom’s ethanol extract exhibits acceptable activity with a low IC50 value (240.11 μg/mL), approximately 2.9 times lower than that of the mushroom Ophiocordyceps sobolifera extract. The ABTS scavenging rate of the extract is around 60% at 500 µg/mL, and the total antioxidant capacity is equivalent to 64.94 ± 1.03 mg of GA/g or 77.42 ± 0.42 μmol of AS/g.  The total phenolics, flavonoids, polysaccharides, and triterpenoids are equivalent to 29.33 ± 0.16 mg of GAE/g, 17.84 ± 0.11 mg of QUE/g (5.04 ± 0.04%), and 4.96 ± 0.04 mg of oleanolic acid/g, respectively. Specifically, the total triterpenoid content has been reported for the first time. The mushroom can have potential biomedical applications

An effective algorithm for reliability-based optimization of stiffened Mindlin plate

Nowadays, stiffened plates have been widely used in many branches of structural engineering such as aircraft, ships, bridges, buildings etc... In comparison with common bending plate structures, stiffened plates not only have larger bending stiffness but also use less amount of material. Hence, it usually has higher economic efficiency. However, to obtain high effectiveness in solving the design problems of the stiffened plate, the reliability-based optimization problems need to be established together with the ordinary numerical computing methods. Therefore, the paper presents an approach to establish and solve the reliability-based optimization problem for the stiffened Mindlin plate. To analyze the behavior of Mindlin plate, we use the recently proposed CS-DSG3 element. The random variables are chosen to be elastic modulus, density of mass and external force. The design variables are the thickness, the width and the height of the stiffened plate. The objective function can be the strain energy or the mass of the structure and subjected to the constraints of displacement or vibration frequency. The reliability-based optimization algorithm used in this paper is a three-step closed loop: 1) Estimating the random variables by the Reliability Index (RI) method; 2) Solving the optimization problem using Sequential Quadratic Programming (SQP) method; 3) Checking and estimating the reliability by the first-order reliability method (FORM) in which the limit state function is the limit of displacement or vibration frequency of the structure

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Viral Metagenomic Analysis of Cerebrospinal Fluid from Patients with Acute Central Nervous System Infections of Unknown Origin, Vietnam.

Central nervous system (CNS) infection is a serious neurologic condition, although the etiology remains unknown in >50% of patients. We used metagenomic next-generation sequencing to detect viruses in 204 cerebrospinal fluid (CSF) samples from patients with acute CNS infection who were enrolled from Vietnam hospitals during 2012-2016. We detected 8 viral species in 107/204 (52.4%) of CSF samples. After virus-specific PCR confirmation, the detection rate was lowered to 30/204 (14.7%). Enteroviruses were the most common viruses detected (n = 23), followed by hepatitis B virus (3), HIV (2), molluscum contagiosum virus (1), and gemycircularvirus (1). Analysis of enterovirus sequences revealed the predominance of echovirus 30 (9). Phylogenetically, the echovirus 30 strains belonged to genogroup V and VIIb. Our results expanded knowledge about the clinical burden of enterovirus in Vietnam and underscore the challenges of identifying a plausible viral pathogen in CSF of patients with CNS infections