Simple Objects Research Articles

Wearable teleoperation controller with 2-DoF robotic arm and haptic feedback for enhanced interaction in virtual reality.

Teleoperation is an essential component in fields such as medicine and manufacturing, enabling human operators to interact with remote robotic systems. A wearable device has been designed and manufactured to enhance sensitivity, wearability, and synchronization, providing users with the ability to experience the sensation of grasping virtual objects. The device incorporates a 2-DoF robotic arm, haptic sensors for finger gestures, and a Unity-powered virtual scene system. Its effectiveness was evaluated through user experiments, where participants were asked to rank the weights of three virtual balls and identify the direction of force applied to a virtual ball in separate tests. Additionally, the device's ability to render various shapes was also examined. The experiments showed that 73.3% of participants accurately ranked the balls by mass, and an overall correctness rate of 87.3% was achieved for force direction identification. For shape rendering, the device yielded more accurate results for simple objects like spheres, whereas rendering more complex objects such as cups and cones was challenging. The findings indicate that this wearable device has potential applications in haptic feedback and virtual reality contexts. Despite the challenges with complex shape rendering, the device shows promising capability in enhancing the user's immersive experience in virtual environments.

Intelligent Object Avoidance Method Design of Railroad Inspection Robot Based on Particle Swarm Algorithm

<p>In order to make the railroad inspection robot better adapt to its complex working environment, it is especially important to study the robot object avoidance algorithm. The WOA algorithm has simple and understandable structure and strong optimization ability but is prone to local convergence. IWOA-PSO is used for railway inspection robots. The performance of IWOA-PSO in the experimental results is better than that of WOA and PSO, and the average accuracy and standard deviation of the IWOA-PSO can better reach the theoretical optimal value in the function tests, and it has performance close to the theoretical value. In the simple environment object avoidance route planning, the minimum path length of IWOA-PSO is 850 mm, which is 53.6% less than that of the PSO algorithm, and the search time is 13.12 seconds, which is 5.11 seconds less than that of PSO algorithm; in the ordinary environment object avoidance route planning, the minimum path length of IWOA-PSO is 830 mm, while the path length of PSO algorithm is 1339 mm, the former is 38% less than the latter, and the search time of IWOA-PSO is 14.05 seconds less than PSO algorithm, so the method has better effect on object avoidance.</p> <p> </p>

Application of Convolutional Neural Network to Gripping Comfort Evaluation Using Gripping Posture Image

Gripping comfort evaluation was crucial for designing a product with good gripping comfort. In this study, a novel evaluation method using gripping posture image was constructed based on convolutional neural network (CNN). Human subject experiment was conducted to acquire gripping comfort scores and gripping posture images while gripping seven objects with simple shape and eleven manufactured products. The scores and the images were used as training set and validation set for CNN. Classification problem was employed to classify gripping posture images as comfort or discomfort. As a result, accuracies were 91.4% for simple shape objects and 76.2% for manufactured products. Regression problem was utilized to predict gripping comfort scores from gripping posture images while gripping cylindrical object. Gripping posture images of radial and dorsal sides in direction of hand were used to investigate effect of direction of hand on prediction accuracy. Consequently, mean absolute errors (MAE) of gripping comfort scores were 0.132 for radial side and 0.157 for dorsal side in direction of hand. In both problems, the results indicated that these evaluation methods were useful to evaluate gripping comfort. The evaluation methods help designers to evaluate products and enhance gripping comfort.

How to make a block of rubber count to ten

On July 3, 2023, Amolf reported on a block of rubber that counts to ten and even remembers in which sequence it was compressed. Physicists Martin van Hecke and Lennard Kwakernaak (Leiden and AMOLF Amsterdam) share a fascination for complexity in simple objects. They wrote an article about a counting block of rubber, which they classify as a metamaterial, and published it in the journal Physical Review Letters.

Simple Objective Assessment to Determine Nutritional Status of Hemodialysis Patients

Objective: The objective is to determine nutritional status of hemodialysis (HD) patients using simple objective assessment (SOA) method. Methods: A total of 54 stable patients were evaluated at our dialysis center. All patients were undergoing maintenance HD for at least 6 months. Nutritional status was assessed by subjective global assessment (SGA) and SOA which included two parameters, i.e., change over past 6 months in weight and albumin. Results: The SGA scoring showed that 39 (72.22%) patients were well nourished, 14 (25.92%) mild to moderately malnourished, and 1 (1.85%) severely malnourished. Using the SOA scoring, 40 (74.07%) patients were well nourished, 13 (24.07%) mild to moderately malnourished, and 1 (1.85%) severely malnourished. There was good correlation between the two scoring methods with a correlation coefficient (R) of 0.85. Conclusion: Our study shows that SOA using delta weight and delta albumin over 6 months provides as good information as SGA (R = 0.85) and is easy to implement in routine clinical practice.

Preferences of young adults on the development of a new digital add-on alcohol intervention for depression treatment: A qualitative study

AimTo explore the preferences of young adults with regard to the development of a new digital add-on alcohol intervention to complement depression treatment. MethodsThis qualitative study included young adults (18–35 years) with experience of either problematic alcohol use or depression or both (n = 29). Two rounds of focus groups were conducted, with two focus groups in each round. All focus groups were recorded, transcribed and analysed deductively and inductively on the basis of qualitative content analysis of the intervention type, features and design. ResultsYoung adults preferred a mobile health application with a clear and simple objective and navigation which was also accessible on a computer. With regard to intervention features, participants indicated a preference for in-depth, gain-framed information on alcohol use and a main feature enabling them to record their alcohol use and mood, which would be rewarded. Other preferences included personal goal-setting and monitoring, an activity list, experience stories, peer contact, guidance from experts by experience or volunteers and receiving notifications from the application. In terms of design, participants preferred short, animated videos and animation figure illustrations to complement written text. Moreover, participants rated the design of the intervention as highly important, yet very personal. Generally, participants preferred a light pastel colour scheme. Once again, participants indicated a need for a clear dashboard using pictograms to reduce the amount of text and fast, easy-to-use navigation. ConclusionThe preferences indicated by young adults with regard to the intervention type, features and design may enhance the development of a new digital add-on alcohol intervention to complement depression treatment.

SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation

We propose a novel solution for unpaired image-to-image (I2I) translation. To translate complex images with a wide range of objects to a different domain, recent approaches often use the object annotations to perform per-class source-to-target style mapping. However, there remains a point for us to exploit in the I2I. An object in each class consists of multiple components, and all the sub-object components have different characteristics. For example, a car in CAR class consists of a car body, tires, windows and head and tail lamps, etc., and they should be handled separately for realistic I2I translation. The simplest solution to the problem will be to use more detailed annotations with sub-object component annotations than the simple object annotations, but it is not possible. The key idea of this paper is to bypass the sub-object component annotations by leveraging the original style of the input image because the original style will include the information about the characteristics of the sub-object components. Specifically, for each pixel, we use not only the per-class style gap between the source and target domains but also the pixel’s original style to determine the target style of a pixel. To this end, we present Style Harmonization for unpaired I2I translation (SHUNIT). Our SHUNIT generates a new style by harmonizing the target domain style retrieved from a class memory and an original source image style. Instead of direct source-to-target style mapping, we aim for source and target styles harmonization. We validate our method with extensive experiments and achieve state-of-the-art performance on the latest benchmark sets. The source code is available online: https://github.com/bluejangbaljang/SHUNIT.

Deep learning estimation of modified Zernike coefficients and recovery of point spread functions in turbulence.

Recovering the turbulence-degraded point spread function from a single intensity image is important for a variety of imaging applications. Here, a deep learning model based on a convolutional neural network is applied to intensity images to predict a modified set of Zernike polynomial coefficients corresponding to wavefront aberrations in the pupil due to turbulence. The modified set assigns an absolute value to coefficients of even radial orders due to a sign ambiguity associated with this problem and is shown to be sufficient for specifying the intensity point spread function. Simulated image data of a point object and simple extended objects over a range of turbulence and detection noise levels are created for the learning model. The MSE results for the learning model show that the best prediction is found when observing a point object, but it is possible to recover a useful set of modified Zernike coefficients from an extended object image that is subject to detection noise and turbulence.

Kostant’s problem for fully commutative permutations

We give a complete combinatorial answer to Kostant’s problem for simple highest weight modules indexed by fully commutative permutations.We also propose a reformulation of Kostant’s problem in the context of fiab bicategories and classify annihilators of simple objects in the principal birepresentations of such bicategories generalizing the Barbasch–Vogan theorem for Lie algebras.

Forest fire detection in aerial vehicle videos using a deep ensemble neural network model

PurposeThe purpose of this paper is to present a deep ensemble neural network model for the detection of forest fires in aerial vehicle videos.Design/methodology/approachPresented deep ensemble models include four convolutional neural networks (CNNs): a faster region-based CNN (Faster R-CNN), a simple one-stage object detector (RetinaNet) and two different versions of the you only look once (Yolo) models. The presented method generates its output by fusing the outputs of these different deep learning (DL) models.FindingsThe presented fusing approach significantly improves the detection accuracy of fire incidents in the input data.Research limitations/implicationsThe computational complexity of the proposed method which is based on combining four different DL models is relatively higher than that of using each of these models individually. On the other hand, however, the performance of the proposed approach is considerably higher than that of any of the four DL models.Practical implicationsThe simulation results show that using an ensemble model is quite useful for the precise detection of forest fires in real time through aerial vehicle videos or images.Social implicationsBy this method, forest fires can be detected more efficiently and precisely. Because forests are crucial breathing resources of the earth and a shelter for many living creatures, the social impact of the method can be considered to be very high.Originality/valueThis study fuses the outputs of different DL models into an ensemble model. Hence, the ensemble model provides more potent and beneficial results than any of the single models.

Bayesian persuasion: Reduced form approach

We introduce reduced form representations of Bayesian persuasion problems where the variables are the probabilities that the receiver takes each of her actions. These are simpler objects than, say, the joint distribution over states and actions in the obedience formulation of the persuasion problem. This can make a difference in computational and analytical tractability, which we illustrate with two applications. The first shows that with quadratic receiver payoffs, the worst-case complexity scales with the number of actions and not the number of states. If |A| and |S| denote the number of actions and states respectively, the worst case complexity of the obedience formulation is O(|A||S|(|S|+|A|)1.5L) where L is its input size. The worst-case complexity of the reduced form representation is O(|A|2.5L). In the second application, the reduced form leads to a simple greedy algorithm to determine the maximum value a sender can achieve in any cheap talk equilibrium.

Bayesian Optimization for Microwave Devices Using Deep GP Spectral Surrogate Models

In microwave design, Bayesian optimization (BO) techniques have been widely applied to the optimization of the frequency response of components and devices. The common approach in BO is to model and maximize an objective function over the design parameters, in order to find the optimal spectral response. Such an approach avoids the direct modeling of spectral responses, which is a challenging task for the typical data-efficient surrogate models used in BO. Simple objective functions may lead to a suboptimal solutions, while complicated objectives require more powerful and less data-efficient surrogate models. To resolve this issue, this article proposes to adopt a deep Gaussian process (DGP) to directly model all relevant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> coefficients over the frequency and the design parameter ranges of interest. Subsequently, an objective probability distribution is retrieved from the DGP model and maximized using a BO scheme. The proposed approach is tested on two suitable microwave examples and compared to the standard BO approach. Results show increased accuracy in identifying the optimal frequency response for the given design parameters and the desired objective, while maintaining high data efficiency.

CONNOTATION OF MICRONUCLEUS AS A BIOMARKER IN CERVICAL INTRAEPITHELIAL LESIONS AND CARCINOMA

Introduction: The Pap smears study is the most rampant and cost-effective method for cervical cancer screening. The role of micronuclei in exfoliated cells of pap smears is the greater beneficial tool used in the screening of cervical cancers. They are intracytoplasmic inclusions made up of chromatin fragments that suggest instability in the chromosomes and they are found in great numbers in carcinogen-exposed tissues long before any clinical signs occur. Aims: To evaluate the predominance of Micronuclei as a biomarker in various premalignant and malignant lesions of cervixand to explore the association between various grades of cervical lesions and micronuclei frequency. Materials &amp;Methods:A prospective study was done in the cytopathology section on 250 cervical pap smears collected in the Pathology laboratoryRajarajeswari medical college, Bangalore. The study period was from January 2021 to June 2021. Slides containing the cell material were stained with Papanicolaou stain, The results were analyzed as per the Micronuclei Scoring done per 1000 exfoliated squamous cells under light microscopy Results: In this study of 250 cervical pap smear screening, there was a stepwise gradual increase in micronuclei count(MN score)from Inflammatory lesions to carcinoma cervix. Inflammatory 2.04 ± 0.43, Atypicalsquamous cells of undetermined significance 3.9 ± 1.73, Atypical squamous cells - High grade5.64 ±1.83,Low grade squamous intraepithelial lesion 7.68 ±1.48, High grade squamous intraepithelial lesion08 12.32 ± 2.68and Intraepithelial Carcinoma 18.60 ± 2.83. Conclusion: MN scoring on cervix squamous epithelial cells is a simple and reliable objective test that may be performed on routine Pap-stained smears and can be used as adjunctive to the screening of cervical cancers and provide a potential pathway for disease monitoring in the future.

VWM-based bias in conscious access can be extended to a new sandwich masking task and real-life stimuli.

Previous studies using breaking continuous flash suppression observed that the content of visual working memory (VWM) influences the priority for accessing visual awareness. However, most studies have used simple stimuli, whereas real-life objects are typically more meaningful and contain more perceptual information than simple objects. In this study, we intermixed a delayed match-to-sample task to manipulate the content of VWM, and a breaking repeated masking suppression (b-RMS) task to investigate whether this memory-based effect in conscious access can be extended to a new sandwich masking task and real-life stimuli. The results revealed that memory-congruent objects broke RMS faster than incongruent objects for both simple and real-life objects. Specifically, for simple objects, color-matching targets broke RMS faster than color-mismatching targets, whereas state-matching targets broke RMS faster than state-mismatching targets for real-life objects. These results suggest that the faster detection of VWM-matching over VWM mismatching stimuli-which has been mostly studied using only one type of task (b-CFS) and stimulus (colored shapes)-extends to (1) yet another masking technique (b-RMS) and to (2) a novel stimulus type (real-life objects), providing evidence that memory-based biases in conscious access are a ubiquitous phenomenon.

Распознавание объектов по составляющим их примитивам и отношениям между ними

The paper’s goal is to develop a methodology and algorithm for the recognition of objects in the environment, keeping the quality with an increasing number of objects. For this purpose, the following problems were solved: recognition of the shape features, estimation of relations between features, and matching between the found features and relations and the defined templates (descriptions of complex and simple objects of the real world). A convolutional neural network is used for the shape feature recognition. In order to train it we used artificially generated images with shape features (3D primitive objects) that were randomly placed on the scene with different properties of their surfaces. The set of relations necessary to recognize objects, which can be represented as a combination of shape features, is formed. Testing on photos of real-world objects showed the ability to recognize real-world objects regardless of their type (in cases where different models and modifications are possible). This paper considers an example of outdoor luminaire recognition. The example shows the algorithm's ability not only to detect an object in the image but also to estimate the position of its components. This solution makes it possible to use the algorithm in the task of object manipulation performed by robotic systems.

Differentiable Curl-Noise

We present Differentiable Curl-Noise, a C1 procedural method to animate strictly incompressible fluid flows in two dimensions. While both the original Curl-Noise method of Bridson et al. [2007] and a recent modification by Chang et al. [2022] have been used to design incompressible flow fields, they often suffer from non-smoothness in their handling of obstacles, owing in part to properties of the underlying Euclidean distance function or closest point function. We therefore propose a differentiable scheme that modulates the background potential in a manner that respects arbitrary solid simple polygonal objects placed at any location, without introducing discontinuities. We demonstrate that our new method yields improved flow fields in a set of two dimensional examples, including when obstacles are in close proximity or possess concavities.

Beating a Benchmark: Dynamic Programming May Not Be the Right Numerical Approach

We analyze dynamic investment strategies for benchmark outperformance using two widely used objectives of practical interest to investors: (i) maximizing the information ratio (IR), and (ii) obtaining a favorable tracking difference (cumulative outperformance) relative to the benchmark. In the case of the tracking difference, we propose a simple and intuitive objective function based on the quadratic deviation (QD) from an elevated benchmark. In order to gain some intuition about these strategies, we provide closed-form solutions for the controls under idealized assumptions. For more realistic cases, we represent the control using a neural network (NN) and directly solve a sampled optimization problem, which approximates the original optimal stochastic control formulation. Unlike the typical approach based on dynamic programming (DP), e.g., reinforcement learning, solving the sampled optimization with an NN as a control avoids computing conditional expectations and leads to an optimization problem with a small number of variables. In addition, our NN parameter size is independent of the number of portfolio rebalancing times. Under some assumptions, we prove that a traditional DP approach results in a high-dimensional problem, whereas directly solving for the control without using DP yields a low-dimensional problem. Our analytical and numerical results illustrate that, compared with IR-optimal strategies with the same expected value of terminal wealth, the QD-optimal investment strategies result in comparatively more diversified asset allocations during certain periods of the investment time horizon.

Multidimensional voiceprint feature assessment system for identifying the depression in children and adolescents: a diagnostic test.

We designed a diagnostic test to evaluate the effectiveness and accuracy of a multidimensional voiceprint feature diagnostic assessment (MVFDA) system vs. the 24-item Hamilton Rating Scale for Depression (HAMD-24) for adjunctive diagnosis of children and adolescents with major depressive disorder (MDD). This study included 55 children aged 6-16 years who were clinically diagnosed with MDD according to the DSM-5 and analyzed by professional physicians, and 55 healthy children (typically developing). Each subject completed a voice recording and was scored on the HAMD-24 scale by a trained rater. We calculated the validity indices, including sensitivity, specificity, Youden's index, likelihood ratio, and other indices including predictive value, diagnostic odds ratio, diagnostic accuracy, and area under the curve (AUC), to assess the effectiveness of the MVFDA system in addition to the HAMD-24. The sensitivity (92.73 vs. 76.36%) and the specificity (90.91 vs. 85.45%) of the MVFDA system are significantly higher than those of the HAMD-24. The AUC of the MVFDA system is also higher than that of the HAMD-24. There is a statistically significant difference between the groups (p < 0.05), and both of them have high diagnostic accuracy. In addition, the diagnostic efficacy of the MVFDA system is higher than that of HAMD-24 in terms of the Youden index, diagnostic accuracy, likelihood ratio, diagnostic odds ratio, and predictive value. The MVFDA has performed well in clinical diagnostic trials for the identification of MDD in children and adolescents by capturing objective sound features. Compared with the scale assessment method, the MVFDA system could be further promoted in clinical practice due to its advantages of simple operation, objective rating, and high diagnostic efficiency.

Online Trajectory Planning Method for Midcourse Guidance Phase Based on Deep Reinforcement Learning

Concerned with the problem of interceptor midcourse guidance trajectory online planning satisfying multiple constraints, an online midcourse guidance trajectory planning method based on deep reinforcement learning (DRL) is proposed. The Markov decision process (MDP) corresponding to the background of a trajectory planning problem is designed, and the key reward function is composed of the final reward and the negative step feedback reward, which lays the foundation for the interceptor training trajectory planning method in the interactive data of a simulation environment; at the same time, concerned with the problems of unstable learning and training efficiency, a trajectory planning training strategy combined with course learning (CL) and deep deterministic policy gradient (DDPG) is proposed to realize the progressive progression of trajectory planning learning and training from satisfying simple objectives to complex objectives, and improve the convergence of the algorithm. The simulation results show that our method can not only generate the optimal trajectory with good results, but its trajectory generation speed is also more than 10 times faster than the hp pseudo spectral convex method (PSC), and can also resist the error influence mainly caused by random wind interference, which has certain application value and good research prospects.

Vehicle Detection Using YOLOv5

Object detection is a computer vision method that allows for the identification and localization of specific classes of objects in images or videos. It goes beyond simple object classification and helps provide a better understanding of the object in question. Object detection has numerous applications, including automating business processes like inventory management in retail. It can detect objects that occupy between 2% and 60% of an image's area and clusters of objects as a single entity. Additionally, it can localize objects at high speeds, typically greater than 15 frames per second. Vehicle detection is a crucial component in the development of autonomous vehicles, enabling them to identify and perceive objects in their environment. It involves identifying and locating vehicles in image or video frames and has various applications in surveillance and security systems. There are different techniques and models for object detection, including traditional image processing methods and modern deep learning networks. Traditional methods like Viola- Jones, SIFT, and histogram of oriented gradients do not require historical data for training and are unsupervised. Popular image processing tools like OpenCV can be used for these techniques. On the other hand, modern deep learning networks like CNN, RCNN, YOLO, ResNet, RetinaNet, and MANet are supervised and efficient for object detection.