Different Types Of Objects Research Articles

Self-powered temperature pressure sensing arrays with stepped microcone structure and Bi2Te3-based films for deep learning-assisted object recognition

Flexible temperature-pressure bimodal sensing arrays can detect multiple types of information, including force and heat, making them crucial for applications such as object classification, human-machine interaction, and artificial intelligence. However, current sensors primarily focus on single-parameter and single-point measurements, while lacking a continuous and stable power supply. This study developed flexible, self-powered temperature-pressure sensing arrays by integrating a stepped microcone structure with thermoelectric materials. This stepped distribution microstructure design enabled effective pressure measurements across a wide range, with high sensitivity and fast response. Temperature-independent measurements were achieved synchronously over a wide temperature range (35–173 °C) by incorporating high-performance Bi2Te3-based thermoelectric films. These temperature and pressure sensing units can discern temperature and pressure stimuli without mutual interference. Furthermore, with the assistance of deep learning, these bimodal sensing arrays performed spatial mapping of temperature and pressure simultaneously, demonstrating their ability to identify different types of objects with an accuracy exceeding 98 %. Therefore, this study shows promise for advancing human-machine interaction, artificial intelligence, and self-powered electronic skins.

Repository (R)evolution: Metadata, Interoperability, and Sustainability

Introduction: Successfully managing an open-access repository requires constant attention to user community priorities in order to inform the development or selection of a platform that fulfills constantly evolving functional demands in an increasingly complex operational environment. This paper uses AgEcon Search (AES) as an example of the way that varying platforms address the metadata and other platform needs of a repository. AES is a successful subject repository with an international scope that has resided on several different platforms in its 25-year lifespan. Elements and Considerations: Critical among the technical requirements of a repository is interoperability with other information sources and the ability to accommodate and describe different types of objects, including data. Experienced in the use of easy and widely used Dublin Core (DC), as well as Machine-Readable Cataloging 21 (MARC 21)-based repository platforms, we discuss both metadata schemas from administrative and user perspectives. Reconsidering underlying metadata issues might positively impact both technical and administrative issues that are currently restricting the development of robust, interoperable systems. As managers of AES, we are uniquely placed to discuss both technical and sustainability issues. Conclusions: Although many institutional and subject repositories are on platforms that use DC for their metadata, other options are available. MARC, the well-established library standard, can provide the wide range of fields needed to fully and accurately describe the variety of document and data types that are included in repositories.

Research of Options for Constructing Information Management Systems Based on Network Models of Queuing Systems

The use of information management systems (IMSs) for the management of technical facilities is currently one of the directions for further improvement and increase in the effectiveness of the use of technical facilities in solving their target tasks. The existing modern IMSs are a set of hardware and software tools designed for collecting, processing and storing information and management. In the presence of a large amount of information and contradictory factors affecting the quality of management, making informed and timely decisions in the management process is impossible without the use of IMSs. The IMSs currently being developed are, for the most part, specialized systems and are designed to solve specific tasks. In this regard, the development and design of IMSs should be carried out taking into account the relationship with the target indicators and features of the management facilities, the results of a comprehensive analysis of information about the IMS elements in the process of functioning, and structural and algorithmic parameters that affect performance indicators. The use of mathematical models for the study of options for the construction of an IMS is the basis for the design and development of devices and subsystems of an IMS. The IMS models currently being developed make it possible to conduct research for single-stage management processes with the presence of similar service facilities in the system. At the same time, modern technical facilities and control systems are complex complexes with cyclically repeating control processes of various types of means. As a rule, such complexes have a set of parallel operating devices (control channels) that provide control of different types of objects at various stages of information processing. In this case, the structure of the IMS must be represented as a multiphase multichannel technical system in which the process of simultaneous management of several objects of various types takes place. In this regard, the purpose of the article is to develop a mathematical model of an IMS with two phases of management and the presence of an arbitrary number of serviced different types of management facilities. The basis of the model is a multiphase CFR network model with a limited waiting time for an application in the service queue. The study on the model allows choosing an option for building an IMS, in particular, choosing the optimal number of control channels for various types of objects according to the criterion of optimality and restrictions on the cost and time of management. An algorithm for selecting an option for building an IMS has been developed, and an example of calculating the number of control channels for managing three types of objects is given.

Deep machine learning for STEM image analysis

The universal, user-friendly online iOk Platform for automatic recognition of any type of objects in images based on deep machine learning is presented. Services aggregated in the iOk Platform significantly reduce the time spent on quantitative image analysis, decrease the influence of the subjective factor and increase the accuracy of the analysis by expanding the set of data that can be analyzed automatically. It is shown how the services can be used to analyze scanning transmission electron microscopy images obtained in heterogeneous catalysis studies, allowing for measurements of thousands of objects in an image, as well as simultaneous analysis of objects of different types, namely: nanoparticles and single sites.

A pixel-level grasp detection method based on Efficient Grasp Aware Network

Abstract This work proposes a novel grasp detection method, the Efficient Grasp Aware Network (EGA-Net), for robotic visual grasp detection. Our method obtains semantic information for grasping through feature extraction. It efficiently obtains feature channel weights related to grasping tasks through the constructed ECA-ResNet module, which can smooth the network’s learning. Meanwhile, we use concatenation to obtain low-level features with rich spatial information. Our method inputs an RGB-D image and outputs the grasp poses and their quality score. The EGA-Net is trained and tested on the Cornell and Jacquard datasets, and we achieve 98.9% and 95.8% accuracy, respectively. The proposed method only takes 24 ms for real-time performance to process an RGB-D image. Moreover, our method achieved better results in the comparison experiment. In the real-world grasp experiments, we use a 6-degree of freedom (DOF) UR-5 robotic arm to demonstrate its robust grasping of unseen objects in various scenes. We also demonstrate that our model can successfully grasp different types of objects without any processing in advance. The experiment results validate our model’s exceptional robustness and generalization.

Communication in multiplex transportation networks

AbstractComplex networks are made up of vertices and edges. The edges, which may be directed or undirected, are equipped with positive weights. Modeling complex systems that consist of different types of objects leads to multilayer networks, in which vertices in distinct layers represent different kinds of objects. Multiplex networks are special vertex-aligned multilayer networks, in which vertices in distinct layers are identified with each other and inter-layer edges connect each vertex with its copy in other layers and have a fixed weight $$\gamma >0$$ γ > 0 associated with the ease of communication between layers. This paper discusses two different approaches to analyze communication in a multiplex. One approach focuses on the multiplex global efficiency by using the multiplex path length matrix, the other approach considers the multiplex total communicability. The sensitivity of both the multiplex global efficiency and the multiplex total communicability to structural perturbations in the network is investigated to help to identify intra-layer edges that should be strengthened to enhance communicability.

‘I heard about the negotiation with Agostini’

Abstract This article focuses on negotiations that took place between 1667 and 1673 leading to the purchase by Prince-Cardinal Leopoldo de’ Medici (1617–1675) of the bulk of the collections assembled by the antiquary Leonardo Agostini (1593–1676). This was one of the most important acquisitions by the Medici family in the second half of the seventeenth century, and it brought different types of objects into the grand-ducal collections: medals, intaglios, cameos and ancient metal objects. Several go-betweens and experts were involved, the most important being the antiquary Ottavio Falconieri (1636–1675), Leopoldo’s agent in Rome. Recently discovered letters exchanged between Leopoldo and Falconieri shed crucial new light on the whole story.

Study of the dependence of accuracy in vehicles search on the size of the object using UAV images

Digital images are increasingly used to analyze different types of objects and their localization and classification. There are many areas for using this information; it is often employed for surveillance systems, automatic driving of vehicles, or exploration of new territories. At the same time, there are a fairly large number of neural networks that allow implementation of this functionality by training them using data sets of various types and classifications. Often, data sets created with the help of unmanned aerial vehicles are frequently used for research tasks. Such datasets allow the recognition of various types of objects without direct access to them, which allows safe exploration of different territories. The use of unmanned aerial vehicles is quite common nowadays, especially in the fields of photography and videography. Many photographers use unmanned aerial vehicles to take pictures of landscapes and use automatic tracking systems for movement. Automatic movement systems and object search systems are quite sensitive to the size of the object and the quality of the search algorithm. Because of the wide applicability of this task, as well as the small amount of initial data, the topic of our work is the study of the dependence of the accuracy of localization and classification of objects on their area in images obtained using unmanned aerial vehicles. The main subject of this study is the quality of neural networks that allow obtaining information about objects, as well as research by obtaining statistical data and a test set of data on the dependence of detection accuracy on the size of the object. The goal of this study was to obtain statistics on the accuracy of localization and classification depending on the size of the object and to determine the accuracy thresholds using the obtained statistics. The task of this study is to train common neural networks with an open architecture on a set of data obtained using unmanned aerial vehicles and to determine their characteristics, particularly the dependence of recognition accuracy on the size of the object. The expected result of the work is the threshold values of the size of the object, which are permissible for a sufficiently accurate classification and localization of objects, as well as the metrics of the quality of the work of the studied neural networks. Because of this work, conclusions are given that reflect the threshold values of object sizes, on which the recognition accuracy depends.

Serpent-inspired multimodal flexible sensor for multi-signal measurement based on PVDF-TrFE/Fe3O4 nanofibers

Flexible sensors demonstrate enormous potential in remote monitoring, electronic healthcare, human-machine interfaces, and soft robotics. Multi-functional sensing devices with the capability of performing various tasks play an indispensable role in flexible sensors. Herein, polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE)/Fe3O4-based nanofibers with a diameter concentrated around 140 nm were prepared using electrospinning technology. Particularly, in the case of adding 5 wt% Fe3O4 nanoparticles (NPs), the β-phase content of these nanofibers exceeds 94 %, exhibiting a sensitivity of 5.02 V/N. Simultaneously, inspired by biomimicry, a serpent-shaped multimodal flexible (SMF) sensor is proposed, consisting of stretchable sinusoidal-shaped silver electrodes, a composite nanofiber membrane, and a PDMS substrate with varying concentration ratios. Subsequently, the SMF sensor is installed on the end effector of the Arm32 robot, capable of recognizing tactile and slip signals, ensuring a success rate of over 92 % in 50 repeated experiments of grasping four different types of objects. Furthermore, thermosensitive tests indicate that the SMF sensor has the ability to perceive different temperatures. Cycling vibration aging experiments have confirmed that the sensor demonstrates excellent robustness and stability. These experimental results highlight the promising application prospects of SMFS in soft robotics and intelligent electronic products.

Comparative Analysis of Investment Objectives: a Study Comparing the Investment Objectives of Different Types of Investors, Such as Individual Investors, Institutional Investors, and Retirement Funds

The research is about individual investors with a wider range of investment objectives than institutional investors and retirement funds. Institutional investors and retirement funds typically have more specific investment objectives, such as preserving capital, generating a steady income stream, and meeting specific liability obligations. Individual investors are more likely to focus on growing their wealth over time and generating income. Institutional investors are more likely to focus on preserving capital and generating a steady income stream. Retirement funds are more likely to focus on providing investors with a comfortable retirement.

A framework for developing an attitude determination and control system simulator for Cubesats: Processor-in-loop testing approach

The outbreak of miniaturized satellites is increasingly demanding well-performing attitude determination and control systems (ADCS). Testing an ADCS directly using hardware is extremely costly and risky, whereas testing it using only computer simulation does not yield realistic enough results. Therefore, a detailed framework for developing an ADCS simulator with processor-in-the-loop testing for low-earth orbit Cubesats is established in this paper. In particular, the study focuses on the implementation of the algorithms on a STM32 with ARM Cortex CPU for efficient testing and prototyping. This is done by formulating a quaternion-based satellite dynamic model equiped with a three-axis magnetorquers and a set of pyramidal cluster of reaction wheels for high pointing capabilities. Prominently used sensors are modeled so that the attitude can be estimated using a multiplicative extended Kalman filter. For attitude control, several modes are developed based on different mission stages, and a simplified less burdensome control switching strategy is proposed to maintain stability with minimal computation and energy resources. To test the applicability of the simulator, an ADCS is designed to meet the requirements of the recent low-atmosphere measurement missions. The results are analyzed in accordance with the ESA ECSS standards, and the assessment shows that the specifications can be met with a safe margin, leaving room for unmodeled errors to be taken into account in later stages of the design. Additionally, the proposed control strategy gives various possibilities for missions with different types of objectives, furthermore the framework's flexibility provides easy modification of any part for the user's specific needs.

Friends Aren't Food: Pinyon Jays (Gymnorhinus cyanocephalus) Show Context-Dependent Quantity Preference

Animals must often choose between different quantities of objects in their environment, from food items to conspecifics. Yet we know little about how quantitative cognitive abilities compare across different types of objects. Previous research shows individuals use both the numerical difference (large − small) and numerical ratio (small/large) between two quantities to choose between them. This study investigated whether numerical difference and ratio predict preferences for quantities of food items and conspecifics in pinyon jays (Gymnorhinus cyanocephalus) using quantity preference tasks. In two replications of the food experiment (N = 12), pinyon jays chose larger quantities of mealworms more when numerical differences were large and numerical ratios were small. However, numerical difference did not influence food choice independently of ratio. In two replications of the social experiment (N = 20), when choosing between groups of conspecifics, pinyon jays did not prefer the larger over smaller group sizes and did not show numerical difference or ratio effects. Therefore, pinyon jays may use quantity information differently when deciding between quantities of food items and conspecifics. Whereas quantity was important for selecting food items, additional factors such as individual identity may be more important for selecting social groups to join. Thus, the type of objects offered can influence how animals use quantity information to choose among quantities.

An ultra-low-computation model for understanding sign languages

In artificial intelligence applications, advanced computational models, such as deep learning, are employed to achieve high accuracy, often requiring the execution of numerous operations. Conversely, lightweight computational models are typically more resource-efficient, making them suitable for various devices, including smartphones, tablets, and wearable technology. This paper presents an ultra-low-computation solution for interpreting sign languages to assist deaf and hard-of-hearing individuals without needing specialized hardware or significant computational resources. The proposed approach initially performs data abstraction on the input data. During this process, the image is systematically scanned from various perspectives, and the collected information is then encoded into a one-dimensional vector. Subsequently, the abstracted information undergoes processing through a Fully Connected Neural Network (FCN), resulting in highly accurate output. We also introduced two abstraction methods, namely Opaque and Glass, inspired by the interaction of light with different types of objects. The proposed abstractions facilitate the comprehension of the hand gesture’s outer boundary as well as its row-wise and column-wise density of pixels. Our experiments on three datasets confirm the efficiency of the proposed method, achieving an accuracy of 99.4% in recognizing American Sign Language, 99.96% accuracy in recognizing Indian Sign Language, and 99.95% accuracy in recognizing Bangla Sign Language. Notably, the model size and the number of MAC operations are significantly smaller than state-of-the-art computational models trained on the same datasets.

Investigation of HIFU beam propagation through acoustic holograms in water

We investigated variations in the HIFU beam of a clinical system as it traverses acoustic holograms—small artificial objects of different types—within a tank that was filled in with the degassed deionized water. The pressure distribution within the HIFU beam was assessed in multiple planes perpendicular to the beam axis. The ultrasound transducer with the focal length of 14 cm inside the Sonalleve table top (Profound Medical, Mississauga, ON, Canada) generated the focusing ultrasonic beam, which propagated through the water. Each hologram was positioned 3 cm below the focal plane, with precise adjustments of positions and angles for each measurement. Pressure measurements were conducted using the needle hydrophone HNA-0400 (ONDA Corp, Sunnyvale, CA, USA) mounted on a 3D positioning system. The motors of the positioning system were controlled, and pressure values were recorded using a GUI generated in LabVIEW. The displacement step created by the motors on each axis was 0.01 mm. The properties of the holograms were reconstructed, considering the experimental data obtained. Our findings demonstrated that the shape, size, elasticity, and placement of each acoustic hologram significantly influenced the observed field pattern. This work was supported by the German Federal Ministry of Education and Research (“MR-HIFU-Pancreas”, FKZ:13GW0364D).

Reliability of Partitioning Metric Space Data

The process of sorting or categorizing objects or information about these objects into clusters according to certain criteria is a fundamental procedure in data analysis. Where it is feasible to determine the distance metric for any pair of objects, the significance and reliability of the separation can be evaluated by calculating the separation/segregation power (SP) index proposed herein. The latter index is the ratio of the average inter distance to the average intra distance, independent of the scale parameter. Here, the calculated SP value is compared to its statistical distribution obtained by a simulation study for a given partition under the homogeneity null hypothesis to draw a conclusion using standard statistical procedures. The proposed concept is illustrated using three examples representing different types of objects under study. Some general considerations are given regarding the nature of the SP distribution under the null hypothesis and its dependence on the number of divisions and the amount of data within them. A detailed modus operandi (working method) for analyzing a metric data partition is also offered.

Indoor air pollutants and their seasonal monitoring in European museums

The monitoring of indoor air pollutants is an important part of the management of heritage collections. In this work, acetic acid, formic acid, formaldehyde, acetaldehyde and NO2 were measured with passive samplers along with temperature and relative humidity once per season in seven European institutions of different sizes with different types of objects. The measurements were carried out in a variety of locations, from modern and old display cases in exhibition rooms of different sizes to storage rooms and their enclosures for different types of objects. The results were evaluated based on the characteristics of the sampling locations and the extent to which the changing seasons affect pollutant concentrations were estimated. The dataset obtained from this study can be a valuable asset as a snapshot of the current state of the environment in European heritage collections.

Deep learning denoising diffusion probabilistic model applied to holographic data synthesis.

In this Letter, we demonstrate for the first time, to our knowledge, a holographic data synthesis based on a deep learning probabilistic diffusion model (DDPM). Several different datasets of color images corresponding to different types of objects are converted to complex-valued holographic data through backpropagation. Then, we train a DDPM using the resulting holographic datasets. The diffusion model is composed of a noise scheduler, which gradually adds Gaussian noise to each hologram in the dataset, and a U-Net convolutional neural network that is trained to reverse this process. Once the U-Net is trained, any number of holograms with similar features as those of the datasets can be generated just by inputting a Gaussian random noise to the model. We demonstrate the synthesis of holograms containing color images of 2D characters, vehicles, and 3D scenes with different characters at different propagation distances.

Семантика глаголов перемещения объекта в хакасском и алтайском языках

The paper deals with the semantic features of verbs of object transfer in Khakass and Altaic languages. The use of lexemes describing the situation of physical transfer with subjects and objects of different types is analyzed. An attempt has been made to systematize verbs by components incorporated into semantics. All these verbs include obligatory signs characteristic of the situation of transfer (semantics of transfer, starting and ending points, object and subject of action). Besides, these tokens contain additional components incorporated into their semantics: “the path,” “route,” “manner of transfer transfer,” “means of transfer” (“transport” and “tool”), and “object.” According to the typological classification developed by L. Talmi, the Turkic languages belong to the verbal type, with their verb roots expressing an indication of the “the path.” The verbs with an incorporated “manner of transfer” component can have their “direction” parameter specified with satellites. The subject of the verbs under study can be a person, less often an animal, or a technical means. When secondary meanings of physical action are expressed, the subject can be expressed by natural phenomena and objects (wind, river, waves). The objects of physical movement are represented by various physical objects and living beings. The examined languages exhibit considerable resemblance in terms of the composition and semantic structures of the verbs associated with transfer. Each language has its own set of verbs used in everyday life and work activities of specific ethnic and linguistic groups.

Cartographic materials for studying the historical geography of Northwest Russia

The historical process in the North-West Russia is characterized by complexity and versatility. Over the centuries, this region has witnessed a variety of historical events, changes in political and administrative boundaries, the founding of cities, the establishment of new communication routes, and the decline of old ones, alongside the growth of industry and trade. These changes were interconnected and mutually influential, highlighting the need for a comprehensive examination of all these phenomena. The spatial positioning of various geographical objects and phenomena is particularly significant, as it helps us understand why their interactions occurred in the ways they did. To grasp these spatial relationships, however, complex maps are necessary, ones that simultaneously depict different types of objects and phenomena. This article addresses the portrayal of North-West Russia's historical development on complex historical and geographical maps spanning from the 5th-6th centuries AD to the early 21st century. It includes a review of the sources used in creating the maps. The article also analyzes the challenges faced in mapping various types of objects and outlines solutions to these problems. Additionally, it presents a periodization of the historical process in the region, developed during the creation of the maps.

The problem of segmenting frequency histograms in the analysis of medical and biological data

Histogram segmentation is the task of identifying statistically significant peaks in a histogram corresponding to different types of objects. This could be identifying different cell types in cytometry (gating), finding objects in images, identifying different types of patients in gene expression data, analyzing a histogram of p-values in a multiple comparisons problem. A statistically motivated search for significant peaks requires the introduction of a null hypothesis describing a histogram in which there are no significant peaks. If a uniform distribution is accepted as the null hypothesis, then we considered the search for peaks and pits for frequency data, as well as clusters and voids in the primary data in previous publications. In practice, a more complex situation often arises when a unimodal distribution is considered as the null hypothesis without specifying its functional form. The paper proposes an approach to the problem of histogram segmentation, when structureless data is described by a unimodal distribution that best matches the original one. In addition, a comparison of two histograms is considered, which allows identifying local regions of divergence, thereby complementing the global chi-square test. The method can be used in bioinformatics, cytometry, and computer image analysis. As an example of a histogram segmentation problem, we consider Katz’s classical data on proving the discreteness of transmitter release into the synaptic gap.