Object Identification Research Articles

Play it Again, Sam? Reference-Point Formation and Product Differentiation in the Music Industry

Newly released music is never assessed in isolation by audiences, who tend to compare objective (sonic attributes) and subjective (genre affiliations) elements of its identity with the previous musical catalog of the corresponding artist, his or her musically proximal peers, and the most successful releases in the market. In this paper, we provide a general framework that disentangles the objective and subjective identities of new musical releases and evaluates how differentiation affects audience reactions. We posit that radio stations, who seek commercial success, have different preferences toward differentiation in comparison with critics, who grant cultural legitimacy in the industry. Combining play numbers, reviews, and music description data from different sources, we find that radio stations prefer consistency in the musical style of successive releases by the focal artist and especially favor albums that sound similar to chart toppers. Critics, however, exhibit opposite preferences, preferring novelty compared with past works of the artist, while remaining uninfluenced by contemporaneous music dynamics. Our moderation checks reveal that decisions around genre affiliations and scheduling of new releases aid music producers to effectively manage audience expectations. This suggests that data-driven decision support systems can help artists to strategically release new products that cater to heterogeneous tastes. This paper was accepted by David Simchi-Levi, entrepreneurship and innovation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03319 .

Amyloid-β deposition in basal frontotemporal cortex is associated with selective disruption of temporal mnemonic discrimination.

Cerebral amyloid-beta (Aβ) accumulation, a hallmark pathology of Alzheimer's disease (AD), precedes clinical impairment by two to three decades. However, it is unclear whether Aβ contributes to subtle memory deficits observed during the preclinical stage. The heterogenous emergence of Aβ deposition may selectively impact certain memory domains, which rely on distinct underlying neural circuits. In this context, we tested whether specific domains of mnemonic discrimination, a neural computation essential for episodic memory, exhibit specific deficits related to early Aβ deposition. We tested 108 cognitively unimpaired human older adults (66% female) who underwent 18F-florbetapir positron emission tomography (Aβ-PET), and a control group of 35 young adults, on a suite of mnemonic discrimination tasks taxing object, spatial, and temporal domains. We hypothesized that Aβ pathology would be selectively associated with temporal discrimination performance due to Aβ's propensity to accumulate in the basal frontotemporal cortex, which supports temporal processing. Consistent with this hypothesis, we found a dissociation in which generalized age-related deficits were found for object and spatial mnemonic discrimination, while Aβ-PET levels were selectively associated with deficits in temporal mnemonic discrimination. Further, we found that higher Aβ-PET levels in medial orbitofrontal and inferior temporal cortex, regions supporting temporal processing, were associated with greater temporal mnemonic discrimination deficits, pointing to the selective vulnerability of circuits related to temporal processing early in AD progression. These results suggest that Aβ accumulation within basal frontotemporal regions may disrupt temporal mnemonic discrimination in preclinical AD, and future work is needed to determine whether assessing temporal mnemonic discrimination can aid in predicting emerging AD progression.Significance Statement Identifying subtle cognitive changes that reflect emerging Aβ accumulation could lead to the development of cognitive tasks to detect individuals at risk of AD and sensitively assess clinical intervention outcomes for Aβ-lowering therapeutics. Temporal mnemonic discrimination, the ability to distinguish between events occurring at different times, is supported by basal frontotemporal regions which are among the earliest impacted by Aβ. We demonstrate that Aβ-positivity, as well as increased Aβ deposition in basal frontotemporal regions, is selectively associated with deficits in temporal mnemonic discrimination, but not other domains of mnemonic discrimination such as object identity or spatial relationships. These results suggest that Aβ disrupts cortical circuits supporting temporal processing and highlights inclusion of temporal mnemonic discrimination tasks in future clinical assessments.

RODDMS-IoT: A Radar Based Real-Time Moving Object Detection and Distance Measurement System Using IoT for Enhanced Safety

The main users of RADAR (Radio Detection and Ranging) in its early development for security and monitoring purposes were the navy, the military, the aviation services, as well as the space organizations. Nowadays, the demand of radar is expanding. But it takes a while to detect, has a limited detection area, lacks target specificity due to its wide range, oversensitive, costly, etc. A cheaper, easy and effective alternate solution is to use ultrasonic sensor which use sound waves for detection and ranging. Therefore, this study offers a technique for using the Ultrasonic Sensor (HC-SR04) as RADAR. For the purpose of rotation and movement, the Servo Motor (SG90) is connected to the Ultrasonic Sensor (HCRS04). The SIM808 IoT integrated GPS-GSM module is also utilized to send out SMS notifications for object identification. These components are connected to an Arduino Uno and a Raspberry Pi3 to detect and notify the object. Additionally, the goal of this work is to capturing the distance, direction, and radar pulse duration and object shape simultaneously. An algorithm was used to determine the object's velocity, or its speed toward or away from the radar, in order to acquire the precision. By comparing the actual values with the data acquired by the radar, the accuracy of the measurement of both angle and distance is ensured. There are both metal and non-metal objects used in this study. This work's novelty lies in its ability to accurately detect real-time moving objects and measurement the distance of the objects. Besides proper distance and velocity, a large set of data was taken to find the accuracy of the radar for objects of different shapes. The results show the object detected with its distance and angle in a java based Graphical User Interface (GUI), different ranges of object in cm at which it is detected and the detection notification sent to the admin via SMS.

Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals.

In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST). The anatomy of a 5G NR frame as a sequence of OFDM symbols is presented, and different PRS configurations are described. It is shown that spectral analysis of time-varying channel impulse response weights, estimated with the help of PRS pilots, can be used for the detection of transmitted signal reflections from moving vehicles and the calculation of their time and frequency/Doppler shifts. Different PRS configurations with varying time and frequency reference signal densities are tested in simulations. The peak-to-noise-floor ratio (PNFR) of the calculated radar range-velocity maps (RVM) is used for quantitative comparison of PRS-based radar scenarios. Additionally, different echo signal strengths are simulated while also checking various observation window lengths (FFT lengths). This study proves the practicality of using PRS pilots in remote sensing; however, it shows that the most dense configurations do not provide notable improvements, while also demanding considerably more resources.

3D Terahertz Confocal Imaging with Chromatic Metasurface

AbstractTerahertz confocal imaging allows 3D see‐through of a non‐metallic object with high resolution. Conventional methods acquiring 3D images of thick objects suffer from limited depth‐of‐field, constrained depth resolution, and/or inconsistent spatial resolution at different depths. To address these limitations, the intrinsic chromatic aberration of a typical focusing metasurface is exploited to achieve frequency‐dependent focal lengths. An object located within this extended focal range can be readily 3D inspected by performing 2D raster scans. A rigorous analysis reveals that the focal spot maintains a constant waist diameter of 2.4 mm (equivalent to 2.2 at 275 GHz) and migrates 68.1 mm (equivalent to 62.4, or 16.4 times of Rayleigh length, or 1.4‐fold of the designed focal length at 275 GHz) from 175 to 525 GHz, and thus achieving a consistent spatial resolution and a large depth‐of‐field for 3D imaging. Importantly, this large depth‐of‐field is achieved with a relatively high numerical aperture of around 0.42. Measurements conducted between 220 and 330 GHz exhibit close agreement with the calculation. To demonstrate its imaging functionality, two stacked papers with different texts, a mobile phone, and earphones concealed in a charging case are imaged, where a short‐time Fourier transform is implemented in the time‐domain terahertz images to enhance image contrast. The presented metasurface is technologically significant for imaging systems to rapidly inspect objects in 3D with exceptional resolutions. Its potential applications include in‐situ defect detection and object identification in security screening.

Effect of roughness on object identification using OAM spectrum

Effect of roughness on object identification using OAM spectrum

CRFUSION: Fine-grained Object Identification Using RF-image Modality Fusion

CRFUSION: Fine-grained Object Identification Using RF-image Modality Fusion

From Fantômas to The Black Doll

American author and illustrator Edward Gorey (1925–2000) was open in interviews about the importance of silent cinema for his work, and in particular the films of the prolific French film director Louis Feuillade (1873–1925). Focusing on this underexplored aspect of Gorey’s work, the paper examines how Gorey employs visual, compositional, and narrative elements from Feuillade’s films, as well as their development of uncertainty within the areas of space, narrative, and character and object identity within the genre of nonsense. The influence of Feuillade is traced across such works as La Malle Saignante (The Bleeding Trunk, 1975), a bilingual homage to silent cinema, and Gorey’s silent screenplay The Black Doll (1973), while also identifying possible conventions of nonsense within Feuillade’s own silent films.

On the depth of identification of objects in the study of the influence of the density of the grid of wells on the degree of production of oil reserves

The article studies the effect of the well grid density on the final oil recovery coefficient under conditions of various groups of objects in carbonate reservoirs of the Volga-Ural oil and gas province. The issue being solved within the framework of this work is one of the most relevant and important when making various management decisions in conditions of uncertainty, significant variability and heterogeneity of geological and field data. More than 500 deposits of carbonate reservoirs were selected for the study, the development of which is carried out on the territory of the Ural-Volga region. To implement an integrated approach, the construction of models was carried out in several interrelated stages. Within the framework of basic modeling, field statistical methods were used to identify dependencies describing at a high quantitative and qualitative level the relationship between the oil recovery coefficient and the density of the well grid before flooding. To obtain the most representative results, a separate approach based on the preliminary division of objects into groups was also used. At the second stage, in addition, a deep differentiation of the initial objects was implemented to determine hidden patterns within already formed groups and a similar construction of models according to which interpretation was performed. When combining the results obtained, it was reliably established that models for assessing the degree of reserve production from the density of the well grid should be built not only in general by tectonic and stratigraphic elements, but also differentially by groups identified as a result of in-depth identification of development sites. It is proposed to use the obtained dependencies to solve the problems of optimizing the density of the well grid for both long-term oil deposits under development and newly commissioned and at the stage of drafting the first design documents. Keywords: well grid density; oil recovery coefficient; modeling; grouping of deposits of the Volga-Ural oil and gas province; deep identification of objects; carbonate reservoirs.

Modeling of the oil recovery process for managing the development of deposits using artificial intelligence

Based on a comprehensive study of the process of oil recovery from deposits in carbonate reservoirs of the Tournaisian tier of the Birskaya saddle, a forecast of the final coefficient of oil recovery for deposits that have been in development for a long time was carried out. The choice of the research object is due to the presence of a significant proportion of residual oil reserves, which can potentially be extracted as part of the implementation of an effective and sound asset management strategy. Previously, more than 60 objects were divided into four groups using one of the well-known methods of reducing the dimensionality of the problem ‒ the method of principal components, after which typical dependencies were established that make it possible to successfully group objects. Using statistical forecasting methods, models of the process of developing oil reserves were obtained differentially for various groups of selected objects, including a number of different parameters reflecting, among other things, geological heterogeneity. Based on the results of the interpretation of the obtained dependencies, the areas of their use in the plane of application of artificial intelligence systems in the development of deposits in the natural regime are established. Within the framework of determining the relationships between various parameters and evaluating the reliability of the obtained models, the need for object identification in solving a wide range of tasks to improve the efficiency of operation of both objects involved in the analysis and similar ones in terms of geological and commercial characteristics has been established. Keywords: oil recovery coefficient; oil field development; computer modeling; carbonate reservoirs of the Tournaisian tier of the Birskaya saddle; identification of objects.

Biomechanics based computer simulation of rural landscape design using remote sensing image technology

Introduction: The strategy and restoration of rural areas and landscape in biomechanics, with a particular emphasis on cellular and molecular biomechanics, is crucial for sustainable rural landscape design. At the cellular and molecular level, plants’ biomechanical properties, such as the rigidity and elasticity of cell walls, determine their growth patterns and responses to environmental factors. These properties are essential in understanding how plants can be effectively incorporated into the rural landscape to enhance its stability and functionality. Aim: The objective of this research is to develop a novel computer simulation model for rural landscape planning using remote sensing imaging technology. Research methodology: We introduce a novel Adaptive YOLOv7 method driven by Starling Murmuration search. UAVs are used to collect extensive visual data for training the model. By considering cellular and molecular biomechanics, we can analyze how the mechanical forces within plants affect their ability to resist wind, retain water, and interact with the surrounding soil and other organisms. This knowledge can be integrated into the model to better predict the long-term viability and adaptability of different plant species in the rural landscape. The combination of the 3D GIS virtual image strategy model and our proposed model, along with SM optimization, not only improves object identification but also takes into account the biomechanical aspects for more accurate simulations. Crowdsourcing helps in precisely mapping rural landscapes and structures, while the incorporation of biomechanical principles ensures better adaptability to changing environmental and ecological conditions. Findings and Conclusion: Implemented in Python software, our SM-AYOLOv7 model shows excellent performance, with metrics like f1 score (93.64%), recall (92.34%), accuracy (91.72%), and IoU (90.23%). Our method outperforms conventional ones, demonstrating enhanced accuracy and flexibility, especially in handling changing configurations, due to the integration of cellular and molecular biomechanical insights.

Semantic segmentation of urban airborne LiDAR data of varying landcover diversity using XGBoost

AbstractSemantic segmentation of aerial LiDAR dataset is a crucial step for accurate identification of urban objects for various applications pertaining to sustainable urban development. However, this task becomes more complex in urban areas characterised by the coexistence of modern developments and natural vegetation. The unstructured nature of point cloud data, along with data sparsity, irregular point distribution, and varying sizes of urban objects, presents challenges in point cloud classification. To address these challenges, development of robust algorithmic approach encompassing efficient feature sets and classification model are essential. This study incorporates point‐wise features to capture the local spatial context of points in datasets. Furthermore, an ensemble machine learning model based on extreme boosting is utilised, which integrates sequential training for weak learners, to enhance the model’s resilience. To thoroughly investigate the efficacy of the proposed approach, this study utilises three distinct datasets from diverse geographical locations, each presenting unique challenges related to class distribution, 3D terrain intricacies, and geographical variations. The Land‐cover Diversity Index is introduced to quantify the complexity of landcover in 3D by measuring the degree of class heterogeneity and the frequency of class variation in the dataset. The proposed approach achieved an accuracy of 90% on the regionally complex, higher landcover diversity dataset, Trivandrum Aerial LiDAR Dataset. Furthermore, the results of the study demonstrate improved overall predictive accuracy of 91% and 87% on data segments from two benchmark datasets, DALES and Vaihingen 3D.

Природоохоронні інституції Вінниччини другої половини 1940-х – початку 1970-х років

The purpose of the article is to consider the issues of nature protection in Vinnytsia region in the second half of the 1940s and early 1970s. It is shown that during this period, the solution to this problem was seen as the identification of certain valuable natural objects, their withdrawal from the sphere of economic use and complete protection. The research methodology is based on the principle of objectivity and historicism, a systematic approach to the study of historical, environmental and socio-economic phenomena in their development and interrelationships. The use of methods of analysis and synthesis, problematic and thematic, descriptive and historical and chronological approaches allowed the research to optimally solve the research objectives. The scientific novelty lies in the systematic use of a comparative analysis of the work of environmental institutions in Ukraine in general and in Vinnytsia region in particular. Conclusions. It has been established that nature protection was not a priority area of state policy in the USSR, but later the first environmental protection measures began to be implemented. In the 1960s, protected areas and nature reserves began to appear in the Vinnytsia region, and programmes were developed to protect water and soil. However, the impact of these measures was limited due to insufficient funding, lack of a systematic approach, and lack of environmental education.

Remote intelligent identification of suspended foreign objects in substation inspection images based on edge optimization faster R-CNN algorithm

Remote intelligent identification of suspended foreign objects in substation inspection images based on edge optimization faster R-CNN algorithm

Solving the problem of technological object identification

Abstract. Problem. The identification task is formulated as follows: based on the results of observations of the input and output variables of a system, a model, i.e. a formalized representation of this system, should be built that is optimal in some sense. This shows the connection between the identification task and the above general scheme of establishing regularities based on the results of observations. The identification task is based on modern control theory. To solve it, modern computers are used. The latter, with their high speed and virtually unlimited memory capacity, create the prerequisites for receiving, transmitting and processing large arrays of observations that are necessary for building adequate models of real objects. Goal. Studying the possibilities of solving the problem of identifying technological objects using MATLAB+Simulink. Job tasks: to consider the tasks of identifying technological objects of management; to choose an identification method; identify the electric motor model. Methodology. The research problems should be solved by methods of analysis and synthesis of automatic control systems by the method of least squares. Results. In the work, experimental data on the dependence of the engine idling speed on the excitation current were obtained and solved the identification problem using the MATLAB+SIMULINK package. Originality. Based on the analysis of existing solutions, it was found that the works did not consider the calculation of absolute and relative errors of the results of identifying the object and the model developed using the least squares method. Thus, there is a need to develop a system for identifying a model of a technological object made by the least squares method. To solve the identification problem, an electric motor is chosen as the object of study. Practical value. The paper examines the methods used to identify complex objects. The comparative analysis showed that the most acceptable method is the least squares method, due to simple mathematical calculations in comparison with the other methods. To illustrate the application of this method, two tasks were solved: identification of the model of an electric motor; identification of the model of an alarm system. The studies have shown that model identification can be performed using the MATLAB+Simulink package. The research results obtained on the model differ from the experimental data by no more than 4 %

Identification of a dangerous technogenic object by orbital near-earth space monitoring means

Identification of a dangerous technogenic object by orbital near-earth space monitoring means

The individuation of mathematical objects.

Against mathematical platonism, it is sometimes objected that mathematical objects are mysterious. One possible elaboration of this objection is that the individuation of mathematical objects cannot be adequately explained. This suggests that facts about the numerical identity and distinctness of mathematical objects require an explanation, but that their supposed nature precludes us from providing one. In this paper, we evaluate this nominalist objection by exploring three ways in which mathematical objects may be individuated: by the intrinsic properties they possess, by the relations they stand in, and by their underlying 'substance'. We argue that only the third mode of individuation raises metaphysical problems that could substantiate the claim that mathematical objects are somehow mysterious. Since the platonist is under no obligation to accept this thesis over the alternatives, we conclude that, at least as far as individuation is concerned, the nominalist objection has no bite.

IDENTIFICATION OF AN ARBITRARY SHAPE RIGID OBSTACLE ILLUMINATED BY FLAT ACOUSTIC WAVE USING NEAR FIELD DATA

The inverse problem concerning the identification of rigid surfaces of scattering objects formulated in the frequency domain is presented in this paper. Differences in the identification of concave objects, such as kite-shaped, and convex objects (circle) are indicated. The reader’s attention is focused on the conventional boundary element method with small number of boundary elements and the small number of sensors, which is significant for inverse problems.

АЛГОРИТМ КОМПЛЕКСИРОВАНИЯ СЕНСОРНЫХ ДАННЫХ ДЛЯ ЗАДАЧ АВТОМАТИЧЕСКОГО УПРАВЛЕНИЯ ПОДВИЖНЫМ СОСТАВОМ

the article analyzes the research and works on processing Sensor data processing and navigation for mobile objects, including those with automatic control. automatic control. It is pointed out the necessity of integration of solutions based on vision and neural networks, taking into account the state of the environment. on the basis of vision and neural networks, taking into account the state of the environment. environment. The description of the algorithm and methods for complexing data received from sensor sensors of the onboard vision system of the rolling stock. Within the framework of the algorithm work the trajectory data\ processing: confirmation (removal) of the observed objects, construction of their trajectories, estimation of velocities and coordinates, formation of the list of global objects. To calculate the object motion model, an extended Kalman filter is applied Kalman filter, Hungarian algorithm and calculation of the Mahalanobis distance. Detailed The stages of detection, tracking and identification of obstacle objects are described in detail, as well as determination of their parameters (class, coordinates and velocity). The Experimental indicators used in this study. Relevance relevance and shown practical applicability of the described approach for the tasks of automatic control of rolling stock in the conditions of a digital railroad

Design of Polarization Spectroscopy Integrated Imaging System

To simultaneously acquire the spectral and polarization information of the target and achieve the monitoring and identification of the target object, a polarization spectral integrated imaging system is proposed in this paper. Firstly, the structural principle of the polarization spectral integrated imaging system is introduced. The relationship between the spatial resolution, spectral resolution, and the system’s structural parameters is analyzed. The design of the optical part of the polarization spectral integrated imaging system is completed, along with the tolerance analysis. Secondly, the mechanical structure of the polarization spectral integrated imaging system is designed. Finally, by using a drone to carry the polarization spectral integrated imaging system, a simulation experiment for sea surface oil spill monitoring is conducted, and the hyperspectral and polarization information of the ocean, crude oil, fuel oil, palm oil, diesel, and gasoline are obtained. The polarization and spectral information were integrated. The integration of hyperspectral and polarization data yields remarkable enhancement outcomes, allowing for the clear delineation of previously challenging-to-identify crude oil contamination areas against the marine background in the fused images, characterized by sharper boundaries and improved discriminability. This accomplishment underscores the feasibility of our system for the rapid identification of large-scale oil spill events.