Meredithmiller4482

Customer activity (CA) in retail environments, which ranges over various shopper situations in store spaces, provides valuable information for store management and marketing planning. Several systems have been proposed for customer activity recognition (CAR) from in-store camera videos, and most of them use machine learning based end-to-end (E2E) CAR models, due to their remarkable performance. Usually, such E2E models are trained for target conditions (i.e., particular CA types in specific store spaces). Accordingly, the existing systems are not malleable to fit the changes in target conditions because they require entire retraining of their specialized E2E models and concurrent use of additional E2E models for new target conditions. This paper proposes a novel CAR system based on a hierarchy that organizes CA types into different levels of abstraction from lowest to highest. The proposed system consists of multiple CAR models, each of which performs CAR tasks that belong to a certain level of the hierarchy on the lower level's output, and thus conducts CAR for videos through the models level by level. Since these models are separated, this system can deal efficiently with the changes in target conditions by modifying some models individually. Experimental results show the effectiveness of the proposed system in adapting to different target conditions.In recent years, the space industry has experienced a significant change mainly due to the incursion of private companies, which has shaken up the sector. This new situation allows for a reduction regarding the reliability of conventional instrumentation for space while reducing the development time and manufacturing volume. Consequently, even though it has been typical to use equipment that was previously tested in space, this could be the right moment to introduce new technologies due to the previously mentioned reasons. One of the interesting technologies with great potential is the rotary sensor in applications with motors. Historically, the resistive potentiometer has been the most used due to its simplicity and robustness; however, it has several drawbacks. Due to this, the aim of this paper is to identify an interesting rotary sensor. Hence, in this article, different sensor types are studied. Then, we review the literature regarding resolvers in order to find the best topology. We designed and compared different single speed absolute position resolvers to find the ones that offered the best results. In this process, a novel resolver topology was designed that improved on the performances of any other studied topology.Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems-especially in the context of rail vehicle subsystem and track subsystem monitoring-have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems' condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.This paper presents an automatic classification of plastic material's inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1-1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier's robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms.This paper summarized the research status, imaging model, systems calibration, distortion correction, and panoramic expansion of panoramic vision systems, pointed out the existing problems and put forward the prospect of future research. According to the research status of panoramic vision systems, a panoramic vision system with single viewpoint of refraction and reflection is designed. The systems had the characteristics of fast acquisition, low manufacturing cost, fixed single-view imaging, integrated imaging, and automatic switching depth of field. Based on these systems, an improved nonlinear optimization polynomial fitting method is proposed to calibrate the monocular HOVS, and the binocular HOVS is calibrated with the Aruco label. This method not only improves the robustness of the calibration results, but also simplifies the calibration process. Finally, a real-time method of panoramic map of multi-function vehicle based on vcam is proposed.In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle's condition as well as the driver's behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO2 emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle's fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers' behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver's behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.Due to their lightweight properties, fiber-reinforced composites are well suited for large and fast rotating structures, such as fan blades in turbomachines. To investigate rotor safety and performance, in situ measurements of the structural dynamic behaviour must be performed during rotating conditions. An approach to measuring spatially resolved vibration responses of a rotating structure with a non-contact, non-rotating sensor is investigated here. The resulting spectra can be assigned to specific locations on the structure and have similar properties to the spectra measured with co-rotating sensors, such as strain gauges. The sampling frequency is increased by performing consecutive measurements with a constant excitation function and varying time delays. The method allows for a paradigm shift to unambiguous identification of natural frequencies and mode shapes with arbitrary rotor shapes and excitation functions without the need for co-rotating sensors. Deflection measurements on a glass fiber-reinforced polymer disk were performed with a diffraction grating-based sensor system at 40 measurement points with an uncertainty below 15 μrad and a commercial triangulation sensor at 200 measurement points at surface speeds up to 300 m/s. A rotation-induced increase of two natural frequencies was measured, and their mode shapes were derived at the corresponding rotational speeds. A strain gauge was used for validation.Constant light power operation of an ultraviolet (UV) LED based on portable low-cost instrumentation and a monolithically integrated monitoring photodiode (MPD) has been reported for the first time. UV light irradiation has become one of the essential measures for disinfection and sterilization. Monitoring and maintaining a specified light power level is important to meet the criteria of sterilization. We built a module composed of a monolithically integrated UV LED and MPD, a transimpedance amplifier, an Arduino Uno card, a digital-to-analog converter and a Bluetooth transceiver. An Android App that we wrote remotely controlled the UV LED module via Bluetooth. The Arduino Uno card was programmed to receive demands from the smartphone, sent a driving voltage to the LED and returned the present MPD voltage to the smartphone. A feedback loop was used to adjust the LED voltage for maintaining a constant light output. We successfully demonstrated the functioning of remote control of the App, and the resultant UV LED measured power remained the same as the setting power. Proteasome assay This setup can also be applied to visible or white LEDs for controlling/maintaining mixed light's chromaticity coordinates or color temperature. With such controlling and internet capability, custom profiling and maintenance of precision lighting remotely would be possible.Due to the blur information and content information entanglement in the blind deblurring task, it is very challenging to directly recover the sharp latent image from the blurred image. Considering that in the high-dimensional feature map, blur information mainly exists in the low-frequency region, and content information exists in the high-frequency region. In this paper, we propose a encoder-decoder model to realize disentanglement from the perspective of frequency, and we named it as frequency disentanglement distillation image deblurring network (FDDN). First, we modified the traditional distillation block by embedding the frequency split block (FSB) in the distillation block to separate the low-frequency and high-frequency region. Second, the modified distillation block, we named frequency distillation block (FDB), can recursively distill the low-frequency feature to disentangle the blurry information from the content information, so as to improve the restored image quality. Furthermore, to reduce the complexity of the network and ensure the high-dimension of the feature map, the frequency distillation block (FDB) is placed on the end of encoder to edit the feature map on the latent space.