Components In Numerous Applications Research Articles

2D/3D Expression Generation Using Advanced Learning Techniques and the Emotion Wheel

Facial expression analysis is a critical component in numerous applications, ranging from human- computer interaction to digital character animation. Despite the availability of extensive datasets, most focus predominantly on basic emotions, limiting the expressiveness and applicability of models trained on them. This article introduces a novel approach to generating compound emotional expressions by leveraging the Emotion Wheel, a principle that captures the complex interrelations between basic emotions. Our method integrates the EMOCA (Emotion Driven Monocular Face Capture and Animation) [1] framework, which enhances 3D facial reconstruction by incorporating emotion recognition, stacking models [2, 54] with a sophisticated expression blending algorithm to synthesize nuanced 2D and 3D facial animations. Utilizing the VKIST dataset, which includes high-resolution facial images of Vietnamese individuals, we build a comprehensive database of emotion parameters. Through principal component analysis (PCA) and correlation-driven blending, our approach not only enhances the realism of generated facial expressions but also preserves the subtle nuances that are characteristic of human emotions. Experimental results demonstrate that our method consistently outperforms traditional linear interpolation techniques, producing more distinct and recognizable blended expressions. A user study further validates the naturalness and quality of the generated expressions, with average ratings indicating a strong preference for the proposed method over existing approaches. These findings suggest significant potential for improving emotional expressiveness in both static and dynamic digital character representations.

CMOS Based Voltage Reference Designs for Sub - 1V

This review paper presents an analysis of the most recent advancements in sub-1V voltage references, addressing the growing demand for ultra-low power consumption and high precision in modern integrated circuits (ICs). Voltage references are critical components in numerous applications, including IoT devices, wearable electronics, and energy-harvesting systems, where power efficiency and accuracy are paramount. It briefly discusses the challenges associated with designing voltage references at such low voltages, such as limited headroom, reduced noise margin, and process variations. Topics include high-order curvature compensation, modified differential pair configurations, and energy-efficient solutions for integrated energy harvesting. These advancements enhance precision and reliability in low-voltage circuits, paving the way for sustainable, low-power electronics and compact devices in the modern digital landscape. It emphasizes the importance of benchmarking different designs against criteria such as power consumption, line regulation, temperature stability, and supply voltage rejection ratio (PSRR). The paper include insights into the state-of-the-art sub-1V voltage reference designs, identification of design trade-offs, and recommendations for future research directions. It underscores the importance of continuous innovation in voltage reference design to address the evolving requirements of ultra-low power electronics. The study here is setting the stage for a detailed analysis of the latest developments in sub-1V voltage references.

A continuous ultra-narrow impulse synchronizer using a monolithic field programmable gate array for fast deployment and scalability.

Ultra-narrow pulses serve as critical components in numerous applications. These pulses have ultra-fast leading edges that typically function as precision trigger signals to synchronize various instruments. Ultra-narrow pulses inherently exhibit an ultra-wide bandwidth, gaining significant attention in diverse electronic systems encompassing communications, radar imaging, electronic warfare, and others. Although several techniques have been explored for generating ultra-narrow pulses, field programmable gate arrays (FPGAs) offer a promising alternative in terms of flexibility and integration. This study introduces a scalable delay pulse synchronizer method with a resolution of 23 ps. A programmable, successive, narrow pulse sequence operating at a 1-GHz repetition frequency is implemented within a monolithic FPGA. The performance of the proposed method is evaluated using an existing board with a general commercial FPGA in the laboratory. This new method presents a convenient and efficient approach of achieving ultra-narrow pulse synchronization, being applicable across various fields.

Representation of the fermionic boundary operator

The boundary operator is a linear operator that acts on a collection of high-dimensional binary points (simplices) and maps them to their boundaries. This boundary map is one of the key components in numerous applications, including differential equations, machine learning, computational geometry, machine vision and control systems. We consider the problem of representing the full boundary operator on a quantum computer. We first prove that the boundary operator has a special structure in the form of a complete sum of fermionic creation and annihilation operators. We then use the fact that these operators pairwise anticommute to produce an $O(n)$-depth circuit that exactly implements the boundary operator without any Trotterization or Taylor series approximation errors. Having fewer errors reduces the number of shots required to obtain desired accuracies.

Approximate Stability Dynamics of Concentric Cylindrical Metasurfaces

The conditions securing stability in configurations that employ gain media, carry vital information for the operation and functionality of the respective systems. These constraints are determined for a generic photonic setup comprising two concentric, passive or active, cylindrical impedance metasurfaces, by assuming their size small compared to the oscillation wavelength. Such an approximation, permits the analytical determination of the unstable poles across the complex frequency plane, which is otherwise a computationally cumbersome task. The absorbing and scattering response of the structure, when excited by various primary sources, unveils the dynamics behind the supported resonances and reveals the limits in its performance while respecting stability restrictions. The reported results may assist substantially the modeling and stable design of devices involving this ubiquitous cylindrical component in numerous applications spanning from radiation pattern control and angular momentum transformation to active cloaking and reconfigurable wavefront manipulation.

An energy balance evaluation in lithium-ion battery module under high temperature operation

Lithium-ion battery systems are becoming a major component in numerous applications. The thermal behaviour of cells should be monitored for many reasons. In this paper, the investigation of the energy balance for lithium-ion battery system is described. The electrothermal model allows to describe heat generation and heat dissipation processes in all components of a battery pack (BP). Moreover, this model can be easily adapted for bigger, modular battery systems, since the primitive in the proposed model is a single cell. A method to determine the energy balance through experimental investigations and to validate the model is also proposed. Four types of BPs with sixteen cylindrical 18650 lithium-ion cells were tested. The influence of both busbar material and the presence of heatsinks on the BP thermal conditions was investigated. The results show that the heat accumulated in cells can be decreased by 20% and the cell temperature lowered by 8 °C if additional elements are used in a BP.

Facial Recognition System Using Mixed Transform and Multilayer Sigmoid Neural Network Classifier

Facial recognition systems are critical components in numerous applications. They are used, for example, to prevent retail crime, unlock phones, find missing persons, protect law enforcement, and aid forensic investigations. In such real-world applications, the identification of facial information must be both quick and exact. The purpose of this study is to improve both the accuracy and speed of facial recognition. The proposed system reduces overall computational complexity by using a few simple algorithms and transforms. The grayscaling algorithm enhances the image, and the salient features are extracted using a mix of two transform families: the two-dimensional discrete wavelet transform and the two-dimensional discrete cosine transform. This combination exploits the nonorthogonality of the coefficients in both domains to preserve the essential details and perceptual qualities of the original image. A multilayer sigmoid neural network is used for classification since the expensive training stage can be performed offline. The trained network, which uses efficient computations, can be embedded in an online system for rapid classification. The efficiency of the system is an attractive property when processing massive information datasets with limited resources. The recognition system is tested with four freely accessible datasets: the ORL, YALE, FERET-c, and FEI. A test set based on the combination of all datasets is also utilized to evaluate the system performance. Results show that despite the reduction in complexity, the system still maintains high recognition rates as compared to the popular existing methods.

Object Tracking in Satellite Videos Based on Convolutional Regression Network With Appearance and Motion Features

Object tracking is one of the most important components in numerous applications of computer vision. Remote sensing videos provided by commercial satellites make it possible to extend this topic into the earth observation domain. In satellite videos, typical moving targets like vehicles and planes only cover a small area of pixels, and they could easily be confused with surrounding complex ground scenes. Similar objects nearby in satellite videos can hardly be differed by appearance details due to the resolution constraint. Thus, tracking drift caused by distractions is also a thorny problem. Facing challenges, traditional tracking methods such as correlation filters with hand-crafted visual features achieve unsatisfactory results in satellite videos. Methods based on deep neural networks have demonstrated their superiority in various ordinary visual tracking benchmarks, but their results on satellite videos remain unexplored. In this article, deep learning technologies are applied to object tracking in satellite videos for better performance. A simple regression network is used to combine a regression model with convolutional layers and a gradient descent algorithm. The regression network fully exploits the abundant background context to learn a robust tracker. Instead of handcrafted features, both appearance features and motion features, which are extracted by pretrained deep neural networks, are used for accurate object tracking. In cases when the tracker encounters ambiguous appearance information, the motion features could provide complementary and discriminative information to improve tracking performances. Experimental results on various satellite videos show that the proposed method achieves better tracking performance than other state-of-the-arts.

Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders.

Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).

A Broadband Multi-Mode Compressive Sensing Current Sensor SoC in 0.16<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>m CMOS

Broadband current sensors are key components in numerous applications, including power conversion, motor control, and smart-metering. We present a compressive sensing (CS) current sensor system-on-chip (SoC) designed and fabricated in STM 0.16μm Bipolar-CMOS-DMOS technology. The SoC is capable of measuring currents with amplitudes of up to 10 A peak with a sensing bandwidth of 1 MHz. Two broadband current sensing cores, each consisting of a Hall-effect probe, an AFE, and a 2 MS/s 9-bit ADC, are monolithically integrated together with a digital multi-mode compressive sensing encoder (DCSE) for data-rate reduction. We focus on the evaluation of CS as data compression codec for current sensing applications and on the details of the DCSE architecture designed for general purpose use. We introduce multi-block decoding that is a decoding modality to improve the reconstruction quality of “off-grid sparse” signals commonly occurring in practice. Moreover, we provide measurements of both the compression performance and power consumption of the SoC employed in two exemplary real-world applications; namely, sparse current sensing, and electrical appliance detection for non-intrusive load monitoring based on compressive measurements.

Wide area condition monitoring of power electric drives in wind power generation system using radiated electromagnetic fields

Electric components in numerous applications (particularly wind generation) are not straightforwardly accessible for monitoring. Therefore, the monitoring and protection through voltage/current measurement may not be dependable since the current value passes numerous segments to reach the observing element. Accordingly, finding an unusual phenomenon of a specific element is difficult. To resolve this issue, using transmitted electromagnetic field of an element for wide area condition monitoring is proposed. It is planned to diagnose and locate short-circuit in induction generator drive such as interturn, intercoil and terminal-to-turn failures. The frequency characteristics of the propagated field is then utilized for finding the short-circuit. The theoretical foundation that relate the behavior of each elements to their frequency response is analyzed and used. To utilize the derived technique for different practical circumstances, two distinctive methods are used for locating the short-circuit. As the experimental test of major fault cases could destruct the winding, the full three-dimensional finite element analysis is used in these cases and some are verified experimentally through the wide area communication. Identifying the areas of partial faults Prevents the whole winding failure prior to a massive destruction, which is costly especially for cases in inaccessible situations such as offshore wind towers.

Multiple person tracking based on slow feature analysis

Object tracking is one of the most important components in numerous applications of computer vision. However, it still has many challenges to be solved, such as occlusion, matching, data association, etc. In this paper, we proposed to utilize slow feature analysis (SFA) method to handle the multiple person tracking problem. First, the part-based model is utilized to detect pedestrian in each frame. Then, a set of reliable tracklets is generated by utilizing spatial-temporal information of detection results. Third, SFA method is leveraged to extract slow-feature for these reliable tracklets. Finally, the traditional graph matching method is utilized to handle data association problem and consequently generate the final trajectory for individual tracking object. Some popular datasets are used in this study. The extensive comparison experiments demonstrate the superiority of the proposed method.

Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion

Visual tracking is one of the most important components in numerous applications of computer vision. Although correlation filter based trackers gained popularity due to their efficiency, there is a need to improve the overall tracking capability. In this paper, a tracking algorithm based on the kernelized correlation filter (KCF) is proposed. First, fused features including HOG, color-naming, and HSV are employed to boost the tracking performance. Second, to tackle the fixed template size, a scale adaptive scheme is proposed which strengthens the tracking precision. Third, an adaptive learning rate and an occlusion detection mechanism are presented to update the target appearance model in presence of occlusion problem. Extensive evaluation on the OTB-2013 dataset demonstrates that the proposed tracker outperforms the state-of-the-art trackers significantly. The results show that our tracker gets a 14.79% improvement in success rate and a 7.43% improvement in precision rate compared to the original KCF tracker, and our tracker is robust to illumination variations, scale variations, occlusion, and other complex scenes.

Investigation on Thermal-Induced Decay of Fiber Bragg Grating

A fiber Bragg grating (FBG), with advantages such as high anti-interference ability, a simple structure, and multiplexing, is widely used as a core component in numerous applications to monitor adverse environments of high temperature and air pressure. When FBGs are exposed to these extreme conditions, especially high temperature, performance decay may occur, bringing serious impact on the stability and reliability of the instruments. Therefore, it is necessary to make a detailed analysis on the mechanism of the thermal-induced decay of a FBG. One commonly used theory is proposed by Erdogn, which is based on a power function and aging curve method. However, these empirical equations are limited in application because only one single type of FBG can be analyzed this way. This paper focuses on the mechanism of a FBG, and presents a detailed analysis on the theory of the thermal-induced decay of a FBG using the electron dipole mode. Theoretical relationships between reflectivity and time or temperature were obtained, and a corresponding thermal-induced decay testing system was designed. The experimental and theoretical reflectivity decline under different temperatures of $$700\,^\circ \hbox {C}$$ and $$800\,^\circ \hbox {C}$$ are plotted, and the curves of reduction derived from the theoretical model fit the experimental data well. Thus, this model can be applied to predict the performance decay of FBGs at high temperature.

Online Pedestrian Tracking with Kalman Filter and Random Ferns

Object tracking is one of the most important components in numerous applications of computer vision. Much progress has been made in recent years. The Tracking-Learning-Detection (TLD) algorithm achieves excellent performance on a set of challenging video sequences. However, classical TLD algorithm fails to track non-rigid pedestrian as complicated appearance, varying viewpoints, shape changes and occlusions. In this paper, we follow the TLD and propose a robust KMD framework which consists of the Kalman filter tracker, random ferns pedestrian detector and the pedestrian model. During the tracking process the tracker and detector are complementary: the Kalman filter tracker predicts the motion, the pedestrian detector searches the best-match appearance and the pedestrian model combines performance of both to determine final result and generate new samples for learning. Experimental results show our framework comparatively improves performance for pedestrian tracking in surveillance videos.

Austempered Materials for Powertrain Applications

Austempered irons and steels offer the design engineer alternatives to conventional material/process combinations. Depending on the material and the application, Austempering may provide the following benefits to producers of powertrain components such as gears and shafts: ease of manufacturing, increased bending and/or contact fatigue strength, better wear resistance and enhanced dampening characteristics resulting in lower noise. Austempered materials have been used to improve the performance of powertrain components in numerous applications for a wide range of industries, from gears and shafts to clutch plates and crankshafts. This paper focuses on Austempered solutions for powertrain applications with an emphasis on gear and shaft solutions.

Diffractive incremental and absolute coding principle for optical rotary sensors

Rotary sensors are an essential component in numerous applications where a rotation movement has to be detected. With optical encoders, a high angular resolution can be achieved. As a disadvantage, the resolution enhancement is associated with increasing cost. To overcome this issue, a coding principle is presented that uses a diffractive solid measure on a microstructured plastic disc. Like a DVD, this encoder disc can be manufactured in a cost effective injection molding process. For this approach, a differential incremental code, as well as an absolute code, has been developed.