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The stress status of a soil pressure cell placed in soil is very different from its stress state in a uniform fluid medium. The use of the calibration coefficient provided by the soil pressure cell manufacturer will produce a large error. In order to improve the measurement accuracy of the interface-type earth pressure cell placed in soil, this paper focuses on a single-membrane resistive earth pressure cell installed on the surface of a structure, analyzing the influence of loading and unloading cycles, the thickness and particle size of the sand filling, and the depth of the earth pressure cell inserted in the structure on the calibration curve and matching error, which were analyzed through calibration tests. The results show that when the sand filling thickness is less than D (D is the diameter of the earth pressure cell), the calibration curve is unstable in relation to the increase in the number of loading and unloading cycles, which will cause the sand calibration coefficient used for stress conversion to not be used normally. When the sand filling thickness in the calibration bucket increases from 0.285D to 5D, the absolute value of the matching error first decreases and then increases, such that the optimal sand filling thickness is 3D. The output of the earth pressure cell increases with the decrease in sand particle size under the same load, and there is a significant difference between the theoretical calculation value and the experimental value of the matching error; aiming at this difference, an empirical formula is derived to reflect the ratio of the diameter of the induction diaphragm of the earth pressure cell to the maximum particle size of the sand filling. When the depth of the earth pressure cell inserted in the structure is "0", the sensing surface is flush with the structure and the absolute value of the matching error is the smallest. Changes in the horizontal placement of the soil pressure cell in the calibration bucket result in significant differences in both the output and hysteresis of the calibration curve. To improve the measurement accuracy of soil pressure cells in scaled tests for applications such as in the retaining walls of excavation pits, tunnel outer surfaces, pile tops, pile ends, and soil pressure measurements in soil, calibration of the soil pressure cells is required before testing. Due to the considerable difference in the stress states of the soil pressure cell between granular media and uniform fluid media, calibration in soil is essential. During in-soil calibration, factors such as cyclic loading and unloading, soil compression, sand thickness and particle size, and the placement of the soil pressure cell all affect the calibration results. This paper primarily investigates the influence of these factors on the calibration curve and matching error. This study found that, as the sand thickness increases, the matching error decreases initially and then increases. [ABSTRACT FROM AUTHOR]

Flexible sensor arrays have attracted extensive attention in human‐computer interaction. However, realizing high‐performance sensor units with programmable properties, and expanding them to multi‐pixel flexible arrays to maintain high sensing consistency is still struggling. Inspired by the contact behavior of octopus antenna, this paper proposes a programmable multistage dome structure‐based flexible sensing array with robust sensing stability and high array consistency. The biomimetic multistage dome structure is pressurized to gradually contact the electrode to achieve high sensitivity and a large pressure range. By adjusting the arrangement of the multistage dome structure, the pressure range and sensitivity can be customized. More importantly, this biomimetic structure can be expanded to a multi‐pixel sensor array at the wafer level with high consistency through scalable and high‐precision imprinting technologies. In the imprinting process, the conductive layer is conformally embedded into the multistage dome structure to improve the stability (maintain stability over 22 000 cycles). In addition, the braced isolation structure is designed to effectively improve the anti‐crosstalk performance of the sensor array (crosstalk coefficient: 26.62 dB). Benefitting from the programmable structural design and high‐precision manufacturing process, the sensor array can be customized and is demonstrated to detect human musculation in medical rehabilitation applications. [ABSTRACT FROM AUTHOR]

This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system's effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Flexible sensor arrays have attracted extensive attention in human‐computer interaction. However, realizing high‐performance sensor units with programmable properties, and expanding them to multi‐pixel flexible arrays to maintain high sensing consistency is still struggling. Inspired by the contact behavior of octopus antenna, this paper proposes a programmable multistage dome structure‐based flexible sensing array with robust sensing stability and high array consistency. The biomimetic multistage dome structure is pressurized to gradually contact the electrode to achieve high sensitivity and a large pressure range. By adjusting the arrangement of the multistage dome structure, the pressure range and sensitivity can be customized. More importantly, this biomimetic structure can be expanded to a multi‐pixel sensor array at the wafer level with high consistency through scalable and high‐precision imprinting technologies. In the imprinting process, the conductive layer is conformally embedded into the multistage dome structure to improve the stability (maintain stability over 22 000 cycles). In addition, the braced isolation structure is designed to effectively improve the anti‐crosstalk performance of the sensor array (crosstalk coefficient: 26.62 dB). Benefitting from the programmable structural design and high‐precision manufacturing process, the sensor array can be customized and is demonstrated to detect human musculation in medical rehabilitation applications.

Conventionally, the electromechanical system requires the installation of auxiliary displacement sensors and only the amount on the drive part and motion end, which increases volume, cost, and measurement error in the system. This paper presents an integrated measurement method with a sensing head, which takes the equal division characteristics of mechanical structures as part of the sensor, thus, the so-called self-sensing system. Moreover, the displacement is measured by counting the time pulses. The sensing head is integrated with the entire electromechanical system, including the driving, transmitting, and moving parts. Thus, the integration of the sensing part is greatly improved. Taking the rotary table as a special example, and the sensing head embedded into each part of the system, displacement information is obtained by the common processing system and fused by the adaptive weighted average method. The results of the experiment show that the fusion precision of each component is higher than only the motor position information as the feedback. The proposed method is a practical self-sensing technology with significant volume reduction and intelligent control benefits in the industry, especially suitable for extremely small and narrow spaces. [ABSTRACT FROM AUTHOR]

Hand gesture recognition using a camera provides an intuitive and promising means of human-computer interaction and allows operators to execute commands and control machines with simple gestures. Research in hand gesture recognition-based control systems has garnered significant attention, yet the deploying of microcontrollers into this domain remains relatively insignificant. In this study, we propose a novel approach utilizing micro-hand gesture recognition built on micro-bottleneck Residual and micro-bottleneck Conv blocks. Our proposed model, comprises only 42K parameters, is optimized for size to facilitate seamless operation on resource-constrained hardware. Benchmarking conducted on STM32 microcontrollers showcases remarkable efficiency, with the model achieving an average prediction time of just 269ms, marking a 7× faster over the state-of-art model. Notably, despite its compact size and enhanced speed, our model maintains competitive performance result, achieving an accuracy of 99.6% on the ASL dataset and 92% on OUHANDS dataset. These findings underscore the potential for deploying advanced control methods on compact, costeffective devices, presenting promising avenues for future research and industrial applications. [ABSTRACT FROM AUTHOR]

Because of the availability of more than an actor and a wireless component in an e-health application, providing more security and safety to users of this type of applications is expected. Moreover, ensuring protection of data user available or shared within different services from any security attack becomes an important requirement. In this paper, we are interested essentially in the authentication process, and we propose an improved Landmarkbased algorithm as a tool to extract, firstly, key features from analysed faces, and hence to accelerate the authentication operation. The suggested approach beats other state-of-the-art works in terms of accuracy and speed-up attaining time execution constraint, according to experimental evaluations. [ABSTRACT FROM AUTHOR]

An ordered hypergroupoid is a hypergroupoid together with a partial order such that satisfies the monotone condition. In this paper, we introduce the notion of semihypergroup actions on ordered hypergroupoids. Some results in this respect are investigated. In particular, we prove that if S is a commutative semihypergroup acting on an ordered hypergroupoid H, then there exists a commutative semihypergroup S acting on the ordered hypergroupoid ... in such a way that $H$ is embedded in H. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the development of digital technology and computer technology, the theory of digital economy has also had far-reaching development, but at present, the digital economy is still not able to clearly define whether the digital economy is good or bad for the high-quality development of China's economy. Therefore, it is necessary to research the spillover effect of the digital economy and the high-quality development of the economy with the help of embedded system technology. OBJECTIVES: To solve the issue of the poor coupling between the digital economy and high-quality development, to raise the degree of China's digital economic development, to improve the high energy consumption in the course of China's economic high-quality development, and to improve the application level of system technology in the study of China's economic development. METHODS: The digital economy model is established using the entropy weight method; its efficiency is evaluated using embedded system technology; the spillover effect analysis of high-quality development is then conducted using the digital economy index; and lastly, the overall assessment of the spillover effect is conducted using the comprehensive evaluation results of the entropy weight method. RESULTS: Embedded system technology has a noticeable promotion effect on the development of the digital economy; although the development of the digital economy and economic high-quality development are poorly coupled, their promotion effect is more significant; embedded system technology can indirectly promote the development of economic high-quality development through the digital economy. CONCLUSION: China should vigorously develop embedded system technology to promote the application of new technology in the economy and should further standardize the market economic order to maintain the orderly development of the digital economy. [ABSTRACT FROM AUTHOR]

In order to improve the ability of bad data identification and fault location accuracy in power system risk control, a new risk control method for power systems using embedded technology was designed.S3C2440 chip was used as the central processing unit, the phasor measurement unit (PMU) technology was used to identify bad data in the power system, improving the efficiency of identification.Improved on the basis of traditional multiverse algorithms, fault localization was based on data collected by PMU, effectively improving the accuracy of fault localization.In the simulation experiment, the efficiency of identifying bad data by the proposed method has been improved by at least 33.76%, the accuracy of fault localization has been improved by 26.33%, and the efficiency has been improved by at least 43.4%.The proposed method has good risk control capabilities in the power system.

The stress status of a soil pressure cell placed in soil is very different from its stress state in a uniform fluid medium. The use of the calibration coefficient provided by the soil pressure cell manufacturer will produce a large error. In order to improve the measurement accuracy of the interface-type earth pressure cell placed in soil, this paper focuses on a single-membrane resistive earth pressure cell installed on the surface of a structure, analyzing the influence of loading and unloading cycles, the thickness and particle size of the sand filling, and the depth of the earth pressure cell inserted in the structure on the calibration curve and matching error, which were analyzed through calibration tests. The results show that when the sand filling thickness is less than D (D is the diameter of the earth pressure cell), the calibration curve is unstable in relation to the increase in the number of loading and unloading cycles, which will cause the sand calibration coefficient used for stress conversion to not be used normally. When the sand filling thickness in the calibration bucket increases from 0.285D to 5D, the absolute value of the matching error first decreases and then increases, such that the optimal sand filling thickness is 3D. The output of the earth pressure cell increases with the decrease in sand particle size under the same load, and there is a significant difference between the theoretical calculation value and the experimental value of the matching error; aiming at this difference, an empirical formula is derived to reflect the ratio of the diameter of the induction diaphragm of the earth pressure cell to the maximum particle size of the sand filling. When the depth of the earth pressure cell inserted in the structure is “0”, the sensing surface is flush with the structure and the absolute value of the matching error is the smallest. Changes in the horizontal placement of the soil pressure cell in the calibration bucket result in significant differences in both the output and hysteresis of the calibration curve. To improve the measurement accuracy of soil pressure cells in scaled tests for applications such as in the retaining walls of excavation pits, tunnel outer surfaces, pile tops, pile ends, and soil pressure measurements in soil, calibration of the soil pressure cells is required before testing. Due to the considerable difference in the stress states of the soil pressure cell between granular media and uniform fluid media, calibration in soil is essential. During in-soil calibration, factors such as cyclic loading and unloading, soil compression, sand thickness and particle size, and the placement of the soil pressure cell all affect the calibration results. This paper primarily investigates the influence of these factors on the calibration curve and matching error. This study found that, as the sand thickness increases, the matching error decreases initially and then increases.

In the current literature, there is no practical formula to calculate the horizontal displacement of cantilever walls. To fill this gap, in the present study, eight formulae for the estimation of wall displacement were developed based on 431 FE wall model configurations. Each formula considers factors such as the wall height, embedment depth, surcharge load, unit weight, internal friction angle, elastic modulus of the surrounding soil, and flexural rigidity of the wall. The FE model, which was used in the development of the formula, was also validated against a physical laboratory study. In addition, the outputs obtained from the formulae were compared with the results of two laboratory studies and a real site study. Finally, a parametric study was performed to estimate the influence of formula input parameters on wall displacement. [ABSTRACT FROM AUTHOR]

Willmore defined embedded surfaces on f : S → E 3 , which is the embedding of S into Euclidean 3-space. He investigated the Euclidean metric of E 3 , inducing a Riemannian structure on f (S) . The expression analogous to the left-hand member of the curvature K is replaced by the mean curvature H 2 on f (S). Our aim is to observe the Gaussian and mean curvatures of curve–surface pairs using embedded surfaces in different curve–surface pairs and to define some developable operations on their curve–surface pairs. We also investigate the embedded surfaces using the Willmore method. We first recall the Darboux curve–surface and derive the new characterizations. This curve–surface pair is called the osculating Darboux curve–surface if its position vector always lies in the osculating Darboux plane spanned by a Darboux frame. Thus, we observed an osculating Darboux curve–surface pair. We also obtained the D ∼ -scroll of the curve–surface pair and involute D ∼ -scroll of the curve–surface pair with some differential geometric elements and found D ∼ (α , M) (s) and D ∗ ∼ (α , M) (s) -scrolls of the curve–surface pair (α , M) . [ABSTRACT FROM AUTHOR]

Negative features in microdevices find a wide range of applications. The process of 3D printing has revolutionized their fabrication due to the combination of good resolution and integration capability. Herein, we report on a systematic study of the effects of materials and print directions on the 3D printing of microfluidic channels as negative features under PolyJet technology. Specifically, the Statasys Objet500 printer was used for this study. We printed two sets of chips ( n = 10 each), each of which contains channel pairs of a high-contrast reference material and a sacrificial material, respectively. Both materials were embedded in a clear photopolymer resin. The channel pairs ranged in planned width from 64 to 992 μm. To explore the effect on print orientation, channels were printed either parallel or perpendicular with respect to the jetting head's movement. The width of each channel of a pair was compared for each planned width and each combination of materials. The effect of print orientation on channel morphology was also investigated. We found that reproducibility and accuracy were highest at a planned channel width of approximately ≥600 μm and that channel morphology was most suitable when the jetting head of the printer moved parallel to the channel's longitudinal axis. The results should be of interest to any users who wish to create negative features using PolyJet 3D technology. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hand gesture recognition using a camera provides an intuitive and promising means of human-computer interaction and allows operators to execute commands and control machines with simple gestures. Research in hand gesture recognition-based control systems has garnered significant attention, yet the deploying of microcontrollers into this domain remains relatively insignificant. In this study, we propose a novel approach utilizing micro-hand gesture recognition built on micro-bottleneck Residual and micro-bottleneck Conv blocks. Our proposed model, comprises only 42K parameters, is optimized for size to facilitate seamless operation on resource-constrained hardware. Benchmarking conducted on STM32 microcontrollers showcases remarkable efficiency, with the model achieving an average prediction time of just 269ms, marking a 7× faster over the state-of-art model. Notably, despite its compact size and enhanced speed, our model maintains competitive performance result, achieving an accuracy of 99.6% on the ASL dataset and 92% on OUHANDS dataset. These findings underscore the potential for deploying advanced control methods on compact, cost-effective devices, presenting promising avenues for future research and industrial applications.

This paper proposes a Design Space Exploration for Edge machine learning through the utilization of the novel MathWorks FPGA Deep Learning Processor IP, featured in the HDL Deep Learning toolbox. With the ever-increasing demand for real-time machine learning applications, there is a critical need for efficient and low-latency hardware solutions that can operate at the edge of the network, in close proximity to the data source. The HDL Deep Learning toolbox provides a flexible and customizable platform for deploying deep learning models on FPGAs, enabling effective inference acceleration for embedded IoT applications. In this study, our primary focus lies in investigating the impact of parallel processing elements on the performance and resource utilization of the FPGA-based processor. By analyzing the trade-offs between accuracy, speed, energy efficiency, and hardware resource utilization, we aim to gain valuable insights into making optimal design choices for FPGA-based implementations. Our evaluation is conducted on the AMD-Xilinx ZC706 development board, which serves as the target device for our experiments. We consider all the compatible Convolutional Neural Networks available within the HDL Deep Learning toolbox to comprehensively assess the performances.

The fairness & bias of narrow coverage of AI becomes another challenge for AI researchers. If a commercial AI trains with a biased dataset, there will be severe gender or racial fairness and bias issues. Since the researchers use primary language datasets to train AI, the broad audience cannot be satisfied if a novel LLM (Large Language Model) AI shows a knowledge or creativity limitation on their specific spoken language. Narrow coverage of the LLMs can lead the audience to misinterpretation and confusion if the service involves STT (Speech-To-Text). In this paper, to overcome this issue of data diversity, we propose the idea that the embedded, extracted features have captured semantic proximity information that can be useful to mitigate diversity issues. This project focused on the Korean language food dataset for STT services, where a narrow-trained A.I. is prone to show its limitations, such as lifestyle-related elements. To present our proof of concept, we trained a baseline model, GPT2, with the Korean Wikipedia dataset in 2022. Then, we employed DistilBERT and KoBERT for comparison. The extracted hidden_state_output features from each model were utilized to build feature-extraction-based text search engines. We used the same idea of Local Sensitive Hashing (LSH) but effectively located a similar hash by applying transposed weights. We also present conventional classification benchmarks for performance comparison using top-k measurements, times for training and memory & disc consumptions. In the discussion, we proposed that our idea can mitigate the diversity problem without re-training the model and tokenizer.

Zero-shot learning (ZSL) in a multi-model environment presents significant challenges and opportunities for improving cross-modal retrieval and object detection in unseen data. This study introduced a novel embedding approach of vector space clustering to address image-to-text and text-to-image retrieval problems effectively. We proposed an iterative training strategy; unlike the CLIP model, which directly compares visual and textual modalities, our model concatenates by clustering trained image and text features in common vector space. We use cross-modal contrastive and multi-stage contrast loss to improve the unsupervised learning of our model. This integration makes it possible to achieve proper clustering on embedding, which enhances the image-text matching problem in zero-shot learning tasks. We rigorously evaluate our model performance on standard benchmark datasets, including Flickr30K, Flickr8K, and MSCOCO 5K, achieving notable improvements with accuracies of 91.3%, 88.8%, and 90.3%, respectively. The results demonstrate the better performance of our model over existing methods but also show its effectiveness in enhancing cross-modal retrieval in zero-shot learning.

This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system’s effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes.

Abstract IoT and Embedded devices grow at an exponential rate, however, without adequate security mechanisms in place. One of the key challenges in the cyber world is the security of these devices. One of the main reasons that these devices are active targets for large-scale cyber-attacks is a lack of security standards and thorough testing by manufacturers. Manufacturer-specific operating systems or firmware of various architectures and characteristics are typically included with these devices. However, due to a lack of security testing and/or late patching, the underlying firmware or operating systems are vulnerable to numerous types of vulnerabilities. Reverse engineering and in-depth research of the firmware is required to detect the vulnerabilities. In this paper, we've delved into various aspects of IoT and embedded devices. This includes a comprehensive survey on the architecture of firmware, techniques for firmware extraction, and state-of-the-art vulnerability analysis frameworks for the detection of vulnerabilities using various approaches like static, dynamic, and hybrid approaches. Furthermore, we’ve scrutinized the challenges of existing vulnerability analysis frameworks and proposed a novel framework to address these issues.

Many works have been done to reduce complexity in terms of time and memory space. The feature selection process is one of the strategies to reduce system complexity and can be defined as a process of selecting the most important feature among feature space. Therefore, the most useful features will be kept, and the less useful features will be eliminated. In the fault classification and diagnosis field, feature selection plays an important role in reducing dimensionality and sometimes might lead to having a high classification rate. In this paper, a comprehensive review is presented about feature selection processing and how it can be done. The primary goal of this research is to examine all of the strategies that have been used to highlight the (selection) selected process, including filter, wrapper, Meta-heuristic algorithm, and embedded. Review of Nature-inspired algorithms that have been used for features selection is more focused such as particle swarm, Grey Wolf, Bat, Genetic, wale, and ant colony algorithm. The overall results confirmed that the feature selection approach is important in reducing the complexity of any model-based machine learning algorithm and may sometimes result in improved performance of the simulated model.

Branch prediction is a momentous technology guarantee for processor performance, especially for the widely used superscalar processor. The properties of the branch predictor significantly affect the overall performance, power consumption, and area of the processor. To obtain a more cost-effective branch predictor in the superscalar processor, an attempt is made to use a single TAGE (tagged geometric history length) predictor to predict the branches within the fetch width. The championship branch prediction platform is used to evaluate the performance of the predictor, and its prediction ability is sufficient to meet the prediction conditions. However, in practice, conflicts in both the predictor and branch target buffer affect its performance. To solve the above problem, this paper adds additional prediction paths based on a single TAGE branch predictor and independently saves and predicts additional branch instruction information. This predictor is implemented in the processor using hardware description language and compared with a single TAGE branch predictor to perform standard benchmark programs for embedded processors, dhrystone, coremark and embench. Experimental results show that the performance of the optimized branch predictor is improved by 14.1 percentage points, while the storage overhead is only increased by 9.06%. Finally, through the analysis of the experimental data, it is found that this scheme is not only conducive to the prediction of additional branch instructions, but also can achieve more accurate prediction of single branch instruction through more accurate acquisition of branch history information.

Because of the high complementarity between global navigation satellite systems (GNSSs) and visual-inertial odometry (VIO), integrated GNSS-VIO navigation technology has been the subject of increased attention in recent years. In this paper, we propose an embedded high-precision multi-sensor fusion suite that includes a multi-frequency and multi-constellation GNSS module, a consumption-grade inertial measurement unit (IMU), and a grayscale camera. The suite uses an NVIDIA Jetson Xavier NX as the host and develops a Field Programmable Gate Array-based controller for hardware time synchronization between heterogeneous sensors. A multi-state constraint Kalman filter is used to generate the tightly-coupled estimation from the camera and the IMU. As a result, the GNSS output is loosely coupled to facilitate the acquisition of the global drift-free estimation. Results from the calibration reveal that the time synchronization accuracy of the suite is better than 30 ^s (standard deviation [STD]) and that the projection error of camera-IMU is less than 0.1 pixels (STD); these results highlight the advantage of this hardware time synchronization mechanism. Results from the vehicle-mounted tests reveal reductions in the three-dimensional (3D) positioning error from 8.455 m to 5.751 m (root mean square) on experimental urban roads, which significantly improves the accuracy and continuity of GNSS individual positioning. In underground sites where the satellite signal is completely unavailable, the 3D position error drift of the suite is only 1.58 %o, which also shows excellent performance. [ABSTRACT FROM AUTHOR]

Narcotics should be strictly controlled as they can cause great disruption to society. Narcotics mostly flow into ports from major narcotic makers via transit points and through cargo containers. To prevent narcotic entry through smuggling, airports use animals or detect narcotics through X-rays. However, the use of animals in ports is not practical, and the method using X-rays sometimes does not detect substance narcotics with low atomic numbers. In this paper, we aimed to detect and classify narcotics using ion mobility spectrometry (IMS) data generated by inhaling air inside the container. To classify narcotic IMS data consisting of time-series data, the performance was improved using a time-series classification machine learning algorithm instead of the threshold method previously used. To this end, K-nearest neighbor, time-series forest, and random convolutional kernel algorithms were applied to the proposed algorithm considering the features of narcotic IMS data. The results demonstrate that the proposed algorithm outperforms the existing algorithm, and it reduces the classification performance processing time up to 5 s with more than 0.9 accuracy level. [ABSTRACT FROM AUTHOR]

Conventionally, the electromechanical system requires the installation of auxiliary displacement sensors and only the amount on the drive part and motion end, which increases volume, cost, and measurement error in the system. This paper presents an integrated measurement method with a sensing head, which takes the equal division characteristics of mechanical structures as part of the sensor, thus, the so-called self-sensing system. Moreover, the displacement is measured by counting the time pulses. The sensing head is integrated with the entire electromechanical system, including the driving, transmitting, and moving parts. Thus, the integration of the sensing part is greatly improved. Taking the rotary table as a special example, and the sensing head embedded into each part of the system, displacement information is obtained by the common processing system and fused by the adaptive weighted average method. The results of the experiment show that the fusion precision of each component is higher than only the motor position information as the feedback. The proposed method is a practical self-sensing technology with significant volume reduction and intelligent control benefits in the industry, especially suitable for extremely small and narrow spaces.

The sector-wide challenge of student mental wellbeing within higher education (HE) requires universities to systematically and holistically enhance their practice and approaches. This paper recounts how an interdisciplinary team of academics, senior leaders, professional services and Students’ Union representatives across seven institutions responded to this need by collaboratively identifying definitions, good-practice for replication, and benefits of activity to embed mental wellbeing in the curriculum. Considering and exploring definitions across HE, our study provides clarity and reference points for definitions, particularly around the nature of “embedding” in relation to mental wellbeing for students. Five core themes for mental wellbeing in HE were identified and defined through this study: flourishing or thriving, balance, community and belonging, dynamic, and inclusive. Furthermore, a five-point definition of embeddedness developed; it being modeled in practice, tailored to needs, inherent in values, ethos and culture, a holistic partnership approach, and represented in strategy supported by appropriate resource. These are represented as a Mental Wellbeing Embeddedness Framework. The study sought good-practice examples from across seven HE providers, presented as example resources for replication by others in the sector seeking to address similar challenges within their institutions. Twenty-seven examples were developed into an online open educational resource toolkit, providing clear examples for use in a range of settings and responding to different needs around student mental wellbeing. Each example sharing its original rationale for development and clear list of benefits to be seen from replicating the initiative. Sector feedback to date indicates referencing the definitions activity and expanding this for use in other contexts, replication of good-practice examples in different settings, and support for identification of the benefits that can be achieved from embedding mental wellbeing in the curriculum. This paper is a call to action, shaping the future direction of student mental wellbeing, supported by policy, practice, and constant innovation.

In recent times, the rapid advancements in technology have led to a digital revolution in urban areas, and new computing frameworks are emerging to address the current issues in monitoring and fault detection, particularly in the context of the growing renewable decentralized energy systems. This research proposes a novel framework for monitoring the condition of decentralized photovoltaic systems within a smart city infrastructure. The approach uses edge computing to overcome the challenges associated with costly processing through remote cloud servers. By processing data at the edge of the network, this concept allows for significant gains in speed and bandwidth consumption, making it suitable for a sustainable city environment. In the proposed edge-learning scheme, several machine learning models are compared to find the best suitable model achieving both high accuracy and low latency in detecting photovoltaic faults. Four light and rapid machine learning models, namely, CBLOF, LOF, KNN, ANN, are selected as best performers and trained locally in decentralized edge nodes. The overall approach is deployed in a smart solar campus with multiple distributed PV units located in the R&D platform Green & Smart Building Park. Several experiments were conducted on different anomaly scenarios, and the models were evaluated based on their supervision method, f1-score, inference time, RAM usage, and model size. The paper also investigates the impact of the type of supervision and the class of the model on the anomaly detection performance. The findings indicated that the supervised artificial neural network (ANN) had superior performance compared to other models, obtaining an f1-score of 80 % even in the most unfavorable conditions. The findings also showed that KNN was the most suitable unsupervised model for the investigated experiments achieving good f1-scores (100 %, 95 % and 92 %) in 3 out of 4 scenarios making it a good candidate for similar anomaly detection tasks.

IoT and Embedded devices grow at an exponential rate, however, without adequate security mechanisms in place. One of the key challenges in the cyber world is the security of these devices. One of the main reasons that these devices are active targets for large-scale cyber-attacks is a lack of security standards and thorough testing by manufacturers. Manufacturer-specific operating systems or firmware of various architectures and characteristics are typically included with these devices. However, due to a lack of security testing and/or late patching, the underlying firmware or operating systems are vulnerable to numerous types of vulnerabilities. Reverse engineering and in-depth research of the firmware is required to detect the vulnerabilities. In this paper, we've delved into various aspects of IoT and embedded devices. This includes a comprehensive survey on the architecture of firmware, techniques for firmware extraction, and state-of-the-art vulnerability analysis frameworks for the detection of vulnerabilities using various approaches like static, dynamic, and hybrid approaches. Furthermore, we've scrutinized the challenges of existing vulnerability analysis frameworks and proposed a novel framework to address these issues. [ABSTRACT FROM AUTHOR]

The integration of rigid electronic components into stretchable materials is a complex challenge due to contrary material properties. This paper shows a novel approach for the embedding of electronic components into flexible and stretchable polymer substrates using a stereolithographic process that leads to stress reduction during stretching. The key factor is the multi-material printing with various elastic material properties. The paper shows a miniaturized packaged component (e.g. 0201 LED) in a flexible and stretchable substrate based on three different materials. The first material has rigid properties (stiffness 42.25 N/mm) and surrounds a small area of the LED with about 500 µm peripheral frame. The main purpose of this material is to round up the sharp edges of the LED to the next material. The second material is more elastic (stiffness 0.11 N/mm) and surrounds the first embedded LED. The purpose of the implementation of the second intermediate material is to spread the load between the LED and stretchable substrate caused by stretching. The third material has even higher elastic properties (stiffness 0.05 N/ mm) and builds up the main part of the stretchable substrate (size of (4.5x34) mm2). After the fabrication of the multi-material substrate with the embedded LED, horseshoe conductive traces are screen printed with conductive paste. Finally, the embedded LED substrate with the conductive traces is cured at 125 °C. The final stretchable substrate with the embedded LED was able to stretch up to about 25 % for 30 cycles without disturbing the electrical function. [ABSTRACT FROM AUTHOR]

In this study, various diffusers are applied to highly efficient ultra-thin emission layer (EML) structure-based blue phosphorescent organic light-emitting diodes (PHOLEDs) to improve the electroluminescence (EL) characteristics and viewing angle. To achieve highly efficient blue PHOLEDs, the EL characteristics of ultra-thin EML PHOLEDs with the various diffusers having different structures of pattern–shape (hemisphere/sphere), size (4~75 μm), distribution (surface/embedded), and packing (close-packed/random) were systematically analyzed. The diffusers showed different enhancements in the overall EL characteristics of efficiencies, viewing angle, and others. The EL characteristics showed apparent dependency on their structure. The external quantum efficiency (EQE) was enhanced mainly by following the orders of pattern, size, and shape. Following the pattern size, the EQE enhancement gradually increased; the largest-sized diffuser with a 75 μm closed-packed hemisphere (diffuser-1) showed a 1.47-fold EQE improvement, which was the highest. Meanwhile, the diffuser with a ~7 μm random embedded sphere with a low density (diffuser 5) showed the lowest 1.02-fold-improved EQE. The reference device with ultra-thin EML structure-based blue PHOLEDs showed a maximum EQE of 16.6%, and the device with diffuser 1 achieved a maximum EQE of 24.3% with a 5.1% wider viewing angle compared to the reference device without a diffuser. For the in-depth analysis, the viewing angle profile of the ultra-thin EML PHOLED device and fluorescent green OLEDs were compared. As a result, the efficiency enhancement characteristics of the diffusers show a difference in the viewing angle profile. Finally, the application of the diffuser successfully demonstrated that the EL efficiency and viewing angle could be selectively improved. Additionally, we found that it was possible to realize a wide viewing angle and achieve considerable EQE enhancement by further investigations using high-density and large-sized embedded structures of light-extraction film. [ABSTRACT FROM AUTHOR]

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Virtualization has become one of the main tools for making efficient use of the resources offered by multicore embedded platforms. In recent years, even sectors such as space, aviation, and automotive, traditionally wary of adopting this type of technology due to the impact it could have on the safety of their systems, have been forced to introduce it into their day-to-day work, as their applications are becoming increasingly complex and demanding. This article provides a comprehensive review of the research work that uses or considers the use of a hypervisor as the basis for building a virtualized safety-critical embedded system. Once the hypervisors developed or adapted for this type of system have been identified, an exhaustive qualitative comparison is made between them. an exhaustive qualitative comparison is made between them. To the best of our knowledge, this is the first time that all this information is collected in a single article. Therefore, the main contribution of this article is that it collects and categorizes the information of each hypervisor and compares them with each other, so that this article can be used as a starting point for future researchers in this area, who will be able to quickly check which hypervisor is best suited to their research needs.

The use of Convolutional Neural Networks (CNN) for the application of wood defects detection has gained significant attention in recent years. In industrial settings, these tasks are typically performed in a strict and consistent environment, making the use of large and complex CNN models unnecessary. Despite this, recent research has continued to focus on the use of such models to achieve increasingly accurate detections. These models require costly machines for inference, making adoption less likely especially for manufacturers in the developing nations. In view of this limitation, this paper proposes a set of strategies to achieve a highly efficient CNN model for fast and accurate wood defects detection based on the YOLOv4-Tiny architecture. The model has been improved with Efficient Channel Attention (ECA) to better select multi-scale features from the backbone network and has been drastically reduced in size through an iterative pruning and recovery process. This results in an 88% reduction in model parameters while retaining accuracy comparable to most state-of-the-art (SOTA) methods. Consequently, the model can perform near real-time inference directly on a general-purpose embedded processor without external hardware accelerators. This research hopes to motivate the development of efficient defect detectors that can run on low-cost embedded devices.

The development of information technology has promoted the expansion of the application field of facial recognition technology. Its mainstream recognition methods rely on deep learning algorithms for calculation, but the problem of large data computation brought by its system makes it difficult to apply to embedded platform devices. As a result, this study focuses on improving recognition systems built on lightweight backend networks and builds an embedded platform system environment using multi-scale feature fusion, anchor box size optimization, the addition of channel attention mechanism weighted features, affine face alignment, and file compilation. The experimental results showed that when the number of iterations was 300, the loss value (0.46) of the improved embedded algorithm was much smaller than that of other comparison algorithms (1.42, 1.73, 2.01), and its ACC value (0.924) was significantly better than other comparison algorithms (0.915, 0.909, 0.894). The minimum system testing time consumedwas7 ms. This deep learning embedded facial recognition algorithm has high recognition accuracy, a fast running speed, and is less limited by environmental conditions and data types. It is ideally suited for use in embedded hardware devices, broadening the scope of equipment matching and facial recognition algorithms’ applications. As a result, it is better suited to satisfy the demands of embedded devices and massive data processing jobs.

In this work, a 3-5 μm mid-infrared broadband perfect absorber is proposed. The proposed absorber is only composed of an aluminium oxide (Al2O3) dielectric layer on the top, a silicon (Si) dielectric layer with an embedded titanium (Ti) ring in the middle and a high loss metal substrate. It exhibits an extremely perfect absorption performance in the entire range of 3-5 μm mid-infrared band, with the highest and average absorption rates of 99.91% and 98.57%, respectively. The excellent absorption at 3-5 μm is attributed to the intrinsic absorption of the two dielectrics and the excitation of the surface plasmon resonance (SPR). Moreover, the average absorption rate can be still maintained above 91.33% when the incident angle of TE/TM or unpolarized wave reaches 60°, and there is almost unchanged in the absorption rate when the polarization angle of the incident wave gradually changes from 0° to 90°, which shows the insensitivity of the absorber to the incident and polarization angle. The absorber with simple structure and high stability has potential application prospects in infrared stealth and detection.

Electricity is the foundation of the entire national economy, and nuclear power has become an important part of the power system with its unique advantages. The main process of nuclear power is the process of the nuclear fuel cycle, including the source and preparation of nuclear fuel, nuclear power plants and nuclear spent fuel processing. In the whole process, nuclear safety is the top priority and the key to the sustainable development of nuclear power. In this paper, aiming at the objective reality of high security risk and strict security requirements for nuclear fuel storage used in nuclear power, based on the analysis of nuclear fuel radiation characteristics and safety monitoring requirements, a nuclear fuel embedded system is designed from the aspects of system overall, software, hardware, data processing, etc. Security intelligent monitoring system. The system uses MCU as the control core, is equipped with motion detection and electronic seal devices, and realizes alarm functions such as identity authentication, unauthorized movement, and illegal unpacking real-time monitoring. Experiments and use show that the system can be easily integrated, installed and deployed on the nuclear fuel packaging container, and the operation is stable and reliable, which effectively promotes the steady improvement of the operating efficiency and operating quality of the nuclear energy system.

In this paper, a compact quasi-Yagi antenna with embedded resistor-loaded arms is proposed to obtain a filtering response with four radiation nulls. The embedded resistor-loaded arms achieve two additional radiation nulls caused by reverse currents and absorb the unwanted out-of-band resonant points brought by themselves. The director close to the driver provides a resonant point and a radiation null caused by opposite currents between the driver and the director. Compared with other filtering quasi-Yagi antennas, the proposed one can achieve a filtering response with a compact size along the endfire direction. For demonstration, a balun-integrated prototype covering the 5G band N78 (3.3–3.8 GHz) is designed with the size along the endfire direction (without ground) of 0.13 λ0 (λ0 is the wavelength in the free space at center frequency), and the measured results show a 10 dB impedance-matching bandwidth of 22.9% (3.21–4.04 GHz), four radiation nulls, and a peak gain of 4.73 dBi. [ABSTRACT FROM AUTHOR]

This paper presents a GPU-based parallelisation of an optimised versatile video decoder (VVC) adaptive loop filter (ALF) filter on a resource-constrained heterogeneous platform. The GPU has been comprehensively utilised to maximise the degree of parallelism, making the programme capable of exploiting the GPU capabilities. The proposed approach enables to accelerate the ALF computation by an average of two times when compared to an already fully optimised version of the software decoder implementation over an embedded platform. Finally, this work presents an analysis of energy consumption, showing that the proposed methodology has a negligible impact on this key parameter. [ABSTRACT FROM AUTHOR]

Willmore defined embedded surfaces on f:S→E3, which is the embedding of S into Euclidean 3-space. He investigated the Euclidean metric of E3, inducing a Riemannian structure on f(S). The expression analogous to the left-hand member of the curvature K is replaced by the mean curvature H2 on f(S). Our aim is to observe the Gaussian and mean curvatures of curve–surface pairs using embedded surfaces in different curve–surface pairs and to define some developable operations on their curve–surface pairs. We also investigate the embedded surfaces using the Willmore method. We first recall the Darboux curve–surface and derive the new characterizations. This curve–surface pair is called the osculating Darboux curve–surface if its position vector always lies in the osculating Darboux plane spanned by a Darboux frame. Thus, we observed an osculating Darboux curve–surface pair. We also obtained the D∼-scroll of the curve–surface pair and involute D∼-scroll of the curve–surface pair with some differential geometric elements and found D∼(α,M)(s) and D∗∼(α,M)(s)-scrolls of the curve–surface pair (α,M).

In the current literature, there is no practical formula to calculate the horizontal displacement of cantilever walls. To fill this gap, in the present study, eight formulae for the estimation of wall displacement were developed based on 431 FE wall model configurations. Each formula considers factors such as the wall height, embedment depth, surcharge load, unit weight, internal friction angle, elastic modulus of the surrounding soil, and flexural rigidity of the wall. The FE model, which was used in the development of the formula, was also validated against a physical laboratory study. In addition, the outputs obtained from the formulae were compared with the results of two laboratory studies and a real site study. Finally, a parametric study was performed to estimate the influence of formula input parameters on wall displacement.

A range of biotech applications, e.g., microfluidic benthic biofuel cells, require devices with the simultaneous capabilities of embedded electrical wiring, aqueous fluidic access, 3D arrays, biocompatibility, and affordable upscalability. These are very challenging to satisfy simultaneously. As a potential solution, herein we present a qualitative experimental proof of principle of a novel self-assembly technique in 3D printed microfluidics towards embedded wiring combined with fluidic access. Our technique uses surface tension, viscous flow, microchannel geometries, and hydrophobic/hydrophilic interactions to produce self-assembly of two immiscible fluids along the length of the same 3D printed microfluidic channel. The technique demonstrates a major step towards the affordable upscaling of microfluidic biofuel cells through 3D printing. The technique would be of high utility to any application that simultaneously requires distributed wiring and fluidic access inside 3D printed devices. [ABSTRACT FROM AUTHOR]

This work proposes a comparison among GaN and SiC device main parameters measured with a dedicated and low-cost embedded system, employing an STM32 microcontroller designed to the purpose. The system has the advantage to avoid the use of expensive laboratory measurement equipment to test the devices, allowing to obtain their behavior in operating conditions. The following KPIs (Key Performance Indicators) are measured and critically compared: threshold voltage, on-resistance and input capacitance. All the measurements are carried out in a short time interval and on a wide range of switching frequencies, ranging from 10 kHz to 1 MHz. This investigation is focused on the deviation of the figures of merit when the switching frequency changes, since it is crucial for wide-bandgap devices. The devised, low-cost, microcontroller unit allows high flexibility and system portability, while the employed equivalent-time sampling technique overcomes some issues related to the need of high sampling frequency. It allows good performances with common microcontroller embedded AD converters. To validate the proposed system, the obtained results have been compared with the time-domain waveforms acquired with a traditional laboratory oscilloscope and a study of the system's measurement errors has been carried out. Results show that GaN devices achieve a higher efficiency with respect to SiC devices in the considered range of switching frequencies. The on-resistance exhibited by GaN devices shows, as expected, an increase with frequency, which happens to switching losses, too. On the other hand, GaN devices are more sensitive to parasitic effects and the high dV/dt, due to the reduced switching times, can excite unwanted ringing phenomena. [ABSTRACT FROM AUTHOR]

This work proposes a real-time multivariable adaptive robust control based on an embedded controller. The designed controller is evaluated over a twin rotor MIMO system by considering an eigenstructure assignment approach for its design in state space. The controller structure considers a full state observer for state estimation, where the design of the observer is also developed based on the eigenstructure assignment. The method's robustness stands in the good selection of the eigenvector subspaces corresponding to each closed loop eigenvalue. It is worth noting that the twin rotor MIMO system is a nonlinear system that is identified using the MOESP subspace approach for multivariable model identification in order to obtain a linear multivariable discrete state-space model. The proposed approach is compared with two PID structures using decoupling and coupled approaches. The robust controller is evaluated over a twin rotor MIMO simulation and an embedded C2000 Texas Instruments F28379d processor. [ABSTRACT FROM AUTHOR]

This research experimentally evaluates the behavior and performance of prefabricated segmental bridge CFDST columns constructed with non-emulative systems as compared to emulative systems under eccentric gravity loadings. The experimental program included testing 12 short columns of the exact dimensions divided into five groups. One group was dedicated to investigating the performance of emulative systems in CFDST columns with an embedded connection at the footing. Three groups were used to examine the performance of the non-emulative system in prefabricated segmental post-tensioned CFDST columns with rocking connections. Finally, one group was used to investigate a hybrid embedded-rocking system that involves prefabricated segmental post-tensioned CFDST columns associated with an embedded connection at the footing. The results indicate that increasing the number of column segments in a non-emulative system improves the overall stress distribution along the column under eccentric loading. Such a technique promotes better engagement of the whole column to rotate along with the column cap under the applied eccentric load, preventing a localized stress concentration and leading to a more stable superstructure system. In addition, it can greatly contribute to mitigating the reduction in moment capacity of non-emulative systems under eccentric loading compared to the emulative systems. Among the studied systems, the hybrid embedded-rocking system showed the least reduction in moment capacity under eccentric loadings compared to the emulative system. The research emphasized the significance of using additional energy dissipation components to achieve the maximum capacity of emulative columns under eccentric loads. Finally, the research addressed several parameters believed to influence the behavior of prefabricated CFDST columns but are not yet considered by available design codes, including the construction system, the post-tension effect, and the stress-strain relationship of the localized infill concrete under eccentric loading. • Performance evaluation of prefabricated bridge segmental CFDST columns under eccentric gravity loadings. • Increasing the number of column segments leads to a more efficient and stable bridge system. • Hybrid system incorporating features of emulative and non-emulative systems performed the best in terms of load-carrying capacity. [ABSTRACT FROM AUTHOR]