Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3485405
Yinhao Ren;Kecheng Yuan;Guofang Xu;Chunyou Ye;Feng Liu;Bensheng Qiu;Xiang Nan;Jijun Han
This study aimed to improve the accuracy of electrical properties tomography (EPT) by proposing a fat-water quantification-based EPT (FW-EPT) using the Dixon technique and provided a feasible approach for obtaining electrical properties (EPs) from current clinical routing modalities. Nine human liver-mimicking phantoms were built with varying fat-water (FW) content at 64 MHz. The EPs were measured using the open-ended coaxial probe method, and an FW signal was obtained through Dixon scanning. Subsequently, three sets of fit models were established: F-EPs, considering only fat information; W-EPs, considering only water information; and FW-EPs, considering both fat and water information. To assess the accuracy of these models, FW-EPT experiments were conducted on two healthy subjects, and the results were evaluated using literature values as a reference benchmark. Experiments showed that the FW-EPs fitted model offered the best accuracy. Compared with the literature values, the average relative errors for human liver conductivity and relative permittivity at 1.5T magnetic resonance imaging (MRI) were lower than 2.89% and 5.37%, respectively. The scanning time for clinical human magnetic resonance (MR) experiments was approximately 22 s. FW-EPT enabled faster, higher resolution, and more precise imaging of EPs in human liver tissue. The findings of this study offered new insights for clinical EPT.
{"title":"Fat-Water Signal-Based Electrical Properties Tomography Using the Dixon Technique","authors":"Yinhao Ren;Kecheng Yuan;Guofang Xu;Chunyou Ye;Feng Liu;Bensheng Qiu;Xiang Nan;Jijun Han","doi":"10.1109/TIM.2024.3485405","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485405","url":null,"abstract":"This study aimed to improve the accuracy of electrical properties tomography (EPT) by proposing a fat-water quantification-based EPT (FW-EPT) using the Dixon technique and provided a feasible approach for obtaining electrical properties (EPs) from current clinical routing modalities. Nine human liver-mimicking phantoms were built with varying fat-water (FW) content at 64 MHz. The EPs were measured using the open-ended coaxial probe method, and an FW signal was obtained through Dixon scanning. Subsequently, three sets of fit models were established: F-EPs, considering only fat information; W-EPs, considering only water information; and FW-EPs, considering both fat and water information. To assess the accuracy of these models, FW-EPT experiments were conducted on two healthy subjects, and the results were evaluated using literature values as a reference benchmark. Experiments showed that the FW-EPs fitted model offered the best accuracy. Compared with the literature values, the average relative errors for human liver conductivity and relative permittivity at 1.5T magnetic resonance imaging (MRI) were lower than 2.89% and 5.37%, respectively. The scanning time for clinical human magnetic resonance (MR) experiments was approximately 22 s. FW-EPT enabled faster, higher resolution, and more precise imaging of EPs in human liver tissue. The findings of this study offered new insights for clinical EPT.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488148
Xingxing Zeng;Yong Yan;Xiangchen Qian;Kamel Reda;Yunlong Lu
The characterization of the velocity and concentration of pneumatically conveyed particles in the upstream of the waveguide protruded into the flow is essential for the measurement of the mass flow rate and size distribution of particles using acoustic emission (AE) methods. However, the protrusion of the waveguide affects the movement of particles, and there is a challenge in quantifying its effects on particle velocity and concentration due to the complexity of the dynamics of particle flow. Therefore, the computational fluid dynamics-discrete element method (CFD-DEM) is employed in this study to simulate the collisions between particles and waveguides with a varying protrusion depth in both circular and square vertical pipes. The modeling data indicate that in circular and square pipes, the waveguide protruded into the flow between 2 and 10 mm results in a reduction in particle velocity of about 30.6%–32.7% and 30.8%–32.9%, respectively, and an increase in particle concentration of about 3.5%–15.6% and 4.0%–17.3%, respectively. Based on the modeling data, a sensing system incorporating electrostatic sensors is developed to measure the particle velocity and concentration in the upstream of the waveguide. Experimental tests were carried out on both circular and square vertical pipes on a particle flow test rig. Experimental results show that in circular and square pipes, the waveguide protruded into the flow between 2 and 10 mm results in a reduction in particle velocity of approximately 32.5%–34.5% and 32.7%–34.8%, respectively, and an increase in particle concentration of approximately 4.1%–19.5% and 4.6%–21.8%, respectively. The experimental results show a close agreement with the modeling data.
{"title":"Characterization of the Velocity and Concentration of Pneumatically Conveyed Particles in the Upstream of an Acoustic Emission Waveguide Through CFD-DEM Modeling and Electrostatic Sensing","authors":"Xingxing Zeng;Yong Yan;Xiangchen Qian;Kamel Reda;Yunlong Lu","doi":"10.1109/TIM.2024.3488148","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488148","url":null,"abstract":"The characterization of the velocity and concentration of pneumatically conveyed particles in the upstream of the waveguide protruded into the flow is essential for the measurement of the mass flow rate and size distribution of particles using acoustic emission (AE) methods. However, the protrusion of the waveguide affects the movement of particles, and there is a challenge in quantifying its effects on particle velocity and concentration due to the complexity of the dynamics of particle flow. Therefore, the computational fluid dynamics-discrete element method (CFD-DEM) is employed in this study to simulate the collisions between particles and waveguides with a varying protrusion depth in both circular and square vertical pipes. The modeling data indicate that in circular and square pipes, the waveguide protruded into the flow between 2 and 10 mm results in a reduction in particle velocity of about 30.6%–32.7% and 30.8%–32.9%, respectively, and an increase in particle concentration of about 3.5%–15.6% and 4.0%–17.3%, respectively. Based on the modeling data, a sensing system incorporating electrostatic sensors is developed to measure the particle velocity and concentration in the upstream of the waveguide. Experimental tests were carried out on both circular and square vertical pipes on a particle flow test rig. Experimental results show that in circular and square pipes, the waveguide protruded into the flow between 2 and 10 mm results in a reduction in particle velocity of approximately 32.5%–34.5% and 32.7%–34.8%, respectively, and an increase in particle concentration of approximately 4.1%–19.5% and 4.6%–21.8%, respectively. The experimental results show a close agreement with the modeling data.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488132
Marco Mestice;Gabriele Ciarpi;Daniele Rossi;Sergio Saponara
Reliability is an important characteristic of electronic systems, and it could be undermined by several issues. Among these, a wide temperature range represents a threat to the correct operation of electronic systems. Indeed, wide temperature ranges can be experienced in various fields, such as the oil and gas industry, the avionics and automotive fields, or space applications. In these cases, temperatures can reach maximum values up to 160 °C and minimum values down to -40 °C. In addition, in these fields, the ever-improving sensors’ technology is pushing for increasingly higher data rates to the control units. This implies the use of high-speed point-to-point connections, which usually exploit phase-locked loops (PLL) to synchronize the communication. These PLLs should then be able to operate in harsh environments and the gigahertz range. In this article, we present the design and the experimental verification of a 6.25-GHz PLL for harsh temperature conditions from -40 °C up to 160 °C prototyped in a standard 65-nm CMOS technology. We describe the transistor-level design, and we discuss the setups for all the performed measures. The proposed PLL shows a limited performance dependence on temperature variations, which can be compensated further thanks to a tunable bandwidth. Moreover, it achieves fast locking with low area, low power, and a phase noise below −98 dBc/Hz at 1 MHz.
{"title":"Design and Experimental Verification of a 6.25-GHz PLL for Harsh Temperature Conditions in 65-nm CMOS Technology","authors":"Marco Mestice;Gabriele Ciarpi;Daniele Rossi;Sergio Saponara","doi":"10.1109/TIM.2024.3488132","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488132","url":null,"abstract":"Reliability is an important characteristic of electronic systems, and it could be undermined by several issues. Among these, a wide temperature range represents a threat to the correct operation of electronic systems. Indeed, wide temperature ranges can be experienced in various fields, such as the oil and gas industry, the avionics and automotive fields, or space applications. In these cases, temperatures can reach maximum values up to 160 °C and minimum values down to -40 °C. In addition, in these fields, the ever-improving sensors’ technology is pushing for increasingly higher data rates to the control units. This implies the use of high-speed point-to-point connections, which usually exploit phase-locked loops (PLL) to synchronize the communication. These PLLs should then be able to operate in harsh environments and the gigahertz range. In this article, we present the design and the experimental verification of a 6.25-GHz PLL for harsh temperature conditions from -40 °C up to 160 °C prototyped in a standard 65-nm CMOS technology. We describe the transistor-level design, and we discuss the setups for all the performed measures. The proposed PLL shows a limited performance dependence on temperature variations, which can be compensated further thanks to a tunable bandwidth. Moreover, it achieves fast locking with low area, low power, and a phase noise below −98 dBc/Hz at 1 MHz.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488154
Somesh Ganguly;Arijit Baral;Sivaji Chakravorti
The polarization and depolarization current (PDC) gets affected by de-trapped charges. Analysis of such currents leads to an inaccurate assessment of the transformer insulation condition. Existing insulation model-based methods assume the polarization current to be free of the de-trapped charges. This assumption does not always hold good; moreover, most of the time series forecasting methods available in the literature are based on this assumption. A novel method has been proposed for obtaining the depolarization current, which is less affected by the de-trapping current, from short-duration de-trapped charge affected polarization current. The result presented in the article demonstrates that the use of data that is less affected by de-trapped charges leads to reliable diagnosis. The proposed analysis has been formulated and tested on data collected from various real-life transformers.
{"title":"Power Transformer Insulation Diagnosis Using De-Trapped Charge Affected Short-Duration Dielectric Response","authors":"Somesh Ganguly;Arijit Baral;Sivaji Chakravorti","doi":"10.1109/TIM.2024.3488154","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488154","url":null,"abstract":"The polarization and depolarization current (PDC) gets affected by de-trapped charges. Analysis of such currents leads to an inaccurate assessment of the transformer insulation condition. Existing insulation model-based methods assume the polarization current to be free of the de-trapped charges. This assumption does not always hold good; moreover, most of the time series forecasting methods available in the literature are based on this assumption. A novel method has been proposed for obtaining the depolarization current, which is less affected by the de-trapping current, from short-duration de-trapped charge affected polarization current. The result presented in the article demonstrates that the use of data that is less affected by de-trapped charges leads to reliable diagnosis. The proposed analysis has been formulated and tested on data collected from various real-life transformers.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The gesture recognition technology based on flexible strain sensors has attracted widespread research interest in fields such as human–computer interaction. However, the complex fabrication process of current flexible strain sensors limits their mass production capacity. Herein, we introduce a fabrication method for strain sensors based on 3-D printing technology. A custom serpentine-shaped flexible strain substrate was constructed using low-cost thermoplastic polyurethane (TPU) elastomer as printing material, which met the needs of mass production of the flexible substrate and improvement of mechanical properties of the sensor. Soaking the substrate in a specific proportion of N, N-dimethylformamide (DMF)/carbon black (CB) solution, the sensing layer based on a stable conductive network was constructed on its surface using ultrasound technology, and further softening enhanced its deformation ability. The proposed strain sensor exhibits excellent sensing performance with a wide strain range of up to 200%, high sensitivity of 58.08, fast response time of about 0.1 s, a nd high durability (1000 cycles under 50% strain), achieving detection of human joint motion status. Finally, a wearable gesture recognition system was established based on the data glove integrated with flexible sensors and a support vector machine (SVM), achieving an accuracy rate of 96% for recognizing ten commonly used gestures and translating them into audible speech in real-time. The experimental results demonstrate the practical value and potential application of the designed sensor in wearable devices, and to build communication channels for people with language disorders in the future.
{"title":"Highly Stretchable Serpentine-Shaped Strain Sensor Based on 3-D Printing for Gestures Recognition","authors":"Peng Zhang;Changbo Guo;Liangsong Huang;Yuxia Li;Kun Zhang;Yu Zhang","doi":"10.1109/TIM.2024.3488139","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488139","url":null,"abstract":"The gesture recognition technology based on flexible strain sensors has attracted widespread research interest in fields such as human–computer interaction. However, the complex fabrication process of current flexible strain sensors limits their mass production capacity. Herein, we introduce a fabrication method for strain sensors based on 3-D printing technology. A custom serpentine-shaped flexible strain substrate was constructed using low-cost thermoplastic polyurethane (TPU) elastomer as printing material, which met the needs of mass production of the flexible substrate and improvement of mechanical properties of the sensor. Soaking the substrate in a specific proportion of N, N-dimethylformamide (DMF)/carbon black (CB) solution, the sensing layer based on a stable conductive network was constructed on its surface using ultrasound technology, and further softening enhanced its deformation ability. The proposed strain sensor exhibits excellent sensing performance with a wide strain range of up to 200%, high sensitivity of 58.08, fast response time of about 0.1 s, a nd high durability (1000 cycles under 50% strain), achieving detection of human joint motion status. Finally, a wearable gesture recognition system was established based on the data glove integrated with flexible sensors and a support vector machine (SVM), achieving an accuracy rate of 96% for recognizing ten commonly used gestures and translating them into audible speech in real-time. The experimental results demonstrate the practical value and potential application of the designed sensor in wearable devices, and to build communication channels for people with language disorders in the future.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488152
Qingquan Xu;Jie Dong;Kaixiang Peng;Qichun Zhang
In the process industry production, the online sensing of process performance is very important for the optimization and control of the manufacturing process. However, the information island is formed by long processes and multiple systems of complex production processes. The process data are characterized by high dimensional heterogeneity, nonlinearity, and strong coupling, and the offline assay of process performance is characterized by high discretization and irregular sampling period. In order to solve the above problems, a cloud-edge collaborative soft sensing framework for multiperformance indicators prediction of manufacturing processes with nonregular sampling is proposed. Also, some experiments are carried out with the actual hot strip rolling process, which realizes the joint real-time sensing of the three performance indicators of yield strength (YS), tensile strength (TS), and elongation (EL) with good accuracy.
{"title":"A Cloud-Edge Collaborative Soft Sensing Framework for Multiperformance Indicators of Manufacturing Processes With Irregular Sampling","authors":"Qingquan Xu;Jie Dong;Kaixiang Peng;Qichun Zhang","doi":"10.1109/TIM.2024.3488152","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488152","url":null,"abstract":"In the process industry production, the online sensing of process performance is very important for the optimization and control of the manufacturing process. However, the information island is formed by long processes and multiple systems of complex production processes. The process data are characterized by high dimensional heterogeneity, nonlinearity, and strong coupling, and the offline assay of process performance is characterized by high discretization and irregular sampling period. In order to solve the above problems, a cloud-edge collaborative soft sensing framework for multiperformance indicators prediction of manufacturing processes with nonregular sampling is proposed. Also, some experiments are carried out with the actual hot strip rolling process, which realizes the joint real-time sensing of the three performance indicators of yield strength (YS), tensile strength (TS), and elongation (EL) with good accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488136
Xiaohua Huang;Jiahao Zhu;Ying Huo
In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring high-quality product production and preventing significant economic losses in the industry. However, existing models within the you only look once (YOLO) family, commonly employed for steel surface defect detection, have exhibited limited effectiveness. In this article, we propose an improved version of YOLO, namely, YOLO enhanced by a convolution squeeze-and-excitation (CSE) module, Conv2d-BatchNorm-SiLU (CBS) with Swin transformer (CST) module, and adaptive spatial feature fusion (ASFF) detection head module, i.e., SSA-YOLO, specifically tailored for end-to-end surface defect detection. Our approach incorporates several key modifications aimed at improving performance. First, we integrate a channel attention mechanism module into the shallow convolutional network module of the backbone. This enhancement focuses on channel information to improve feature extraction related to small defects while reducing redundant information in candidate boxes. In addition, we fuse a Swin transformer (Swin-T) module into the neck to enhance feature representation for detecting diverse and multiscale defects. Finally, the ASFF is introduced in YOLO to increase cross-interaction between high and low levels in the feature pyramid network (FPN). Experimental results demonstrate the superior performance and effectiveness of our SSA-YOLO model compared to other state-of-the-art models. Our approach achieves higher accuracy and sensitivity in detecting surface defects, offering significant advancements in steel strip production quality control. The code is available at https://github.com/MIPIT-Team/SSA-YOLO .
{"title":"SSA-YOLO: An Improved YOLO for Hot-Rolled Strip Steel Surface Defect Detection","authors":"Xiaohua Huang;Jiahao Zhu;Ying Huo","doi":"10.1109/TIM.2024.3488136","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488136","url":null,"abstract":"In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring high-quality product production and preventing significant economic losses in the industry. However, existing models within the you only look once (YOLO) family, commonly employed for steel surface defect detection, have exhibited limited effectiveness. In this article, we propose an improved version of YOLO, namely, YOLO enhanced by a convolution squeeze-and-excitation (CSE) module, Conv2d-BatchNorm-SiLU (CBS) with Swin transformer (CST) module, and adaptive spatial feature fusion (ASFF) detection head module, i.e., SSA-YOLO, specifically tailored for end-to-end surface defect detection. Our approach incorporates several key modifications aimed at improving performance. First, we integrate a channel attention mechanism module into the shallow convolutional network module of the backbone. This enhancement focuses on channel information to improve feature extraction related to small defects while reducing redundant information in candidate boxes. In addition, we fuse a Swin transformer (Swin-T) module into the neck to enhance feature representation for detecting diverse and multiscale defects. Finally, the ASFF is introduced in YOLO to increase cross-interaction between high and low levels in the feature pyramid network (FPN). Experimental results demonstrate the superior performance and effectiveness of our SSA-YOLO model compared to other state-of-the-art models. Our approach achieves higher accuracy and sensitivity in detecting surface defects, offering significant advancements in steel strip production quality control. The code is available at \u0000<uri>https://github.com/MIPIT-Team/SSA-YOLO</uri>\u0000.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-17"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we present a novel relaxed-intensified exploration algorithm (RIEA) to synthesize large-aperture sensor arrays producing good array sparsity and optimal weight vector of the sparse sensor arrays for sensing unmanned aerial vehicles (UAVs) in airspace. The proposed algorithm is based on the compressed-sensing framework integrated with a kind of relaxed-intensified optimization thought, which comprises two core stages: the relaxed optimization stage and the intensified reoptimization stage. The relaxed-intensified exploration algorithm (RIEA) is tailored to accelerate array synthesis efficiency and promote global optimization. For the proposed algorithm, the ability to approach the global convergence is embodied in two key stages. The first stage aims to generate an optimal sparse sensor array with arbitrary upper mask constraints, whose upper mask is slightly relaxed to expand the solution space for further enhancing the array sparsity. Meanwhile, direction dimension reduction is further conducted to relax the radiating direction matrix for reducing massive computational cost. For the intensified reoptimization stage, the “relaxed” upper mask is first readjusted back to the strictly constrained strength and the weight vector of the designed sparse sensor array in the previous stage is then further optimized to approach the global optimal solution. Finally, the presence of element pattern for an individual sensor and array beam-scanning capability are also considered and investigated in synthesizing the sparse sensor arrays for precise positioning and sensing of UAVs. Several representative examples of the small/large-aperture sparse sensor arrays are performed to demonstrate the superiority, effectiveness, and robustness of the proposed RIEA.
{"title":"Synthesis of Large Sparse Sensor Arrays Utilizing Relaxed-Intensified Exploration Algorithm (RIEA) for Optimal UAVs Beamforming","authors":"Zhigang Zhou;Cao Zeng;Lan Lan;Guisheng Liao;Shengqi Zhu;Baixiao Chen","doi":"10.1109/TIM.2024.3488133","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488133","url":null,"abstract":"In this study, we present a novel relaxed-intensified exploration algorithm (RIEA) to synthesize large-aperture sensor arrays producing good array sparsity and optimal weight vector of the sparse sensor arrays for sensing unmanned aerial vehicles (UAVs) in airspace. The proposed algorithm is based on the compressed-sensing framework integrated with a kind of relaxed-intensified optimization thought, which comprises two core stages: the relaxed optimization stage and the intensified reoptimization stage. The relaxed-intensified exploration algorithm (RIEA) is tailored to accelerate array synthesis efficiency and promote global optimization. For the proposed algorithm, the ability to approach the global convergence is embodied in two key stages. The first stage aims to generate an optimal sparse sensor array with arbitrary upper mask constraints, whose upper mask is slightly relaxed to expand the solution space for further enhancing the array sparsity. Meanwhile, direction dimension reduction is further conducted to relax the radiating direction matrix for reducing massive computational cost. For the intensified reoptimization stage, the “relaxed” upper mask is first readjusted back to the strictly constrained strength and the weight vector of the designed sparse sensor array in the previous stage is then further optimized to approach the global optimal solution. Finally, the presence of element pattern for an individual sensor and array beam-scanning capability are also considered and investigated in synthesizing the sparse sensor arrays for precise positioning and sensing of UAVs. Several representative examples of the small/large-aperture sparse sensor arrays are performed to demonstrate the superiority, effectiveness, and robustness of the proposed RIEA.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488140
Huang-Chen Lee;Soun-Cheng Wang;Zih-Hua Lin
A runner’s duty factor (DF) is defined as the ratio of ground contact time (GCT) to stride time. Fast runners tend to have short GCTs as well as a small DF. In the current method of DF measurement, the runner needs to run on a treadmill and use a high-speed motion capture camera for video recording to examine manually when the runner’s foot touches and leaves the ground. This method is labor costly, slow, and inefficient. To ease the DF measurement, we proposed a novel method by designing a special wearable sensor system, the Tag, can collect the acceleration of runners and compute DFs automatically. The Tag can be installed on the head, waist, or ankle to obtain the acceleration of runners for DF calculation. However, different runners will generate significantly varying characteristics of acceleration as their body shapes and running habits may not be similar. Therefore, a machine-learning algorithm was introduced to overcome this issue. The proposed system was evaluated on 27 runners with different running professions, genders, heights, and weights. Results indicate that by using acceleration data measured from the runner’s head and training data based on the runner’s profession category, the proposed design can accurately measure the DF, with a mean absolute error (MAE) of 5%. To facilitate the development of this domain, this study features the first open-source wearable sensor design for this application. New sensing components and data processing algorithms may be introduced to enhance the performance and open additional possibilities to apply this technology in this area.
{"title":"An Open-Source Wearable Sensor System for Measuring the Duty Factor of Runners","authors":"Huang-Chen Lee;Soun-Cheng Wang;Zih-Hua Lin","doi":"10.1109/TIM.2024.3488140","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488140","url":null,"abstract":"A runner’s duty factor (DF) is defined as the ratio of ground contact time (GCT) to stride time. Fast runners tend to have short GCTs as well as a small DF. In the current method of DF measurement, the runner needs to run on a treadmill and use a high-speed motion capture camera for video recording to examine manually when the runner’s foot touches and leaves the ground. This method is labor costly, slow, and inefficient. To ease the DF measurement, we proposed a novel method by designing a special wearable sensor system, the Tag, can collect the acceleration of runners and compute DFs automatically. The Tag can be installed on the head, waist, or ankle to obtain the acceleration of runners for DF calculation. However, different runners will generate significantly varying characteristics of acceleration as their body shapes and running habits may not be similar. Therefore, a machine-learning algorithm was introduced to overcome this issue. The proposed system was evaluated on 27 runners with different running professions, genders, heights, and weights. Results indicate that by using acceleration data measured from the runner’s head and training data based on the runner’s profession category, the proposed design can accurately measure the DF, with a mean absolute error (MAE) of 5%. To facilitate the development of this domain, this study features the first open-source wearable sensor design for this application. New sensing components and data processing algorithms may be introduced to enhance the performance and open additional possibilities to apply this technology in this area.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/TIM.2024.3488135
Yong-Un Jeong;Joo-Hyung Chae
To achieve a high input and output (I/O) bandwidth, memory interfaces adopt a parallel I/O structure, in which the offset caused by systemic and random mismatches can limit the I/O bandwidth; thus, offset measurement and correction are required. A receiver with a loop-unrolled decision feedback equalizer (DFE) can have a per-DFE offset between two loop-unrolled data paths, degrading the overall performance. We propose an offset calibration method that can identify and adjust the per-DFE offset, thereby correcting each input-referred offset between multiple lanes. We implemented a prototype that has a two-lane receiver adopting a one-tap weighted-VREF-based DFE in a 28-nm CMOS process. Its energy efficiency and area are 0.21 pJ/bit/lane and 0.004 mm2/lane, respectively. Through the offset calibration with the DFE, a bit error rate (BER) of 10-12 and an improved eye shmoo were achieved at 12 Gb/s in a total of six data lanes in three chips.
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