Pub Date : 2024-08-21DOI: 10.1109/TCE.2024.3446895
Amar Kapic;Andromachi Tsirou;Piero Giorgio Verdini;Sandro Carrara
Monitoring humidity and temperature in silicon-based high-energy physics (HEP) detectors is indispensable but challenging due to space restrictions, radiation, sub-zero temperatures, and strong magnetic fields. This manuscript presents humidity and temperature monitoring systems with radiation compensation suitable for integration in HEP environments. The humidity monitoring system is based on the MK33-W sensor, which exhibits linear output capacitance change with accumulated fluence. The sensor is insensitive to strong magnetic field variations, and its temperature dependence is compensated using the inverse second-degree calibration function. The designed readout circuit is based on commercial off-the-shelf (COTS) components that are not radiation/magnetic field immune and must be placed far away (~100 m) from the sensor. Passive and active shielding methods are applied to minimize the parasitic capacitance introduced by the cables. Furthermore, the readout unit effectively nullifies the sensor internal parasitic resistance. The Pt1000 Resistance Temperature Detector (RTD) is chosen for temperature monitoring due to its high radiation tolerance. The change in resistance of an RTD is equivalent to 2.3 °C after accumulating a dose of $4 cdot 10^{16}$ protons/cm2 which is the highest expected dose in the HL-LHC experiments after 10 years of operation. A cost-effective, embedded-based solution for a massive-temperature readout system that conditions up to 24 RTDs is proposed.
{"title":"Multichannel Radiation-Compensated Systems for Temperature and Humidity Monitoring for High Energy Physics Detectors","authors":"Amar Kapic;Andromachi Tsirou;Piero Giorgio Verdini;Sandro Carrara","doi":"10.1109/TCE.2024.3446895","DOIUrl":"10.1109/TCE.2024.3446895","url":null,"abstract":"Monitoring humidity and temperature in silicon-based high-energy physics (HEP) detectors is indispensable but challenging due to space restrictions, radiation, sub-zero temperatures, and strong magnetic fields. This manuscript presents humidity and temperature monitoring systems with radiation compensation suitable for integration in HEP environments. The humidity monitoring system is based on the MK33-W sensor, which exhibits linear output capacitance change with accumulated fluence. The sensor is insensitive to strong magnetic field variations, and its temperature dependence is compensated using the inverse second-degree calibration function. The designed readout circuit is based on commercial off-the-shelf (COTS) components that are not radiation/magnetic field immune and must be placed far away (~100 m) from the sensor. Passive and active shielding methods are applied to minimize the parasitic capacitance introduced by the cables. Furthermore, the readout unit effectively nullifies the sensor internal parasitic resistance. The Pt1000 Resistance Temperature Detector (RTD) is chosen for temperature monitoring due to its high radiation tolerance. The change in resistance of an RTD is equivalent to 2.3 °C after accumulating a dose of \u0000<inline-formula> <tex-math>$4 cdot 10^{16}$ </tex-math></inline-formula>\u0000 protons/cm2 which is the highest expected dose in the HL-LHC experiments after 10 years of operation. A cost-effective, embedded-based solution for a massive-temperature readout system that conditions up to 24 RTDs is proposed.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7535-7543"},"PeriodicalIF":4.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantum technology has the transformative potential to impact various industries, including consumer technology, by applying quantum systems. However, the quantum systems are inherently sensitive to errors and decoherence, necessitating the development of quantum error correction (QEC) codes to mitigate these issues and preserve the integrity of quantum information while ensuring the reliability of quantum operations. Motivated by this, the main objective of this paper is to provide an analysis of the QEC code, emphasizing its key features and potential benefits. The analysis includes a comprehensive review of current QEC codes, a detailed theoretical examination of prevailing methodologies, and an exploration of quantum gate circuits to evaluate code feasibility and practical implementation. The findings demonstrate that integrating QEC codes enhances quantum state fidelity and error reduction, ensuring the reliability and stability of quantum devices for more accurate and dependable quantum operations. This analysis enhances the understanding of the potential of QEC codes to improve the performance and feasibility of quantum devices in consumer applications.
{"title":"Quantum Error Correction Codes in Consumer Technology: Modeling and Analysis","authors":"Vikram Singh Thakur;Atul Kumar;Jishnu Das;Kapal Dev;Maurizio Magarini","doi":"10.1109/TCE.2024.3442472","DOIUrl":"10.1109/TCE.2024.3442472","url":null,"abstract":"Quantum technology has the transformative potential to impact various industries, including consumer technology, by applying quantum systems. However, the quantum systems are inherently sensitive to errors and decoherence, necessitating the development of quantum error correction (QEC) codes to mitigate these issues and preserve the integrity of quantum information while ensuring the reliability of quantum operations. Motivated by this, the main objective of this paper is to provide an analysis of the QEC code, emphasizing its key features and potential benefits. The analysis includes a comprehensive review of current QEC codes, a detailed theoretical examination of prevailing methodologies, and an exploration of quantum gate circuits to evaluate code feasibility and practical implementation. The findings demonstrate that integrating QEC codes enhances quantum state fidelity and error reduction, ensuring the reliability and stability of quantum devices for more accurate and dependable quantum operations. This analysis enhances the understanding of the potential of QEC codes to improve the performance and feasibility of quantum devices in consumer applications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7102-7111"},"PeriodicalIF":4.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-19DOI: 10.1109/tce.2024.3444792
Ziyi Bai, Man-Chung Wong
{"title":"A Multifunctional Inverter Integrated With Smart Substations for Grid-Connected and Island Operations","authors":"Ziyi Bai, Man-Chung Wong","doi":"10.1109/tce.2024.3444792","DOIUrl":"https://doi.org/10.1109/tce.2024.3444792","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"11 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-19DOI: 10.1109/TCE.2024.3445769
Shiv Shankar Sinha;Praveen Nambisan;Munmun Khanra
Lithium-ion batteries (LIBs) are a widely used energy storage technology owing to their excellent energy density, minimal self-discharge property, and high cycle life. Despite these promising features, their performance is affected by both low and high temperatures. When the internal temperature exceeds a certain threshold, the battery may experience thermal runaway, leading to fire and explosion. Moreover, this process is accelerated at high charge/discharge currents. Therefore, in high current applications, accurate monitoring of the internal temperature of the battery becomes critically important to ensure the safety. Hence, an improved coupled electrothermal model (ICETM) has been proposed by combining a novel three-state thermal model with an existing electrical equivalent circuit model through temperature dependent electrical parameters and heat generation. The primary aim is to improve the accuracy of internal temperature estimation of the battery at high currents while accounting for time efficiency in thermal model parameterization. The ICETM is parameterized through experimental and simulation studies using a LiFePO4/graphite battery. The effectiveness of the proposed model and parameterization method is validated experimentally using two case studies. The results show 14% improvement in accuracy and 140–160 hours time reduction over its existing counterparts in estimating core temperature and model parameterization, respectively.
{"title":"Improved Coupled Electrothermal Model of Lithium-Ion Battery for Accurate Core Temperature Estimation at High Current","authors":"Shiv Shankar Sinha;Praveen Nambisan;Munmun Khanra","doi":"10.1109/TCE.2024.3445769","DOIUrl":"10.1109/TCE.2024.3445769","url":null,"abstract":"Lithium-ion batteries (LIBs) are a widely used energy storage technology owing to their excellent energy density, minimal self-discharge property, and high cycle life. Despite these promising features, their performance is affected by both low and high temperatures. When the internal temperature exceeds a certain threshold, the battery may experience thermal runaway, leading to fire and explosion. Moreover, this process is accelerated at high charge/discharge currents. Therefore, in high current applications, accurate monitoring of the internal temperature of the battery becomes critically important to ensure the safety. Hence, an improved coupled electrothermal model (ICETM) has been proposed by combining a novel three-state thermal model with an existing electrical equivalent circuit model through temperature dependent electrical parameters and heat generation. The primary aim is to improve the accuracy of internal temperature estimation of the battery at high currents while accounting for time efficiency in thermal model parameterization. The ICETM is parameterized through experimental and simulation studies using a LiFePO4/graphite battery. The effectiveness of the proposed model and parameterization method is validated experimentally using two case studies. The results show 14% improvement in accuracy and 140–160 hours time reduction over its existing counterparts in estimating core temperature and model parameterization, respectively.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"6463-6471"},"PeriodicalIF":4.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-19DOI: 10.1109/TCE.2024.3445629
Shuqi Qin;Shenghao Liu;Shengjie Ye;Xiaoxuan Fan;Minmin Cheng;Yuanyuan He;Xianjun Deng;Jong Hyuk Park
Anomalies within data flows in the Internet of Things environment can potentially result in security vulnerabilities in consumer electronics. Therefore, it is crucial to effectively detect anomaly data to safeguard the reliability and continuous functionality of consumer electronics. Existing related works either learn from unlabeled data using unsupervised methods or leverage the limited labeled data to improve detection performance by semi-supervised methods. However, these methods usually overfit specific types of known anomalies or ignore the uncertainty when model training. To this end, we design a novel approach to jointly optimize the end-to-end detection of labeled and unlabeled anomalies. Specifically, the anomaly data detection problem investigated is first reformulated as a Markov decision process. Then, a partially labeled anomaly data detection approach (PANDA) based on prioritized deep deterministic policy gradient is proposed, which considers uncertainty when the agent makes decisions and can learn from the labeled known anomalies while continuously exploring and detecting prospective anomalies in unlabeled data. Extensive experiments on 13 datasets show that PANDA improves the AUC-ROC and AUC-PR by 3.0%-10.3% and 10.0%-73.5% and its robustness under the impact of anomaly contamination rates compared with four state-of-the-art competing methods.
{"title":"A Partially Labeled Anomaly Data Detection Approach Based on Prioritized Deep Reinforcement Learning for Consumer Electronics Security","authors":"Shuqi Qin;Shenghao Liu;Shengjie Ye;Xiaoxuan Fan;Minmin Cheng;Yuanyuan He;Xianjun Deng;Jong Hyuk Park","doi":"10.1109/TCE.2024.3445629","DOIUrl":"10.1109/TCE.2024.3445629","url":null,"abstract":"Anomalies within data flows in the Internet of Things environment can potentially result in security vulnerabilities in consumer electronics. Therefore, it is crucial to effectively detect anomaly data to safeguard the reliability and continuous functionality of consumer electronics. Existing related works either learn from unlabeled data using unsupervised methods or leverage the limited labeled data to improve detection performance by semi-supervised methods. However, these methods usually overfit specific types of known anomalies or ignore the uncertainty when model training. To this end, we design a novel approach to jointly optimize the end-to-end detection of labeled and unlabeled anomalies. Specifically, the anomaly data detection problem investigated is first reformulated as a Markov decision process. Then, a partially labeled anomaly data detection approach (PANDA) based on prioritized deep deterministic policy gradient is proposed, which considers uncertainty when the agent makes decisions and can learn from the labeled known anomalies while continuously exploring and detecting prospective anomalies in unlabeled data. Extensive experiments on 13 datasets show that PANDA improves the AUC-ROC and AUC-PR by 3.0%-10.3% and 10.0%-73.5% and its robustness under the impact of anomaly contamination rates compared with four state-of-the-art competing methods.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"6452-6462"},"PeriodicalIF":4.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Asynchronous Remote Distributed Key Generation Method for Securing User Data in the Metaverse","authors":"Yintong Wang, Guowei Fang, Shitao Huang, Zhuotao Lian, Yongjun Ren","doi":"10.1109/tce.2024.3445382","DOIUrl":"https://doi.org/10.1109/tce.2024.3445382","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"77 6 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel deep-learning method for detecting and tracking vehicles in autonomous driving scenarios, with a focus on vehicle failure situations. The primary objective is to enhance road safety by accurately identifying and monitoring vehicles. Our approach combines YOLOv8 models with Transformers-based convolutional neural networks (CNNs) to address the limitations of traditional CNNs in capturing high-level semantic information. A key contribution is the integration of a modified pyramid pooling model for real-time vehicle detection and kernelized filter-based techniques for efficient vehicle tracking with minimal human intervention. The proposed method demonstrates significant improvements in detection accuracy, with experimental results showing increases of 4.50%, 4.46%, and 3.59% on the DLR3K, VEDAI, and VAID datasets, respectively. Our qualitative and quantitative analysis highlights the model’s robustness in handling shadows and occlusions in traffic scenes, outperforming several existing methods. This research contributes a more effective solution for real-time multi-vehicle detection and tracking in autonomous driving systems.
{"title":"Smart Traffic Monitoring Through Real-Time Moving Vehicle Detection Using Deep Learning via Aerial Images for Consumer Application","authors":"Avaneesh Singh;Mohammad Zia Ur Rahma;Preeti Rani;Navin Kumar Agrawal;Rohit Sharma;Elham Kariri;Daniel Gavilanes Aray","doi":"10.1109/TCE.2024.3445728","DOIUrl":"10.1109/TCE.2024.3445728","url":null,"abstract":"This paper presents a novel deep-learning method for detecting and tracking vehicles in autonomous driving scenarios, with a focus on vehicle failure situations. The primary objective is to enhance road safety by accurately identifying and monitoring vehicles. Our approach combines YOLOv8 models with Transformers-based convolutional neural networks (CNNs) to address the limitations of traditional CNNs in capturing high-level semantic information. A key contribution is the integration of a modified pyramid pooling model for real-time vehicle detection and kernelized filter-based techniques for efficient vehicle tracking with minimal human intervention. The proposed method demonstrates significant improvements in detection accuracy, with experimental results showing increases of 4.50%, 4.46%, and 3.59% on the DLR3K, VEDAI, and VAID datasets, respectively. Our qualitative and quantitative analysis highlights the model’s robustness in handling shadows and occlusions in traffic scenes, outperforming several existing methods. This research contributes a more effective solution for real-time multi-vehicle detection and tracking in autonomous driving systems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7302-7309"},"PeriodicalIF":4.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things (IoT) has proliferated ubiquitous information exchange between the physical and cyber worlds through consumer electronics, with a focus on moving computing power to edge terminals. Computing-in-memory (CIM) technology has emerged as a competitive candidate for edge computing because of its low power consumption and high performance. In order to achieve accurate inference for neural network models, it is crucial to comprehend the source of errors in the CIM-based analog computing paradigm. In this work, we analyzed the impact of random noises and output stabling times on the Programmable Linear Random Access Memory (PLRAM)-based CIM chip. Experimental results show that the impact of random noise is negligible. The output stabling time can be treated as RC delay, which is related to the weight distribution. We proposed a weight reordering strategy to achieve better performance without sacrificing computation accuracy. Experiments with a commercial 11-keyword speech recognition model show a 74.4% runtime reduction while maintaining a 95.6% classification accuracy.
{"title":"Mitigating RC-Delay Induced Accuracy Loss in Analog In-Memory Computing: A Non-Compromising Approach","authors":"Saike Zhu;Cimang Lu;Xiang Qiu;Shifan Gao;Xiang Ding;Youngseo Kim;Yi Zhao","doi":"10.1109/TCE.2024.3445341","DOIUrl":"10.1109/TCE.2024.3445341","url":null,"abstract":"The Internet of Things (IoT) has proliferated ubiquitous information exchange between the physical and cyber worlds through consumer electronics, with a focus on moving computing power to edge terminals. Computing-in-memory (CIM) technology has emerged as a competitive candidate for edge computing because of its low power consumption and high performance. In order to achieve accurate inference for neural network models, it is crucial to comprehend the source of errors in the CIM-based analog computing paradigm. In this work, we analyzed the impact of random noises and output stabling times on the Programmable Linear Random Access Memory (PLRAM)-based CIM chip. Experimental results show that the impact of random noise is negligible. The output stabling time can be treated as RC delay, which is related to the weight distribution. We proposed a weight reordering strategy to achieve better performance without sacrificing computation accuracy. Experiments with a commercial 11-keyword speech recognition model show a 74.4% runtime reduction while maintaining a 95.6% classification accuracy.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7544-7550"},"PeriodicalIF":4.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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