Pub Date : 2023-11-28DOI: 10.1080/02564602.2023.2271719
Mamidala Jagadesh Kumar
Published in IETE Technical Review (Vol. 40, No. 6, 2023)
发表于IETE技术评论(第40卷第6期，2023年)
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Pub Date : 2023-11-27DOI: 10.1080/02564602.2023.2280187
Aaqib A. Patel, Abdul Mateen Ahmed, B. Praveen Sai, Mohammed Zafar Ali Khan
Short block length “block” codes are typically not used in wireless standards as soft decision decoding is computationally intensive and hard decision decoding results in performance loss. However,...
{"title":"Parity Check Codes for Second Order Diversity","authors":"Aaqib A. Patel, Abdul Mateen Ahmed, B. Praveen Sai, Mohammed Zafar Ali Khan","doi":"10.1080/02564602.2023.2280187","DOIUrl":"https://doi.org/10.1080/02564602.2023.2280187","url":null,"abstract":"Short block length “block” codes are typically not used in wireless standards as soft decision decoding is computationally intensive and hard decision decoding results in performance loss. However,...","PeriodicalId":13252,"journal":{"name":"IETE Technical Review","volume":"15 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138515782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-11-20DOI: 10.1080/02564602.2023.2276914
Udayabhaskar Pattapu, Rajasekhar Manda, Sushrut Das
The RF (Radio Frequency) signals Carry both energy and information. Harvesting energy from these RF signals leads to new technology such as WEH (Wireless Energy Harvesting). In this paper, the desi...
{"title":"A Spurious Free 2.45 GHz Array Antenna for Energy Harvesting Applications","authors":"Udayabhaskar Pattapu, Rajasekhar Manda, Sushrut Das","doi":"10.1080/02564602.2023.2276914","DOIUrl":"https://doi.org/10.1080/02564602.2023.2276914","url":null,"abstract":"The RF (Radio Frequency) signals Carry both energy and information. Harvesting energy from these RF signals leads to new technology such as WEH (Wireless Energy Harvesting). In this paper, the desi...","PeriodicalId":13252,"journal":{"name":"IETE Technical Review","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138515793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-11-16DOI: 10.1080/02564602.2023.2279155
Md Hasan Maruf, Syed Iftekhar Ali
Researchers are currently emphasizing the development of memory design based on memristors as a solution to the challenges posed by MOSFET-based designs. In this paper, a memristor-based ternary co...
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Pub Date : 2023-11-06DOI: 10.1080/02564602.2023.2277375
Permvir Singh
AbstractIn-band emission represents a critical metric for assessing the reliability of a mobile communication network and ensuring quality of service (QoS) for end users. In the 5G network deployment, due to the limited availability of frequency spectrum, mobile users and internet-of-things (IoT) devices will transmit on the same frequency bands, necessitating more stringent in-band interference management to facilitate interference-free communication. In order to ensure that high-power user equipment (UEs) accessing the shared 5G uplink channel are able to communicate without disruption and without interfering with low-power devices, it is imperative to undertake in-band emissions analysis and control. This paper explores the issue of in-band emission interference, which pertains to the interference that arises in unallocated sub-bands within a specific bandwidth. Specifically, this investigation delves into the modeling approach and regulated in-band emissions within a non-standalone (NSA) 5G network. To verify compliance with 3GPP requirements for in-band emissions regulations, an over-the-air (OTA) testing system is employed to examine in-band emissions in the physical uplink shared channel (PUSCH). The study employs various resource block distributions, offsets, and modulation techniques within the specified NR channel bandwidth. All simulations are based on 3GPP specifications.KEYWORDS: Cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM)Frequency spectrumIn-band emissionsNon-standalone (NSA) networkOver-the-air (OTA) measurementPhysical uplink shared channel (PUSCH)Resource block (RB) allocationTransmission bandwidth Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsPermvir SinghPermvir Singh received his MSc degree from the University of Sussex, UK in 2020. He graduated from the Institution of Electronics and Telecommunication Engineers (IETE), New Delhi, India in 2007. He served in leading telecom companies. Currently, he is with Rohde & Schwarz (India) in the Department of Test and Measurement. His research interests include wireless communication, physical layer, multi-user MIMO communications, massive MIMO, millimeter wave communication, joint communication and sensing, antennas and microwaves, and terahertz frequencies. Corresponding author. Email: permvir.s@gmail.com
{"title":"A Review of the 3GPP-Compatible Model of 5G in-Band Emissions in Uplink Resource Allocation in Hybrid Network","authors":"Permvir Singh","doi":"10.1080/02564602.2023.2277375","DOIUrl":"https://doi.org/10.1080/02564602.2023.2277375","url":null,"abstract":"AbstractIn-band emission represents a critical metric for assessing the reliability of a mobile communication network and ensuring quality of service (QoS) for end users. In the 5G network deployment, due to the limited availability of frequency spectrum, mobile users and internet-of-things (IoT) devices will transmit on the same frequency bands, necessitating more stringent in-band interference management to facilitate interference-free communication. In order to ensure that high-power user equipment (UEs) accessing the shared 5G uplink channel are able to communicate without disruption and without interfering with low-power devices, it is imperative to undertake in-band emissions analysis and control. This paper explores the issue of in-band emission interference, which pertains to the interference that arises in unallocated sub-bands within a specific bandwidth. Specifically, this investigation delves into the modeling approach and regulated in-band emissions within a non-standalone (NSA) 5G network. To verify compliance with 3GPP requirements for in-band emissions regulations, an over-the-air (OTA) testing system is employed to examine in-band emissions in the physical uplink shared channel (PUSCH). The study employs various resource block distributions, offsets, and modulation techniques within the specified NR channel bandwidth. All simulations are based on 3GPP specifications.KEYWORDS: Cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM)Frequency spectrumIn-band emissionsNon-standalone (NSA) networkOver-the-air (OTA) measurementPhysical uplink shared channel (PUSCH)Resource block (RB) allocationTransmission bandwidth Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsPermvir SinghPermvir Singh received his MSc degree from the University of Sussex, UK in 2020. He graduated from the Institution of Electronics and Telecommunication Engineers (IETE), New Delhi, India in 2007. He served in leading telecom companies. Currently, he is with Rohde & Schwarz (India) in the Department of Test and Measurement. His research interests include wireless communication, physical layer, multi-user MIMO communications, massive MIMO, millimeter wave communication, joint communication and sensing, antennas and microwaves, and terahertz frequencies. Corresponding author. Email: permvir.s@gmail.com","PeriodicalId":13252,"journal":{"name":"IETE Technical Review","volume":"20 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135684661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-17DOI: 10.1080/02564602.2023.2268582
Sunil Kumar Gautam, Savita Nema, R.K. Nema
AbstractThis article proposes a novel hybrid technique of order abatement for large-scale models that combines the Mihailov stability method (MSM) and the stability equation method (SEM). In this approach, the denominator coefficients of the higher-order system (HOS) are estimated using the MSM, while the numerator coefficients are computed using the SEM. The suggested approach is based on the MSM, which guarantees the stability of the estimated model if the actual model is stable. The MSM also makes sure that important factors of the original plant, such as dominant poles and stability, are retained in the reduced order system (ROS). The suggested approach is compared to several current conventional reduction methods using error indicators, and the smallest performance error indices values reflect the supremacy of the method. The transfer function (TF) of the ROS is then used to design controllers by employing the moment matching technique. When the controller designed with the approximated model is applied to the real HOS, it indicates that the response of the closed-loop system of the real model entirely overlaps with the response of the reference plant. To further demonstrate the efficiency of the proposed schemes, time-domain specifications are produced and time responses are plotted.KEYWORDS: Controller designHigher order modelMihailov stability methodModel order reductionReduced order modelStability equation method Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article.Additional informationNotes on contributorsSunil Kumar GautamSunil Kumar Gautam received the Btech degree in electrical engineering from Uttar Pradesh Technical University, Uttar Pradesh, India, in 2013, and the Mtech degree in control and instrumentation from Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India, in 2019. He is currently pursuing a PhD in control systems from the Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India. His current research interests include mathematical modelling of electrical systems, model order reduction, and controller design. Corresponding author. Email: sunilgautam827@gmail.comSavita NemaSavita Nema was born in Jabalpur, Madhya Pradesh, India. She is currently a professor in the Department of Electrical Engineering at the Maulana Azad National Institute of Technology (MANIT), Bhopal, India. She received her B.E. degree in electrical engineering and her M.E. degree in control systems from Jabalpur Engineering College, Madhya Pradesh, India, in 1990 and 1993, respectively. She received her PhD degree from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) Bhopal, India, in 2011. She has 30 years’ experience in teaching and research. She has published more than 100 research papers in national and international journals and conferences,
摘要本文提出了一种结合Mihailov稳定性法(MSM)和稳定性方程法(SEM)的大尺度模型阶消减混合技术。在该方法中，使用MSM估计高阶系统(HOS)的分母系数，而使用SEM计算分子系数。该方法基于MSM，在实际模型稳定的情况下，保证了估计模型的稳定性。MSM还确保了原植物的重要因素，如优势极和稳定性，在降阶系统(ROS)中被保留。将该方法与目前几种使用误差指标的常规约简方法进行了比较，最小的性能误差指标值反映了该方法的优越性。然后利用ROS的传递函数(TF)通过矩匹配技术来设计控制器。将逼近模型设计的控制器应用于实际对象时，结果表明，实际模型闭环系统的响应与参考对象的响应完全重合。为了进一步证明所提方案的有效性，给出了时域规范并绘制了时间响应图。关键词:控制器设计高阶模型mihailov稳定性方法模型降阶模型稳定性方程方法披露声明作者未报告潜在的利益冲突。数据可用性声明作者确认在文章中可以获得支持本研究结果的数据。sunil Kumar Gautam于2013年获得印度北方邦Uttar Pradesh技术大学电气工程学士学位，并于2019年获得印度北方邦阿拉哈巴德Motilal Nehru国立理工学院控制和仪器仪表硕士学位。他目前正在印度中央邦博帕尔Maulana Azad National Institute of Technology攻读控制系统博士学位。他目前的研究兴趣包括电气系统的数学建模、模型降阶和控制器设计。相应的作者。NemaSavita Nema出生于印度中央邦贾巴尔普尔。她目前是印度博帕尔Maulana Azad国立理工学院(MANIT)电气工程系的教授。她分别于1990年和1993年在印度中央邦贾巴尔普尔工程学院获得电气工程学士学位和控制系统硕士学位。她于2011年在印度博帕尔获得拉吉夫·甘地(Rajiv Gandhi)博士学位。她有30年的教学和科研经验。她在国内和国际期刊和会议上发表了100多篇研究论文，并与人合著了四本书。她目前的研究兴趣包括可再生能源、光伏、控制系统、电力驱动和电动汽车。电子邮件:s_nema@yahoo.comR.K。NemaRajesh Kumar Nema于1963年出生在印度中央邦的贾巴尔普尔。他分别于1986年和1992年获得博帕尔大学(Bhopal University)电气工程学士学位和硕士学位。他于2004年在博帕尔的Barkatullah University获得电气工程博士学位。他一直在曼尼特博帕尔大学电气工程系担任教授。他发表了150多篇文章。他目前的研究兴趣包括多级逆变器、太阳能光伏控制器、混合能源系统、控制系统和可再生能源应用的电力电子转换器。电子邮件:rk_nema@yahoo.com
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Pub Date : 2023-10-15DOI: 10.1080/02564602.2023.2265897
Anil Sharma, Ila Sharma, Anil Kumar
AbstractThe last few decades have emerged as a remarkable era for exploring and employing electromyography (EMG) signals and their attributes in various applications such as clinical assessment and rehabilitation engineering. An EMG signal-based system encapsulates different domains of signal acquisition and processing, statistical analysis, and control systems in a single framework. This survey attempts to highlight and distinguish the time- and frequency-based signal processing according to the applications of EMG signals. When EMG signals are used for clinical assessment, time–frequency analysis involves transforming the signals in different domains and extracting useful physiological information. On the other hand, the concept of time and frequency deals with extracting time, frequency, or time–frequency-based features when EMG signals are used for pattern recognition-based control applications such as robotics and augmented reality. It is often very difficult and confusing to distinguish and establish a clear understanding between these domains reported in various literature. Hence, this study first presents different signal acquisition systems and pre-processing techniques, followed by comprehending the concepts in time, frequency, and time–frequency-based approaches based on the applications. Next, the review of various post-processing techniques, different feature extraction routines, and a survey of different classifiers used in the pattern recognition step is done. The work concludes with a study of innovative applications of EMG signals reported in recent years, provides an overview of EMG signal-based limb prosthetics, and suggests a few futuristic research ideas.KEYWORDS: Biomedical engineeringData acquisitionElectromyographyFeature extractionPattern classificationProstheticsSignal processing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsAnil SharmaAnil Sharma received a BTech degree in electronics and communication and an MTech degree in mechatronics engineering. Currently, he is pursuing PhD in the electronics and communication engineering department from Malaviya National Institute of Technology (MNIT), Jaipur. His current research interest includes biomedical signal processing, EMG signal acquisition and control, machine learning, and robotics. Corresponding author. Email: 2020rec9510@mnit.ac.inIla SharmaIla Sharma received a PhD degree in electronic and communication engineering from the PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur, India. Currently, she is an assistant professor with the electronics and communication engineering department at Malaviya National Institute of Technology (MNIT), Jaipur, India. Her current research interests includemulti-ate filter banks, digital signal processing, multiplier-less filters and filter banks, wireless communication, and cognitive radio. Email: ila.ece@mnit.ac.
{"title":"Signal Acquisition and Time–Frequency Perspective of EMG Signal-based Systems and Applications","authors":"Anil Sharma, Ila Sharma, Anil Kumar","doi":"10.1080/02564602.2023.2265897","DOIUrl":"https://doi.org/10.1080/02564602.2023.2265897","url":null,"abstract":"AbstractThe last few decades have emerged as a remarkable era for exploring and employing electromyography (EMG) signals and their attributes in various applications such as clinical assessment and rehabilitation engineering. An EMG signal-based system encapsulates different domains of signal acquisition and processing, statistical analysis, and control systems in a single framework. This survey attempts to highlight and distinguish the time- and frequency-based signal processing according to the applications of EMG signals. When EMG signals are used for clinical assessment, time–frequency analysis involves transforming the signals in different domains and extracting useful physiological information. On the other hand, the concept of time and frequency deals with extracting time, frequency, or time–frequency-based features when EMG signals are used for pattern recognition-based control applications such as robotics and augmented reality. It is often very difficult and confusing to distinguish and establish a clear understanding between these domains reported in various literature. Hence, this study first presents different signal acquisition systems and pre-processing techniques, followed by comprehending the concepts in time, frequency, and time–frequency-based approaches based on the applications. Next, the review of various post-processing techniques, different feature extraction routines, and a survey of different classifiers used in the pattern recognition step is done. The work concludes with a study of innovative applications of EMG signals reported in recent years, provides an overview of EMG signal-based limb prosthetics, and suggests a few futuristic research ideas.KEYWORDS: Biomedical engineeringData acquisitionElectromyographyFeature extractionPattern classificationProstheticsSignal processing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsAnil SharmaAnil Sharma received a BTech degree in electronics and communication and an MTech degree in mechatronics engineering. Currently, he is pursuing PhD in the electronics and communication engineering department from Malaviya National Institute of Technology (MNIT), Jaipur. His current research interest includes biomedical signal processing, EMG signal acquisition and control, machine learning, and robotics. Corresponding author. Email: 2020rec9510@mnit.ac.inIla SharmaIla Sharma received a PhD degree in electronic and communication engineering from the PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur, India. Currently, she is an assistant professor with the electronics and communication engineering department at Malaviya National Institute of Technology (MNIT), Jaipur, India. Her current research interests includemulti-ate filter banks, digital signal processing, multiplier-less filters and filter banks, wireless communication, and cognitive radio. Email: ila.ece@mnit.ac.","PeriodicalId":13252,"journal":{"name":"IETE Technical Review","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135758631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AbstractAutomated identification of mathematical expressions (MEs) is essential in transforming scientific and engineering documents into electronic form. Even though character and symbol recognizers have achieved commendable performance for digitizing documents, structure analysers still face a challenge in correctly interpreting the maths expressions. This review paper compares the salient aspects of past works dealing with structure analysis of printed and handwritten MEs. To the best of our knowledge, no previous work has done a systematic study of structural analysis methods in mathematical expression recognition. We present distinguishing aspects of different grammars and their production rules for semantic parsing of ME. Our study contributes by providing information on the existing datasets, their desirable properties, different evaluation measures, distinguishing aspects of techniques used and future research directions in structural analysis.KEYWORDS: Deep learningMathematical expression recognitionOfflineOnlineStructural analysisSyntactic parsing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRidhi AggarwalRidhi Aggarwal received a BTech in electrical and electronics engineering and an ME in electronics product design and technology. She is currently working towards her PhD degree from IIT Jodhpur.Shilpa PandeyShilpa Pandey (PhD 2019) is an assistant professor in the Department of CSE at Pandit Deendayal Energy University, Gujarat. Email: shilpaiitj@gmail.comAnil Kumar TiwariAnil Kumar Tiwari (member, IEEE) is a professor at Department of EE at IIT Jodhpur. His research interests include development of algorithms for image and video compression, noise minimization, interpolation, healthcare and watermarking. Email: akt@iitj.ac.inGaurav HaritGaurav Harit (PhD 2007) is a professor in the Department of CSE at the IIT Jodhpur since 2010. His areas of interest are document image analysis and video analysis. Email: gharit@iitj.ac.in
{"title":"Survey of Structural Analysis in Mathematical Expression Recognition","authors":"Ridhi Aggarwal, Shilpa Pandey, Anil Kumar Tiwari, Gaurav Harit","doi":"10.1080/02564602.2023.2265864","DOIUrl":"https://doi.org/10.1080/02564602.2023.2265864","url":null,"abstract":"AbstractAutomated identification of mathematical expressions (MEs) is essential in transforming scientific and engineering documents into electronic form. Even though character and symbol recognizers have achieved commendable performance for digitizing documents, structure analysers still face a challenge in correctly interpreting the maths expressions. This review paper compares the salient aspects of past works dealing with structure analysis of printed and handwritten MEs. To the best of our knowledge, no previous work has done a systematic study of structural analysis methods in mathematical expression recognition. We present distinguishing aspects of different grammars and their production rules for semantic parsing of ME. Our study contributes by providing information on the existing datasets, their desirable properties, different evaluation measures, distinguishing aspects of techniques used and future research directions in structural analysis.KEYWORDS: Deep learningMathematical expression recognitionOfflineOnlineStructural analysisSyntactic parsing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRidhi AggarwalRidhi Aggarwal received a BTech in electrical and electronics engineering and an ME in electronics product design and technology. She is currently working towards her PhD degree from IIT Jodhpur.Shilpa PandeyShilpa Pandey (PhD 2019) is an assistant professor in the Department of CSE at Pandit Deendayal Energy University, Gujarat. Email: shilpaiitj@gmail.comAnil Kumar TiwariAnil Kumar Tiwari (member, IEEE) is a professor at Department of EE at IIT Jodhpur. His research interests include development of algorithms for image and video compression, noise minimization, interpolation, healthcare and watermarking. Email: akt@iitj.ac.inGaurav HaritGaurav Harit (PhD 2007) is a professor in the Department of CSE at the IIT Jodhpur since 2010. His areas of interest are document image analysis and video analysis. Email: gharit@iitj.ac.in","PeriodicalId":13252,"journal":{"name":"IETE Technical Review","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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