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Dr. Satya Singh
Lee Kong Chian School of Medicine, Nanyang Technological University, 608232 Singapore

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0 Computed Tomography
0 DTI
0 Machine Learning
0 Signal Processing
0 Medical Image Analysis

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Machine Learning
Computed Tomography
Medical Image Analysis
3D deep learning

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Journal article
Published: 23 August 2021 in Sustainability
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This study was designed to research the impact of pandemic situations such as COVID-19 in digital transformation (DT). Our proposed study was designed to research whether COVID-19 is a driver of digital transformation and to look at the three most positive and negative DT disruptors. Our study suggests that COVID-19 is a driver of digital transformation, since 94 percent of respondents agreed that COVID-19 is a driver of DT. The second phase of our study shows that technology, automation, and collaboration (TAC) is the most positive significant factor which enables work from anywhere (WFA) (or work from home) arrangements and also leads to the third positive factor of a work-life balance (WLB). The top three negative factors are no work-life balance (NWL), social employment issues (SEI), and data security and technology issues (DST). The negative factors show a contradictory result since NWL is the most negative factor, even though WLB is the third most positive factor. While the pandemic situation is leading to a positive situation for economies and organizations at a micro level, the negative impacts, which will affect overall economic growth as well as social, health, and wealth wellbeing, need to be kept in mind. The motivation of this study was to research positive and negative effects of COVID-19 on DT, since COVID-19 is impacting everyone and everyday life, including businesses. Our study developed a unique framework to address both positive and negative adoption. Our study also highlights the need for organizations and the economy to establish mitigation plans, as the pandemic has already been disrupting the entire world for the past three quarters.

ACS Style

Radhakrishnan Subramaniam; Satya P. Singh; Parasuraman Padmanabhan; Balázs Gulyás; Prashobhan Palakkeel; Raja Sreedharan. Positive and Negative Impacts of COVID-19 in Digital Transformation. Sustainability 2021, 13, 9470 .

AMA Style

Radhakrishnan Subramaniam, Satya P. Singh, Parasuraman Padmanabhan, Balázs Gulyás, Prashobhan Palakkeel, Raja Sreedharan. Positive and Negative Impacts of COVID-19 in Digital Transformation. Sustainability. 2021; 13 (16):9470.

Chicago/Turabian Style

Radhakrishnan Subramaniam; Satya P. Singh; Parasuraman Padmanabhan; Balázs Gulyás; Prashobhan Palakkeel; Raja Sreedharan. 2021. "Positive and Negative Impacts of COVID-19 in Digital Transformation." Sustainability 13, no. 16: 9470.

Journal article
Published: 22 June 2021 in IEEE Transactions on Instrumentation and Measurement
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The design of an antenna, for its subsequent characterization by means of real sets of measurement, requires a specific modeling that allows a best approximation to the actual construction of the device and true measured values. Like Fourier series expansion, the characteristic mode analysis (CMA) creates a set of orthogonal true current on a conducting material with arbitrary geometry. In this work, a hexagon-shaped wideband circularly polarized (CP) antenna using CMA is presented. The design of the proposed antenna is completed in two stages: first, a hexagon-shaped monopole is designed to achieve circular polarization in the upper frequency band, and subsequently a hexagon-shaped slotted patch is implemented for obtaining circular polarization in the lower frequency band. This procedure helps in achieving the wideband CP antenna. In place of using the conventional method of analyzing simulated surface current distribution to verify CP radiation, CMA is used to understand the different modes of the antenna and their contribution toward CP radiation of antenna. A prototype of the proposed antenna is fabricated with dimensions $0.36\lambda \times 0.36\lambda \times 0.013\lambda $ ( $\lambda $ corresponds to the wavelength for lowest working frequency) to validate the simulated results. The impedance bandwidth of the proposed antenna is 2.3–7.6 GHz and the 3-dB axial ratio bandwidth (ARBW) of the antenna is 3.4–6.5 GHz. The measured peak gain of the antenna is 4.6 dBic. The proposed antenna exhibits a consistent radiation pattern, and the operating frequency band of the antenna effectively includes the international mobile telecommunication (IMT), industrial, scientific, and medical band (ISM), and WiMAX spectrums.

ACS Style

Ankit Sharma; Deepak Gangwar; Ravi Prakash Singh; Rashi Solanki; Saurabh Rajpoot; Binod Kumar Kanaujia; Satya P. Singha; Aime Lay-Ekuakille. Design of Compact Wideband Circularly Polarised Hexagon Shaped Antenna using Characteristics Mode Analysis. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -1.

AMA Style

Ankit Sharma, Deepak Gangwar, Ravi Prakash Singh, Rashi Solanki, Saurabh Rajpoot, Binod Kumar Kanaujia, Satya P. Singha, Aime Lay-Ekuakille. Design of Compact Wideband Circularly Polarised Hexagon Shaped Antenna using Characteristics Mode Analysis. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-1.

Chicago/Turabian Style

Ankit Sharma; Deepak Gangwar; Ravi Prakash Singh; Rashi Solanki; Saurabh Rajpoot; Binod Kumar Kanaujia; Satya P. Singha; Aime Lay-Ekuakille. 2021. "Design of Compact Wideband Circularly Polarised Hexagon Shaped Antenna using Characteristics Mode Analysis." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-1.

Journal article
Published: 09 February 2021 in Applied Sciences
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Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

ACS Style

Shabana Urooj; Satya Singh; Areej Malibari; Fadwa Alrowais; Shaeen Kalathil. Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network. Applied Sciences 2021, 11, 1574 .

AMA Style

Shabana Urooj, Satya Singh, Areej Malibari, Fadwa Alrowais, Shaeen Kalathil. Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network. Applied Sciences. 2021; 11 (4):1574.

Chicago/Turabian Style

Shabana Urooj; Satya Singh; Areej Malibari; Fadwa Alrowais; Shaeen Kalathil. 2021. "Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network." Applied Sciences 11, no. 4: 1574.

Review
Published: 29 January 2021 in IEEE Sensors Journal
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The detection is the main feature of a sensor, and, in particular, nanosensor. This latter may detain high capability of detection, given its dimensions. Optoelectronics comes to help the field of nanosensors with innovations regarding new solutions. This paper provides an overview of the most important significant milestones with clear applications, namely in the fields of pressure nanosensors, nanomaterials to be characterized in an optical viewpoint, using artificial intelligence in the design of nanosensors, optical communications with dedicated circuits, and nanosensors for extreme radiation. These cases illustrate the importance of detection, especially the efficiency of the sensor/nanosensor.

ACS Style

Aime Lay-Ekuakille; Alessandro Massaro; Satya P. Singh; Ireneusz Jablonski; Zia Ur Rahman; Fabrizio Spano. Optoelectronic and Nanosensors Detection Systems: A Review. IEEE Sensors Journal 2021, 21, 12645 -12653.

AMA Style

Aime Lay-Ekuakille, Alessandro Massaro, Satya P. Singh, Ireneusz Jablonski, Zia Ur Rahman, Fabrizio Spano. Optoelectronic and Nanosensors Detection Systems: A Review. IEEE Sensors Journal. 2021; 21 (11):12645-12653.

Chicago/Turabian Style

Aime Lay-Ekuakille; Alessandro Massaro; Satya P. Singh; Ireneusz Jablonski; Zia Ur Rahman; Fabrizio Spano. 2021. "Optoelectronic and Nanosensors Detection Systems: A Review." IEEE Sensors Journal 21, no. 11: 12645-12653.

Journal article
Published: 16 December 2020 in IEEE Sensors Journal
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Decoding human activity accurately from wearable sensors can aid in applications related to healthcare and context awareness. The present approaches in this domain use recurrent and/or convolutional models to capture the spatio-temporal features from time-series data from multiple sensors. We propose a deep neural network architecture that not only captures the spatio-temporal features of multiple sensor time-series data but also selects, learns important time points by utilizing a self-attention mechanism. We show the validity of the proposed approach across different data sampling strategies on six public datasets and demonstrate that the self-attention mechanism gave a significant improvement in performance over deep networks using a combination of recurrent and convolution networks. We also show that the proposed approach gave a statistically significant performance enhancement over previous state-of-the-art methods for the tested datasets. The proposed methods open avenues for better decoding of human activity from multiple body sensors over extended periods of time. The code implementation for the proposed model is available at https://github.com/isukrit/encodingHumanActivity.

ACS Style

Satya P. Singh; Madan Kumar Sharma; Aime Lay-Ekuakille; Deepak Gangwar; Sukrit Gupta. Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors. IEEE Sensors Journal 2020, 21, 8575 -8582.

AMA Style

Satya P. Singh, Madan Kumar Sharma, Aime Lay-Ekuakille, Deepak Gangwar, Sukrit Gupta. Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors. IEEE Sensors Journal. 2020; 21 (6):8575-8582.

Chicago/Turabian Style

Satya P. Singh; Madan Kumar Sharma; Aime Lay-Ekuakille; Deepak Gangwar; Sukrit Gupta. 2020. "Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors." IEEE Sensors Journal 21, no. 6: 8575-8582.

Review
Published: 07 September 2020 in Sensors
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The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

ACS Style

Satya P. Singh; Lipo Wang; Sukrit Gupta; Haveesh Goli; Parasuraman Padmanabhan; Balázs Gulyás. 3D Deep Learning on Medical Images: A Review. Sensors 2020, 20, 5097 .

AMA Style

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás. 3D Deep Learning on Medical Images: A Review. Sensors. 2020; 20 (18):5097.

Chicago/Turabian Style

Satya P. Singh; Lipo Wang; Sukrit Gupta; Haveesh Goli; Parasuraman Padmanabhan; Balázs Gulyás. 2020. "3D Deep Learning on Medical Images: A Review." Sensors 20, no. 18: 5097.

Journal article
Published: 17 June 2020 in IEEE Sensors Journal
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A triple-band low radar cross-section (RCS) high isolation antenna is proposed for 24 GHz ISM band sensing and automotive radar applications. The proposed design consists of 2 × 2 patch array that acts as transmit and receive antennas. Low RCS and high isolation are achieved at 24 GHz by designing a metamaterial absorber (MA), which consists of a square ring with a resistor connected in its diagonal arm for the absorption of electromagnetic waves. The proposed MA shows near unity normalized impedance at 24.1 GHz with 90% absorptivity bandwidth of 1 GHz. An array of MA is placed in between and around the transmit/receive antennas to suppress surface current and reduce in-band RCS of the radar sensor. The -10 dB impedance bandwidths of the triple-band sensor antenna are 20.8 to 21.24 GHz, 23.94 to 24.55 GHz, and 27.18 to 27.5 GHz. The proposed sensor antenna achieves isolation of 34 dB between the transmit and receive ports, and peak RCS reduction of 11 dB, as compared to the reference antenna. The half-power beamwidth of the proposed sensor antenna is 38° for E-plane and 52° for H-plane at 24 GHz.

ACS Style

Ankit Sharma; Santanu Dwari; Binod Kumar Kanaujia; Deepak Gangwar; Sachin Kumar; Satya P. Singh; Aime’ Lay-Ekuakille. In-Band RCS Reduction and Isolation Enhancement of a 24 GHz Radar Antenna Using Metamaterial Absorber for Sensing and Automotive Radar Applications. IEEE Sensors Journal 2020, 20, 13086 -13093.

AMA Style

Ankit Sharma, Santanu Dwari, Binod Kumar Kanaujia, Deepak Gangwar, Sachin Kumar, Satya P. Singh, Aime’ Lay-Ekuakille. In-Band RCS Reduction and Isolation Enhancement of a 24 GHz Radar Antenna Using Metamaterial Absorber for Sensing and Automotive Radar Applications. IEEE Sensors Journal. 2020; 20 (21):13086-13093.

Chicago/Turabian Style

Ankit Sharma; Santanu Dwari; Binod Kumar Kanaujia; Deepak Gangwar; Sachin Kumar; Satya P. Singh; Aime’ Lay-Ekuakille. 2020. "In-Band RCS Reduction and Isolation Enhancement of a 24 GHz Radar Antenna Using Metamaterial Absorber for Sensing and Automotive Radar Applications." IEEE Sensors Journal 20, no. 21: 13086-13093.

Articles
Published: 19 May 2020 in International Journal of Electronics
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In this work, a low radar cross-section (RCS) dual-band with one band circularly polarized (CP) slot antenna consisting of an L-shaped metallic strip along with a combination of Split Ring Resonator (SRR) and complementary SRR (CSRR) is proposed. In the dual-band, one wideband operation is obtained by creating a wide slot in the ground plane of the antenna and a narrow band is achieved by loading the antenna with a combination of SRR-CSRR. The proposed antenna achieves impedance bandwidth of 1.5 to 3.26 GHz and 3.6 to 4.1 GHz. The proposed design has 3-dB axial ratio bandwidth (ARBW) of 2.5 to 3 GHz and the peak gain of the antenna is 4.2 and 3.3 dBi in the respective bands. The proposed antenna exhibits significant RCS reduction in the wideband of 4.7 to 18 GHz and peak RCS reduction of 26 dB is achieved at 11.3 GHz. Also, the sense of polarization of the proposed antenna can be changed by 90o rotating the L-shaped metallic strip with reference to the slot in the ground plane.

ACS Style

Hridesh Kumar Verma; R.S. Meena; Mithilesh Kumar; Satya P Singh. A low rcs compact circularly polarized dual band slot antenna loaded with SRR and CSRR for satellite applications. International Journal of Electronics 2020, 107, 1790 -1807.

AMA Style

Hridesh Kumar Verma, R.S. Meena, Mithilesh Kumar, Satya P Singh. A low rcs compact circularly polarized dual band slot antenna loaded with SRR and CSRR for satellite applications. International Journal of Electronics. 2020; 107 (11):1790-1807.

Chicago/Turabian Style

Hridesh Kumar Verma; R.S. Meena; Mithilesh Kumar; Satya P Singh. 2020. "A low rcs compact circularly polarized dual band slot antenna loaded with SRR and CSRR for satellite applications." International Journal of Electronics 107, no. 11: 1790-1807.

Articles
Published: 02 May 2020 in Journal of Electromagnetic Waves and Applications
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In this paper, a compact wideband circularly polarized slot antenna consisting of two L-shaped resonators is proposed. The slot antenna is loaded with a band-stop frequency selective surface (FSS) reflector to improve the gain of the antenna and to reduce the profile of the antenna. The radiation and scattering characteristics of FSS loaded slot antenna are compared with the metallic reflector loaded slot antenna, and the proposed antenna shows improved impedance bandwidth of 1.75–2.65 GHz. The measured results show that the peak gain of the slot antenna with FSS is enhanced by 4 dB. The proposed antenna with FSS also exhibits 3-dB ARBW of 2.2–2.65 GHz, and the scattering performance shows an average RCS reduction of 7.34 dB is achieved by slot antenna with FSS as compared to slot antenna with metallic reflector with peak RCS reduction of 24 dB at 1.2 GHz.

ACS Style

Hridesh Kumar Verma; Mithilesh Kumar; R. S. Meena; Satya P. Singh. A low RCS wideband high gain CP slot antenna loaded with frequency selective surface. Journal of Electromagnetic Waves and Applications 2020, 34, 940 -959.

AMA Style

Hridesh Kumar Verma, Mithilesh Kumar, R. S. Meena, Satya P. Singh. A low RCS wideband high gain CP slot antenna loaded with frequency selective surface. Journal of Electromagnetic Waves and Applications. 2020; 34 (7):940-959.

Chicago/Turabian Style

Hridesh Kumar Verma; Mithilesh Kumar; R. S. Meena; Satya P. Singh. 2020. "A low RCS wideband high gain CP slot antenna loaded with frequency selective surface." Journal of Electromagnetic Waves and Applications 34, no. 7: 940-959.

Journal article
Published: 28 February 2020 in IEEE Sensors Journal
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In this work, a novel Ultra-wideband –Multi-Input-Multi-Output Antenna Sensor (UMAS) probe is designed for the detection of the malignant cells in the breast. The Sensor probe has four radiating elements and it is operated within the 2.8 GHz to 20 GHz ultra-wide band range. Isolation between the radiating element is more than 20 dB. Further, three kinds of the breast phantoms (i.e. normal phantom, phantom with single and multiple tumors) are fabricated using tissue mimicking material. The electrical characteristics of the malignant cells are different from non-malignant cells of the breast. The S-parameter and Specific Absorption Rate (SAR) analysis are best approaches to detect the malignant cells in the breast. The UMAS sensing probe is embedded on the phantoms and S-parameters of the probe are recorded from the Vector Network Analyzer (VNA). Measured S-parameters of the probe for normal and malignant phantoms are differ from each other. The statistical machine learning concept of Principal Component Analysis (PCA) is also applied on the measured S-Parameters. Which exhibits clear detection of normal and malignant breast phantoms. Further verification is done by using Simulation based specific absorption rate (SAR) study of the phantom models for tumor detection. The obtained maximum SAR results are well differentiating the normal phantom.

ACS Style

Madan Kumar Sharma; Mithilesh Kumar; J. P. Saini; Deepak Gangwar; Binod K. Kanaujia; Satya P. Singh; Aime' Lay Ekuakille. Experimental Investigation of the Breast Phantom for Tumor Detection Using Ultra-Wide Band–MIMO Antenna Sensor (UMAS) Probe. IEEE Sensors Journal 2020, 20, 6745 -6752.

AMA Style

Madan Kumar Sharma, Mithilesh Kumar, J. P. Saini, Deepak Gangwar, Binod K. Kanaujia, Satya P. Singh, Aime' Lay Ekuakille. Experimental Investigation of the Breast Phantom for Tumor Detection Using Ultra-Wide Band–MIMO Antenna Sensor (UMAS) Probe. IEEE Sensors Journal. 2020; 20 (12):6745-6752.

Chicago/Turabian Style

Madan Kumar Sharma; Mithilesh Kumar; J. P. Saini; Deepak Gangwar; Binod K. Kanaujia; Satya P. Singh; Aime' Lay Ekuakille. 2020. "Experimental Investigation of the Breast Phantom for Tumor Detection Using Ultra-Wide Band–MIMO Antenna Sensor (UMAS) Probe." IEEE Sensors Journal 20, no. 12: 6745-6752.

Review
Published: 03 October 2019 in Current Pharmaceutical Design
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Background:Multimodal imaging plays an important role in the diagnosis of brain disorders. Neurological disorders need to be diagnosed at an early stage for their effective treatment as later, it is very difficult to treat them. If possible, diagnosing at an early stage can be much helpful in curing the disease with less harm to the body. There is a need for advanced and multimodal imaging techniques for the same. This paper provides an overview of conventional as well as modern imaging techniques for brain diseases, specifically for tumor imaging. In this paper, different imaging modalities are discussed for tumor detection in the brain along with their advantages and disadvantages. Conjugation of two and more than two modalities provides more accurate information rather than a single modality. They can monitor and differentiate the cellular processes of normal and diseased condition with more clarity. The advent of molecular imaging, including reporter gene imaging, has opened the door of more advanced noninvasive detection of brain tumors. Due to specific optical properties, semiconducting polymer-based nanoparticles also play a pivotal role in imaging tumors.Objective:The objective of this paper is to review nanoparticles-mediated brain imaging and disease prognosis by conventional as well as modern modal imaging techniques.Conclusion:We reviewed in detail various medical imaging techniques. This paper covers recent developments in detail and elaborates a possible research aspect for the readers in the field.

ACS Style

Cheng-Tang Pan; Wei-Hsi Chang; Ajay Kumar; Satya P Singh; Aman Chandra Kaushik; Jyotsna Sharma; Zheng-Jing Long; Zhi-Hong Wen; Sunil Kumar Mishra; Chung-Kun Yen; Ravi Kumar Chaudhary; Yow-Ling Shiue. Nanoparticles-mediated Brain Imaging and Disease Prognosis by Conventional as well as Modern Modal Imaging Techniques: a Comparison. Current Pharmaceutical Design 2019, 25, 2637 -2649.

AMA Style

Cheng-Tang Pan, Wei-Hsi Chang, Ajay Kumar, Satya P Singh, Aman Chandra Kaushik, Jyotsna Sharma, Zheng-Jing Long, Zhi-Hong Wen, Sunil Kumar Mishra, Chung-Kun Yen, Ravi Kumar Chaudhary, Yow-Ling Shiue. Nanoparticles-mediated Brain Imaging and Disease Prognosis by Conventional as well as Modern Modal Imaging Techniques: a Comparison. Current Pharmaceutical Design. 2019; 25 (24):2637-2649.

Chicago/Turabian Style

Cheng-Tang Pan; Wei-Hsi Chang; Ajay Kumar; Satya P Singh; Aman Chandra Kaushik; Jyotsna Sharma; Zheng-Jing Long; Zhi-Hong Wen; Sunil Kumar Mishra; Chung-Kun Yen; Ravi Kumar Chaudhary; Yow-Ling Shiue. 2019. "Nanoparticles-mediated Brain Imaging and Disease Prognosis by Conventional as well as Modern Modal Imaging Techniques: a Comparison." Current Pharmaceutical Design 25, no. 24: 2637-2649.

Journal article
Published: 05 August 2019 in New Journal of Chemistry
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Correction for ‘PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches’ by Aman Chandra Kaushik et al., New J. Chem., 2019, DOI: 10.1039/c9nj01902b.

ACS Style

Aman Chandra Kaushik; Ajay Kumar; Chun-Yen Yu; Shiao-Wei Kuo; Shih-Shin Liang; Satya P Singh; Xiangeng Wang; Yan-Jing Wang; Chung-Kun Yen; Xiaofeng Dai; Dong-Qing Wei; Cheng-Tang Pan; Yow-Ling Shiue. Correction: PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches. New Journal of Chemistry 2019, 43, 12511 -12511.

AMA Style

Aman Chandra Kaushik, Ajay Kumar, Chun-Yen Yu, Shiao-Wei Kuo, Shih-Shin Liang, Satya P Singh, Xiangeng Wang, Yan-Jing Wang, Chung-Kun Yen, Xiaofeng Dai, Dong-Qing Wei, Cheng-Tang Pan, Yow-Ling Shiue. Correction: PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches. New Journal of Chemistry. 2019; 43 (31):12511-12511.

Chicago/Turabian Style

Aman Chandra Kaushik; Ajay Kumar; Chun-Yen Yu; Shiao-Wei Kuo; Shih-Shin Liang; Satya P Singh; Xiangeng Wang; Yan-Jing Wang; Chung-Kun Yen; Xiaofeng Dai; Dong-Qing Wei; Cheng-Tang Pan; Yow-Ling Shiue. 2019. "Correction: PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches." New Journal of Chemistry 43, no. 31: 12511-12511.

Paper
Published: 24 June 2019 in New Journal of Chemistry
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A schematic diagram of HCC & TACE; injections of HepaSphere with DOX are made into the femoral artery, abdominal aorta, and hepatic artery to make the tumor shrink to a resectable size due to a shortage of nutrients and drug treatment.

ACS Style

Aman Chandra Kaushik; Ajay Kumar; Chun-Yen Yu; Shiao-Wei Kuo; Shih-Shin Liang; Satya P. Singh; Xiangeng Wang; Yan-Jing Wang; Chung-Kun Yen; Xiaofeng Dai; Dong-Qing Wei; Cheng-Tang Pan; Yow-Ling Shiue. PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches. New Journal of Chemistry 2019, 43, 12241 -12256.

AMA Style

Aman Chandra Kaushik, Ajay Kumar, Chun-Yen Yu, Shiao-Wei Kuo, Shih-Shin Liang, Satya P. Singh, Xiangeng Wang, Yan-Jing Wang, Chung-Kun Yen, Xiaofeng Dai, Dong-Qing Wei, Cheng-Tang Pan, Yow-Ling Shiue. PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches. New Journal of Chemistry. 2019; 43 (31):12241-12256.

Chicago/Turabian Style

Aman Chandra Kaushik; Ajay Kumar; Chun-Yen Yu; Shiao-Wei Kuo; Shih-Shin Liang; Satya P. Singh; Xiangeng Wang; Yan-Jing Wang; Chung-Kun Yen; Xiaofeng Dai; Dong-Qing Wei; Cheng-Tang Pan; Yow-Ling Shiue. 2019. "PCL–DOX microdroplets: an evaluation of the enhanced intracellular delivery of doxorubicin in metastatic cancer cells via in silico and in vitro approaches." New Journal of Chemistry 43, no. 31: 12241-12256.

Journals
Published: 19 June 2019 in RSC Advances
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NP screening through a deep learning approach against Anti-EGFR and validation through docking with AuNP. Biochemical pathway and simulation of AuNP with Anti-EGFR and further implementation in biological circuits.

ACS Style

Aman Chandra Kaushik; Yanjing Wang; Xiangeng Wang; Ajay Kumar; Satya P Singh; Cheng-Tang Pan; Yow-Ling Shiue; Dong-Qing Wei. Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks. RSC Advances 2019, 9, 19261 -19270.

AMA Style

Aman Chandra Kaushik, Yanjing Wang, Xiangeng Wang, Ajay Kumar, Satya P Singh, Cheng-Tang Pan, Yow-Ling Shiue, Dong-Qing Wei. Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks. RSC Advances. 2019; 9 (34):19261-19270.

Chicago/Turabian Style

Aman Chandra Kaushik; Yanjing Wang; Xiangeng Wang; Ajay Kumar; Satya P Singh; Cheng-Tang Pan; Yow-Ling Shiue; Dong-Qing Wei. 2019. "Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks." RSC Advances 9, no. 34: 19261-19270.

Journal article
Published: 10 May 2019 in Sensors
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Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning algorithms can be used to perform medical image classification and assist clinicians in diagnosing radiological scans. In this paper, we apply 3-dimensional convolutional neural networks (3D CNN) to classify computed tomography (CT) brain scans into normal scans (N) and abnormal scans containing subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), acute subdural hemorrhage (ASDH) and brain polytrauma hemorrhage (BPH). The dataset used consists of 399 volumetric CT brain images representing approximately 12,000 images from the National Neuroscience Institute, Singapore. We used a 3D CNN to perform both 2-class (normal versus a specific abnormal class) and 4-class classification (between normal, SAH, IPH, ASDH). We apply image thresholding at the image pre-processing step, that improves 3D CNN classification accuracy and performance by accentuating the pixel intensities that contribute most to feature discrimination. For 2-class classification, the F1 scores for various pairs of medical diagnoses ranged from 0.706 to 0.902 without thresholding. With thresholding implemented, the F1 scores improved and ranged from 0.919 to 0.952. Our results are comparable to, and in some cases, exceed the results published in other work applying 3D CNN to CT or magnetic resonance imaging (MRI) brain scan classification. This work represents a direct application of a 3D CNN to a real hospital scenario involving a medically emergent CT brain diagnosis.

ACS Style

Justin Ker; Satya P. Singh; Yeqi Bai; Jai Rao; Tchoyoson Lim; Lipo Wang. Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans. Sensors 2019, 19, 2167 .

AMA Style

Justin Ker, Satya P. Singh, Yeqi Bai, Jai Rao, Tchoyoson Lim, Lipo Wang. Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans. Sensors. 2019; 19 (9):2167.

Chicago/Turabian Style

Justin Ker; Satya P. Singh; Yeqi Bai; Jai Rao; Tchoyoson Lim; Lipo Wang. 2019. "Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans." Sensors 19, no. 9: 2167.

Journal article
Published: 03 May 2019 in IEEE Access
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With the wide applications of Wireless sensor networks (WSNs) in various fields such as environment monitoring, battlefield surveillance, healthcare, and intrusion detection, trust establishment among sensor nodes becomes vital requirement to improve security, reliability and successful cooperation. The existing trust management approaches for large scale WSN are failed due to their low dependability (i.e. cooperation), higher communication and memory overheads (i.e. resource inefficient). In this paper, we propose a novel and comprehensive trust estimation approach (LTS) for large scale WSN that employs clustering to improve cooperation, trustworthiness, and security by detecting malicious (faulty or selfish) sensor nodes with reduced resource (memory, power) consumption. The proposed scheme (LTS) operates on two levels namely, intra-cluster and inter-cluster along with distributed approach and centralized approach respectively to make accurate trust decision of sensor nodes with minimum overheads. LTS consists of unique features like robust trust estimation function, attack resistant and efficient trust aggregation at cluster head to obtain global feedback trust value. Data trust along with communication trust plays a significant role to cope with malicious nodes. In LTS, punishment and trust severity can be tuned according to the application requirement makes it an innovative trust estimation approach. Moreover, dishonest recommendations (outliers) are eliminated before aggregation at the base station by observing statistical dispersion. Theoretical and mathematical validation along with simulation results exhibit great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes as well as communication overhead.

ACS Style

Tayyab Khan; Karan Singh; Le Hoang Son; Mohamed Abdel-Basset; Hoang Viet Long; Satya P Singh; Manisha Manjul. A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks. IEEE Access 2019, 7, 58221 -58240.

AMA Style

Tayyab Khan, Karan Singh, Le Hoang Son, Mohamed Abdel-Basset, Hoang Viet Long, Satya P Singh, Manisha Manjul. A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks. IEEE Access. 2019; 7 (99):58221-58240.

Chicago/Turabian Style

Tayyab Khan; Karan Singh; Le Hoang Son; Mohamed Abdel-Basset; Hoang Viet Long; Satya P Singh; Manisha Manjul. 2019. "A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks." IEEE Access 7, no. 99: 58221-58240.

Research article electrical engineering
Published: 03 May 2019 in Arabian Journal for Science and Engineering
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Isolation and bandwidth are the two important performance parameters of the multiple-input-multiple-output (MIMO) antenna. A small footprint of an antenna with enhanced isolation and extended bandwidth is highly desirable for space-limited UWB applications. In this paper, we present a compact and computationally optimized MIMO antenna for UWB applications. The proposed antenna consists of two micro-strip-fed semicircular radiating elements. The inverted prism-shaped ground stub is used to enhance isolation. A truncated-shaped partial ground plane with two ground slots is used for impedance matching over the extended UWB. The circular monopole radiating elements of the reference antenna (RA) are converted into semicircular radiating elements for efficient utilization of the available space. The initial design parameters are obtained from the RA. In the next step, the initial design parameters are optimized by a fast and accurate surrogate-assisted optimization model. Using the optimized design parameters, the final design of the antenna is simulated using a computer simulation tool. The prototype of the antenna is fabricated on a Roger substrate (substrate height ‘h’ = 0.8 mm) with a dielectric constant of 3. The manufactured prototype with the size of 31 × 18 mm2 is experimentally evaluated and validated using vector network analyser and anechoic chamber. The proposed MIMO antenna provides extended ultra-wide impedance bandwidth of 3–25 GHz (fractional bandwidth 157%), enhanced isolation S21 ≤ − 27 dB envelope correlation coefficient (ECC = 0.002), good pattern diversity, and constant group delay. Finally, the obtained results are compared with the existing literature.

ACS Style

Madan Kumar Sharma; Mithilesh Kumar; J. P. Saini; Satya P. Singh. Computationally Optimized MIMO Antenna with Improved Isolation and Extended Bandwidth for UWB Applications. Arabian Journal for Science and Engineering 2019, 45, 1333 -1343.

AMA Style

Madan Kumar Sharma, Mithilesh Kumar, J. P. Saini, Satya P. Singh. Computationally Optimized MIMO Antenna with Improved Isolation and Extended Bandwidth for UWB Applications. Arabian Journal for Science and Engineering. 2019; 45 (3):1333-1343.

Chicago/Turabian Style

Madan Kumar Sharma; Mithilesh Kumar; J. P. Saini; Satya P. Singh. 2019. "Computationally Optimized MIMO Antenna with Improved Isolation and Extended Bandwidth for UWB Applications." Arabian Journal for Science and Engineering 45, no. 3: 1333-1343.

Article
Published: 06 October 2018 in Journal of Signal Processing Systems
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Polar harmonic transforms (PHTs) are superior to Zernike moments and Pseudo-Zernike moments in terms of higher speed and numerical stability. Despite all these advantages, there is still a need for fast computing algorithms for real-world applications. So far, the approaches used in boosting the computational speed of PHTs include recursive relation and symmetric/anti-symmetric properties of kernel functions. Taking advantage of these two approaches, a new class of computational framework has been presented to compute the kernel functions with a minimal number of arithmetic operations. The proposed computational framework reduces the number of additions/subtractions from 56 to 24 and the number of multiplications from 12 to 8 compared to existing fast methods. The experimental results show that the proposed method is around 1.4 times faster than that of the existing fastest algorithm.

ACS Style

Satya P. Singh; Shabana Urooj. A New Computational Framework for Fast Computation of a Class of Polar Harmonic Transforms. Journal of Signal Processing Systems 2018, 91, 915 -922.

AMA Style

Satya P. Singh, Shabana Urooj. A New Computational Framework for Fast Computation of a Class of Polar Harmonic Transforms. Journal of Signal Processing Systems. 2018; 91 (8):915-922.

Chicago/Turabian Style

Satya P. Singh; Shabana Urooj. 2018. "A New Computational Framework for Fast Computation of a Class of Polar Harmonic Transforms." Journal of Signal Processing Systems 91, no. 8: 915-922.

Conference paper
Published: 20 January 2018 in Advances in Intelligent Systems and Computing
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The main objective of this paper is to reduce the reconstruction error and fast calculation of Radial Harmonic Fourier Moments (RHFM). In the proposed work, the fast RHFM has been applied on the original gray image for reconstruction. Before applying RHFM on grayscale image, the image in portioned into radial and angular sectors. Results are compared with traditional methods. The proposed approach results in better reconstruction error. Also, moments can be calculated at high speed using proposed approach.

ACS Style

Shabana Urooj; Satya P. Singh; Shevet Kamal Maurya; Mayank Priyadarshi. Fast Radial Harmonic Moments for Invariant Image Representation. Advances in Intelligent Systems and Computing 2018, 533 -538.

AMA Style

Shabana Urooj, Satya P. Singh, Shevet Kamal Maurya, Mayank Priyadarshi. Fast Radial Harmonic Moments for Invariant Image Representation. Advances in Intelligent Systems and Computing. 2018; ():533-538.

Chicago/Turabian Style

Shabana Urooj; Satya P. Singh; Shevet Kamal Maurya; Mayank Priyadarshi. 2018. "Fast Radial Harmonic Moments for Invariant Image Representation." Advances in Intelligent Systems and Computing , no. : 533-538.

Conference paper
Published: 04 October 2017 in Advances in Intelligent Systems and Computing
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This paper presents a novel method to analyze Leukoderma images using Neuro-Fuzzy hybrid (NFH) approach. Skin diseases are the most widespread diseases in India and worldwide. In the proposed work, a hybrid Artificial Neural Fuzzy Inference System (ANFIS) is designed. The advantage of the proposed system is that there is not any connection between fuzzy and neural network. The training data is grouped into several clusters. Each cluster is designed to represent a particular rule. Error rate, output data, and trained data are calculated.

ACS Style

Sudhakar Singh; Shabana Urooj; Satya P Singh. Analysis of Leukoderma Images Using Neuro-Fuzzy Hybrid Technique. Advances in Intelligent Systems and Computing 2017, 651, 93 -101.

AMA Style

Sudhakar Singh, Shabana Urooj, Satya P Singh. Analysis of Leukoderma Images Using Neuro-Fuzzy Hybrid Technique. Advances in Intelligent Systems and Computing. 2017; 651 ():93-101.

Chicago/Turabian Style

Sudhakar Singh; Shabana Urooj; Satya P Singh. 2017. "Analysis of Leukoderma Images Using Neuro-Fuzzy Hybrid Technique." Advances in Intelligent Systems and Computing 651, no. : 93-101.