Displaying publications 1 - 20 of 168 in total

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  1. ECG waveform generation from radar signals: A deep learning perspective
    Chowdhury FA, Hosain MK, Bin Islam MS, Hossain MS, Basak P, Mahmud S, et al.
    Comput Biol Med, 2024 Jun;176:108555.
    PMID: 38749323 DOI: 10.1016/j.compbiomed.2024.108555
    Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ± 18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery.
    Matched MeSH terms: Signal Processing, Computer-Assisted*
  2. Fulltext Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface
    John AA, Subramanian AP, Jaganathan SK, Sethuraman B
    Indian Heart J, 2015 Nov-Dec;67(6):549-51.
    PMID: 26702684 DOI: 10.1016/j.ihj.2015.07.017
    To process the electrocardiogram (ECG) signals using MATLAB-based graphical user interface (GUI) and to classify the signals based on heart rate.
    Matched MeSH terms: Signal Processing, Computer-Assisted
  3. PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal
    Shuzan MNI, Chowdhury MH, Alam SB, Reaz MBI, Khan MS, Murugappan M, et al.
    Phys Eng Sci Med, 2024 Dec;47(4):1705-1722.
    PMID: 39287773 DOI: 10.1007/s13246-024-01482-1
    Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet's performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates.
    Matched MeSH terms: Signal Processing, Computer-Assisted*
  4. Fulltext A novel sample based quadrature phase shift keying demodulator
    Mohamed Moubark A, Ali SH
    ScientificWorldJournal, 2014;2014:107831.
    PMID: 25197687 DOI: 10.1155/2014/107831
    This paper presents a new practical QPSK receiver that uses digitized samples of incoming QPSK analog signal to determine the phase of the QPSK symbol. The proposed technique is more robust to phase noise and consumes up to 89.6% less power for signal detection in demodulation operation. On the contrary, the conventional QPSK demodulation process where it uses coherent detection technique requires the exact incoming signal frequency; thus, any variation in the frequency of the local oscillator or incoming signal will cause phase noise. A software simulation of the proposed design was successfully carried out using MATLAB Simulink software platform. In the conventional system, at least 10 dB signal to noise ratio (SNR) is required to achieve the bit error rate (BER) of 10(-6), whereas, in the proposed technique, the same BER value can be achieved with only 5 dB SNR. Since some of the power consuming elements such as voltage control oscillator (VCO), mixer, and low pass filter (LPF) are no longer needed, the proposed QPSK demodulator will consume almost 68.8% to 99.6% less operational power compared to conventional QPSK demodulator.
    Matched MeSH terms: Signal Processing, Computer-Assisted*
  5. Fulltext Hardware implementation of 32-bit high-speed direct digital frequency synthesizer
    Ibrahim SH, Ali SH, Islam MS
    ScientificWorldJournal, 2014;2014:131568.
    PMID: 24991635 DOI: 10.1155/2014/131568
    The design and implementation of a high-speed direct digital frequency synthesizer are presented. A modified Brent-Kung parallel adder is combined with pipelining technique to improve the speed of the system. A gated clock technique is proposed to reduce the number of registers in the phase accumulator design. The quarter wave symmetry technique is used to store only one quarter of the sine wave. The ROM lookup table (LUT) is partitioned into three 4-bit sub-ROMs based on angular decomposition technique and trigonometric identity. Exploiting the advantages of sine-cosine symmetrical attributes together with XOR logic gates, one sub-ROM block can be removed from the design. These techniques, compressed the ROM into 368 bits. The ROM compressed ratio is 534.2:1, with only two adders, two multipliers, and XOR-gates with high frequency resolution of 0.029 Hz. These techniques make the direct digital frequency synthesizer an attractive candidate for wireless communication applications.
    Matched MeSH terms: Signal Processing, Computer-Assisted/instrumentation*
  6. Studies on the formation, temporal evolution and forensic applications of camera "fingerprints"
    Kuppuswamy R
    Forensic Sci Int, 2006 Jun 2;159(2-3):210-7.
    PMID: 16219441
    A series of experiments was conducted by exposing negative film in brand new cameras of different make and model. The exposures were repeated at regular time intervals spread over a period of 2 years. The processed film negatives were studied under a stereomicroscope (10-40x) in transmitted illumination for the presence of the characterizing features on their four frame-edges. These features were then related to those present on the masking frame of the cameras by examining the latter in reflected light stereomicroscopy (10-40x). The purpose of the study was to determine the origin and permanence of the frame-edge-marks, and also the processes by which the marks may probably alter with time. The investigations have arrived at the following conclusions: (i) the edge-marks have originated principally from the imperfections received on the film mask from the manufacturing and also occasionally from the accumulated dirt, dust and fiber on the film mask over an extended time period. (ii) The edge profiles of the cameras have remained fixed over a considerable period of time so as to be of a valuable identification medium. (iii) The marks are found to be varying in nature even with those cameras manufactured at similar time. (iv) The influence of f/number and object distance has great effect in the recording of the frame-edge marks during exposure of the film. The above findings would serve as a useful addition to the technique of camera edge-mark comparisons.
    Matched MeSH terms: Signal Processing, Computer-Assisted/instrumentation*
  7. Complexity-based classification of EEG signal in normal subjects and patients with epilepsy
    Namazi H, Aghasian E, Ala TS
    Technol Health Care, 2020;28(1):57-66.
    PMID: 31104032 DOI: 10.3233/THC-181579
    Analysis of human brain activity is an important topic in human neuroscience. Human brain activity can be studied by analyzing the electroencephalography (EEG) signal. In this way, scientists have employed several techniques that investigate nonlinear dynamics of EEG signals. Fractal theory as a promising technique has shown its capabilities to analyze the nonlinear dynamics of time series. Since EEG signals have fractal patterns, in this research we analyze the variations of fractal dynamics of EEG signals between four datasets that were collected from healthy subjects with open-eyes and close-eyes conditions, patients with epilepsy who did and patients who did not face seizures. The obtained results showed that EEG signal during seizure has greatest complexity and the EEG signal during the seizure-free interval has lowest complexity. In order to verify the obtained results in case of fractal analysis, we employ approximate entropy, which indicates the randomness of time series. The obtained results in case of approximate entropy certified the fractal analysis results. The obtained results in this research show the effectiveness of fractal theory to investigate the nonlinear structure of EEG signal between different conditions.
    Matched MeSH terms: Signal Processing, Computer-Assisted*
  8. Fulltext Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification
    Qaisar SM, Mihoub A, Krichen M, Nisar H
    Sensors (Basel), 2021 Feb 22;21(4).
    PMID: 33671583 DOI: 10.3390/s21041511
    The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.
    Matched MeSH terms: Signal Processing, Computer-Assisted*
  9. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing
    Ahmad M, Jung LT, Bhuiyan AA
    Comput Methods Programs Biomed, 2017 Oct;149:11-17.
    PMID: 28802326 DOI: 10.1016/j.cmpb.2017.06.021
    BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

    METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

    RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

    CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

    Matched MeSH terms: Signal Processing, Computer-Assisted*
  10. Accurate wavelet thresholding method for ECG signals
    Yu K, Feng L, Chen Y, Wu M, Zhang Y, Zhu P, et al.
    Comput Biol Med, 2024 Feb;169:107835.
    PMID: 38096762 DOI: 10.1016/j.compbiomed.2023.107835
    Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.
    Matched MeSH terms: Signal Processing, Computer-Assisted*
  11. Design of a Sub-Picosecond Jitter with Adjustable-Range CMOS Delay-Locked Loop for High-Speed and Low-Power Applications
    Abdulrazzaq BI, Ibrahim OJ, Kawahito S, Sidek RM, Shafie S, Yunus NA, et al.
    Sensors (Basel), 2016 Sep 28;16(10).
    PMID: 27690040
    A Delay-Locked Loop (DLL) with a modified charge pump circuit is proposed for generating high-resolution linear delay steps with sub-picosecond jitter performance and adjustable delay range. The small-signal model of the modified charge pump circuit is analyzed to bring forth the relationship between the DLL's internal control voltage and output time delay. Circuit post-layout simulation shows that a 0.97 ps delay step within a 69 ps delay range with 0.26 ps Root-Mean Square (RMS) jitter performance is achievable using a standard 0.13 µm Complementary Metal-Oxide Semiconductor (CMOS) process. The post-layout simulation results show that the power consumption of the proposed DLL architecture's circuit is 0.1 mW when the DLL is operated at 2 GHz.
    Matched MeSH terms: Signal Processing, Computer-Assisted
  12. Fulltext Target detection in forward scattering radar
    Mohamed Khalaf alla Hassan Mohamed, Raja Syamsul Azmir Raja Abdullah, Rasid, M.F.A.
    Pertanika Journal of Science & Technology, 2009;17(2):-.
    MyJurnal
    This paper analyses electromagnetic signal scattered from the target crossing the Forward Scattering
    Radar (FSR) system baseline. The aim of the analysis was to extract the Doppler signal of a target under the influence of high ground clutter and noise interference. The extraction was used for the
    automatic target detection (ATD) in the FSR system. Two extraction methods, namely Hilbert Transform and Wavelet Technique, were analyzed. The detection using the Hilbert Transform is only applicable for some conditions; however, the detection using the Wavelet Technique is more robust to any clutter and noise level. From 55 sets of signal, only 4% of false alarm was detected or occurred when the Wavelet Technique was applied as a detection scheme. Two sets of field experimentation were carried out and the target’s signal under the influence of high clutter had successfully been detected using the proposed method.
    Matched MeSH terms: Signal Processing, Computer-Assisted
  13. Fulltext Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A Review
    Thangarajoo RG, Reaz MBI, Srivastava G, Haque F, Ali SHM, Bakar AAA, et al.
    Sensors (Basel), 2021 Dec 20;21(24).
    PMID: 34960577 DOI: 10.3390/s21248485
    Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of '3N' biosignals-nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.
    Matched MeSH terms: Signal Processing, Computer-Assisted
  14. Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems
    Al-Busaidi AM, Khriji L, Touati F, Rasid MF, Mnaouer AB
    J Med Syst, 2017 Sep 12;41(10):166.
    PMID: 28900815 DOI: 10.1007/s10916-017-0817-1
    One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.
    Matched MeSH terms: Signal Processing, Computer-Assisted
  15. Fulltext Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach
    Tan GY, Bakibillah ASM, Chan PY, Tan CP, Nurzaman S
    Comput Biol Med, 2025 Apr;188:109814.
    PMID: 39978094 DOI: 10.1016/j.compbiomed.2025.109814
    Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies; however, this is challenging due to the nonlinear, stochastic, and nonstationary characteristics of PDT, which substantially vary among patients and their activities. Moreover, most models only have one-step prediction capabilities, which causes delays in real-time applications. This paper proposes a self-attention deep temporal convolutional network (SADTCN) model for the real-time prediction of hand-arm PDT signals from different activities and joint angular motions. The SADTCN can capture both short- and long-term dependencies and complex temporal and spatial dynamics of PDT signals and hence, can effectively adapt to varying tremor characteristics. The performance of the proposed model is evaluated using experimental hand-arm PDT data. The results show that the SADTCN outperforms existing deep learning (DL) models by accurately predicting varying tremor amplitudes and frequencies multi-step ahead. Moreover, we performed spectrum analysis on the measured and predicted signal using the short-time Fourier transform (STFT) as a measure of potential active tremor control and found that SADTCN can accurately determine the transience of tremor amplitude in frequency and time. Finally, we run the Wilcoxon signed-rank statistical test and the results show a statistically significant improvement in the proposed model over the other DL models in all conditions. Therefore, the SADTCN can overcome the nonstationary, nonlinear, and stochastic nature of PDT to perform multi-step prediction with high accuracy, robustness, and generalizability in unseen testing data.
    Matched MeSH terms: Signal Processing, Computer-Assisted
  16. Fulltext Improvement of adaptive GAs and back propagation ANNs performance in condition diagnosis of multiple bearing system using grey relational analysis
    Wulandhari LA, Wibowo A, Desa MI
    Comput Intell Neurosci, 2014;2014:419743.
    PMID: 25587265 DOI: 10.1155/2014/419743
    Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data. In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis. However, large number of features extraction will increase the complexity of the diagnosis system. Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy. In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction. The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA.
    Matched MeSH terms: Signal Processing, Computer-Assisted/instrumentation*
  17. Fulltext Investigation of a new handover approach in LTE and WiMAX
    Hindia MN, Reza AW, Noordin KA
    ScientificWorldJournal, 2014;2014:246206.
    PMID: 25379524 DOI: 10.1155/2014/246206
    Nowadays, one of the most important challenges in heterogeneous networks is the connection consistency between the mobile station and the base stations. Furthermore, along the roaming process between the mobile station and the base station, the system performance degrades significantly due to the interferences from neighboring base stations, handovers to inaccurate base station and inappropriate technology selection. In this paper, several algorithms are proposed to improve mobile station performance and seamless mobility across the long-term evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX) technologies, along with a minimum number of redundant handovers. Firstly, the enhanced global positioning system (GPS) and the novel received signal strength (RSS) prediction approaches are suggested to predict the target base station accurately. Then, the multiple criteria with two thresholds algorithm is proposed to prioritize the selection between LTE and WiMAX as the target technology. In addition, this study also covers the intercell and cochannel interference reduction by adjusting the frequency reuse ratio 3 (FRR3) to work with LTE and WiMAX. The obtained results demonstrate high next base station prediction efficiency and high accuracy for both horizontal and vertical handovers. Moreover, the received signal strength is kept at levels higher than the threshold, while maintaining low connection cost and delay within acceptable levels. In order to highlight the combination of the proposed algorithms' performance, it is compared with the existing RSS and multiple criteria handover decision algorithms.
    Matched MeSH terms: Signal Processing, Computer-Assisted/instrumentation*
  18. Fulltext Wideband LTE power amplifier with integrated novel analog pre-distorter linearizer for mobile wireless communications
    Uthirajoo E, Ramiah H, Kanesan J, Reza AW
    PLoS One, 2014;9(7):e101862.
    PMID: 25033049 DOI: 10.1371/journal.pone.0101862
    For the first time, a new circuit to extend the linear operation bandwidth of a LTE (Long Term Evolution) power amplifier, while delivering a high efficiency is implemented in less than 1 mm2 chip area. The 950 µm × 900 µm monolithic microwave integrated circuit (MMIC) power amplifier (PA) is fabricated in a 2 µm InGaP/GaAs process. An on-chip analog pre-distorter (APD) is designed to improve the linearity of the PA, up to 20 MHz channel bandwidth. Intended for 1.95 GHz Band 1 LTE application, the PA satisfies adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) specifications for a wide LTE channel bandwidth of 20 MHz at a linear output power of 28 dBm with corresponding power added efficiency (PAE) of 52.3%. With a respective input and output return loss of 30 dB and 14 dB, the PA's power gain is measured to be 32.5 dB while exhibiting an unconditional stability characteristic from DC up to 5 GHz. The proposed APD technique serves to be a good solution to improve linearity of a PA without sacrificing other critical performance metrics.
    Matched MeSH terms: Signal Processing, Computer-Assisted*
  19. Fulltext Ultra-low-voltage CMOS-based current bleeding mixer with high LO-RF isolation
    Tan GH, Sidek RM, Ramiah H, Chong WK, Lioe de X
    ScientificWorldJournal, 2014;2014:163414.
    PMID: 25197694 DOI: 10.1155/2014/163414
    This journal presents an ultra-low-voltage current bleeding mixer with high LO-RF port-to-port isolation, implemented on 0.13 μm standard CMOS technology for ZigBee application. The architecture compliments a modified current bleeding topology, consisting of NMOS-based current bleeding transistor, PMOS-based switching stage, and integrated inductors achieving low-voltage operation and high LO-RF isolation. The mixer exhibits a conversion gain of 7.5 dB at the radio frequency (RF) of 2.4 GHz, an input third-order intercept point (IIP3) of 1 dBm, and a LO-RF isolation measured to 60 dB. The DC power consumption is 572 µW at supply voltage of 0.45 V, while consuming a chip area of 0.97 × 0.88 mm(2).
    Matched MeSH terms: Signal Processing, Computer-Assisted/instrumentation*
  20. Fulltext Design of high speed and low offset dynamic latch comparator in 0.18 µm CMOS process
    Rahman LF, Reaz MB, Yin CC, Ali MA, Marufuzzaman M
    PLoS One, 2014;9(10):e108634.
    PMID: 25299266 DOI: 10.1371/journal.pone.0108634
    The cross-coupled circuit mechanism based dynamic latch comparator is presented in this research. The comparator is designed using differential input stages with regenerative S-R latch to achieve lower offset, lower power, higher speed and higher resolution. In order to decrease circuit complexity, a comparator should maintain power, speed, resolution and offset-voltage properly. Simulations show that this novel dynamic latch comparator designed in 0.18 µm CMOS technology achieves 3.44 mV resolution with 8 bit precision at a frequency of 50 MHz while dissipating 158.5 µW from 1.8 V supply and 88.05 µA average current. Moreover, the proposed design propagates as fast as 4.2 nS with energy efficiency of 0.7 fJ/conversion-step. Additionally, the core circuit layout only occupies 0.008 mm2.
    Matched MeSH terms: Signal Processing, Computer-Assisted/instrumentation*
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