Khairun Nisa' Minhad's research while affiliated with Xiamen University and other places

One of the essential components of phase-locked loop (PLL) circuits is the phase frequency detector (PFD). PFD generates an error output signal whose phase difference between the phases of the feedback clock and the reference clock is proportionate. PFDs are mostly utilized in demodulators, motor control, radar, and communications systems, servo-mechanisms, and mobile communication systems. While designing a PFD blind zone, power consumption and operational frequency must be considered cautiously for better performance. In this study, a detailed analysis of various designs of PFDs is presented and analyzed for various applications. This may help researchers or designers from academies and industrial sectors choose a suitable device topology for RF transceiver applications.

Dementia is not a specific disease, but a general term for age-related decline or loss of memory, cognitive abilities including problem solving and decision-making, and one’s own language, which significantly interfere with daily life. Researchers around the world have developed ways to automate the diagnosis of dementia through the use of machine learning and data mining approaches. The aim of this research project is to design and develop a day-to-day activity prediction algorithm in order to accurately identify and differentiate the dementia affected patients from the healthy subjects, to ensure early diagnosis of dementia development. This research advocates a novel algorithm called ‘Sequence Prediction via All Discoverable Episodes (SPADE)’ as a statistical tool to map activities of daily life (ADLs) in different groups of people in order to develop a unique parameter for precise diagnosis. The results of our experiment demonstrated a significant difference (i.e. 11 %) in the sequence prediction peak accuracy between the healthy subjects and the residents with dementia. SPADE demonstrated an adequate accuracy (i.e. 80 % on average), with an improvement of about 12 % compared to the performance of M-SPEED in inferring future occurrences of activities. It is thus evident that the algorithms for activity predictions show promise for early detection of dementia symptoms without the use of any expensive clinical procedure.

Injection-locked frequency divider (ILFD) is an essential component of a phase-locked loop (PLL) used in a communication transceiver. Due to the rapid growth of virtual reality and artificial intelligence technologies, high-speed communication has become a crucial factor. As a result, highly efficient ILFDs must be engineered to ensure accurate high-speed communication. An optimized ILFD is anticipated to have a large locking range, a low power consumption, a low phase noise, and a low injection power for better functioning of a high speed PLL. However, there are inevitable trade-offs of frequency, phase noise, division ratio, and power consumption, making it more challenging to design an ILFD with a high division ratio at higher frequencies. Choosing an appropriate topology for different operational frequencies is also a challenging task for RFIC designers. This study exemplified different design architectures available for CMOS ILFDs in different super high frequency (SHF) and extremely high frequency (EHF) frequency ranges. The performance parameters of these architectures are analyzed, and a detailed discussion on the device’s performance is presented. This review will serve as a comparative study and reference for the RFIC designer’s future ILFD designs.

The past decade has witnessed rapid research and developments in wind energy conversion systems and related enabling technologies. The incorporation of wind turbines in the integral design of buildings has become a promising approach to promote on-site renewable energy conversion in urban areas. However, information pertaining the efficiency of the wind turbines, wind speeds and the integration of generated wind power to the grid in urban environments is scarce. This review paper aims to capture the fact that recent advancements in wind energy systems are able to deliver tangible benefits to the renewable energy technology (RET) industry as a whole. A comprehensive review has been carried out on the applied techniques of radial wind turbine (RWTs), contra-rotating wind turbines (CRWTs), hybrid Savonius–Darrieus rotor, piezoelectric generators, flag-type triboelectric nanogenerator and stretching-enhanced triboelectric nanogenerator. This paper provides a knowledge platform for readers to explore possible methods in exploiting the advantages of urban wind profiles with minimal vibration, noise and installation space. Unique approaches pertaining the developments of wind energy system in urban environments are reviewed and reported in this paper. Several research gaps are also identified to guide future study in urban wind energy conversion systems.

With the widespread adoption of the internet of things (IoT), power management of the different electronic (i.e. IoT) devices has become a major challenge. The low-dropout linear regulator (LDO) circuit is widely used for power management applications of electronic devices. This article reports the design and simulation of a low-dropout linear regulator (LDO) circuit, powered by a 1.2 V DC power supply voltage. In order to optimise the power dissipation, low layout silicon area and lower dropout voltage, a current mirror based transistor optimised LDO circuit has been implemented. The simulation results show that the proposed LDO regulator circuit exhibits a 582 mV low-dropout voltage, 1.568 mW power dissipation, and a very compact layout silicon area of 163.84 µm 2(12.795 × 12.805 µm). The proposed LDO linear regulator archi- tecture is designed and validated in 0.13 µm TSMC CMOS process technology using Mentor Graphic EDA tools.

The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance.