Now showing 1 - 8 of 8
  • Publication
    Bending Assessment of Dual-band Split Ring-shaped and Bar Slotted All-Textile Antenna for Off-body WBAN/WLAN and 5G Applications
    This paper presents a dual-band split ring-shaped and bar slotted textile antenna for potential WBAN/WLAN and 5G applications. The antenna is made using textiles and features a full ground plane to possibly alleviate coupling to the human body. The overall size of the antenna is 70 x 70 mm2, with a patch sized at 47.2 x 31 mm2 0.472 \lambda \times 0.031 \lambda. The antenna is made using ShieldIt Super as its conductive textile and felt as its substrate. To enable its dual-band resonance at 2.45 and 3.5 GHz a split ring-shaped and bar slots are integrated onto the patch. The proposed antenna is evaluated when bent under different radii and at different axes to estimate its performance in terms of reflection coefficient, bandwidth, efficiency and gain. A 10-dB impedance bandwidth of 57 % or 135 MHz (from 2.39 to 2.52 GHz) and 70 % or 240 MHz (from 3.45 to 3.56 GHz) are obtained when evaluated in the planar /bent configuration. The maximum realized gain is 6 dBi for at 3.5 GHz. These performances indicate that the antenna proposed in this work can be potentially improved for applications in WBAN/WLAN and 5G bands.
  • Publication
    A hybrid modified method of the sine cosine algorithm using latin hypercube sampling with the cuckoo search algorithm for optimization problems
    The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.
  • Publication
    A hybrid modified sine cosine algorithm using inverse filtering and clipping methods for low autocorrelation binary sequences
    ( 2022-01-01)
    Rosli S.J.
    ;
    ; ; ;
    Mustafa W.A.
    ;
    ; ; ;
    Abdulmalek M.
    ;
    Ariffin W.S.F.W.
    ;
    Alkhayyat A.
    The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains' properties shows that a class of signals is mainlywell suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximumpossibleMF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) using Inverse Filtering (IF) and clipping method to achieve better results. The proposed algorithms, LABS-IF and HMSCACSA-IF, achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237, respectively, where the optimal solutions belong to the skew-symmetric sequences. TheMFoutperformed up to 24.335% and 2.708% against the state-of-the-art LABS heuristic algorithm, xLastovka, and Golay, respectively. These results indicated that the proposed algorithm's simulation had quality solutions in terms of fast convergence curve with better optimal means, and standard deviation.
  • Publication
    1×4 Patch Array All-Textile Antenna for WLAN Applications
    This paper proposes the design of 1×4 patch array all-Textile antenna for Wireless Local Area Networks (WLAN) applications. The wearable antenna needs to have low profile and lightweight since such antenna is intended to operate in the vicinity of the human body. The key parameters are studied to determine their effects towards the performance of the antenna. The proposed design uses ShieldIt as the top radiator and ground plane, while fabric Felt is used as a substrate, sandwiched between the top radiator and ground plane.The obtained results show that there is improvement in the proposed array antenna in terms of gain enhancement and impedance bandwidth, maximum up to 143.6% and 19.08%, respectively, against single patch structure.
  • Publication
    1×4 Patch Array All-Textile Antenna for WLAN Applications
    This paper proposes the design of 1×4 patch array all-Textile antenna for Wireless Local Area Networks (WLAN) applications. The wearable antenna needs to have low profile and lightweight since such antenna is intended to operate in the vicinity of the human body. The key parameters are studied to determine their effects towards the performance of the antenna. The proposed design uses ShieldIt as the top radiator and ground plane, while fabric Felt is used as a substrate, sandwiched between the top radiator and ground plane.The obtained results show that there is improvement in the proposed array antenna in terms of gain enhancement and impedance bandwidth, maximum up to 143.6% and 19.08%, respectively, against single patch structure.
  • Publication
    A hybrid modified method of the sine cosine algorithm using latin hypercube sampling with the cuckoo search algorithm for optimization problems
    The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.
  • Publication
    Mobile Green E-Waste Management Systems using IoT for Smart Campus
    This paper presents the design and development of mobile "green"electronic waste (e-waste) management systems using Internet of Things (IoT) for smart campus. The system uses Raspberry Pi 3 Model B v1.2 microcontroller for monitoring e-waste object detection, e-waste count, and bin percentage level, respectively. TensorFlow Lite application programming interface (API) is used to run Single Shot Multibox Detector (SSD)Lite-MobileNet-v2 model trained on Microsoft Common Objects in Context (MSCOCO) dataset for e-waste object detection in image. All the monitoring data are stored and retrieved in ThingSpeak cloud platform using Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT) protocol over the Internet and displayed via interactive Android-based mobile user interface (UI). Furthermore, automatic e-mail notification will be sent to waste collector for bin collection whenever e-waste bin percentage level is greater than predetermined threshold value of 80% full.
  • Publication
    A hybrid modified sine cosine algorithm using inverse filtering and clipping methods for low autocorrelation binary sequences
    ( 2022-01-01)
    Rosli S.J.
    ;
    ; ; ;
    Mustafa W.A.
    ;
    ; ; ;
    Abdulmalek M.
    ;
    Ariffin W.S.F.W.
    ;
    Alkhayyat A.
    The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains' properties shows that a class of signals is mainlywell suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximumpossibleMF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) using Inverse Filtering (IF) and clipping method to achieve better results. The proposed algorithms, LABS-IF and HMSCACSA-IF, achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237, respectively, where the optimal solutions belong to the skew-symmetric sequences. TheMFoutperformed up to 24.335% and 2.708% against the state-of-the-art LABS heuristic algorithm, xLastovka, and Golay, respectively. These results indicated that the proposed algorithm's simulation had quality solutions in terms of fast convergence curve with better optimal means, and standard deviation.