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POWER OF ALIGNMENT: EXPLORING THE EFFECT OF FACE ALIGNMENT ON ASD DIAGNOSIS USING FACIAL IMAGES
Get PDFAutism Spectrum Disorder (ASD) is a developmental disorder that impacts social communication and conduct. ASD lacks standard treatment protocols or medication, thus early identification and proper intervention are the most effective procedures to treat this disorder. Artificial intelligence could be a very effective tool to be used in ASD diagnosis as this is free from human bias. This research examines the effect of face alignment for the early diagnosis of Autism Spectrum Disorder (ASD) using facial images with the possibility that face alignment can improve the prediction accuracy of deep learning algorithms. This work uses the SOTA deep learning-based face alignment algorithm MTCNN to preprocess the raw data. In addition, the impacts of facial alignment on ASD diagnosis using facial images are investigated using state-of-the-art CNN backbones such as ResNet50, Xception, and MobileNet. ResNet50V2 achieves the maximum prediction accuracy of 93.97% and AUC of 96.33% with the alignment of training samples, which is a substantial improvement over previous research. This research paves the way for a data-centric approach that can be applied to medical datasets in order to improve the efficacy of deep neural network algorithms used to develop smart medical devices for the benefit of mankind.
ABSTRAK: Gangguan Spektrum Autisme (ASD) adalah gangguan perkembangan yang memberi kesan kepada komunikasi dan tingkah laku sosial. Kelemahan dalam rawatan ASD adalah ianya tidak mempunyai protokol rawatan standard atau ubat. Oleh itu pengenalan awal dan campur tangan betul merupakan prosedur paling berkesan bagi merawat gangguan ini. Kecerdasan buatan boleh menjadi alat berkesan bagi diagnosis ASD kerana bebas campur tangan manusia. Penyelidikan ini mengkaji kesan penjajaran muka bagi diagnosis awal ASD menggunakan imej muka dengan kebarangkalian penjajaran muka dapat meningkatkan ketepatan ramalan algoritma pembelajaran mendalam. Kajian ini menggunakan algoritma penjajaran muka MTCNN berasaskan pembelajaran mendalam SOTA bagi pra-proses data mentah. Selain itu, kesan penjajaran muka pada diagnosis ASD menggunakan imej muka disiasat menggunakan CNN terkini seperti ResNet50, Xception dan MobileNet. ResNet50V2 mencapai ketepatan ramalan maksimum sebanyak 93.97% dan AUC 96.33% dengan sampel penjajaran latihan, yang merupakan peningkatan ketara berbanding penyelidikan terdahulu. Kajian ini membuka jalan bagi pendekatan data berpusat yang boleh digunakan pada set data perubatan bagi meningkatkan keberkesanan algoritma rangkaian saraf mendalam dan membangunkan peranti perubatan pintar bermanfaat untuk manusia
ADSORPTION PERFORMANCE OF FIXED-BED COLUMNS FOR THE REMOVAL OF PHENOL USING BAOBAB FRUIT SHELL BASED ACTIVATED CARBON
Get PDFA continuous adsorption study in a fixed-bed column using baobab fruit shell activated carbon (BF-AC) was investigated for phenol removal from an aqueous solution. Baobab fruit shell (BFS) was chemically activated using potassium hydroxide (KOH) at 700 °C in a nitrogen (N2) atmosphere. Scanning electron microscope (SEM), X-ray diffraction (XRD), and BET surface area analyses were performed for the characterization of BF-AC. Fixed-bed experiments were carried out and the effect of feed flowrate (10, 15, 20 mL/min) and bed height (5, 10, 15 cm) on the adsorption were investigated by evaluating the breakthrough curves. BET surface area of BF-AC was 1263 m2/g, indicating its well-developed pores and its good quality as an adsorbent. The findings showed that the exhaustion time (t????) and breakthrough time (tb) reduced as the flowrate augmented, while they increased as the bed height augmented. With the increase in the bed height and the flowrate, phenol solution volume treated was augmented. Also, BF-AC with bed height of 15 cm provided better elimination of phenol with carbon usage rate (CUR) of 1.74 g/L and empty bed contact time (EBCT) of 9.9 minutes. According to the findings, BF-AC is an effective adsorbent for removing phenol from aqueous solutions.
ABSTRAK: Kajian penjerapan berterusan menggunakan kulit buah baobab diaktifkan karbon (BF-AC) telah dikaji mengguna pakai kolum lapisan tetap bagi penyingkiran fenol daripada larutan cecair. Kulit buah Baobab (BFS) diaktifkan secara kimia menggunakan kalium hidroksida (KOH) pada suhu 700 °C dalam atmosfera nitrogen (N2). Imbasan mikroskop elektron (SEM), pembelahan sinar-X (XRD, dan analisis permukaan BET dijalankan bagi pencirian BF-AC. Eksperimen kolum lapisan tetap bagi mengkaji kesan penjerapan pada aliran suapan (10, 15, 20 mL/min) dengan ketinggian (5, 10, 15 cm) dinilai melalui lengkung bulus. Kawasan permukaan BET BF-AC adalah 1263 m2/g, menunjukkan liang yang elok terbentuk dan berkualiti baik sebagai penyerap. Penemuan ini menunjukkan bahawa puncak masa maksima (t????) dan masa terbaik (tb) berkurangan pada kadar aliran bertambah, sebaliknya ianya meningkat pada ketinggian bertambah. Dengan penambahan ketinggian katil dan kadar aliran, jumlah larutan fenol yang dirawat telah bertambah. Selain itu, BF-AC pada ketinggian 15 cm menunjukkan penghapusan fenol terbaik pada kadar penggunaan karbon (CUR) 1.74 g/L dan masa sentuhan kolum kosong (EBCT) 9.9 minit. Ini menunjukkan, BF-AC adalah penyerap yang berkesan bagi menyingkirkan fenol daripada larutan cecair
Attitude UAV Stability Control Using Linear Quadratic Regulator-Neural Network (LQR-NN)
Get PDFThe stability of an Unmanned Aerial Vehicle (UAV) attitude is crucial in aviation to mitigate the risk of accidents and ensure mission success. This study aims to optimize and adaptively control the flight attitude stability of a flying wing-type UAV amidst environmental variations. This is achieved through the utilization of Linear Quadratic Regulator-Neural Network (LQR-NN) control, wherein the Neural Network predicts the optimal K gain value by fine-tuning Q and R parameters to minimize system errors. An online learning neural network adjusts the K value based on real-time error feedback, enhancing system performance. Experimental results demonstrate improved stability metrics: for roll angle stability, a rise time of 0.4682 seconds, settling time of 1.3819 seconds, overshoot of 0.298%, and Steady State Error (SSE) of 0.133 degrees; for pitch angle stability, a rise time of 0.2309 seconds, settling time of 0.7091 seconds, overshoot of 0.1224%, and Steady State Error (SSE) of 0.0239 degrees. The LQR-NN approach effectively reduces overshoot compared to traditional Linear Quadratic Regulator (LQR) control, thereby minimizing oscillations. Furthermore, LQR-NN can minimize the Steady State Error (SSE) to 0.074 degrees for roll rotation motion and 0.035 degrees for pitch rotation motion.
ABSTRAK: Kestabilan perubahan Pesawat Tanpa Pemandu (UAV) adalah penting dalam penerbangan bagi mengurangkan risiko kemalangan dan memastikan kejayaan misi. Kajian ini bertujuan mengoptimum dan menstabilkan perubahan kawalan adaptif penerbangan UAV jenis sayap terbang di tengah-tengah variasi persekitaran. Ini dicapai melalui penggunaan kawalan Rangkaian Linear Kuadratik Pengatur-Neural (LQR-NN), di mana Rangkaian Neural meramal nilai perolehan K optimum dengan meneliti parameter Q dan R bagi mengurangkan ralat sistem. Rangkaian neural pembelajaran dalam talian melaraskan nilai K berdasarkan maklum balas ralat masa nyata, ini meningkatkan prestasi sistem. Dapatan kajian eksperimen menunjukkan metrik kestabilan lebih baik: bagi kestabilan sudut gulungan, masa kenaikan sebanyak 0.4682 saat, masa kestabilan 1.3819 saat, lajakan 0.298% dan Ralat Keadaan Mantap (SSE) 0.133 darjah; bagi kestabilan sudut pic, masa kenaikan 0.2309 saat, masa penetapan 0.7091 saat, lajakan 0.1224%, dan Ralat Keadaan Mantap (SSE) 0.0239 darjah. Pendekatan LQR-NN berkesan mengurangkan lajakan berbanding kawalan tradisi Pengatur Kuadratik Linear (LQR), dengan itu mengurangkan ayunan. Tambahan, LQR-NN dapat mengurangkan Ralat Keadaan Mantap (SSE), sebanyak 0.074 darjah bagi gerakan putaran guling dan 0.035 darjah bagi gerakan putaran anggul
OPTIMAL CLUSTERING OF WIRELESS MULTIPATHS BY UNIFORM MANIFOLD APPROXIMATION AND PROJECTION-ASSISTED DBSCAN
Get PDFUniform Manifold Approximation and Projection (UMAP) is applied to reduce the multipath dataset into 2-dimensions (2D) for visualization and clustering. Density-based spatial clustering of applications with noise (DBSCAN) is used as the clustering approach and the performance of different search radius epsilon ?. The proposed approach was used to cluster semi-urban scenarios of the COST2100 channel model (C2CM), which has many multipath components (MPCs). The approach is validated by comparing the clustering results to the ground truth and computing the Adjusted Rand Index (ARI) and the cluster-wise Jaccard index . The results suggest that lowering the search radius up to 0.3 achieved a median below 0.6 in the multiple-links scenarios due to the overlapping nature of clusters. Nevertheless, the median values above 0.7 and 0.8 for the ARI and Jaccard index , respectively for the single-link scenarios indicate the robsutness of the approach.
ABSTRAK: Anggaran Manifold Seragam dan Unjuran (UMAP) 2-dimensi (2D) digunakan sebagai penggambaran dan pengelasan bagi mengurangkan set data pelbagai laluan. Aplikasi pengelasan ruangan bersama bunyi berdasarkan ketumpatan (DBSCAN) ini mengguna pakai pendekatan pengelasan dan prestasi pelbagai radius carian epsilon ?. Pendekatan yang dicadangkan ini digunakan bagi pengelasan senario separa-bandar model saluran COST2100 (C2CM), di mana komponen ini mempunyai banyak laluan (MPCs). Pendekatan ini disahkan dengan membandingkan dapatan pengelasan kepada kesahihan lapangan, pengiraan Indeks Rawak Terlaras (ARI) dan indeks Jaccard pengelasan ?. Dapatan menunjukkan pengurangan radius carian sehingga 0.3 dicapai pada median di bawah 0.6 dalam senario pelbagai pautan disebabkan oleh sifat pertindihan pengelasan. Walau bagaimanapun, nilai median di atas 0.7 dan 0.8 untuk ARI dan indeks Jaccard ?, masing-masing menunjukkan kaedah ini berkesan bagi senario pautan-tunggal
MOBILE GAS SENSING FOR LABORATORY INFRASTRUCTURE
Get PDFIndoor air quality has become a growing concern in modern society due to prolonged indoor working hours that lead to the frequent exposure to numerous toxic gases from various sources. These pollutants, including volatile organic compounds (VOCs), pose severe health risks such as asthma and lung cancer. To address this critical issue, this project focuses on developing and evaluating an advanced gas detection system that explicitly targets VOCs by integrating two novel metal oxide semiconductor (MOX)-based gas sensors, ENS 160 and TED110. Different sensor parameters, such as the air quality index (AQI) and volatile organic compounds (VOCs), were evaluated using 12 volatile organic chemicals. The findings revealed that the ENS 160 sensor performs excellently, detecting 60 gas samples out of 72, with an average detection rate of approximately 83%. In contrast, the TED110 sensor demonstrated considerably lower performance and response in 24 out of 72 gas samples, with a detection rate of about 33%. The results contribute insights into the gas sensor's characteristics, providing essential information to enhance indoor air quality monitoring technology, particularly in laboratory environments.
ABSTRAK: Setiap hari, banyak gas toksik, letupan dan beracun berlaku di dalam dan di luar rumah daripada pelbagai sumber. Dalam masyarakat moden, kebanyakan orang menghabiskan 90% masa bekerja mereka di dalam rumah; oleh itu, kualiti udara dalaman secara beransur-ansur bertambah buruk daripada suasana luar. Projek ini sedang membangunkan sistem pengesanan dan pemantauan moden yang canggih untuk mengesan pelbagai gas berbahaya, seperti sebatian organik meruap (VOC). Dua penderia gas berasaskan semikonduktor oksida logam (MOX) novel telah diperkenalkan dalam projek ini dengan mikropengawal yang dikemas kini untuk pemerolehan data dan pemprosesan data. Tambahan pula, parameter sensor yang berbeza (AQI, TVOC) telah dinilai dengan 12 bahan kimia organik yang tidak menentu. Semua ujian telah dijalankan dalam tudung kimia tradisional dengan tiga kuantiti sampel yang berbeza (5?L, 10?L, 50?L) pada jarak 40 cm dan 100 cm. Akhir sekali, volum minimum yang boleh dikesan berdasarkan jarak antara nod sensor dan sumber bocor telah dianalisis selepas eksperimen yang meluas dengan kedua-dua sensor. Sensor ENS 160 sedang mengesan 60 sampel gas daripada 72, dalam ketiga-tiga parameter seperti AQI, TVOC dan kadar pengesanan CO2 sekitar 83%. TED110 menunjukkan prestasi yang sangat rendah; ia telah bertindak balas kepada 24 daripada 72 sampel gas, dan kadar pengesanan ialah 33%
SPECTROSCOPY DATA CALIBRATION USING STACKED ENSEMBLE MACHINE LEARNING
Get PDFNear infrared spectroscopy (NIRS) is a widely used analytical technique for non-destructive analysis of various materials including food fraud detection. However, the accurate calibration of NIRS data can be challenging due to the complexity of the underlying relationships between the spectral data and the target variables of interest. Ensemble learning, which combines multiple models to make predictions, has been shown to improve the accuracy and robustness of predictive models in various domains. This paper proposes stacking ensemble machine learning (SEML) for calibration of NIRS data with two levels of learning involved. Eight (8) spectroscopy datasets from public repository and previously published works by the authors are used as the case study. The model well generalized the data in the respective regression tasks with of at least »0.8 in the test samples and in the respective classification tasks with classification accuracy (CA) of at least »0.8 also. In addition, the proposed SEML can improve, or at least reach par with, the accuracy of individual base learners in both train and test samples for all cases of regression and classification datasets. It shows superior performance in test samples for both regression and classification datasets with respectively ranging from 0.86 to nearly 1 and CA ranging from 0.89 to 1.
ABSTRAK: Spektroskopi inframerah dekat (NIRS) adalah teknik analitikal yang banyak digunakan bagi analisa pelbagai bahan tanpa merosakkan bahan termasuk ketika mengesan penipuan makanan. Walau bagaimanapun, kalibrasi yang tepat bagi data NIRS adalah sangat mencabar kerana hubungan antara data spektral dan pemboleh ubah sasaran yang ingin dikaji bersifat kompleks. Gabungan pembelajaran (Ensemble learning), iaitu gabungan pelbagai model bagi membuat prediksi, telah terbukti dapat meningkatkan ketepatan dan kecekapan model prediksi dalam pelbagai bentuk. Kajian ini mencadangkan Turutan Gabungan Pembelajaran Mesin (Stacking Ensemble Machine Learning ) (SEML), bagi teknik penentu ukuran data NIRS melibatkan dua tahap pembelajaran. Lapan (8) set data spektroskopi dari repositori awam dan kajian terdahulu oleh pengarang telah digunakan sebagai kes kajian. Model ini menggeneralisasi data dalam tugas regresi masing-masing sebanyak ?0.8 bagi sampel ujian dan pengelasan tugas masing-masing dengan ketepatan klasifikasi (CA) sekurang-kurangnya ?0.8. Tambahan, SEML yang dicadangkan ini dapat membantu, atau sekurang-kurangnya setanding dengan ketepatan individu dalam pembelajaran berkumpulan dalam kedua-dua sampel latihan dan ujian bagi semua kes set data regresi dan klasifikasi. Ia menunjukkan prestasi terbaik dalam sampel ujian bagi kedua-dua kumpulan set data regresi dan klasifikasi dengan masing-masing antara 0.86 hingga hampir 1 dan antara julat 0.89 hingga 1 bagi CA
A WHEELCHAIR SITTING POSTURE DETECTION SYSTEM USING PRESSURE SENSORS
Get PDFThe usage of machine learning in the healthcare system, especially in monitoring those who are using a wheelchair for their mobility has also helped to improve their quality of life in preventing any serious life-time risk, such as the development of pressure ulcers due to the prolonged sitting on the wheelchair. To date, the amount of research on the sitting posture detection on wheelchairs is very small. Thus, this study aimed to develop a sitting posture detection system that predominantly focuses on monitoring and detecting the sitting posture of a wheelchair user by using pressure sensors to avoid any possible discomfort and musculoskeletal disease resulting from prolonged sitting on the wheelchair. Five healthy subjects participated in this research. Five typical sitting postures by the wheelchair user, including the posture that applies a force on the backrest plate, were identified and classified. There were four pressure sensors attached to the seat plate of the wheelchair and two pressure sensors attached to the back rest. Three classification algorithms based on the supervised learning of machine learning, such as support vector machine (SVM), random forest (RF), and decision tree (DT) were used to classify the postures which produced an accuracy of 95.44%, 98.72%, and 98.80%, respectively. All the classification algorithms were evaluated by using the k-fold cross validation method. A graphical-user interface (GUI) based application was developed using the algorithm with the highest accuracy, DT classifier, to illustrate the result of the posture classification to the wheelchair user for any posture correction to be made in case of improper sitting posture detected.
ABSTRAK: Penggunaan pembelajaran mesin dalam sistem penjagaan kesihatan terutama dalam mengawasi pergerakan pengguna kerusi roda dapat membantu meningkatkan kualiti hidup bagi mengelak sebarang risiko serius seperti ulser disebabkan tekanan duduk terlalu lama di kerusi roda. Sehingga kini, kajian tentang pengesanan postur ketika duduk di kerusi roda adalah sangat kurang. Oleh itu, kajian ini bertujuan bagi membina sistem pengesan postur khususnya bagi mengawasi dan mengesan postur duduk pengguna kerusi roda dengan menggunakan pengesan tekanan bagi mengelak sebarang kemungkinan ketidakselesaan dan penyakit otot akibat duduk terlalu lama. Lima pengguna kerusi roda yang sihat telah dijadikan subjek bagi kajian ini. Terdapat lima postur duduk oleh pengguna kerusi roda termasuk postur yang memberikan tekanan pada bahagian belakang telah di kenalpasti dan dikelaskan. Terdapat empat pengesan tekanan dilekatkan pada bahagian tempat duduk kerusi roda dan dua pengesan tekanan dilekatkan pada bahagian belakang. Tiga algoritma pengelasan berdasarkan pembelajaran terarah melalui pembelajaran mesin seperti Sokongan Vektor Mesin (SVM), Hutan Rawak (RF) dan Pokok Keputusan (DT) telah digunakan bagi pengelasan postur di mana masing-masing memberikan ketepatan 95.44%, 98.72% dan 98.80%. Semua algoritma pengelasan telah dinilai menggunakan kaedah k-lipatan pengesahan bersilang. Sebuah aplikasi grafik antara muka (GUI) telah dibina menggunakan algoritma dengan ketepatan paling tinggi, iaitu pengelasan DT bagi memaparkan keputusan pengelasan postur untuk pengguna kerusi roda bagi membantu pembetulan postur jika postur salah dikesan
COMPACT CPW 4X4 MIMO ANTENNA FOR WI-FI 6 (IEEE802.11.AX) AND 5G(NR77/NR78/NR79) COMMUNICATIONS
Get PDFThis research proposes a compact 4x4 MIMO coplanar waveguide antenna for 5G NR and Wi-Fi 6 applications. The antenna has a size of 34x32x1.6 mm and operates in the 4.2-7 GHz band. By cutting slots on the ground and radiator, the mutual coupling is reduced to less than -15 dB between adjacent and opposite elements and less than -25 dB between diagonal elements. The antenna achieves good measured gains (3-6 dBi) and efficiency (60%-80%). The proposed antenna is suitable for high-performance wireless communication systems that require a small and low-cost MIMO antenna.
ABSTRAK: Kajian ini mencadangkan antena pandu gelombang yang kompak 4x4 MIMO koplanar bagi aplikasi 5G NR dan Wi-Fi6. Antena ini mempunyai saiz 34x32x1.6 mm dan beroperasi dalam kelompok gelombang 4.2-7 GHz. Dengan memotong slot pada tanah dan radiator, mutual coupling dikurangkan sebanyak -15 dB antara adjasen dan elemen bertentangan dan kurang daripada -25 dB antara elemen diagonal. Antena ini mencapai ukuran terbaik pada gain (3-6 dBi) dan kecekapan (60%-80%). Antena yang dicadangkan ini sesuai bagi sistem komunikasi tanpa wayar berprestasi tinggi yang memerlukan antena kecil dan murah seperti antena MIMO.
Recent Advances in Enhanced Polyamidoamine Inhibitors for Silicate Scales in the Petroleum Upstream
Get PDFChemical flooding is regarded as a promising enhanced oil recovery technique to recover more hydrocarbon from reservoirs. However, the dissolution of quartz minerals in a highly alkaline environment poses the risk of silicate scaling near the production well region from the mixing of two different waters. Commercial scale inhibitors are effective, but they are also harmful to the environment. This paper aims to provide insights into current advances in environment-friendly or “green” scale inhibitors for petroleum upstream. Previous research works have demonstrated that green chemicals are effective in mitigating silicate, carbonate, and sulfide scales. Polyamidoamine or amide-based inhibitors have been widely investigated in recent literature due to several advantages. The addition of anionic compounds in these inhibitors enhanced scale inhibition efficiency by roughly 10%. Nevertheless, the reported findings were deliberated for industrial wastewater treatment. Meanwhile, understanding the performance of polyamidoamine or amide-based scale inhibitors in petroleum upstream is inadequate to a certain extent. The formation process of silicate scales inside a reservoir is rather complicated by looking at the influence of water salinity, composition of brine, temperature, pressure, and rock type. Hence, it is essential to study and develop green scale inhibitors that are effective and environmentally friendly to meet increasingly stringent disposal regulations in the petroleum industry.
ABSTRAK: Pembanjiran kimia merupakan teknik pemulihan minyak. Ia berpotensi dalam memperoleh lebih banyak hidrokarbon dari takungan. Namun, pelarut mineral kuarza dalam persekitaran beralkali tinggi memberi risiko penumpukan silikat berhampiran kawasan takungan pengeluaran. Ia disebabkan oleh pencampuran dua jenis cecair berbeza. Perencat penumpukan silikat komersial adalah berkesan, tetapi sangat berbahaya pada alam sekitar. Kajian ini bertujuan bagi menambahbaik kemajuan perencat silikat mesra alam terkini atau perencat silikat hijau bagi bidang saliran petroleum. Kajian terdahulu telah membuktikan bahawa bahan kimia mesra alam adalah berkesan dalam pengurangan penumpukan silikat, karbonat dan sulfida. Perencat poliamidoamina atau perencat bersumber amida telah dikaji secara meluas dalam beberapa kajian sejak kebelakangan ini kerana kelebihannya yang banyak. Penambahan sebatian anionik dalam perencat ini mampu meningkatkan keberkesanan perencat silikat sebanyak 10%. Namun, laporan kajian terdahulu adalah khusus bagi rawatan sisa air industri. Sementara itu, pemahaman tentang prestasi perencat silikat bersumberkan poliamidoamina atau perencat bersumber amida dalam saliran petroleum masih tidak mencukupi. Proses pembentukan penumpukan silikat dalam takungan adalah agak rumit berdasarkan faktor saliniti air, komposisi air garam, suhu, tekanan dan jenis batuan. Oleh itu, kajian dan pembangunan berkesan tentang perencat silikat mesra alam adalah penting bagi memenuhi peraturan pelupusan sisa yang semakin ketat dalam industri petroleum
Enhanced Antenna Performance at 3.5 GHz With a Compact and Intelligent Reflecting Surface
Get PDFIntelligent Reflecting Surface (IRS) is an upbound 5G technology capable of intelligently controlling and altering an electromagnetic (EM) wave. IRS is a planar 2D metamaterial or metasurface made up of many passive element reflecting elements connected to a smart controller, which is capable of introducing an independent phase shift and/or amplitude attenuation (collectively termed as “reflection coefficient”) to the incident signal at each reflecting element. Hence, in this research, an IRS was designed to operate at 3.5 GHz structured by a compact unit cell size of 21.4 mm x 21.4 mm with Circular Patch and Ring. The metasurface consists of FR-4 substrate with a dielectric constant of 4.3 and copper backplane as the ground plane. Generally, the IRS uses a PIN diode or varactor to achieve the configurability by the ON and OFF state. However in this research, the concept is proven by connecting and disconnecting metal strips to indicate the ON and OFF state. The reflection magnitude and phase are the main parameters that were analyzed in this research. In OFF and ON states, the magnitude of the reflection coefficient is -0.32 dB and -0.38 dB respectively with dynamic reflection range of 325?. A prototype for the OFF state has been fabricated and demonstrated as a reflecting surface for a horn antenna. The measured outcome, employing the reflecting surface positioned approximately 10 cm away from the horn antenna, indicates a decrease in return loss of approximately 72.2%. The results show that the proposed reflecting surface can be used as a good reflector in IRS at 3.5 GHz.
ABSTRAK: Permukaan Pemantul Pintar (IRS) merupakan teknologi terbaru 5G yang mampu mengawal dan mengubah gelombang elektromagnetik (EM) secara pintar. IRS adalah ‘bahan meta’ 2D satah atau permukaan meta 2D satah yang terdiri daripada sejumlah besar elemen pemantau pasif yang bersambung dengan pengawal pintar. Ia mampu mengadakan pergeseran fasa bebas dan/atau penurunan amplitud (secara kolektif iaitu sebagai pekali refleksi) kepada isyarat insiden pada setiap unsur reflektif. Oleh itu, kajian ini adalah berkenaan IRS yang beroperasi pada 3.5 GHz dengan struktur sel kompak bersaiz 21.4 mm x 21.4 mm seunit dengan tampalan kuprum berbentuk cincin dan bulatan. Permukaan meta ini terdiri daripada substrat FR-4 dengan pemalar dielektrik 4.3 dan satah kuprum di bahagian belakang. Secara umum, IRS menggunakan diod PIN atau varaktor bagi mencapai keboleh konfigurasi mengikut keadaan BERSAMBUNG dan TIDAK. Walau bagaimanapun, konsep ini dibuktikan dengan menyambung dan memutuskan jalur logam bagi menunjukkan keadaan BERSAMBUNG dan TIDAK. Magnitud pantulan dan fasa pekali merupakan parameter utama yang dikaji dalam kajian ini. Dalam keadaan TIDAK dan BERSAMBUNG, magnitud pekali pantulan ialah -0.32 dB dan -0.38 dB masing-masing dengan julat pantulan dinamik 325?. Prototaip pada keadaan TIDAK telah dibentuk dan menunjukkan sebagai permukaan pantulan bagi antena jenis tanduk. Dapatan hasil menunjukkan permukaan reflektif yang diukur pada jarak 10 cm dari antena tanduk mengalami penurunan kehilangan refleksi sebanyak 72.2%. Ini menunjukkan permukaan reflektif yang dicadangkan dapat digunakan sebagai reflektor IRS yang baik pada frekuensi 3.5 GHz