Persepsi pelajar sarjana muda kejuruteraan elektrik terhadap program latihan industri, Kolej Universiti Teknologi Tun Hussein Onn

Kajian ini dijalankan bertujuan untuk mengetahui persepsi Pelajar Sarjana Muda Kejuruteraan Elektrik Terhadap Program Latihan Industri, KUiTTHO berdasarkan kepada 4 faktor iaitu kesesuaian penempatan program latihan industri, kesesuaian pendedahan pelajaran teori di KUiTTHO dan amali di tempat program latihan industri, tahap kerjasama yang diberikan oleh pihak industri kepada pelajar d a n kesediaan pelajar melakukan kerja yang diberi semasa program latihan industri. Sampel kajian adalah terdiri daripada pelajar-pelajar Sarjana Mud a Kejuruteraan Elektrik di KUITTHO yang telah menjalani program latihan industri. Set soal selidik terdiri daripada 3 bahagian iaitu bahagian A yang bertujuan untuk mendapatkan maklumat diri responden manakala bahagian Bertujuan untuk mengetahui kesesuaian program latihan industri yang telah diikuti oleh pelajar dan bahagian C adalah cadangan untuk meningkatkan mutu program latihan industri. Data - data yang diperolehi dianalisis menggunakan perisisan SPSS 10.0 for Windows (Statistical Package for the Social Science version 10) dan dipersembahkan dalam bentuk peratusan, carta dan keterangan analisis. Dapatan kajian secara umumnya menunjukkan reaksi positif dimana bagi semua aspek menunjukkan min keseluruhan yang tingg

An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities

Fusion of deep representations in multistatic radar networks to counteract the presence of synthetic jamming

Micro-Doppler signatures are extremely valuable in the classification of a wide range of targets. This paper investigates the effects of jamming on the micro-Doppler classification performance and explores a potential deep topology enabling low-bandwidth data fusion between nodes in a multistatic radar network. The topology is based on an array of three independent deep neural networks (DNNs) functioning cooperatively to achieve joint classification. In addition to this, a further DNN is trained to detect the presence of jamming, and from this, it attempts to remedy the degradation effects in the data fusion process. This is applied to the real experimental data gathered with the multistatic radar system, NetRAD, of a human operating with seven combinations of holding a rifle-like object and a heavy backpack that is slung on their shoulders. The resilience of the proposed network is tested by applying synthetic jamming signals into specific radar nodes and observing the networks' ability to respond to these undesired effects. The results of this are compared with a traditional voting system topology, serving as a convenient baseline for this paper