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Abstrak-COVID-19 merupakan virus yang telah dinyatakan sebagai pandemi oleh WHO, dan di indonesia sendiri menetapkan COVID-19 sebagai bencana nasional melalui Keputusan Presiden Nomor 12 Tahun 2020. Sumber utama transmisi dari virus ini berasal dari percikan pernapasan atau droplet yang salah satu pencegahan penyebarannya adalah dengan penggunaan masker. Saat ini, pemerintah sedang memberlakukan new normal. Walaupun beraktivitas di lingkungan luar, protokol kesehatan wajib diikuti dan seluruh masyarakat harus disiplin dalam menjalaninya. Pada studi ini dirancang sebuah sistem otomatis pendeteksi wajah bermasker menggunakan deep learning dalam menjalankan fungsinya. Sistem yang dirancang menggabungkan model deep learning, detektor wajah, dan program tracking dan counting menjadi sebuah sebuah sistem otomatis yang dibantu oleh Graphic User Interface (GUI) serta sebuah perangkat alarm dan platform Internet of Things dalam pemakaiannya. Berdasarkan hasil pengujian yang dilakukan mengikuti batasan masalah yang telah dirumuskan, model memiliki tingkat akurasi klasifikasi pada dataset test sebesar 99%. Implementasi pada Raspberry Pi 4 menunjukkan sistem berbasis model deep learning yang telah dibuat sukses melakukan deteksi, tracking dan counting yang datanya dikirimkan kepada alarm yang dirancang dan sebuah platform IoT, Ubidots. Performa deteksi maksimal dicapai saat objek deteksi bergerak 0,7 m/s, pencahayaan ≥ 100 lux, dan penggunaan modul TensorFlow Lite pada sistem dengan akurasi sebesar 85,7%. Hasil perbandingan dengan metode deteksi lain menunjukkan karakterisasi model deep learning memiliki akurasi deteksi sebesar 82%, lebih tinggi dari metode Haar Classifier dengan akurasi 53%.

This paper studies the energy-optimization design methodology for current-mode (CM) signaling in high-speed on-chip interconnects, using the modified clamped bit-line sense amplifier circuit (MCBLSA) as a case study. Optimization for the CM circuits for on-chip interconnects requires a completely different treatment than the voltagemode circuits, due to the problems such as different effective driver resistance and termination resistance modeling. The methodology will be validated using SPICE simulations. It is shown that when dealing with receiver termination sizing, the optimal size is determined by the required voltage swing at the receiver end to guarantee valid operation under the effect of crosstalk noise. However, sizing the driver and receiver transistors should be done simultaneously as their resistive values which affect the performance are dependent on each other.

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This paper deals with the design aspect of currentsteering D/A converters which is to be incorporated in an oversampling sigma-delta DAC with Dynamic Element Matching (DEM), and particularly with the trade-off between device sizing, output impedance, and ideal systematic nonlinearity. A formula for the estimation of minimum output impedance requirement based on DNL specification is proposed, and the corresponding design guideline is given. As a case study, a 16-bit sigma-delta current-steering DAC is presented. It is shown in this paper that basic cascode current mirror structure is not practical for this purpose. An 8-level current steering DAC with 16-bit accuracy was designed in a 0.18-ȝm CMOS process using the regulated cascode enhanced output impedance current mirror. A worst case INL of 0.09 LSB of a 16-bit converter was achieved.I.

Particulate matter is one of the factors that can affect air quality. The air quality can be determined by the Air Pollution Index, which has several parameters including PM 10 and ozone (O 3 ). Air pollution can be overcome by using a filtration system based on electrostatic precipitation when particles are attached to the static charges. In this study, we have developed a prototype of the electrostatic filter based on fuzzy logic control to reduce air pollution of particulate matters. The electrostatic filter is an ozone generator consisting of plate-type corona discharge and a high voltage dc generator. The experimental results showed that the more ozone generators used as electrostatic filters, the faster the particulate concentration decreases. However, the use of ozone generators may increase the concentration of ozone in the air that can be harmful to human health. Therefore, we have developed an electrostatic precipitation-based air pollution control using fuzzy logic. Semiconductor gas sensor and laser dust sensor are used as feedback signals for the system in regulating the number of charges released by ozone generators. Implementation of this control system can reduce the particulates of PM 10 by 80% within 10 minutes while maintaining a low level of ozone during the air purification process.