This research focuses on the development and performance evaluation of a recommendation system model that leverages time-based data and user preference scores. The model recommends movies to users by analyzing movie data and the preference scores previously assigned by users in the system, with these scores decreasing in significance over time. The research utilizes the MovieLens dataset, which is divided into 80% for model training and 20% for testing. The model employs a Collaborative Filtering technique to determine user similarity, utilizing Pearson Correlation and Cosine Similarity methods. It then selects the nearest neighbors to the user through a two-step process: in the first step, the top K most similar neighbors are identified, and in the second step, the top 10 “friends of friends” for each neighbor are selected. The study tests K values ranging from 10 to 40, incrementing by 5 at each step. The results indicate that as the K value increases, the model's performance also improves. At a K value of 10, the model using Pearson Correlation slightly outperforms the one using Cosine Similarity, with scores of 0.53 and 0.51, respectively. When the K value increases to 15, both models perform similarly, with scores of 0.77 and 0.75, respectively. At K values of 30, 35, and 40, both models achieve equivalent performance, with scores of 0.95, 0.97, and 0.98, respectively. This research demonstrates that the developed model can be effectively applied to recommend movies or other products with similar characteristics, providing a robust foundation for time-sensitive and user-preference-based recommendation systems.
Measuring remote photoplethysmography (rPPG), a contactless facial video-based PPG estimation, requires a large amount of labeled data via supervised methods, leading to significantly increased labor and costs. Existing rPPG studies based on self-supervised methods have utilized temporal and spatial similarities between rPPG data, which help reduce manual labeling costs. Here, we propose a novel self-supervised rPPG estimation method with contrastive learning. The proposed method not only leverages temporal and spatial similarities as representations but also maintains a well-contained representation. A well-contained representation reduces the computational cost associated with calculating negative loss. We use a 3D-CNN model to capture well-contained representation and multiple rPPG signals, which perform on different spaces but are temporally similar. We show that the proposed method outperforms current rPPG methods in terms of heart rate estimation accuracy on two public datasets, i.e., PURE and UBFC.
This study proposed a body movement-based system for efficiently controlling robots in large indoor environments. Traditional hand gesture recognition systems experience significant accuracy loss in large indoor spaces, particularly as the distance between the user and the camera grows. To address this issue, clear and large body movements were used as control signals instead of hand movements. For example, participants could give commands to the robot by raising both hands or lifting one hand in a specific direction. The system utilized MediaPipe Pose to track the user's posture in real time and communicated commands from the server to the robot via TCP/IP socket communication. This structure ensured stable data transmission, enabling smooth robot control even in expansive spaces. Consequently, this research demonstrated the feasibility of effective robot control in large indoor areas, such as screen golf courses, and suggested the potential for system expansion in various indoor environments.
This paper proposes a low-power single-stage single-balanced radio frequency (RF) front-end receiver in which a high performance Colpitts based self-oscillating mixer (SOM) is folded at the output of a cascode low-noise amplifier (LNA). Since the cascode LNA is used in the front-end, the proposed SOM has good reverse isolation from the oscillator output to the RF input. Using a 65 nm CMOS technology, the proposed SOM is optimized for the best phase noise performance. Targeted for WLAN frequency band application, the proposed SOM oscillates at around 2.4 GHz and achieves the phase noise of -77 dBc/Hz, -100 dBc/Hz, and -120 dBc/Hz at 10 kHz, 100 kHz, and 1 MHz offset frequency, respectively. The simulated voltage conversion gain is about 44 dB. The double side-band (DSB) noise figure (NF) is about 6.8 dB at 1 MHz intermediate frequency (IF). The SOM cell consumes 755 μW de power from a 1-V supply.
In recent years, there has been a spike in demand for wearable devices. Low dropout regulators are an integral part of these devices, owing to their high Power Supply Rejection (PSR) and stable output. This paper proposes a high gain, low quiescent current $(10.298\mu \mathrm{A})$, Output Capacitorless Low Dropout Regulator (OCL-LDO) with a fast local loop and unity feedback. Nested Miller frequency Compensation (NMC) technique is used to stabilize the system. The proposed circuit can operate with a load current ranging from $20\text{pA}$ to 20mA. The design encompasses three gain stages with a total open loop gain of 132 dB resulting in enhanced load regulation (0.0831 µV/mA) and line regulation (0.6917 mV/V). The proposed LDO has been simulated in gpdk 90nm CMOS technology using Cadence Virtuoso. The simulation results demonstrate a PSR of −61.03 dB and −41.17 dB at 1kHz and 10kHz respectively.
