Garbage classification is the first step in waste segregation, recycling, or reuse. MobileNet was used to generate a model that classifies common trash according to the following categories: glass, paper, cardboard, plastic, metal, and other trash. A dataset of 2527 trash images in .jpg extension was used for the training. The model used transfer learning from a model trained on the ImageNet Large Visual Recognition Challenge dataset. The TensorFlow for Poets git repository was cloned as a working directory to retrain the MobileNet model in 500 steps. The resulting baseline model, with a final test accuracy of 87.2% was optimized and quantized. In the Andoid app development, the optimized model (with 89.34% confidence) is preferred over the quantized model (with 1.47% confidence) based on the test using a plastic image. The model app was successfully installed in a Samsung Galaxy S6 Edge+ mobile phone. The installed mobile app successfully identified a cardboard material in an image with a cardboard container. It is recommended to rerun the training using more steps as this may improve the quantized model performance since a quantized model is fit for mobile devices than models with no quantization.

In this paper, the spectrum occupancy of the VHF and UHF bands are investigated. The primary objective is to identify the bands that have low utilization and could be accessed for future developments in TV broadcasting, especially in the analog to digital transition as well as other digital technological developments. Results could provide an avenue for emerging technologies such as cognitive radio. The study consists of data collection and analyses in the VHF and UHF TV bands. Average occupancy calculations based on measurements and results taken over a two-day period show an overall average occupancy of about 16% of the total spectrum surveyed. Specifically, average occupancies for the low band VHF, high band VHF and UHF band are about 57%, 60% and 7% of the respective bands. Since the study on white space is at its early stages, this paper serves as a preliminary study, thus further studies are needed to fully optimize the white spaces of the spectrum for future applications in the advancement of the technology.

Traffic violation apprehension is one of the traffic problems here in the Philippines. One example is the No Helmet No Ride Law that is implemented but many motorists still choose to ignore. To alleviate the problem the government has offered many solutions, one of which is the No Contact Traffic Apprehension Policy that uses CCTV Monitoring. To further enhance this solution the government has partnered with the De La Salle University to use artificial intelligence in the system. Computer Vision tasks like image classification and object detection can help automate the traffic apprehension system. Image classification and object detection are technologies which are used in computer vision in defining an image or coordinates of an object in an image. In this work, a novel approach to classifying motorcycle riders between wearing a helmet or not will be developed. It will be demonstrated using deep machine learning, specifically convolutional neural network and by utilizing different pre-trained models to a gathered dataset.

In this paper, we present the development and evaluation of a low-cost RGB-D camera based anthropometric measurement system for Barong Tagalog tailoring shops. Although anthropometric scanners exist in the market today, most of these current systems have high cost and complex installation process. The proposed barong anthropometric measurement system utilizes only frontal and side-view images from off-the-shelf RGB-D sensor, i.e. Kinect camera, which is inexpensive and readily available. The proposed system measures the linear body distances, i.e. arms, shoulder, and upper body lengths based on RGB-D skeleton tracking joints. We also propose three different methods for estimating the circumferential barong measurements for the hips, chest, and waist. The proposed system was evaluated by comparing the measured and actual values for absolute and relative error metrics in every measurements from 200 randomly selected participants – 100 males and 100 females. The average relative errors (%) for the male arm, body, shoulder, chest, waist, and hip girth measurements were 4.09, 3.06, 2.80, 4.51, 4.14, and 3.05, respectively, while generally higher for females, 3.93, 6.53, 5.19, 5.23, 4.37, and 5.27, respectively. The acquired body measurements were utilized to produce barong tailoring patterns and assign standard barong sizes, i.e. Extra Small (XS), Small (S), Medium (M), Large (L), and Extra Large (XL) sizes, to 130 male participants. Notably, only one participant was misallocated by the proposed system as shown in the confusion matrix results. Finally, a website was developed as a proof-of concept for e-commerce potential. Overall, the results illustrated the feasibility of the proposed system.

This paper presents a new algorithm for tracking the hand during palpation in a breast self-examination video capture using a modified KLT feature tracker. This is implemented primarily using Shi-Tomasi corner detection and Lucas-Kanade optical flow. A novel hand initialization technique was developed using Shi-Tomasi corner detection, outlier elimination, ellipse fitting, and target estimation in order to locate specifically the finger pads. Then, continuous hand tracking is achieved using Lucas- Kanade optical flow and a novel evaluation and screening of displacement vectors. A dataset of 14 video sequences was used to test the performance of the proposed algorithm. Experiments revealed efficient tracking capability of the algorithm with an overall F-score of 94.61%.

Amidst the global pandemic, the methodical acquisition, analysis, and evaluation of health-related data is crucial to learn from experience and strengthen preparedness for future challenges of similar nature. The goal of this paper is to survey the recent approaches to disease surveillance or outbreak monitoring in the context of artificial intelligence or machine learning. Utilizing Elsevier's Scopus database, the keywords, “Disease Surveillance” or “Outbreak Monitoring” yielded 12,648 document results (with year duration starting 2016). Then, the documents were reduced to 367 after conjunction with the terms, namely, “Artificial Intelligence” or “Machine Learning (ML)” or “Data Science” and limiting the document type to article and review only. The documents were examined one-by-one leaving only the most recent and those papers which applied machine learning methods. The survey showed four major ML tasks in the recent literature, namely, topic modeling, time series forecasting & regression, classification, and clustering. It was also observed that many disease surveillance systems are still utilizing traditional (shallow) machine learning techniques. However, deep learning-based techniques was also demonstrated, in the literature, to perform better than the traditional models.