The paper introduces a robust image watermark based on an invariant image feature vector. Normalized Zernike moments of an image are used as the vector. The watermark is generated by modifying the vector. The watermark signal is designed with Zernike moments. The signal is added to the cover image in the spatial domain after the reconstruction process. We extract the feature vector from the modified image and use it as the watermark. The watermark is detected by comparing the computed Zernike moments of the test image and the given watermark vector. Rotation invariance is achieved by taking the magnitude of the Zernike moments. An image normalization method is used for scale and translation invariance. The robustness of the proposed method is demonstrated and tested using Stirmark 3.1. The test results show that our watermark is robust with respect to geometrical distortions and compression.

This paper addresses a novel robust watermarking method for digital images using local invariant features. Most previous watermarking algorithms are unable to resist geometric distortions that desynchronize the location where copyright information is inserted. We propose a watermarking method that is robust to geometric distortions. In order to resist geometric distortions, we use a local invariant feature of the image called the scale-invariant feature transform (SIFT), which is invariant to translation and scaling distortions. The watermark is inserted into the circular patches generated by the SIFT. Rotation invariance is achieved using the translation property of the polar-mapped circular patches. Our method belongs to the blind watermark, because we do not need the original image during detection. We have performed an intensive simulation to show the robustness of the proposed method. The simulation results support the contention that our method is robust against geometric distortion attacks as well as signal-processing attacks. We have compared our results with those of other methods, and our method outperforms them.

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a precursor to eventual depression as a disease. Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. We have selected a wearable band with multiple sensors for monitoring elderly people. Depression questionnaire has been surveyed periodically to be used as the labels. Instead of working with depression patients, we recruited 14 single household elderly people from a nearby community. The wearable band provided daily activity and biometric data for 71 days. From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. One general model is generated to be used as the baseline for the initial model deployment. Personal models are also generated for model refinement. The general model has a high recall of 80% in an MLP model. Individual models achieved an average recall of 82.7%. In this study, we have demonstrated that we can generate depressed mood prediction models with data collected from real daily living. Our work has shown the feasibility of using a wearable band as an unobtrusive depression monitoring sensor even for elderly people.

This paper presents an automated power model generation method for smartphones. Having an accurate power model of mobile devices can help identify the sources of fast discharge of the battery and provide opportunities for optimizing battery use. However, current methods rely on either external measurement equipment or internal current sensor support. This study presents a novel method for power modeling that exploits the usage patterns stored on the phone. This method can be applied to any mobile device regardless of the manufacturer and the model of the device. A power consumption analysis tool, and an artificial usage pattern generator tool have been developed for power modeling. The proposed method is able to generate an accurate power model of various smartphones even without detailed knowledge of the phone hardware.

Finite-state machines (FSMs) and the W method have been widely used in software testing. However, the W method fails to detect post-processing errors in the implementation under test (IUT) because it ends testing when it encounters a previously visited state. To alleviate this issue, we propose an enhanced fault-detection W method. The proposed method does not stop the test, even if it has reached a previously visited state; it continues to test and check the points that the W method misses. Through various case studies, we demonstrated software testing using the W method and the proposed method. From the results, it can be inferred that the proposed method can more explicitly determine the consistency between design and implementation, and it is a better option for testing larger software. Unfortunately, the testing time of the proposed method is approximately 1.4 times longer than that of the W method because of the added paths. However, our method is more appropriate than the W method for software testing in safety-critical systems, even if this method is time consuming. This is because the error-free characteristics of a safety-critical system are more important than anything else. As a result, our method can be used to increase software reliability in safety-critical embedded systems.