This paper describes a music information retrieval system that uses humming as the key for retrieval. Humming is an easy way for a user to input a melody. However, there are several problems with humming that degrade the retrieval of information. One problem is the human factor. Sometimes, people do not sing accurately, especially if they are inexperienced or unaccompanied. Another problem arises from signal processing. Therefore, a music information retrieval method should be sufficiently robust to surmount various humming errors and signal processing problems. A retrieval system has to extract the pitch from the user's humming. However, pitch extraction is not perfect. It often captures half or double pitches, which are harmonic frequencies of the true pitch, even if the extraction algorithms take the continuity of the pitch into account. Considering these problems, we propose a system that takes multiple pitch candidates into account. In addition to the frequencies of the pitch candidates, the confidence measures obtained from their powers are taken into consideration as well. We also propose the use of an algorithm with three dimensions that is an extension of the conventional Dynamic Programming (DP) algorithm, so that multiple pitch candidates can be treated. Moreover, in the proposed algorithm, DP paths are changed dynamically to take deltaPitches and IOIratios (inter-onset-interval) of input and reference notes into account in order to treat notes being split or unified. We carried out an evaluation experiment to compare the proposed system with a conventional system [6]. When using three-pitch candidates with conference measure and IOI features, the top-ten retrieval accuracy was 94.1%. Thus, the proposed method gave a better retrieval performance than the conventional system. Index Terms-Continuous DP, dynamic melody representation, humming, multiple pitch candidates, music information retrieval.

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: This paper provides the trend of Internet of Things (IoT) for smart healthcare services and applications. IoT has provided a promising opportunity to build intelligent healthcare system and smart wearable applications by using the growing capability of wireless mobile devices, interactive sensors/actuators, and RFID technologies. For analysis of state-of-art technology of smart healthcare system, this paper includes comparative analysis and investigation of existing standard, network protocol, and devices, etc. In this paper, we examine the market trend of IoT healthcare. In particular, we examine the variety of IoT based healthcare type such as mobile, wearable device. After that, we examine the technologies of IoT healthcare such as standard, sensor, network and security. This survey contributes to better understanding of the challenges in existing IoT healthcare and further new light on future research directions.

BACKGROUND: Eye exam diagnosis is one of the early detection methods. However, such a method is dependent on expensive and unpredictable optical equipment. OBJECTIVE: The eye exam can be re-emerged through an optometric lens attached to a smartphone and come to read the diseases automatically. Therefore, this study aims to provide a stable and predictable model with a given dataset representing the target group domain and develop a new method to identify eye disease with accurate and stable performance. METHODS: The ResNet-18 models pre-trained on ImageNet data composed of 1,000 everyday objects were employed to learn the dataset’s features and validate the test dataset separated from the training dataset. RESULTS: A proposed model showed high training and validation accuracy values of 99.1% and 96.9%, respectively. CONCLUSION: The designed model could produce a robust and stable eye disease discrimination performance.

This paper presents the design and performance evaluation of an integrated hybrid MAC and topology control scheme (abbreviated IMTC) for machine-to-machine (M2M) area networks that require comparatively the high data rate communications. In order to maximize the network performance, IMTC is designed with a cross-layer concept of MAC and network layers, which provides the multi-channel TDMA scheduling based on the information of network topology optimized by transmission power control. Simulation results show that by using the proposed scheme, the network throughput and the energy efficiency can be significantly improved.