Given real-time sensor data streams obtained from machines, how can we continuously predict when a machine failure will occur? This work aims to continuously forecast the timing of future events by analyzing multi-sensor data streams. A key characteristic of real-world data streams is their dynamic nature, where the underlying patterns evolve over time. To address this, we present TimeCast, a dynamic prediction framework designed to adapt to these changes and provide accurate, real-time predictions of future event time. Our proposed method has the following properties: (a) Dynamic: it identifies the distinct time-evolving patterns (i.e., stages) and learns individual models for each, enabling us to make adaptive predictions based on pattern shifts. (b) Practical: it finds meaningful stages that capture time-varying interdependencies between multiple sensors and improve prediction performance; (c) Scalable: our algorithm scales linearly with the input size and enables online model updates on data streams. Extensive experiments on real datasets demonstrate that TimeCast provides higher prediction accuracy than state-of-the-art methods while finding dynamic changes in data streams with a great reduction in computational time.

In this paper, we propose a quantum-native formulation of maximum likelihood detection in random access channel with overloaded multiple-input multiple-output (MIMO), where a number of user terminals share the same channel resource and asynchronously transmit signals. Classical low-complexity linear detectors suffer from significant performance degradation in this scenario, while the exhaustive-search-based maximum likelihood detection achieves the optimal performance but induces an exponential increase in time complexity. To strike this fundamental tradeoff, we formulate the maximum likelihood detection as a binary optimization problem and solve it via the Grover adaptive search (GAS), a quantum exhaustive search algorithm offering quadratic speedup. We further introduce a search space reduction technique using quantum W states and controlled-H gates, which substantially decreases complexity. In addition, we investigate the effects of efficient parameter settings in GAS. Our simulation results show that the proposed detector achieved the optimal detection performance while reducing the query complexity, thereby highlighting its potential as a solution for future quantum-accelerated overloaded MIMO systems.

In this paper, we propose the heartbeat interval estimating using 300GHz sub-terahertz, which is focused on 6G. In this method, to extract the heartbeat interval, audible range body surface displacement is detected using high resolution of sub-terahertz. We compare the experimental results of proposed method with the ECG sensor output, and the result of sub-terahertz reveals that the heartbeat interval performance can be obtained with about 0.5 % root mean squared error (RMSE) compared to that of ECG sensor.

Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and anomalies? Our answer is to focus on two types of patterns, i.e., “regimes” and “components”, over high-order tensor streams, for which we present an efficient and effective method, namely CubeScope. Specifically, it identifies any sudden discontinuity and recognizes distinct dynamical patterns, “regimes” (e.g., weekday/weekend/holiday patterns). In each regime, it also performs multi-way summarization for all attributes (e.g., item, price, brand, and time) and discovers hidden “components” representing latent groups (e.g., item/brand groups) and their relationship. Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice.

For solving the “Digital divide” problem and developing a system which understands human, the data mining technology is being developed. Moreover, within the concept of Kukanchi, the system can not only collect information, but also provide service to user. In this paper, the authors show two experiments which constructed based on “Kukanchi”. These Kukanchi systems include both techniques of action recognition and mining in time-series motion logs. In the first experiment, the authors constructed a service robot system, which makes networked robots exchange and provide the information to customers by recognizing their actions, to show the basis concept of the paper. In the second experiment, the authors show the possibility of using interactive data mining, which uses visualization techniques to help human add judgments, to improve the results of the first experiment. The two experiments show the utility and possibility of Kukanchi and mining in time-series motion logs which supports the system to understand human and help people directly.