User identification is an important task for a variety of purposes such as authentication or providing personalized advice for improving user experience. In this paper, we propose a method for identifying a user who is holding a smartphone out of previously given target users from acceleration data obtained from the accelerometer in the smartphone using a deep neural network. This proposed method preliminary creates the model from the acceleration data of each user while walking in its training phase. This method identifies the user from acceleration data for identification based on this model in the classification phase. We evaluated the proposed method with the acceleration obtained from the actual eight and twelve users in two aspects, which were identifications including no-decision choice and that without no-decision choice. Our evaluation showed that the proposed method achieved accuracies higher than 95% for twoto five-class identification without no-decision. The proposed method identified the user with no or little false positive in evaluations with “no-decision.”
Low power wide area wireless communication technologies are attracting attention particularly from various IoT applications. IEEE 802.11ah and IEEE 802.15.4g are two wireless technologies designed for outdoor IoT applications and installed on consumer devices and systems, for which both technologies operate in frequencies below 1GHz (Sub-1GHz Band). In addition, both technologies have communication range up to 1, 000 meters. Therefore, IEEE 802.11ah and IEEE 802.15.4g networks are likely to coexist. Our simulation results using standard defined coexistence mechanisms show that IEEE 802.11ah network can severely interfere with IEEE 802.15.4g network and lead to significant packet loss in IEEE 802.15.4g network. IEEE 802.15.4g network can also impact on packet latency in IEEE 802.11ah network. Accordingly, IEEE New Standards Committee and Standard Board formed IEEE 802.19.3 Task Group in December 2018 to develop an IEEE 802 standard for the coexistence of IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1GHz frequency bands to guide product deployment. The authors of this paper have been actively leading this standard development. This paper introduces IEEE 802.19.3 standardization activities to address coexistence issues of IEEE 802.11ah and IEEE 802.15.4g systems and summarizes our technical contributions for interference mitigation. Simulation results show that our coexistence technologies achieve better coexistence performance.
Obstacle detection is an essential process in consumer's autonomous mobility systems such as autonomous vehicles inside the dedicated lane to acquire the location of obstacles, and it has become a popular topic in this decade with the blooming of various object detection algorithms and the enhancement of sensor quality. To maintain high accuracy of obstacles' detection in mobility systems outdoor, a sensor fusion system is required to essentially support environmental influence such as lousy weather as well as high moving speeds and adaptably deal with clutter and miss detection based on the incoming measurements from heterogenous sensors with Camera, LiDAR and Radar. Since no current literature about Gaussian mixture probability hypothesis density (GMPHD) handles the above low accuracy fusion problem due to environmental influence for heterogeneous sensors, we propose the concept of integrating GMPHD to heterogeneous sensor fusion with three architectures, Track-to-Track-Fusion (T2TF), Measurement-to-Track-Fusion (M2TF) and Track-to-Association-Fusion (T2AF) and further evaluate their performances respectively in terms of their fusion improvement abilities to determine their practicalities for mobility systems by using the simulation datasets which reproduce ordinary and poorer conditions with the degradation of sensors' performance in the assumption of environmental influences.
The large power consumption of smartphones is an important issue. Smartphone operating systems such as Android have a function that invokes an application without user's operation. That is, an application runs and may communicate using its network interface in the screen-off state without user's operation. This behavior consumes large batteries. Temporarily disabling its network interface is one of the promising methods for reducing power consumption in the screen-off state. Less power is consumed while its network interface is disabled, but processes of disabling and enabling its network interface consume battery. Therefore, it is necessary to keep the interface disabled for a sufficiently long period such that the power consumption decreased by disabling the network interface exceeds the power consumption increased by the transition process of disabling and enabling the interface. In this paper, we focus on a method of reducing power consumption in the screen-off state by repeating to disable and enable the network interface and discuss estimation of its Break-Even Time (BET) with which the sizes of increased and decreased power consumption are the same. We then propose two methods for estimating BET. One method estimates BET by integrating the electric current. The other method estimates it according to the average electric current. We evaluate the methods with practical applications and Android devices and show that the method based on the average electric current can estimate BET accurately. In the case of our experiments, the difference between the actual and estimated BETs was less than 16.4%.
In this paper, the author proposes an Energy-on-Demand (EoD) system based on combinatorial optimization of appliance power consumptions, and describes its implementation and evaluation. EoD is a novel power network architecture of demand-side power management, whose objective is to intelligently manage power flows among power generations under the limitation of available power resource. In an EoD system, when total power consumption exceeds the limit of power resource, a power allocation manager deployed in the system decides the optimal power allocation to all the appliances based on their importance and power consumptions, and controls the amount of power supplied to the appliances in a way that causes minimum undesired effect to quality-of-life of users. Therefore, one of the most crucial factors in an EoD system is the strategy for deciding the optimal power allocation. From a mathematical viewpoint, the power allocation management in an EoD system can be considered as an optimization problem of appliance operation modes. In the developed system, power allocation is based on the multiple-choice knapsack problem (MCKP), a kind of combinatorial optimization problem. The system measures power consumption of appliances, computes the optimal power allocation based on an algorithm for the MCKP, and realizes computed power allocation by controlling IR-controllable appliances and mechanical relays. Through experiments, the developed system is confirmed to work properly as an EoD system by observing system behaviors when the total power consumption exceeds the upper limit of the available power resource.
