To prevent constraints or defects of a single sensor from malfunctions, this paper proposes a fire detection system based on the Dempster-Shafer theory with multi-sensor technology. The proposed system operates in three stages: measurement, data reception and alarm activation, where an Arduino is tasked with measuring and interpreting the readings from three types of sensors. Sensors under consideration involve smoke, light and temperature detection. All the measured data are wirelessly transmitted to the backend Raspberry Pi for subsequent processing. Within the system, the Raspberry Pi is used to determine the probability of fire events using the Dempster-Shafer theory. We investigate moderate settings of the conflict coefficient and how it plays an essential role in ensuring the plausibility of the system's deduced results. Furthermore, a MySQL database with a web server is deployed on the Raspberry Pi for backlog and data analysis purposes. In addition, the system provides three notification services, including web browsing, smartphone APP, and short message service. For validation, we collected the statistics from field tests conducted in a controllable and safe environment by emulating fire events happening during both daytime and nighttime. Each experiment undergoes the No-fire, On-fire and Post-fire phases. Experimental results show an accuracy of up to 98% in both the No-fire and On-fire phases during the daytime and an accuracy of 97% during the nighttime under reasonable conditions. When we take the three phases into account, the accuracy in the daytime and nighttime increase to 97% and 89%, respectively. Field tests validate the efficiency and accuracy of the proposed system.

Artificial lighting is responsible for a large portion of total energy consumption and has great potential for energy saving. This paper designs an LED light control algorithm based on users' localization using multiple battery-less binary human detection sensors. The proposed lighting control system focuses on reducing office lighting energy consumption and satisfying users' illumination requirement. Most current lighting control systems use infrared human detection sensors, but the poor detection probability, especially for a static user, makes it difficult to realize comfortable and effective lighting control. To improve the detection probability of each sensor, we proposed to locate sensors as close to each user as possible by using a battery-less wireless sensor network, in which all sensors can be placed freely in the space with high energy stability. We also proposed to use a multi-sensor-based user localization algorithm to capture user's position more accurately and realize fine lighting control which works even with static users. The system is actually implemented in an indoor office environment in a pilot project. A verification experiment is conducted by measuring the practical illumination and power consumption. The performance agrees with design expectations. It shows that the proposed LED lighting control system reduces the energy consumption significantly, 57% compared to the batch control scheme, and satisfies user's illumination requirement with 100% probability.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

Multi-sensory communications with haptics attract a number of researchers in recent years. To provide services of the communications with high realistic sensations, the researchers focus on the quality of service (QoS) control, which keeps as high quality as possible, and the quality of experience (QoE) assessment, which is carried out to investigate the influence on user perception and to verify the effectiveness of QoS control. In this paper, we report the present status of studies on multi-sensory communications with haptics. Then, we divide applications of the communications into applications in virtual environments and those in real environments, and we mainly describe collaborative work and competitive work in each of the virtual and real environments. We also explain QoS control which is applied to the applications and QoE assessment carried out in them. Furthermore, we discuss the future directions of studies on multi-sensory communications.

The need for efficient movement and precise location of robots in intelligent robot control systems within complex buildings is becoming increasingly important. This paper proposes an indoor positioning and communication platform using Fluorescent Light Communication (FLC) employing a newly developed nine-channel receiver, and discusses a new location estimation method using FLC, that involves a simulation model and coordinate calculation formulae. A series of experiments is performed. Distance errors of less than 25 cm are achieved. The enhanced FLC system yields benefits such as greater precision and ease of use.

In this paper, we propose a method to construct structure models of articulated objects from multiple local observations of their motion using state transition analysis of local geometric constraints. The object model is constructed by a bottom-up approach with three levels. Each level groups sensor data with a constraint among local features observed by the sensor, and constructs the local model. If the sensor data in current model conflict, the model is reconstructed. In each level, the first level estimates a local geometric feature from the local sensor data (eg. edge, feature point) The second level estimates a rigid body from the local geometric feature. The third level estimates an object from the rigid bodies. In the third level, the constraint between rigid bodies is estimated by transition states, which are motions between rigid bodies. This approach is implemented on a blackboard system.

An HDTV still-picture camera that uses four PAL CCD sensors has been developed for use as a high-speed, high-resolution image reader. The CCD sensors are optically coupled to a single lens by a pyramidal mirror. Each CCD sensor reads a quarter of the image and the four quarter-images are combined into one HDTV picture. Discontinuities at the lines where the four images join can be eliminated by white- and dark-level correction and gamma correction. Moreover, smoothing processing using a weighted-mean method is performed to produce a seamless picture. With this processing the camera can consistently produce seamless pictures.