This letter presents a new time-digital single-slope ADC (TDSS) architecture for CMOS image sensors. In the proposed ADC, a conventional single-slope ADC is used in coarse phase and a time to digital convertor is employed in fine phase. Through second comparison of the two different slope voltages (discharge input voltage and ramp voltage), the proposed ADC achieves low bit precision compensation. Compared with multiple-ramp single-slope (MRSS) ADC, the proposed ADC not only has a simple digital judgment circuit, but also increases conversion speed without complicated structure of ramp generator. A 10-bit TDSS ADC consisting of 7-bit conventional single-slope ADC and 3-bit time to digital converter was realized in a 0.13µm CIS process. Simulations demonstrate that the conversion speed of a TDSS ADC is almost 3.5 times faster than that of a single-slope ADC.

Time synchronization is of paramount importance in wireless sensor networks (WSNs) due to the inherent distributed characteristics of WSNs. Border surveillance WSNs, especially, require a highly secure and accurate time synchronization scheme to detect and track intruders. In this paper, we propose a Secure and Efficient Time synchronization scheme for Border surveillance WSNs (SETB) which meets the requirements of border surveillance WSNs while minimizing the resource usage. To accomplish this goal, we first define the performance and security requirements for time synchronization in border surveillance WSNs in detail. Then, we build our time synchronization scheme optimized for these requirements. By utilizing both heterogeneous WSNs and one-way key chains, SETB satisfies the requirements with much less overhead than existing schemes. Additionally, we introduce on-demand time synchronization, which implies that time synchronization is conducted only when an intruder enters the WSN, in order to reduce energy consumption. Finally, we propose a method of deploying time-source nodes to keep the synchronization error within the requirement. Our analysis shows that SETB not only satisfies the performance and security requirements, but also is highly efficient in terms of communication and computation overhead, thus minimizing energy consumption.

To design a wireless sensor network for farms, it is necessary to understand and predict the effect of vegetation. In this study, the change in the propagation loss characteristics in 920-MHz band is examined during the growth of mulberry bushes. The received signal strength indicator (RSSI) is measured as a function of the distance between the transmitting antenna (Tx) and the receiving antenna (Rx) in a 50×50m mulberry field. The Tx and Rx are placed at a height of 1.5m. Moreover, the horizontal and vertical polarizations are measured and the differences are shown. Three empirical vegetation attenuation models are introduced, and the measured data have been fitted to each model. The results show that the non-zero gradient model is the best model at predicting the vegetation attenuation in a mulberry farm regardless of the polarization or mulberry growth. It is found that the attenuation dependence on the plant height is linear. Furthermore, the results have revealed that the horizontal polarization had about 1.5 times as large an effect on the vegetation attenuation as the vertical polarization.

Because redox sensors can detect multi-ions and the concentration within a single sensing area using current and potential signals, they have been studied for chemical analysis applications. A small sensing area and a low concentration measurement typically reduce the output current of a redox sensor. Therefore, we previously fabricated the Amplified Redox Sensor, which has a working electrode combined with a bipolar transistor to amplify a small current signal. However, the current gain of the bipolar transistor had been changed by the redox current because the redox current flows in the base terminal of the bipolar transistor. In this study, we propose a new measurement method in which an offset current is inserted along with the redox current in the base terminal. The proposed measurement method can detect potassium ferricyanide (K3[Fe (CN)6]) concentrations as low as 1μM using the Square Wave Voltammetry method.

Visible light communication is one of the key technologies for intelligent transport systems (ITS). However, current visible light communication systems require high-cost devices, such as high-speed image sensors, to support their high transmission rates. In this paper, we designed a communication system with combination of a low-speed commercial image sensor and a polygon mirror — namely, a fast-blinking light signal is scanned by the polygon mirror and captured as a residual image on the low-speed image sensor — to achieve visible light communication on existing mobile devices with high transmission rates. We also analyzed some required conditions, such as the relationship between the exposure time of the image sensor and the optimal resolution, and conducted experiments for performance evaluation. As a result, we found that the proposed system could achieve a data rate of 120bps, 10 times faster than that of the existing scheme when we compare them using the same image sensor. We also found that the proposed system can achieve a practical bit error rate in a low-noise environment.

The problem of identifying moving objects in a video recording produced by a range sensor camera is due to the limited information available for classifying different objects. On the other hand, the infrared signal from a range sensor camera is more robust for extreme luminance intensity when the monitored area has light conditions that are too bright or too dark. This paper proposes a method of detection and tracking moving objects in image sequences captured by stationary range sensor cameras. Here, the depth information is utilized to correctly identify each of detected objects. Firstly, camera calibration and background subtraction are performed to separate the background from the moving objects. Next, a 2D projection mapping is performed to obtain the location and contour of the objects in the 2D plane. Based on this information, graph matching is performed based on features extracted from the 2D data, namely object position, size and the behavior of the objects. By observing the changes in the number of objects and the objects' position relative to each other, similarity matching is performed to track the objects in the temporal domain. Experimental results show that by using similarity matching, object identification can be correctly achieved even during occlusion.

The RO (Ring-Oscillator)-based sensor is one of easily-implementable variation sensors, but for decomposing the observed variability into multiple unique device-parameter variations, a large number of ROs with different structures and sensitivities to device-parameters is required. This paper proposes an area efficient device parameter estimation method with sensitivity-configurable ring oscillator (RO). This sensitivity-configurable RO has a number of configurations and the proposed method exploits this property for reducing sensor area and/or improving estimation accuracy. The proposed method selects multiple sets of sensitivity configurations, obtains multiple estimates and computes the average of them for accuracy improvement exploiting an averaging effect. Experimental results with a 32-nm predictive technology model show that the proposed averaging with multiple estimates can reduce the estimation error by 49% or reduce the sensor area by 75% while keeping the accuracy. Compared to previous work with iterative estimation, 23% accuracy improvement is achieved.

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.

This paper proposes a method that uses bistatic Multiple-Input Multiple-Output (MIMO) radar to locate living-bodies. In this method, directions of living-bodies are estimated by the MUltiple SIgnal Classification (MUSIC) method at the transmitter and receiver, where the Fourier transformed virtual Single-Input Multiple-Output (SIMO) channel matrix is used. Body location is taken as the intersection of the two directions. The proposal uses a single frequency and so has a great advantage over conventional methods that need a wide frequency band. Also, this method can be used in multipath-rich environments such as indoors. An experiment is performed in an indoor environment, and the MIMO channels yielded by various subject numbers and positions are measured. The result indicates that the proposed method can estimate multiple living-body locations with high accuracy, even in multipath environments.

We have developed a capacitance sensor of frequency modulation for integrated touchpanels using amorphous In-Sn-Zn-O (α-ITZO) thin-film transistors (TFTs). This capacitance sensor consists of a ring oscillator, whose one stage is replaced by a reset transistor, sensing transistor, and sensing electrode. The sensing electrode is prepared as one terminal to form a sensing capacitor when the other terminal is added by a finger. The ring oscillator consists of pseudo CMOS inverters. We confirm that the oscillation frequency changes when the other terminal is added. This result suggests that this capacitance sensor can be applied to integrated touchpanels on flatpanel displays.

Wireless sensor networks (WSNs) are ubiquitous in a wide range of applications requiring the monitoring of physical and environmental variables, such as target localization and identification. One of these applications is the sensing of ferromagnetic objects. In typical applications, the area to be monitored is typically large compared to the sensing radius of each magnetic sensor. On the other hand, the RF communication radii of WSN nodes are invariably larger than the sensing radii. This makes it economical and efficient to design and implement a sparse network in terms of sensor coverage, in which each point in the monitored area is likely to be covered by at most one sensor. This work aims at investigating the sensing potential and limitations (e.g. in terms of localization accuracy on the order of centimeters) of the Honeywell HMC 1002 2-axis magnetometer used in the context of a sparse magnetic WSN. The effect of environmental variations, such as temperature and power supply fluctuations, magnetic noise, and sensor sensitivity, on the target localization and identification performance of a magnetic WSN is examined based on experimental tests. Signal processing strategies that could enable an alternative to the typical “target present/absent” mode of using magnetic sensors, such as providing successive localization information in time, are discussed.

In this paper, a novel approach for central angle estimation of coherently distributed targets that utilizes electric vector sensors in bistatic MIMO radar is proposed. First, the coherently distributed targets signal model in bistatic MIMO radar that equipped with electric vector sensors is reconstructed. The Hadamard product rotation invariance property of the coherently distributed targets' steering vectors is found to get the initial estimation of direction of departure (DOD). 1-D MUSIC is then used to estimate the accurate central angles of direction of arrival (DOA) and DOD. The proposed method can estimate the central angles of DOA and DOD efficiently and accurately without pairing even in the situation where the angular signal distribution functions are unknown. Our method has better performance than Guo's algorithm. Numerical results verify the improvement and performance of the proposed algorithm.

This paper discusses the core ambient sensor network (ASN) technologies in view of their support for global connectivity. First, we enumerate ASN services and use cases and then discuss the underlying core technologies, in particular, the importance of the RESTful approach for ensuring global accessibility to sensors and actuators. We also discuss several profile-handling technologies for context-aware services. Finally, we envisage the ASN trends, including our current work for cognitive behavior therapy (CBT) in mental healthcare. We strongly believe that ASN services will become widely available in the real world and an integral part of daily life and society in the near future.

In indoor localization using sensor networks, performance improvements are required for non-line-of-sight (NLOS) environments in which the estimation error is high. NLOS mitigation schemes involve the detection and elimination of the NLOS measurements. The iterative minimum residual (IMR) scheme, which is often applied to the localization scheme using the time of arrival (TOA), is commonly employed for this purpose. The IMR scheme is a low-complexity scheme and its NLOS detection performance is relatively high. However, when there are many NLOS nodes in a sensor field, the NLOS detection error of the IMR scheme increases and the estimation accuracy deteriorates. Therefore, we propose a new scheme that exploits coarse NLOS detection based on stochastic characteristics prior to the application of the IMR scheme to improve the localization accuracy. Improved performances were confirmed in two NLOS channel models by performing numerical simulations.

Wireless sensor networks play an important role in several industries. Ad-hoc networks with multi-hop transmissions are considered suitable for wireless sensor networks because of their high scalability and low construction cost. However, a lack of centralized control makes it difficult to respond to congestion when system capacity is exceeded. Therefore, the analysis of system capacity is a critical issue for system design. In this paper, we propose a novel zone division model to analyze the capacity of multi-hop wireless sensor networks using carrier sense multiple access with collision avoidance protocols. We divide the one-hop area to a gateway (GW) into two zones: an outer zone, where access nodes (ANs) can relay packets from multi-hop ANs, and an inner zone where ANs cannot relay packets. Using this approach, we calculate the packet loss for each zone to estimate the capacity, considering the difference in the communication range of the GW and ANs, as well as the collision with hidden nodes. Comparisons with simulation results and the conventional method show that our model achieves higher estimation accuracy.

We consider a chain of integrators system that has an uncertain delay in the input. Also, there is a measurement noise in the feedback channel that only noisy output is available. We develop a new output feedback control scheme along with amplification such that the ultimate bounds of all states and output of the controlled system can be made arbitrarily small. We note that the condition imposed on the sensor noise is quite general over the existing results such that the sensor noise is uncertain and is only required to be bounded by a known bound. The benefit of our control method is shown via an example.

This paper investigates active period selection for cluster-based WSNs employing traffic adaptive IEEE 802.15.4 beacon enabled medium access control (MAC) under spatial non-uniform traffic and cluster mobility environments, and proposes a simple autonomous distributed superframe duration (SD) selection scheme to enhance network performance under such environments. The proposed scheme autonomously selects an active SD at each cluster head (CH) by using beacon reception power monitoring (conducted in distributed control manner) and also introduces a beacon status notice from sensor nodes (SNs) to their parent CHs in order to prevent unnecessary SD selection at CHs. Moreover, SD reuse mechanism and joint operation with previously proposed distributed backoff mechanism are proposed for the proposed SD selection scheme to further enhance the network performance. The results of computer simulation show that the proposed scheme can improve both the transmission and power efficiency performance of cluster-based WSNs under spatial non-uniform traffic and cluster mobility environments.

In emerging wearable sensor systems, it is crucial to save energy because these systems are severely energy-constrained. For making the sensors in these systems energy efficient, transmission power control (TPC) is widely used, and thus far, many TPC algorithms have been proposed in the literature. However, these TPC algorithms do not always work well in all wireless body channel conditions, which are capriciously varied due to diverse sensor environments such as sensor placements, body movements, and body locations. In this paper, we propose a simple TPC algorithm that quickly and stably approaches the optimal transmission power level and works well in all wearable sensor environments. We experimentally evaluated the proposed TPC algorithm and proved that it works well under all wireless body channel conditions.

In this paper, we propose a modeling approach for wireless sensor networks (WSNs) that is based on non-volatile two-dimensional cellular automata (CA) and analyze the space-time dynamics of a WSN based on the proposed model. We introduce the fourth circuit element with memory function — memristor into the cells of CA to model a non-volatile CA and employ the non-volatile CA in modeling a WSN. A state transition method is designed to implement the synchronous updates of the states between the central sensor nodes and its neighbors which might behave asynchronously in sending messages to the central one. Therefore, the energy consumption in sensor nodes can be reduced by lessening the amount of exchanged information. Simulations demonstrate that the energy consumption of a WSN can be reduced greatly based on the proposed model and the lifetime of the whole network can be increased.

Recently, the growing concepts that information communication technologies apply to social infrastructures have caused deep interests with wireless sensor networks (WSNs). WSNs can be used for various application areas such as home, health, factory and so on. For the different application areas, there are different technical issues (e.g., security, reliability, real time gathering, long life time, scalability). Efficient information gathering can be potentially obtained if we take a suitable information gathering method with considering the requirements of each WSN application. Thus, we have not persisted all information gathering perfectly and have proposed one of simple information gathering methods in response to the requirements of WSN applications in this paper. In the proposed method, the information is converted to physical-layer parameters of wireless communications, such as frequency and time. Also, simulations are performed to validate the effectiveness of the proposed method in real time gathering and estimating with high precision.