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.

This paper presents an inter-satellite link (ISL) reassignment method to optimize the snapshot routing performance for polar-orbit LEO satellite networks. When the snapshot routing tables are switching simultaneously in all satellites, we propose to reassign the inter-plane ISLs with regularity to improve the quality of the next snapshot, such as snapshot duration, on-board transceiver utilization ratio and end to end delay. Evaluations indicate that our method can attain equal-length snapshots regardless of the latitude of the polar area border, and so is superior to the natural partition method. Meanwhile, compared with the equal partition method which is used in the Iridium system, our method can prolong 82.87% snapshot duration, increase 8.68% on-board transceiver utilization ratio and reduce 5.30% average end to end delay of the whole network. Therefore, we believe that the ISL reassignment method can be efficiently applied in all practical polar-orbit LEO satellite networks.

We propose a novel algorithm for the recovery of non-sparse, but compressible signals from linear undersampled measurements. The algorithm proposed in this paper consists of two steps. The first step recovers the signal by the l1-norm minimization. Then, the second step decomposes the l1 reconstruction into major and minor components. By using the major components, measurements for the minor components of the target signal are estimated. The minor components are further estimated using the estimated measurements exploiting a maximum a posterior (MAP) estimation, which leads to a ridge regression with the regularization parameter determined using the error bound for the estimated measurements. After a slight modification to the major components, the final estimate is obtained by combining the two estimates. Computational cost of the proposed algorithm is mostly the same as the l1-nom minimization. Simulation results for one-dimensional computer generated signals show that the proposed algorithm gives 11.8% better results on average than the l1-norm minimization and the lasso estimator. Simulations using standard images also show that the proposed algorithm outperforms those conventional methods.

This paper describes 0.8-/1.5-GHz-band GaAs-HBT power amplifier modules with a newly designed analog bias control scheme. This scheme has two features. One is to achieve approximately linear quiescent current control using not a BiFET process but only the usual HBT process. The other is to help improve linearity under reduced supply voltage and lower quiescent current operation. The following two key techniques are incorporated into the bias scheme. The first is to employ two different kinds of bias circuits: emitter follower bias and current injection bias. The second is the unique current injection bias block, based on the successful combination of an input buffer with an emitter resistance load and a current mirror. These techniques allow quiescent current control that is almost proportional to an externally applied analog control voltage. To confirm the effectiveness of the scheme, 0.8-GHz-band and 1.5-GHz-band power amplifier modules were designed and fabricated using the usual HBT process. Measurements conducted under the conditions of a 3.4V supply voltage and an HSDPA WCDMA modulated signal are as follows. The 0.8-GHz-band amplifier can deliver a 28-dBm output power (Pout), a 28.4-dB power gain (Gp), and 42% PAE while restricting the ACLR to less than -40dBc. For the 1.5-GHz-band amplifier, 28dBm of Pout, 29dB of Gp, and 41% of PAE are obtained with the same ACLR levels. The measurements also confirm that the quiescent current for the second stage in the amplifiers is approximately linearly changed from 14mA to 58mA over a control voltage ranging from 1.1V to 2.2V. In addition, our measured DG.09-based current dissipation with both supply voltage and analog bias controls is as low as 16.9mA, showing that the analog bias control scheme enables an average current reduction of more than 20%, as compared to a conventional supply voltage and two-step quiescent current control.

A K-user parallel concatenated code (PCC) is proposed for a Gaussian multiple-access channel with symbol synchronization, equal-power, and equal-rate users. In this code, each user employs a PCC with M+1 component codes, where the first component code is a rate-1/q repetition code and the other M component codes are the same rate-1 recursive convolutional (RC) codes. By designing the repetition coding rate and the RC component code, the K-user PCC achieve reliable transmission for a given number of users and noise level. Two decoding schemes are considered: low-density parity-check (LDPC)-like decoding and Turbo-like decoding. For each decoding scheme, a fixed point analysis is given to optimize the parameters: the rate of repetition component code 1/q, the number of RC component codes M, or the RC component codes themselves. The analysis shows that an accumulate code is the optimal RC component code for a K-user PCC, in the sense of achieving the maximum sum rate. The K-user PCC with an accumulate component code achieves a larger sum rate in the high rate region than the conventional scheme of an error correction code serially concatenated with spreading under similar encoding and decoding complexity.

A new algorithm for separating mass spectra into individual substances is proposed for explosives detection. The conventional algorithm based on probabilistic latent component analysis (PLCA) is effective in many cases because it makes use of the fact that non-negativity and sparsity hold for mass spectra in explosives detection. The algorithm, however, fails to separate mass spectra in some cases because uncertainty can not be resolved only by non-negativity and sparsity constraints. To resolve the uncertainty, an algorithm based on shift-invariant PLCA (SIPLCA) utilizing temporal correlation of mass spectra is proposed in this paper. In addition, to prevent overfitting, the temporal correlation is modeled with a function representing attenuation by focusing on the fact that the amount of a substance is attenuated continuously and slowly with time. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms the PLCA-based conventional algorithm and the simple SIPLCA-based one. The main novelty of this paper is that an evaluation of the detection performance of explosives detection is demonstrated. Results of the evaluation indicate that the proposed separation algorithm can improve the detection performance.

This paper proposes and analyses an improved direction finding (DF) method that uses a rotating interferometer. The minimum sampling frequency is deduced in order to eliminate the phase ambiguity associated with a long baseline, the influence of phase imbalance of receiver is quantitatively discussed and the Root Mean Square Error (RMSE) of both bearing angle and pitch angle are also demonstrated. The theoretical analysis of the rotating interferometer is verified by simulation results, which show that it achieves better RMSE performance than the conventional method.

The sparse Fourier transform (SFT) seeks to recover k non-negligible Fourier coefficients from a k-sparse signal of length N (k«N). A single frequency signal can be recovered via the Chinese remainder theorem (CRT) with sub-sampled discrete Fourier transforms (DFTs). However, when there are multiple non-negligible coefficients, more of them may collide, and multiple stages of sub-sampled DFTs are needed to deal with such collisions. In this paper, we propose a combinatorial aliasing-based SFT (CASFT) algorithm that is robust to noise and greatly reduces the number of stages by iteratively recovering coefficients. First, CASFT detects collisions and recovers coefficients via the CRT in a single stage. These coefficients are then subtracted from each stage, and the process iterates through the other stages. With a computational complexity of O(klog klog 2N) and sample complexity of O(klog 2N), CASFT is a novel and efficient SFT algorithm.

This paper proposes a foreground segmentation method for indoor environments using depth images only. It uses a morphological operator and histogram analysis to segment the foreground. In order to compare the accuracy for foreground segmentation, we use metric measurements of false positive rate (FPR), false negative rate (FNR), total error (TE), and a similarity measure (S). A series of experimental results using video sequences collected under various circumstances are discussed. The proposed system is also designed in a field-programmable gate array (FPGA) implementation with low hardware resources.

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.

This paper proposes a rate adaptation scheme (RAS) for a wireless local area network (WLAN) station powered with microwave power transmission (MPT). A WLAN station attempting to transmit data frames when exposed to microwave radiation for MPT, experiences a reduction in the physical (PHY) layer data rate because frames are lost even when the carrier sense mechanism is used. The key idea of the proposed scheme is to utilize the output of the rectenna used for receiving microwave power. Using rectenna output, a WLAN station based on the proposed scheme assesses whether the station is exposed to microwave radiation for MPT. Then, using historical data corresponding to the assessment result, the station selects an appropriate PHY data rate. The historical data are obtained from previous transmission results, e.g., historical data pertaining to the data frame loss ratio. The proposed scheme was implemented and verified through an experiment. Experimental results showed that the proposed scheme prevents the reduction in the PHY data rate, which is caused by the use of historical data stored in a single memory. Thus, the proposed scheme leads to an improvement in the WLAN throughput.

In this paper, we propose an utilization-aware hybrid beacon scheduling method for a large-scale IEEE 802.15.4 cluster-tree ZigBee network. The proposed method aims to enhance schedulability of a target network by better utilizing transmission medium, while avoiding inter-cluster collisions at the same time. To achieve this goal, the proposed scheduling method partially allows beacon overlaps, if appropriate. In particular, this paper answers for the following questions: 1) on which condition clusters can send overlapped beacons, 2) how to select clusters to overlap with minimizing utilization, and 3) how to adjust beacon parameters for grouped clusters. Also, we quantitatively evaluate the proposed method compared to previous works — i.e., non-beacon scheduling and a serialized beacon scheduling algorithm — from several aspects including total duty cycles, packet drop rate, and end-to-end delay.

Multiple subcarrier passive communication is a new research area which enables a type of frequency division multiple access with wireless and batteryless sensor RF tags just by implementing RF switches to produce dedicated subcarriers. Since the mutual interference among subcarriers is unevenly distributed over the frequency band, careless allocations of subcarrier frequencies may result in degraded network performance and inefficient use of the frequency resource. In this paper, we examine four subcarrier frequency allocation schemes using MATLAB numerical simulations. The four schemes are evaluated in terms of the communication capacity and access fairness among sensor RF tags. We found that the subcarrier allocation scheme plays an important role in multiple subcarrier communication and can improves the communication capacity by 35%.

User context and user location in particular play an important role in location-based services (LBS). The location can be determined by various positioning methods. These are typically evaluated with average positioning error or percentile values, which are not the most suitable metrics for evaluation of how a positioning method functions in the semantic space. Therefore, we propose a new method for evaluation of positioning accuracy in the semantic space. We focus on two types of semantic user locations that are widely available in urban areas: the street address and the categories of the surrounding points of interest (POIs). We demonstrate its use on ten different positioning methods: a standalone satellite navigation device, GPS module on a smartphone, two versions of Foursquare positioning service, Google positioning service, a positioning service of the local mobile operator, and four other possible variants of mobile operator-based positioning methods. The evaluation suggests that approach with the street addresses is more promising approach due to either sparse or unevenly distributed POIs. Furthermore, some of the positioning methods that are less accurate in Euclidean space, such as a combination of the GPS data with the mobile operator-based method that relies on the propagation models, performed comparably well in the semantic space as the methods that are using more accurate technologies, such as Google and Foursquare.

This paper gives an outline of key technologies necessary for science-based agriculture. In order to design future agriculture, present agriculture should be redesigned based on the context of smart agriculture that indicates the overall form of agriculture including a social system while the present precision agriculture shows a technical form of agriculture only. Wireless Sensor Network (WSN) and the various type of optical sensors are assumed to be a basic technology of smart agriculture which intends the harmony with the economic development and sustainable agro-ecosystem. In this paper, the current state and development for the optical sensing for environment and plant are introduced.

Potential games form a class of non-cooperative games where the convergent of unilateral improvement dynamics is guaranteed in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.

A key issue in Peer-to-Peer (P2P) live streaming systems is that several participant peers tend to leave within a short time period. For example, such a phenomenon is common at the half time of football games and at the end of the performance of famous artists. Such selfish behavior of the participants causes several problems in P2P networks such as the disconnection of the overlay, the departure of backup peers and the occurrence of cyclic reference to backup peers. In this paper, we propose several techniques for tree-structured P2P live streaming systems to enhance their resilience to the simultaneous departure of some participants. As the baseline of the discussion, we will focus on mTreebone which is a typical churn-resilient P2P live streaming system based on the notion of peer stability. The performance of the proposed techniques is evaluated by simulation. The simulation result indicates that even under high churn rates, the proposed techniques significantly reduce the number of attempts needed to connect to backup peers and the recovery time after a fail.

In this letter, a novel collaborative representation graph based on the local and global consistency label propagation method, denoted as CRLGC, is proposed. The collaborative representation graph is used to reduce the cost time in obtaining the graph which evaluates the similarity of samples. Considering the lacking of labeled samples in real applications, a semi-supervised label propagation method is utilized to transmit the labels from the labeled samples to the unlabeled samples. Experimental results on three image data sets have demonstrated that the proposed method provides the best accuracies in most times when compared with other traditional graph-based semi-supervised classification methods.

Physical layer security is effective in wireless communications because it makes a transmission secure from the beginning of protocols. We have proposed a chaos multiple-input multiple-output (C-MIMO) transmission scheme that achieves both physical layer security and channel coding gain using chaos signals. C-MIMO is a type of encryption modulation and it obtains the coding gain in conjunction with encryption without a decrease in the transmission efficiency. Thus, the error rate performance is improved in C-MIMO. However, decoding complexity increases exponentially with code length because of the use of maximum likelihood sequence estimation (MLSE), which restricts the code length of C-MIMO and thus the channel coding gain. Therefore, in this paper, we consider outer channel code concatenation instead of code length expansion for C-MIMO, and propose an iterative turbo decoding scheme for performance improvement by introducing a log-likelihood ratio (LLR) into C-MIMO and by utilizing turbo principle. The improved performances of the proposed scheme, compared to the conventional scheme when the outer channel codes are convolutional code and low-density parity check (LDPC) code, are shown by computer simulations.

With recent developments in machine learning technology, the predictions by systems incorporating machine learning can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly dependent on personal attributes, such as gender or ethnicity, hiring decisions might be discriminatory. This paper investigates the neutralization of a probabilistic model with respect to another probabilistic model, referred to as a viewpoint. We present a novel definition of neutrality for probabilistic models, η-neutrality, and introduce a systematic method that uses the maximum likelihood estimation to enforce the neutrality of a prediction model. Our method can be applied to various machine learning algorithms, as demonstrated by η-neutral logistic regression and η-neutral linear regression.