Much effort has been applied to research on object detection by statistical learning methods in recent years, and the results of that work are expected to find use in fields such as ITS and security. Up to now, the research has included optimization of computational algorithms for real-time processing on hardware such as GPU's and FPGAs. Such optimization most often works only with particular parameters, which often forfeits the flexibility that comes with dynamic changing of the target object. We propose a hardware architecture for faster detection and flexible target reconfiguration while maintaining detection accuracy. Tests confirm operation in a practical time when implemented in an FPGA board.

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.

Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest determines classifications using the majority of multiple trees; so many decision trees must be built. Performance increases with the number of decision trees, requiring memory, and decreases if the number of decision trees is decreased. Because of this, the algorithm is not well suited to implementation on small-scale hardware as an embedded system. As such, we have proposed Boosted Random Forest, which introduces a boosting algorithm into the Random Forest learning method to produce high-performance decision trees that are smaller. When evaluated using databases from the UCI Machine learning Repository, Boosted Random Forest achieved performance as good or better than ordinary Random Forest, while able to reduce memory use by 47%. Thus, it is suitable for implementing Random Forests on embedded hardware with limited memory.

A salient feature extraction algorithm is proposed to improve the recognition rate of the speech emotion. Firstly, the spectrogram of the emotional speech is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Each map is normalized and down-sampled to form the low resolution feature matrix. Then, each feature matrix is converted to the row vector and the principal component analysis (PCA) is used to reduce features redundancy to make the subsequent classification algorithm more practical. Finally, the speech emotion is classified with the support vector machine. Compared with the tradition features, the improved recognition rate reaches 15%.

Segmenting foreground objects in unconstrained dynamic scenes still remains a difficult problem. We present a novel unsupervised segmentation approach that allows robust object segmentation of dynamic scenes with large displacements. To make this possible, we project motion based foreground region hypotheses generated via standard optical flow onto visual saliency regions. The motion hypotheses correspond to inside seeds mapping of the motion boundary. For visual saliency, we generalize the image signature method from images to videos to delineate saliency mapping of object proposals. The mapping of image signatures estimated in Discrete Cosine Transform (DCT) domain favor stand-out regions in the human visual system. We leverage a Markov random field built on superpixels to impose both spatial and temporal consistence constraints on the motion-saliency combined segments. Projecting salient regions via an image signature with inside mapping seeds facilitates segmenting ambiguous objects from unconstrained dynamic scenes in presence of large displacements. We demonstrate the performance on fourteen challenging unconstrained dynamic scenes, compare our method with two state-of-the-art unsupervised video segmentation algorithms, and provide quantitative and qualitative performance comparisons.

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.

Even though cloud users want to keep their data on clouds secure, it is not easy to protect the data because cloud administrators could be malicious and hypervisor could be compromised. To solve this problem, hardware-based memory isolation schemes have been proposed. However, the data in virtual storage are not protected by the memory isolation schemes, and thus, a guest OS should encrypt the data. In this paper, we address the problems of the previous schemes and propose a hardware-based storage isolation scheme. The proposed scheme enables to protect user data securely and to achieve performance improvement.

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.

This paper summarizes recent progress on modified uni-traveling carrier photodiodes that have achieved RF output power levels of 1.8 Watt and 4.4 Watt in continuous wave and pulsed operation, respectively. Flip-chip bonded discrete photodiodes, narrowband photodiodes, and photodiodes integrated with antennas are described.

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.

We report the adaptability of the burst-mode erbium-doped fiber amplifier (BM-EDFA) for uplink transmission of sharply rising analog radio-over-fiber (RoF) signals by using long-term evolution (LTE) -Advanced format on a mobile front-haul. Recent drastically increased mobile data traffic is boosting the demand for high-speed radio communication technologies for next-generation mobile services to enhance user experience. However, the latency become increasingly visible as serious issues. Analog RoF technology is a promising candidate for a next generation mobile front-haul to realize low latency. For the uplink, an RoF signal may rise sharply in response to a burst of in-coming radio signals. We propose that a newly developed BM-EDFA is applied for such a sharply rising RoF signal transmission. The BM-EDFA that we designed using enhanced intrinsic saturation power EDF to suppress the gain transient caused by received optical power fluctuations with optical feedback. The new BM-EDFA was designed for a wider linear output power range and lower NF than the previous BM-EDFA. The observed range of received optical power satisfying an error vector magnitude of less than 8%rms achieved over 16dB. We consider that our BM-EDFAs with wide linear ranges of output power will be a key device for the LTE-Advanced RoF uplink signal transmission via optical access networks for the next-generation mobile front-haul.

We propose an energy-efficient real-time scheduling algorithm based on T-L Plane abstraction. The algorithm is designed to exploit Dynamic Power Management and generates a new event called event-s to render longer idle intervals, which increases the chances of switching a processor to the sleep mode. We compare the proposed algorithm with previous work and show that it is effective for energy management.

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.

In order to tackle the rapidly increasing traffic, the 5th generation (5G) mobile communication system will introduce small cells using higher frequency bands with wider bandwidth to achieve super high bit rate transmission of several tens of Gbps. Massive multiple input multiple output (MIMO) beamforming (BF) is promising as one of the technologies that can compensate for larger path-loss in the higher frequency bands. Joint analog fixed BF and digital precoding have been proposed to reduce the cost of a Massive MIMO transceiver. However, the conventional scheme assumes the transmission of a few streams using well-known codebook-based precoding as the digital precoding, and both a selection method of the fixed BF weights and a digital precoder design, which are suitable for super high bit rate transmission using multiple streams, have not been studied. This paper proposes a joint fixed BF and CSI-based precoding (called FBCP) scheme for the 5G Massive MIMO systems. FBCP first selects the analog fixed BF weights based on a maximum total received power criterion, and then it calculates an eigenmode (EM) precoding matrix by exploiting CSI. This paper targets a 5G system achieving over 20Gbps in the 20GHz band as one example. Throughput performances of the Massive MIMO using the proposed FBCP are evaluated by link level simulations using adaptive modulation and coding and it is shown that the proposed FBCP with the optimum number of selected beams (baseband chains) can use higher level modulation, up to 256QAM, and higher coding rates and achieve throughputs close to 30Gbps while the cost and complexity can be reduced compared with the fully digital Massive MIMO.