In this letter we propose a practical sensing-based opportunistic spectrum sharing scheme for cognitive radio (CR) downlink MIMO systems. Multi-antennas are exploited at the secondary transmitter to opportunistically access the primary spectrum and effectively achieve a balance between secondary throughput maximization and mitigation of interference probably caused to primary radio link. We first introduce a brief secondary frame structure, in which a sensing phase is exploited to estimate the effective interference channel. According to the sensing result and taking the interference caused by the primary link into account, we propose an enhanced signal-to-leakage-and-noise ratio (SLNR)-based precoding scheme for the secondary transmitter. Compared to conventional schemes where perfect knowledge of the channels over which the CR transmitter interferes with the primary receiver (PR) is assumed, our proposed scheme shows its superiority and simulation results validate this.

High-performance FFT processor is indispensable for real-time OFDM communication systems. This paper presents a CORDIC based design of variable-length FFT processor which can perform various FFT lengths of 64/128/256/512/1024/2048/4096/8192-point. The proposed FFT processor employs memory based architecture in which mixed radix 4/2 algorithm, pipelined CORDIC, and conflict-free parallel memory access scheme are exploited. Besides, the CORDIC rotation angles are generated internally based on the transform of butterfly counter, which eliminates the need of ROM making it memory-efficient. The proposed architecture has a lower hardware complexity because it is ROM-free and with no dedicated complex multiplier. We implemented the proposed FFT processor and verified it on FPGA development platform. Additionally, the processor is also synthesized in 0.18 µm technology, the core area of the processor is 3.47 mm2 and the maximum operating frequency can be up to 500 MHz. The proposed FFT processor is better trade off performance and hardware overhead, and it can meet the speed requirement of most modern OFDM system, such as IEEE 802.11n, WiMax, 3GPP-LTE and DVB-T/H.

This paper aims at channel modeling and bit error rate (BER) performance improvement with diversity reception for in-body to on-body ultra wideband (UWB) communication for capsule endoscope application. The channel characteristics are firstly extracted from 3.4 to 4.8 GHz by using finite difference time domain (FDTD) simulations incorporated with an anatomical human body model, and then a two-path impulse response channel model is proposed. Based on the two-path channel model, a spatial diversity reception technique is applied to improve the communication performance. Since the received signal power at each receiver location follows a lognormal distribution after summing the two path components, we investigate two methods to approximate the lognormal sum distribution in the combined diversity channel. As a result, the method matching a short Gauss-Hermite approximation of the moment generating function (MGF) of the lognormal sum with that of a lognormal distribution exhibits high accuracy and flexibility. With the derived probability density function (PDF) for the combined diversity signals, the average BER performances for impulse-radio (IR) UWB with non-coherent detection are investigated to clarify the diversity effect by both theoretical analysis and computer simulation. The results realize an improvement around 10 dB on Eb/No at BER of 10-3 for two-branch diversity reception.

Compressing a JPEG image twice will greatly decrease the values of some of its DCT coefficients. This effect can be easily detected by statistics methods. To defend this forensic method, we establish a model to evaluate the security and image quality influenced by the re-compression. Base on the model, an optimized adjustment of the DCT coefficients is achieved by Genetic Algorithm. Results show that the traces of double compression are removed while preserving image quality.

This paper proposes non-coherent multiple-input multi-ple-output (MIMO) communication systems employing per transmit antenna differential mapping (PADM), which generates an independent differentially encoded sequence for each of the multiple transmit antennas by means of space-time coding and mapping. At a receiver, the proposed PADM employs adaptive maximum-likelihood detection (MLD). The features of PADM are as follows: 1) it has excellent tracking performance for fast time-varying fading channels, because it can detect transmitted data without needing channel state information (CSI); 2) it can be applied not only to transmit diversity (TD) but also to spatial multiplexing (SM). In this paper, we analyze the adaptive MLD based on pseudo matrix inversion and derive its metric for data detection. In order to satisfy requirements on multiple transmitted sequences for the adaptive MLD, this paper proposes a mapping rule for PADM. Next, this paper describes a receiver structure based on per-survivor processing (PSP), which can drastically reduce the complexity of adaptive MLD. Finally, computer simulations confirm that the proposed non-coherent MIMO communication systems employing PADM have excellent tracking capability for TD and SM on fast time-varying fading channels.

Ground truth based image segmentation evaluation paradigm plays an important role in objective evaluation of segmentation algorithms. So far, many evaluation methods in terms of comparing clusterings in machine learning field have been developed. However, most traditional pairwise similarity measures, which only compare a machine generated clustering to a “true” clustering, have their limitations in some cases, e.g. when multiple ground truths are available for the same image. In this letter, we propose utilizing an information theoretic measure, named NJMI (Normalized Joint Mutual Information), to handle the situations which the pairwise measures can not deal with. We illustrate the effectiveness of NJMI for both unsupervised and supervised segmentation evaluation.

A 60 GHz direct conversion transceiver which employs amplitude/phase imbalance cancellation technique is newly proposed. By using the proposed technique, the receive path of the transceiver achieves less than 0.2 dB of amplitude error and less than 3 of phase error at 60 GHz bands over a 10 GHz bandwidth, which relaxes the design accuracy required for baluns used in the transceiver. It also employs a simple and fast calibration algorithm to adjust the locking range of the divide-by-3 injection locked divider in the phase locked loop. Fabricated in 90 nm CMOS technology, the transceiver achieves a low power consumption of 230 mW in transmit mode and 173 mW in receive mode. The output spectrum of 1.76 Gsps π/2-BPSK/QPSK modulation shows the excellent distortion and spurious suppression that meet the IEEE802.11ad draft standard.

We demonstrate how Hsiang and Shih's authentication scheme can be compromised and then propose an improved scheme based on the Rabin cryptosystem to overcome its weaknesses. Furthermore, we discuss the reason why we should use an asymmetric encryption algorithm to secure a password-based remote user authentication scheme using smart cards. We formally prove the security of our proposed scheme using the BAN logic.

All points on the Pareto boundary can be obtained by solving the weighted sum rate maximization problem for some weighted coefficients. Unfortunately, the problem is non-convex and difficult to solve without performing an exhaustive search. In this paper, we propose an optimal distributed beamforming strategy for the two-user multiple-input single-output (MISO) interference channel (IC). Through minimizing the interference signal power leaked to the other receiver for fixed useful signal power received at the intended receiver, the original non-convex optimization problem can be converted into a family of convex optimization problems, each which can be solved in distributed manner with only local channel state information at each transmitter. After some conversion, we derive the closed-form solutions to all Pareto optimal points based on a game-theoretic viewpoint which indicates that linear combinations of the maximum-ratio transmit (MRT) and zero-forcing (ZF) beamforming strategies can achieve any point on the Pareto boundary of the rate region for the two-user MISO interference channel, and the only computation involved is to solve a basic quadratic equation. Finally, the result is validated via numerical simulations.

The research on body-centric wireless communications (BCWCs) is becoming very hot because of numerous applications, especially the application of E-health systems. Therefore, a small multi-band and low-profile planar inverted-F antenna (PIFA) with tuning function is presented for BCWCs in this paper. In order to achieve multi-band operation, there are two branches in the antenna: the longer branch low frequency band (950–956 MHz), and the shorter branch with a varactor diode embedded for high frequency bands. By supplying different DC voltages, the capacitance of the varactor diode varies, so the resonant frequency can be tuned without changing the dimension of the antenna. While the bias is set at 6 V and 14 V, WiMAX and ISM bands can be covered, respectively. From the radiation patterns, at 950 MHz, the proposed antenna is suitable for on-body communications, and in WiMAX and ISM bands, they are suitable for both on-body and off-body communications.

This paper presents a new incremental approach for robot navigation using associative memory. We defined the association as node→action→node where node is the robot position and action is the action of a robot (i.e., orientation, direction). These associations are used for path planning by retrieving a sequence of path fragments (in form of (node→action→node) → (node→action→node) →…) starting from the start point to the goal. To learn such associations, we applied the associative memory using Self-Organizing Incremental Associative Memory (SOIAM). Our proposed method comprises three layers: input layer, memory layer and associative layer. The input layer is used for collecting input observations. The memory layer clusters the obtained observations into a set of topological nodes incrementally. In the associative layer, the associative memory is used as the topological map where nodes are associated with actions. The advantages of our method are that 1) it does not need prior knowledge, 2) it can process data in continuous space which is very important for real-world robot navigation and 3) it can learn in an incremental unsupervised manner. Experiments are done with a realistic robot simulator: Webots. We divided the experiments into 4 parts to show the ability of creating a map, incremental learning and symbol-based recognition. Results show that our method offers a 90% success rate for reaching the goal.

A new information detection method has been proposed for a very fast and efficient search engine. This method is implemented on hardware system using FPGA. We take advantages of Content Addressable Memory (CAM) which has an ability of matching mode for designing the system. The CAM blocks have been designed using available memory blocks of the FPGA device to save access times of the whole system. The entire memory can return multi-match results concurrently. The system operates based on the CAMs for pattern matching, in a parallel manner, to output multiple addresses of multi-match results. Based on the parallel multi-match operations, the system can be applied for pattern matching with various required constraint conditions without using any search principles. The very fast multi-match results are achieved at 60 ns with the operation frequency 50 MHz. This increases the search performance of the information detection system which uses this method as the core system.

One of promising application offered by implant body area networks (BANs) is a capsule endoscope localization system. To begin with, this paper performs finite-difference time-domain (FDTD) simulations on implant BAN propagation with a numerical human model, and investigates the propagation characteristics of implant BAN signals at 400 MHz medical implant communication service (MICS) band. Then, the paper presents a capsule endoscope localization system which utilizes only received signal strength indicator (RSSI) and two estimation methods, such as a maximum likelihood (ML) estimation method and a least squares (LS) method. Furthermore, we evaluate the two localization methods by two computer simulation scenarios. Our computer simulation results demonstrate that the ML localization can improve the location estimation accuracy as compared with the LS localization, that is, our performance comparison reveals that a careful consideration the propagation characteristics of implant BANs signals is efficient in terms of estimation performance improvement in capsule endoscope localization.

PMIPv6 is the IETF standard for a network-based localized mobility management protocol. In PMIPv6, MNs are topologically anchored at an LMA, which forwards all data for registered MNs. However, since all data packets destined for MNs always traverse the MNs' LMA, the end-to-end packet delay is increased. Therefore, this paper proposes an RO scheme in single and multiple LMA environments. For efficient RO possibility detection, an IPv6 RO extension header and initial RO procedure are proposed. Plus, an effective post-handover RO procedure is presented, along with a packet forwarding scheme to avoid the race condition problem during an RO operation. A Performance evaluation confirms that the proposed scheme can significantly reduce the end-to-end delay, signaling overhead, and RO latency when compared with existing RO schemes.

This paper presents a novel approach for mobile positioning in IEEE 802.11a wireless LANs with acceptable computational complexity. The approach improves the positioning accuracy by utilizing the time and frequency domain channel information obtained from the orthogonal frequency-division multiplexing (OFDM) signals. The simulation results show that the proposed approach outperforms the multiple signal classification (MUSIC) algorithm, Ni's algorithm and achieve a positioning accuracy of 1 m with a 97% probability in an indoor scenario.

Nowadays, there are a great deal of e-documents being accessed on the Internet. It would be helpful if those documents and significant extract contents could be automatically analyzed. Similarity analysis and topic extraction are widely used as document relation analysis techniques. Most of the methods being proposed need some processes such as stemming, stop words removal, and etc. In those methods, natural language processing (NLP) technology is necessary and hence they are dependent on the language feature and the dataset. In this study, we propose novel document relation analysis and topic extraction methods based on text compression. Our proposed approaches do not require NLP, and can also automatically evaluate documents. We challenge our proposal with model documents, URCS and Reuters-21578 dataset, for relation analysis and topic extraction. The effectiveness of the proposed methods is shown by the simulations.

The need for the OpenGL-family of the 3D rendering API's are highly increasing, especially for graphical human-machine interfaces on various systems. In the case of safety-critical market for avionics, military, medical and automotive applications, OpenGL SC, the safety critical profile of the OpenGL standard plays the major role for graphical interfaces. In this paper, we present an efficient way of implementing OpenGL SC 3D graphics API for the environments with hardware-supported OpenGL 1.1 and its multi-texture extension facility, which is widely available on recent embedded systems. Our approach achieved the OpenGL SC features at the low development cost on the embedded systems and also on general personal computers. Our final result shows its compliance with the OpenGL SC standard specification. From the efficiency point of view, we measured its execution times for various application programs, to show a remarkable speed-up.

In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.

In camera-based object recognition and classification, surface color is one of the most important characteristics. However, apparent object color may differ significantly according to the illumination and surface conditions. Such a variation can be an obstacle in utilizing color features. Geusebroek et al.'s color invariants can be a powerful tool for characterizing the object color regardless of illumination and surface conditions. In this work, we analyze the estimation process of the color invariants from RGB images, and propose a novel invariant feature of color based on the elementary invariants to meet the circular continuity residing in the mapping between colors and their invariants. Experiments show that the use of the proposed invariant in combination with luminance, contributes to improve the retrieval performances of partial object image matching under varying illumination conditions.

A novel online speaker clustering method based on a generative model is proposed. It employs an incremental variant of variational Bayesian learning and provides probabilistic (non-deterministic) decisions for each input utterance, on the basis of the history of preceding utterances. It can be expected to be robust against errors in cluster estimation and the classification of utterances, and hence to be applicable to many real-time applications. Experimental results show that it produces 50% fewer classification errors than does a conventional online method. They also show that it is possible to reduce the number of speech recognition errors by combining the method with unsupervised speaker adaptation.