To ensure the security of image transmission, this paper presents a new image encryption algorithm based on a genetic algorithm (GA) and a piecewise linear chaotic map (PWLCM), which adopts the classical diffusion-substitution architecture. The GA is used to identify and output the optimal encrypted image that has the highest entropy value, the lowest correlation coefficient among adjacent pixels and the strongest ability to resist differential attack. The PWLCM is used to scramble pixel positions and change pixel values. Experiments and analyses show that the new algorithm possesses a large key space and resists brute-force, statistical and differential attacks. Meanwhile, the comparative analysis also indicates the superiority of our proposed algorithm over a similar, recently published, algorithm.

An on-channel repeater (OCR) performing simultaneous reception and transmission at the same frequency is beneficial to improve spectral efficiency and coverage. In an OCR, it is important to cancel the feedback interference caused by imperfect isolation between the transmit and receive antennas, and least mean square (LMS) based adaptive filters are commonly used for this purpose. In this paper, we analyze the performance of the LMS based adaptive feedback canceller in terms of its transient behavior and the steady-state mean square error (MSE). Through a theoretical analysis, we derive iterative equations to compute transient MSEs and provide a procedure to simply evaluate steady-state MSEs for the adaptive feedback canceller. Simulation results performed to verify the theoretical MSEs show good agreement between the proposed theoretical analysis and the empirical results.

In this paper, we report our analysis of path loss variation characteristics in the low millimeter wave band based on measurement results obtained by using 26.365GHz. We analyze the fading scale of the measurement results by applying a wavelet analysis method. It is clarified that a fading of unique scale between the scale of fast fading and shadowing can be seen in the low millimeter band. Finally, it is shown that the variation characteristics can be evaluated by taking reflection paths at building walls into account, in addition to the ground waves and LOS paths.

This paper proposes a novel iterative multiple-input multiple-output (MIMO) receiver for orthogonal frequency division multiplexing (OFDM) systems, named as an “iterative MIMO receiver employing virtual channels with a Turbo decoder.” The proposed MIMO receiver comprises a MIMO detector with virtual channel detection and a Turbo decoder, between which signals are exchanged iteratively. This paper proposes a semi hard input soft output (SHISO) iterative decoding for the iterative MIMO receiver that achieves better performance than a soft input soft output (SISO) iterative decoding. Moreover, this paper proposes a new criterion for the MIMO detector to select the most likely virtual channel. The performance of the proposed receiver is verified in a 6×2 MIMO-OFDM system by computer simulation. The proposed receiver achieves better performance than the SISO MAP iterative receiver by 1.5dB at the bit error rate (BER) of 10-4, by optimizing the number of the Turbo iteration per the SHISO iteration. Moreover, the proposed detection criterion enables the proposed receiver to achieve a gain of 3.0dB at the BER of 10-5, compared with the SISO MAP iterative receiver with the Turbo decoder.

This paper describes characteristics of direct and scattered waves that are extracted from measurement channel data obtained using a 3.35GHz vector channel sounder in an indoor environment. For the scattered waves, a ray number, n, is assigned to each ray in order of the received levels and the relationship between n and the characteristics of each ray such as the received level, delay and azimuth angle of arrival (AOA) are investigated. The distribution of the received level for each n, which is normalized to the received level that is calculated based on free space at each measurement point and includes the received level of all measurement points, is a log normal distribution. Moreover, the median received level of each n of the scattered waves is approximated with two different gradient linear lines as a function of n. Furthermore, the azimuth AOA for the ray of scattered waves whose received level is relatively high is biased in the base station antenna direction and the distribution of the azimuth AOA becomes uniform with a decrease in the received ray level. Finally, a spatio-temporal channel model is proposed based on the above mentioned analysis.

Recently, the ratio of probability density functions was demonstrated to be useful in solving various machine learning tasks such as outlier detection, non-stationarity adaptation, feature selection, and clustering. The key idea of this density ratio approach is that the ratio is directly estimated so that difficult density estimation is avoided. So far, parametric and non-parametric direct density ratio estimators with various loss functions have been developed, and the kernel least-squares method was demonstrated to be highly useful both in terms of accuracy and computational efficiency. On the other hand, recent study in pattern recognition exhibited that deep architectures such as a convolutional neural network can significantly outperform kernel methods. In this paper, we propose to use the convolutional neural network in density ratio estimation, and experimentally show that the proposed method tends to outperform the kernel-based method in outlying image detection.

Weak target detection is a key problem in passive bistatic radar (PBR). Track-before-detect (TBD) is an effective solution which has drawn much attention recently. However, TBD has not been fully developed in PBR. In this paper, the transition function and the selection of parameters in dynamic programming are analyzed in PBR. Then a novel processing scheme of dynamic programming based TBD is proposed to reduce the computation complexity without severely decreasing the detection performance. Discussions including complexity, detection performance, threshold determination, selection of parameters and detection of multitarget, are presented in detail. The new method can provide fast implementation with only a slight performance penalty. In addition, good multitarget detection performance can be achieved by using this method. Simulations are carried out to present the performance of the proposed processing scheme.

In this paper, we investigate iterative detection and decoding, a.k.a. turbo detection, for multiple-input multiple-output (MIMO) transmission. Specifically, we consider using a low complexity soft-in/soft-out MIMO detector based on belief propagation over a pair-wise graph that accepts a priori information feedback from a channel decoder. Simulation results confirm that considerable performance improvement can be obtained with only a few detection-and-decoding iterations if convolutional channel coding is used. A brief estimate is given of the overall complexity of turbo detectors, to verify the key argument that the performance of a maximum a posteriori (MAP) detector (without turbo iteration) can be achieved, at much lower computation cost, by using the low complexity soft-in/soft-out MIMO detector under consideration.

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.

Small cell networks have been promoted as an enabling solution to enhance indoor coverage and improve spectral efficiency. Users usually deploy small cells on-demand and pay no attention to global profile in residential areas or offices. The reduction of cell radius leads to dense deployment which brings intractable computation complexity for resource allocation. In this paper, we develop a semi-distributed resource allocation algorithm by dividing small cell networks into clusters with limited inter-cluster interference and selecting a reference cluster for interference estimation to reduce the coordination degree. Numerical results show that the proposed algorithm can maintain similar system performance while having low complexity and reduced information exchange overheads.

Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.

To enhance the privacy of vehicle owners, combinatorial certificate management schemes assign each certificate to a large enough group of vehicles so that it will be difficult to link a certificate to any particular vehicle. When an innocent vehicle shares a certificate with a misbehaving vehicle and the certificate on the misbehaving vehicle has been revoked, the certificate on the innocent vehicle also becomes invalid and is said to be covered. When a group of misbehaving vehicles collectively share all the certificates assigned to an innocent vehicle and these certificates are revoked, the innocent vehicle is said to be covered. We point out that the previous analysis of the vehicle cover probability is not correct and then provide a new and exact analysis of the vehicle cover probability.

In this work, resistive switching random-access memory (RRAM) devices having a structure of metal/Si$_{3}$N$_{4}$/Si with different top electrode metals were fabricated to investigate the changes in switching and conduction mechanisms depending on electrode metals. It is shown that the metal workfunction is not strongly related with either high-resistance state (HRS) and forming voltage. Top electrodes (TEs) of Al, Cu, and Ni show both bipolar and unipolar switching characteristics. The changes of resistances in these devices can be explained by the different defect arrangements in the switching layer (SL). Among the devices with different TE metals, one with Ag electrode does not show unipolar switching unlike the others. The conducting filaments of Ag-electrode device in the low-resistance state (LRS) demonstrated metallic behaviors in the temperature-controlled experiments, which supports that Ag substantially participates in the conduction as a filament source. Moreover, the difference in switching speed is identified depending on TE metals.

Tonal signals are shown as spectral peaks in the frequency domain. When the number of spectral peaks is small and the spectral signal is sparse, Compressive Sensing (CS) can be adopted to locate the peaks with a low-cost sensing system. In the CS scheme, a time domain signal is modelled as $oldsymbol{y}=Phi F^{-1}oldsymbol{s}$, where y and s are signal vectors in the time and frequency domains. In addition, F-1 and $Phi$ are an inverse DFT matrix and a random-sampling matrix, respectively. For a given y and $Phi$, the CS method attempts to estimate s with l0 or l1 optimization. To generate the peak candidates, we adopt the frequency-domain information of $ esmile{oldsymbol{s}}$ = $oldsymbol{F} esmile{oldsymbol{y}}$, where $ esmile{y}$ is the extended version of y and $ esmile{oldsymbol{y}}left(oldsymbol{n} ight)$ is zero when n is not elements of CS time instances. In this paper, we develop Gaussian statistics of $ esmile{oldsymbol{s}}$. That is, the variance and the mean values of $ esmile{oldsymbol{s}}left(oldsymbol{k} ight)$ are examined.

Developing a practical and accurate statistical parser for low-resourced languages is a hard problem, because it requires large-scale treebanks, which are expensive and labor-intensive to build from scratch. Unsupervised grammar induction theoretically offers a way to overcome this hurdle by learning hidden syntactic structures from raw text automatically. The accuracy of grammar induction is still impractically low because frequent collocations of non-linguistically associable units are commonly found, resulting in dependency attachment errors. We introduce a novel approach to building a statistical parser for low-resourced languages by using language parameters as a guide for grammar induction. The intuition of this paper is: most dependency attachment errors are frequently used word orders which can be captured by a small prescribed set of linguistic constraints, while the rest of the language can be learned statistically by grammar induction. We then show that covering the most frequent grammar rules via our language parameters has a strong impact on the parsing accuracy in 12 languages.

In this paper, we propose a zero-forcing (ZF) Tomlinson-Harashima precoding (THP) with substream permutations based on the bit rate maximization for single-user MIMO (SU-MIMO) systems. We study the effect of substream permutations on the ZF-THP SU-MIMO systems, when the mean squared error (MSE) and the bit rate are adopted for the selection of the permutation matrix as criteria. Based on our analysis, we propose a method to increase the bit rate by substream permutations, and derive QR and Cholesky decomposition-based algorithms which realize the proposed method. Furthermore, to improve the error rate performance, we apply zero transmission to subchannels with low signal-to-noise ratios. Numerical examples are provided to demonstrate the effectiveness of the proposed THP MIMO system.

This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.

Nowadays, computer systems are limited by the power and memory wall. As the Dynamic Random Access Memory (DRAM) has dominated the power consumption in modern devices, developing power-saving approaches on DRAM has become more and more important. Among several techniques on different abstract levels, scheduling-based power management policies can be applied to existing memory controllers to reduce power consumption without causing severe performance degradation. Existing power-aware schedulers cluster memory requests into sets, so that the large portion of the DRAM can be switched into the power saving mode; however, only the target addresses are taken into consideration when clustering, while we observe the types (read or write) of requests can play an important role. In this paper, we propose two scheduling-based power management techniques on the DRAM controller: the inter-rank read-write aware clustering approach greatly reduces the active standby power, and the intra-rank read-write aware reordering approach mitigates the performance degradation. The simulation results show that the proposed techniques effectively reduce 75% DRAM power on average. Compared with the existing policy, the power reduction is 10% more on average with comparable or less performance degradation for the proposed techniques.

Large-scale content transfer, especially video transfer, is now a dominant traffic component in the Internet. Originally, content transfer had a content-oriented feature, i.e., “Users do not care where content is retrieved. Users only take care of what content they obtain.” Conventional traffic engineering (TE) aims to obtain optimal routes for traffic between ingress and egress router pairs, i.e., TE has focused on a location-oriented approach that takes care of where to connect. With increased demand for content-oriented features for content traffic, TE needs to focus on content-oriented routing design. In this study, we therefore propose a novel approach to content-oriented TE, called content aware routing (CAR). In CAR, routes are designed for content and egress router pairs, i.e., content traffic toward a receiver-side router. Content demand can be flexibly distributed to multiple servers (i.e., repositories) providing the same content, meaning that content can be obtained from anywhere. CAR solves the optimization problem of minimizing maximum link utilization. If there are multiple optimal solutions, CAR selects a solution in which resource usage is minimized. Using numerical examples formulated by the linear programming problem, we evaluated CAR by comparing it with combinations of conventional content delivery networks and TE, i.e., location-oriented designs. Our numerical results showed that CAR improved maximum link utilization by up to 15%, with only a 5% increase of network resource usage.

In this short correspondence, (1+uv)-constacyclic codes over the finite non-chain ring R[v]/(v2+v) are investigated, where R=F2+uF2 with u2=0. Some structural properties of this class of constacyclic codes are studied. Further, some optimal binary linear codes are obtained from these constacyclic codes.