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This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system with statistical channel state information (CSI). The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI. Firstly, we reveal the power scaling laws and derive the closed-form expressions for the uplink achievable rate which holds for arbitrary numbers of base station (BS) antennas. Based on the theoretical expressions, we discuss the rate performance under some special cases and provide the average asymptotic rate when using random phase shifts. Then, we consider the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS. However, these two optimization problems are challenging to solve due to the complicated data rate expression. To solve these problems, we propose a novel genetic algorithm (GA) with low complexity but can achieve considerable performance. Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Besides, our simulations demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems., 48 pages, 10 figures. Keywords: Intelligent reflecting surface (IRS), reconfigurable intelligent surface (RIS), massive MIMO

Emotions can be evoked in humans by images. Previous reports on Recognition of Emotions induced by Visual Content of images (REVC) mainly focused on numerous features to improve recognition performance. To devise a more robust REVC system, this paper examines the performance of a wide range of classifiers using color histogram as a single feature. Different numbers of color histogram bins in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces are considered in the examination and the overall classification performance is compared across the bin sizes. This investigation shows that features are not the only important factors affecting the performance of REVC systems, but also the type of classifiers and their parameters. This study shows that the HSV color space is better suited than the RGB color space for REVC systems. This paper proposes a new optimization algorithm called Optimizing Parameters of Ensemble RUSboosted Tree (OPERT) to boost the performance of the REVC system. Furthermore, a novel REVC system called Color histogram with Optimized RUSboosted Tree (CORT) is introduced. It is shown that our method is simpler, faster, and more efficient than the state-of-the-art, while providing comparable recognition performance. The robustness of the CORT system is validated over three different image datasets.

In millimeter wave (mmWave) systems, it is challenging to ensure reliable communication links due to the high sensitivity to the presence of blockages. In order to improve the robustness of mmWave systems in the presence of random blockages, we consider the deployment of multiple reconfigurable intelligent surfaces (RISs) to enhance the spatial diversity gain, and the design of robust beamforming schemes based on stochastic optimization methods that minimize the maximum outage probability among multiple users so as to ensure fairness. Under the stochastic optimization framework, we adopt the stochastic majorization–minimization (SMM) method and the stochastic successive convex approximation (SSCA) method to construct deterministic surrogate problems at each iteration, and to obtain closed-form solutions of the precoding matrix at the base station (BS) and the beamforming vectors at the RISs. Both stochastic optimization methods are proved to converge to the set of stationary points of the original stochastic problems. Simulation results show that the proposed robust beamforming for RIS-aided systems can effectively compensate for the performance loss caused by the presence of random blockages, especially when the blockage probability is high.

This paper investigates the reconfigurable intelligent surface (RlS)-aided multiple-input-single-output (MISO) systems with imperfect channel state information (CSI), where RIS-related channels are modeled by Rician fading. Considering the overhead and complexity in practical systems, we employ the low-complexity maximum ratio combining (MRC) at the base station (BS), and configure the phase shifts of the RIS based on long-term statistical CSI. Specifically, we first estimate the overall channel matrix based on the linear minimum mean square error (LMMSE) estimator, and evaluate the performance of mean square error (MSE) and normalized MSE (NMSE). Then, with the estimated channel, we derive the closed-form expressions of the ergodic rate. The derived expressions show that with Rician RIS-related channels, the rate can maintain at a non-zero value when the transmit power is scaled down proportionally to 1 /M or 1 /N2, where M and N are the number of antennas and reflecting elements, respectively. However, if all the RIS-related channels are purely Rayleigh, the transmit power of each user can only be scaled down proportionally to $1/\sqrt M $ or 1/N. Finally, numerical results verify the promising benefits from the RIS to traditional MISO systems.

Different from conventional wired line connections, industrial control through wireless transmission is widely regarded as a promising solution. However, mission-critical applications impose stringent quality of service (QoS) requirements that entail ultra-reliability low-latency communications (URLLC). The primary feature of URLLC is finite blocklength (FBL), and conventional Shannon Capacity is not applicable. In this paper, we consider URLLC in a factory automation (FA) scenario, where wireless signal are easy to be blocked by densely deployed machines. To address this issue, we propose to deploy intelligent reflecting surface (IRS) to create an alternative transmission link when the direct link is blocked, which can enhance the transmission reliability. We focus on the performance analysis for IRS-aided URLLC-enabled communications. Both the average data rate (ADR) and the average decoding error probability (ADEP) are derived and asymptotic analysis is performed to obtain more design insights. Extensive numerical results are provided to verify the accuracy of our derived results.

In this paper, reconfigurable intelligent surface (RIS) is proposed to enhance the physical layer security in the Rician fading channel where the angular direction of the eavesdropper is aligned with a legitimate user. In this scenario, we consider a two-phase communication system under the passive eavesdropping. Particularly, in the first phase, the base station avoids direct transmission to the attacked user. While, in the second phase, other users cooperate to forward signals to the attacked user with the help of RIS and energy harvesting technology. An average secrecy rate maximization problem is formulated, which is addressed by a low complexity algorithm. Numerical results show that the negative effect of the eavesdropper’s channel error is greater than that of the legitimate user.

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system with statistical channel state information (CSI). The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI. Firstly, we derive the closed-form expressions for the uplink achievable rate which holds for arbitrary numbers of base station (BS) antennas. Then, we propose a genetic algorithm (GA)-based method to maximize the minimum user rate by optimizing the phase shifts at the RIS. Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems.

In practice, residual transceiver hardware impairments inevitably lead to distortion noise which causes the performance loss. In this letter, we study the robust transmission design for a reconfigurable intelligent surface (RIS)-aided secure communication system in the presence of transceiver hardware impairments. We aim for maximizing the secrecy rate while ensuring the transmit power constraint on the active beamforming at the base station and the unit-modulus constraint on the passive beamforming at the RIS. To address this problem, we adopt the alternate optimization method to iteratively optimize one set of variables while keeping the other set fixed. Specifically, the successive convex approximation (SCA) method is used to solve the active beamforming optimization subproblem, while the passive beamforming is obtained by using the semidefinite program (SDP) method. Numerical results illustrate that the proposed transmission design scheme is more robust to the hardware impairments than the conventional non-robust scheme that ignores the impact of the hardware impairments.

A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we are exploited to deal with the link outage issue caused by a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of the beamforming in the presence of random blockages, we formulate a stochastic optimization problem with the aim of minimizing the outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic majorization-minimization method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.

In this paper, we investigate the robust transmission design for the intelligent reflecting surface (IRS) aided secure wireless communication systems, where a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers via IRS. The estimation error of the imperfect cascaded AP-IRS-user channels is considered in the robust beamforming. Specifically, a transmit power minimization problem is formulated subject to the outage rate probability of information leakage to Eves under the statistical cascaded CSI error model, and the beamformer at the transmitter, the covariance matrix of artificial noise (AN), and the IRS phase shifts are jointly optimized. To handle the resulting non-convex optimization problem, we first approximate the rate outage probability constraints by using the Bernsteintype inequality. Then, we develop a suboptimal algorithm based on the alternating optimization, penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power while ensures the system security.

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

Intelligent reflecting surface (IRS) has been proved to be an efficient technology to improve the spectrum and energy efficiency in cognitive radio (CR) networks. Unfortunately, due to the fact that the primary users (PUs) and the secondary users (SUs) are non-cooperative, it is challenging to obtain the perfect PUs-related channel sate information (CSI). In this paper, we investigate the robust beamforming design based on the statistical CSI error model for PU-related cascaded channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) matrix and phase shifts to minimize the SU’s total transmit power, meanwhile subject to the quality of service (QoS) of SUs, the interference imposed on the PU and unit-modulus of the reflective beamforming. The non-convex optimization problems are transformed into two second-order cone programming (SOCP) subproblems and efficient algorithms are proposed for solving these subproblems. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST’s transmit power and feasibility rate of the optimization problem.

In oil pipeline leak detection and location, noise in the pressure signal collected at the end of the pipeline affects the accuracy of leak detection and the error of leakage location. To reduce the noise interference, an improved local mean decomposition signal analysis method is proposed. The production functions (PFs) that are related to the leak signal can be exacted, and it is necessary to know the characteristics of leak signals or noise in advance. According to the cross-correlation function, there is a significant peak between the measured signals, which are decomposed into a number of PFs. These reconstructed principal PF components are obtained, and a wavelet analysis is used to remove the noise in the reconstructed signal. On this basis, the signal features are extracted according to the time-domain feature and the waveform feature, which are input into the least squares twin support vector machine (LSTSVM), to recognize pipeline leaks. According to the reconstructed signal after wavelet denoising, the time-delay estimate of the negative pressure signal at the end of the pipeline is obtained by the cross-correlation function, and the leak location is ultimately calculated by combining the time delay with the leak signal propagation velocity. A flow model for pipeline leakage is proposed based on the Flowmaster software, where the collected data of the different working conditions are processed. The experimental results show that the proposed method can effectively identify different working conditions and accurately locate the leakage point.

Images can evoke emotions in the viewers, but are rarely indexed based on their emotional contents because emotions are subjective. Given the ubiquitous use of images, it would therefore be desirable to have an automated classification system for recognition of human emotions induced by the visual content of images (REVC), which can index images based on the emotions that they may induce. The focus of research in REVC systems has been on identification of features that evoke different emotions and their strength. These features are then used to develop REVC systems that can accurately predict emotions, which a given image may evoke. This paper investigates the performance of a color moment-based REVC system, by identifying the suitable color moments, and measuring their effectiveness for emotion recognition. Using a combination of these features, a new REVC system is developed. The proposed method outperforms a commonly used benchmark that relies on more than 100 features to predict emotional content of the image, signifying the fact that using combination of multiple features does not necessarily improve the system performance.

The number of time steps used for machine learning methods on the hourly prediction of solar photovoltaic (PV) generation is typically set to one. In this paper, we investigate the larger-than-one time steps for long short-term memory (LSTM)-based method to improve the accuracy of prediction with a limited dataset. Besides, we analyze the contribution of all available features toward a different number of time steps and select proper features for better training. By using real-world dataset, the experimental results show that the proposed method can effectively outperform the conventional one whilst using the same dataset.

Natural gas hydrate is an environmentally friendly and efficient new energy source. Drilling core is the most direct way to identify gas hydrates. The impact tube shoe is located at the lowermost end of the drilling coring device, and works together with the impact tube. In the coring process, the impact tube shoe is a key part to ensure that the coring work can be carried out smoothly. This paper uses ANSYS Workbench18.0 finite element analysis software to optimize the size of the impact tube shoes. The optimization content includes: The length of the cutting surface along the axial direction l, angle of front angle α, combination optimization of axial length l 1 and angle of front angle α, and the thickness δ of the end face. This paper explores the variation of the maximum stress and penetration resistance of the impact tube shoes when the shoes of different sizes penetrate into the mudstone layer. According to the calculation results, the optimized shoe size: l=35mm, a=20°, l 1 =13mm, and δ=1.5mm. The optimized shoe model not only has the sufficient strength, but also exhibits good penetration performance, and smaller penetration resistance reduces drag during coring. And the impact tube shoe could work under the load of 254.17kN, which is the load under the seabed with the water depth of 1300m.

It takes a long time to train people to master correct postures and movements in some sports, such as martial arts, dance and other difficult sports skills. In the past, without the direction of professionals, instructional videos such as DVD, online videos and other self-study ways were used for training. But these training ways cannot detect movement data and provide personalized information. Hence, this study took martial art movements as the content and used Kinect to establish a motion sensing martial art learning system. In this study, 20 students from a national university located in Mid-Taiwan were invited to test this system and fill out the system usability scale. The scale shows a good grade, which indicates that this system can be widely acceptable. In the future, updated equipment and technologies will be used to improve this system to provide better personalized information.

Origami is not only a kind of lifelization game and art but also a kind of effective hand and brain skill training and learning activity. The origami learning activity can effectively cultivate students' ability to develop spatial visualization and can help elementary school students transform the graphic representation of the plane into the visualization ability of three-dimensional object of solid figure. The process of operating origami through tablet's interactive e-book can be used to train elementary school students' folding and unfolding ability, their concept for the thinking in three-dimensional space as well as the cognitive ability for shapes, so as to reduce their cognitive load. This research took the third grade elementary school students as the subjects, and applied the dynamic visual feedback process of origami animation and interactive e-book to conduct the teaching experiment. The research results are as follows: (1) The elementary school students who apply the e-book cooperative learning teaching have the significantly better origami learning effectiveness than those using the generally traditional cooperative learning teaching. (2) In origami learning activity, the elementary school students' learning motivation achieves the significant difference and the e-book cooperative learning teaching is superior to the generally traditional cooperative learning teaching in learning motivation. (3) The e-book cooperative learning teaching can make the elementary school students reduce their cognitive load during their learning of origami.

In this paper, two digital watermark algorithms are compared for stereoscopic three-dimensional (3-D) video protection. Dual-tree complex wavelets transform (DT-CWT) and Haar discrete wavelet transform (Haar DWT) are employed to embed an image watermark to left and right views of a stereoscopic video stream. The digital watermark embedded in the selected coefficients of the CB-channel to avoid visual perception of the embedded image by the human eye, which is compared favorably with the widely used watermark embedding approach in YCBCR domain in terms of picture quality and has higher capacity for watermark payload. Results show that the DT-CWT demonstrates better performance than the Harr DWT in terms of robustness, watermark payload and visual transparency. Objective and subjective assessments were conducted on the watermarked 3-D video sequences for performance evaluation. The methods are benchmarked against the state of the art methods such as QIM-NTSS (quantization index modulation — new three-step search) and IProtect, and have survived various types of watermark attacks.

Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

Pictures play an important role in school children education. Lively and vivid pictures or illustrations are able to attract the most attention from children and enhance their interest in learning. What's more, relevant psychological data prove that people accept different information and knowledge at different paces. Human brain responds to verbal information at the lowest pace, and the consequent judgement and reasoning is also delayed. However, iconic information can come into people's minds once or twice times as fast as verbal information, from which it can be inferred that pictures can provide some teaching functions superior to languages and words. Information infused teaching can enhance students' understanding of word problems through direct iconic representation. It can improve the learning interest of students, reduce their anxiety about mathematics and improve their learning effectiveness. The research aims to design a mathematic system of animation teaching materials based on iconic representation and explore the learning effectiveness of the students in Grade 4 of elementary schools in the central of Taiwan in terms of the study unit, mathematical word problems. According to the research results, (1) the effects of Mathematics teaching is obvious with the help of animation teaching materials based on iconic representation, especially for multiplication and division methods; (2) the students hold positive and affirmative attitudes towards animation teaching materials based on iconic representation.

In traditional industrial applications, the thread plug gauge is used to check the pitch diameter of the internal thread of a nut. However, the thread plug gauge can only be used to determine whether the nut was manufactured successfully rather than to measure the value of the pitch diameter. In recent years, several studies have employed a reflected light method such as laser triangulation to scan the outline of an object. However, most research studies using laser triangulation have developed measurement algorithms for specific objects because the properties of an object's appearance and the reflective laser from that object differ greatly. Existing algorithms are consequently not suitable for scanning the outline of the internal thread of a nut and measuring its pitch diameter with laser triangulation. In this paper, we propose a novel, real-time algorithm for measuring the pitch diameter of the internal thread of a nut using laser triangulation. First, a specific image preprocessing procedure designed for a reflective laser in the internal thread of a nut is presented. The result of preprocessing critically affects the measurement accuracy of the proposed method. Thereafter, we propose a measurement algorithm designed for the appearance properties of the internal thread of a nut. Our method can operate in real time because the proposed algorithm can measure the pitch diameter of the internal thread of a nut in only a few steps and simple calculations.

As high-resolution fingerprint images are becoming more common, the pores have been found to be one of the promising candidates in improving the performance of automated fingerprint identification systems (AFIS). This paper proposes a deep learning approach towards pore extraction. It exploits the feature learning and classification capability of convolutional neural networks (CNNs) to detect pores on fingerprints. Besides, this paper also presents a unique affine Fourier moment-matching (AFMM) method of matching and fusing the scores obtained for three different fingerprint features to deal with both local and global linear distortions. Combining the two aforementioned contributions, an EER of 3.66% can be observed from the experimental results.