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The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose. Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to web-based and online intelligent education systems, and ultimately with the use of embedded computer systems, together with other technologies, the use of humanoid robots and web-based chatbots to perform instructors' duties and functions independently or with instructors. Using these platforms, instructors have been able to perform different administrative functions, such as reviewing and grading students' assignments more effectively and efficiently, and achieve higher quality in their teaching activities. On the other hand, because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students' needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.

Device-to-Device (D2D) communication with caching technology has emerged as a promising technique for offloading the traffic and boosting the throughput of the fifth generation (5G) cellular networks. The combined impact of cache memory size of user equipments (UEs) and content sizes, which are two crucial factors in D2D-assisted caching networks, were usually ignored in the existing researches. In this paper, an optimization algorithm is proposed to maximize the cache hit probability and cache-aided throughput, with the consideration of various cache memory sizes of UEs and content sizes. Firstly, users are grouped according to the cache memory sizes and the content sizes. Then the general mathematical expressions for the optimization of cache hit probability and cache-aided throughput with the constraints of cache memory sizes and content sizes are obtained. Subsequently, a Packet Cache Strategy (PCS) algorithm is proposed to obtain the caching probability matrix with the maximum cache hit probability and cache-aided throughout by taking user caching probability of a file as a variable. Finally, numerical results show that the sizes of the requested files affect the caching willingness of users, and the proposed PCS can achieve the highest cache hit probability and the best cache-aided throughput comparing with two other existing methods.

Hotel managers can learn about hotel customer satisfaction by analyzing online reviews. There are differences in the experience of reviewers, the release time and the degree of recognition for different online reviews, which leads to different reliability in reflecting customer satisfaction. The issue of how to determine hotel customer satisfaction more rationally with this reliability in mind is still a problem. To address this problem, this paper proposes a method for measuring hotel customer satisfaction based on the Dempster-Shafer (D-S) evidence theory, which considers the reliability of online reviews and information from multiple online travel review websites. This method is composed of three stages. First, considering the difference in online reviews’ reliability, the basic probability assignment is generated based on online reviews. Next, the aggregation of the evaluation information is conducted based on the entropy weight method and Dempster’s rule of combination. Finally, the utility function is used to calculate the expected utility, and customer satisfaction is analyzed based on the calculated expected utility. Therefore, improvement strategies of customer satisfaction can be developed according to customer satisfaction ranking. To verify the feasibility and effectiveness of this method, a case study for four hotels is presented. The proposed method is expected to help hotel managers understand the hotel’s customer satisfaction, and develop corresponding improvement strategies to enhance its competitiveness.

This paper investigates the decoding performance of channel-coded ship-based satellite communications on-the-move (SSCOTM) between ships and satellites. In practical communications, ship antennas deviated from satellites by wind waves suffer rapid channel degradation with serious burst errors and even losing all the data. To address this issue, we first establish a mathematical model to study the influence of wind waves and derive the effective signal-to-noise ratio (SNR) in SSCOTM. Second, we propose a vertical Reed-Solomon (RS) with cyclic redundancy check (CRC) and low-density parity check (VRC-LDPC) code, where the RS code and LDPC code are treated as the inner code and outer code to correct both burst and random errors, respectively. Moreover, both the encoder and the decoder of the proposed VRC-LDPC can be carried out in parallel implementations. Finally, simulation results show that the proposed VRC-LDPC performs much better than the conventional codes of the Consultative Committee for Space Data Systems (CCSDS) standard. Thus, this paper provides an excellent alternative channel code for reliable error-correction systems of SSCOTM.

The existing physical-layer network coding (PNC) only considers channel codes of a constant coding rate. In this paper, we propose a rate-compatible (RC) channel coding scheme for PNC in two-way relay systems. Our contributions are as follows. First, we propose a two-edge-type-low-density-paritycheck-aided PNC (JTNC) scheme due to its simple implementation and excellent performance. Second, we design an RC scheme, i.e., choice of locations and symbols, for the proposed JTNC in the relay. The simulation results show that the proposed RC-JTNC has good error correction performance and achieves large gains over the existing puncturing algorithms aided PNC over a wide range of rates.

We developed a method using a digital camera combined with fitting algorithm and T-S fuzzy neural network (T-S fnn) to measure the water turbidity accurately and efficiently. A turbidity-measuring device and image-processing software based on the common camera were designed to obtain the image of a standard solution after a constant light source passed through the sample and the RGB values, Lab values corresponding to the image were obtained. These RGB values were used as the input of the fuzzy neural network prediction model, the corresponding standard turbidity values were used as the output, and a camera-based fuzzy neural network turbidity measurement method was established. Simultaneously, the standard curve of turbidity measurement is established by fitting the turbidity using color component and color difference. The proposed method was applied to the measurement of standard turbidity solution and the results were compared with those of turbidmeter. The accuracy of the fuzzy neural network and the fitting algorithm is higher than that of the turbidimeter, and the accuracy of the fuzzy neural network method is the highest, the measurement error was only ±0.89%, and the accuracy much higher than that of an ordinary turbidimeter. By comparing the independent sample t-tests of the actual water samples, the fuzzy neural network method had the same trend as the turbidimeter and there was no significant difference of the results. The water turbidity measurement with the camera-T-S fuzzy neural network assembly can be applied to the actual water samples. This method can replace the traditional photoelectric detection method to measure turbidity, and it can reduce measurement error and cost. It may be used in environmental detection, biomedicine, and other fields.

Thanks to its superior features of non-volatility, fast read/write speed, high endurance, and low power consumption, spin-torque transfer magnetic random access memory (STT-MRAM) has become a promising candidate for the next generation non-volatile memories (NVMs) and storage class memories (SCMs). However, it has been found that the write errors and read errors caused by thermal fluctuation and process variation severely degrade the reliability of STT-MRAM. Moreover, process imperfection also causes a diversity of the raw bit error rate (BER) among different dies of STT-MRAM. In this paper, we propose the design of novel rate-compatible protograph low-density parity-check (RCP-LDPC) codes to correct memory cell errors and mitigate the raw BER diversity of STT-MRAM. In particular, to deal with the asymmetric property of the STT-MRAM channel, we first apply an independent and identically distributed (i.i.d.) channel adapter to symmetrize the STT-MRAM channel. We then present a modified protograph extrinsic information transfer (P-EXIT) algorithm for the symmetrized STT-MRAM channel. We further propose a combined guideline, including the modified P-EXIT algorithm, the asymptotic weight enumerator (AWE) analysis, as well as the actual error rate performance, for designing protograph LDPC codes with short information word lengths for STT-MRAM. By further applying a code extension approach, we design novel RCP-LDPC codes that can work with a single encoder/decoder. Simulation results show that our proposed RCP-LDPC codes outperform the well-known rate-compatible AR4JA protograph codes as well as the fixed-rate quasi-cyclic (QC) LDPC codes in terms of both the error rate performance and the convergence speed over the STT-MRAM channel.

Face hallucination is an example of the image super-resolution problem, where the higher resolution face images can be obtained from the lower resolution ones. Many methods based on the sparse representation have been proposed to solve this problem. These methods use the position-patch strategy, which divides the input image into several small patches and represents each patch by the patches at the same position in the training set. An effective image prior is critical to improve the quality of the estimated superresolution images. Thus, we try to exploit the gradient information during the patch representation to achieve better hallucination result. In this paper, we propose a novel face hallucination model based on the sparse representation, called iterative gradient constrained weighted sparse representation method. Our model incorporates both the gradient information of the images and the l1 reweighted constraint into the sparse representation to achieve better performance. An iterative algorithm is proposed to refine these reconstructed high-resolution images. The experiments on several face databases show the better performance of our algorithm compared with other baseline algorithms.

A recently proposed network-coded multiple access (NCMA), consisting of XOR-based channel decoding and multi-user complete decoding (MUD-CD), can significantly improve throughput over conventional successive interference cancellation (SIC) decoding under near power-balanced channels. In this paper, we propose an improved NCMA (I-NCMA) for non-orthogonal multiple access (NOMA) in the 5G wireless communications, consisting of multiple users and a single base station (BS). At the transmitter side, the proposed scheme enables all users to simultaneously transmit messages to the BS in the same time and frequency. At the receiver side, we propose a joint-user channel decoding (JU-CD) to jointly decode all user messages by a single decoder. Then, the extrinsic messages from JU-CD can be jointly utilized by MUDCD for decoding all user messages. Also, other than conventional NCMA that mainly deals with two users, I-NCMA adapts to the NOMA networks with an arbitrary number of users. Simulation results indicate that the proposed I-NCMA achieves a better decoding performance than both SIC and conventional NCMA over near power-balanced channels in the low-to-medium SNR regime, while retaining a performance close to conventional NCMA in the high SNR regime.

This paper proposes a novel encoder architecture of low-density parity-check (LDPC) generator matrix in frequency modulation-China digital radio (CDR), which was promulgated in August 2013. We utilize the specific structure of LDPC parity matrix to parallelize row and column encoding operations. An optimized method is also proposed to control memories, which can be reused for the LDPC code with different code rates to improve the utilization of hardware resources. The proposed LDPC encoder and decoder are implemented on Xilinx FPGA. According to simulation results of ModelSim and MATLAB, we also verify that the proposed method has the advantages of reduced resource consumption, low power, and high accuracy. The proposed encoder can achieve throughput up to 400 Mbps. In particular, with Lena binary image as the test transmission data, we find that the decoded result meets the error requirements of CDR.

Differential-chaos-shift-keying cooperative communication (DCSK-CC) has attracted a great deal of attention in the past five years, because it can overcome the negative effect of wireless multipath fading. However, the conventional DCSK-CC system suffers from a major disadvantage of low data rate due to the resource consumption in cooperation. To address the issue, this paper proposes an efficient cooperative scheme, called partial-sequence CC (PS-CC) scheme, for the DCSK-CC system. In the PS-CC framework, the source and relays only transmit a partial chaotic sequence corresponding to one bit to the destination, while consuming the same transmission energy as DCSK-CC. To validate the feasibility of the DCSK PS-CC system, this paper carries out the error performance, diversity order, and data rate analyses under different cooperative conditions over multipath fading channels. Both numerical and simulated results show that the proposed PS-CC system outperforms the DCSK-based non-cooperative system and DCSK-CC systems over the range of signal-to-noise ratio under study. Moreover, the proposed system can enhance data rate and reduce the computational overhead of the relays as compared with the DCSK-CC system. In addition, it will be demonstrated that the DCSK PS-CC system preserves its advantages in typical ultra-wideband (UWB) channels. Overall, the PS-CC appears to be a desirable cooperative scheme for those applications featured by high-data-rate, low-power, and low-complexity, such as wireless sensor networks using the transmitted reference UWB technique.