Convolutional neural networks (CNNs) have shown great promise in human activity recognition, but long-term dependencies in time series data can be difficult to capture using standard CNNs. This study introduces a new CNN architecture that incorporates a multi-head attention mechanism (CNN-MHA) to address this challenge. This mechanism is composed of several attention heads, each independently calculating attention weights for distinct segments of the input. The attention head outputs are then concatenated and processed through a fully connected layer to produce the final attention representation. The multi-head attention mechanism allows the network to focus on relevant features and maintain long-term dependencies in the input data. The proposed model is evaluated on the physical activity monitoring for aging people dataset (PAMAP2) from the UCI machine learning repository, which is preprocessed by cleaning, normalization, segmentation, and reshaping before splitting into training, validation, and testing sets. The experimental results demonstrate that the CNN-MHA model outperforms existing models, achieving F1-score of 95.7%. Particularly, the multi-head attention mechanism significantly improves the model’s ability to recognize complex activity patterns. Furthermore, our model attained an average inference latency of 0.304 seconds, which can be crucial in real-time applications. The findings clearly demonstrate the substantial promise of the proposed CNN-MHA architecture for optimizing human activity recognition tasks, offering a powerful tool for advancing the state-of-the-art in this domain.

With the rise of AI, the recognition of Sign Language (SL) through sign-to-text has gained significance in the field of computer vision and deep machine learning. However, there are only a few medium to large open datasets available for this task, as it requires a vast dataset of thousands of signs for words/phrases in different environments, which is a time-consuming and tedious process. Furthermore, there has been very little effort towards Arabic Sign Language Recognition (ArSLR). This research paper presents the results of fine-tuning the Vision Transformer (ViT) model on a small-scale in-house dataset of ArSL. The main goal is to attain satisfactory results by utilizing minimal computing power and a small dataset involving less than 10 individuals, with only one recording made for each sign in every environment. The dataset comprises 49 classes/signs, all of which were made with two hands and belong to the Level I category in terms of popularity. To enhance the dataset, three types of augmentations - translation, shear, and rotation were employed. The ViT model, pre-trained on the Kinetics dataset, was trained on the variation of augmented datasets with 2 to 40 times samples for each original video, where the training set includes original and augmented videos of 8 volunteers and the test set includes only original videos of one particular volunteer. Experimental results reveal that the combination of rotation and shear outperformed the others, achieving an accuracy of 93% on the 20 times augmented samples per class per signer dataset. We believe this study sheds light on small-scale dataset-based SLR tasks and video/action recognition in general.

In recent years, artificial intelligence (AI) technology has been used in computer vision to extract and analyze specific information from images. This research aims to apply AI in the field of agriculture. In this study, the you only look once version4 (YOLOv4) based on scaled-up feature fusion (YOLOv4-SUFF) has been implemented for rice fields detection from high-resolution images taken by an unmanned aerial vehicle (UAV). YOLOv4-SUFF consists of an extra layer which can extract special feature maps for the detector. This makes the proposed model YOLOv4-SUFF get more information during the stage of feature fusion. The experimental results show that the YOLOv4-SUFF has provided the best performance in terms of an average precision at 86.78% compared with other models.

Synthetic Aperture Radar (SAR) imagery has been widely used in many maritime applications due to its high resolution, wide coverage, and real-time monitoring characteristics. Nevertheless, the size of SAR images is significantly large for real-time application. In recent years, High-Performance Computing (HPC)-related methods have been used to improve the precision and detection rate of SAR imagery analysis. In this paper, motivated by the state-of-the-art real time object detection You Only Look Once version 3 (YOLOv3), an enhanced GPU-based deep learning method has been proposed, namely Accelereated-YOLOv3 (A-YOLOv3), to detect ships from the SAR images. A-YOLOv3 aims to reduce the computational time with relatively competitive detection accuracy by constructing a new architecture with less layers and channels. The proposed A-YOLOv3 architecture achieves Average Precision (AP) of 97.4% on the Expand Diversified SAR Ship Detection Dataset (EDSSDD).

In the last few decades, the number of elderly people living alone surge worldwide due to an increase in human life expectancy. Researchers suggest different types of methods to monitor elderly residents living alone to prevent them from being unable to get help on time after some incidents such as accidental falling or heart attack. High-resolution data generating methods in monitoring elderly people with RGB cameras or wearable devices are inconvenient for elderly residents. While the former raises a privacy concern, the latter is not practical to wear on and off or charge its battery frequently. One of the solutions that solve both aforementioned problems is ultra-low resolution infrared (IR) sensor arrays. We offer a dataset for Activities of Daily Living (ADL) collected with 8×8 IR sensor arrays from 74 volunteers. For ADL recognition, four types of deep learning models, Convolutional Neural Network (CNN), two types of Recurrent Neural Networks (RNN), and Transformer models are employed. Among them, CNN and Transformer models showed promising results. We believe the dataset is a good contribution to versatile data sources for researchers to accelerate their work on the development of privacy-preserved ADL recognition systems.

Indoor posture recognition is vital for monitoring/detecting exercises, activities of daily living, accidental falls, unusual behavior, etc. However, high-resolution image based systems have a high accuracy, they are considered as intrusive and most of the current state-of-the-art image classifiers (VGG, ImageNet, ResNext) are not applicable for ultra-low resolution (<; 32 pixels in extent) image classification due to their downsizing feature extraction architecture. Thus, we propose a shallow LowNet model for classifying privacy preserved 16x16 posture images with its feature preserving architecture, variable ReLU slopes, and a custom loss function. LowNet outperformed, with an Accuracy of 98.94% and F1-score of 79.86%, the existing models (LeNet, ResNet1, ResNet-2) which can run on our Ultra lowresolution Thermal Posture Image (UTPI38) dataset (offered here) with 38 classes (4374 samples) collected from 23 volunteers. More experimental results are discussed on the custom loss, and variable ReLU slopes which gave 8.2% performance increase. Thus, we conclude that LowNet is useful in a multiclass ultra-low-resolution thermal posture image classification task.

This study develops a point of familiarity (POF)-based navigation system for people with mild cognitive impairment (PMCI) based on wandering detection. The proposed system has four main functionalities: optimal path planning, POF-based navigation, wandering detection, and remote route tracking. An improved A*algorithm which introduced the θ_WD approach is employed to plan an optimal path for the PMCI. As any wandering behavior is detected, the PMCI who holds a mobile phone will receive the pof data sent by the remote monitoring system for guiding to the point of familiarity (POF) to wait for rescue. The travelling status of the PMCI will be recorded by the remote route tracking unit. Experiment results show that the proposed system achieves the accuracy of 91.7% for wandering detection in the normal GPS environments. Furthermore, the POF-based navigation saves about 19% of the traveling time as compared to the MAP-based navigation approach. Therefore, the system has great potential in helping the PMCI to go home safely.

In recent years, the number of yoga practitioners has been drastically increased and there are more men and older people practice yoga than ever before. Internet of Things (IoT)-based yoga training system is needed for those who want to practice yoga at home. Some studies have proposed RGB/Kinect camera-based or wearable device-based yoga posture recognition methods with a high accuracy; however, the former has a privacy issue and the latter is impractical in the long-term application. Thus, this paper proposes an IoT-based privacy-preserving yoga posture recognition system employing a deep convolutional neural network (DCNN) and a low-resolution infrared sensor-based wireless sensor network (WSN). The WSN has three nodes (x, y, and z-axes) where each integrates 8 × 8 pixels' thermal sensor module and a Wi-Fi module for connecting the deep learning server. We invited 18 volunteers to perform 26 yoga postures for two sessions each lasted for 20 s. First, recorded sessions are saved as .csv files, then preprocessed and converted to grayscale posture images. Totally, 93200 posture images are employed for the validation of the proposed DCNN models. The tenfold cross-validation results revealed that F1-scores of the models trained with xyz (all 3-axes) and y (only y-axis) posture images were 0.9989 and 0.9854, respectively. An average latency for a single posture image classification on the server was 107 ms. Thus, we conclude that the proposed IoT-based yoga posture recognition system has a great potential in the privacy-preserving yoga training system.

Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion. Unobtrusive activity recognition is the most preferred solution for monitoring daily activities of the elderly people living alone rather than the camera and wearable devices based systems. Thus, we propose an unobtrusive activity recognition classifier using deep convolutional neural network (DCNN) and anonymous binary sensors that are passive infrared motion sensors and door sensors. We employed Aruba annotated open data set that was acquired from a smart home where a voluntary single elderly woman was living inside for eight months. First, ten basic daily activities, namely, Eating, Bed_to_Toilet, Relax, Meal_Preparation, Sleeping, Work, Housekeeping, Wash_Dishes, Enter_Home, and Leave_Home are segmented with different sliding window sizes, and then converted into binary activity images. Next, the activity images are employed as the ground truth for the proposed DCNN model. The 10-fold cross-validation evaluation results indicated that our proposed DCNN model outperforms the existing models with F1-score of 0.79 and 0.951 for all ten activities and eight activities (excluding Leave_Home and Wash_Dishes), respectively.

Human posture recognition is the foundation of the human activity monitoring. The activity monitoring system is in high demand for the elderly living alone to monitor their health status and accidental fall since the world elderly population will be doubled by 2050. Researchers have developed many camera or wearable device-based human recognition systems; however, they are considered to be privacy-invasive and/or not practical for the long-term monitoring. We propose a device-free unobtrusive indoor human posture recognition system leveraging a low-resolution infrared sensor-based wireless sensor network and deep convolutional neural network (DCNN). We integrated AMG8833 sensor module with 8×8 thermal sensors for sensing the human body temperature and WiFi module for a wireless sensor network. Three wireless sensor nodes are used to capture 3-axis human thermal image. Totally, 15063 samples are collected from four volunteers while they had performed eight human postures as the ground truth for the 10-fold cross-validation of DCNN models. Experimental results indicate that the highest average F1-score for the eight postures was 0.9981. Thus, the proposed system has the high potential for monitoring elderly daily activities, exercise, and fall in emergency cases. Moreover, we believe that our proposed system will be a milestone in the device-free unobtrusive sensing technology.

Many elderly persons prefer to stay alone in a single-resident house for seeking an independent life and reducing the cost of health care. However, the independent life cannot be maintained if the resident develops dementia. Thus, an early detection of dementia is essential for the elderly to extend their independent lifetime. One of the early symptoms of dementia is forgetting something when the person leaves the house. In this study, we introduce a novel front-door events (exit, enter, visitor, other, and brief-return-and-exit (BRE)) and their classification scheme that validated by using open datasets (n = 10) collected from ten single-resident testbeds by anonymous binary sensors. BRE events occur when four consecutive events (exit-enter-exit-enter) happen in some certain time intervals (t1, t2, and t3), and some of them may be the forget events. Each testbed had one older adult (aged 73 years and over) during the experimental period (μ = 583.1 ± 297.3 days). The algorithm automatically classifies the resident's front-door events and ignores visitor's entrance and exit events. The experimental results reveal the significance of the tiparameters for the number of BRE events. Since BRE events may include forget events, the proposed algorithm could be a useful tool for the forget event detection.

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. Therefore, we propose a Simple Particle Swarm Optimization (SPSO) algorithm for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. The simulation results show that with less population size of M=10, the SPSO can perform quickly convergence to sub-optimal solution in the 100'hgeneration. Therefore, as compared to the random allocation (Rand) method, the proposed SPSO can obtain sub-optimum performance with more 2 UEs than the Rand method.

This paper proposes a contactless charging platform with reconfigurable coils which compose a hexagonal coil array complied with wireless power consortium A3 standard. The function of contactless charging platform (CCP) that switches one of them to excite is to avoid the current cancellation phenomenon and to prevent the magnetic field attenuation. The CCP transmits wireless power to the secondary circuit and the measured results by a DC electronic load exhibit the maximum output power of 2.48W with its maximum transmission efficiency of 63%. It demonstrates that the designed CCP system is potentially to be applied for charging portable electronic appliances and communication systems.

In this paper we represent our proposed novel morphological filter developed under the scope of Taiwan-Mongolian co-project. We applied the implemented filter in vehicle detection from CCTV video signal. Our goalwas to develop a filter that can reduce the noise in background subtracted binary image, which created by camera shake, and unnecessary moving objects such as wave of the tree etc. We compared our filter performance with morphological open, close, erosion, dilation, and median filters. PSNR (Peak Signal to Noise Ratio) is employed for evaluating the performance of the filters, our filter's PSNR was relatively higher (21.39) than the other method. Furthermore, we used our filter for vehicle detection, and detection rate was 100% as the other methods. Thus, we conclude the new filter is sufficient for denoising binary image, and suitable for vehicle detection.

Cognitive radio (CR) allows unauthorized users to access authorized band without interfering the authorized user, thereby improving the bandwidth efficiency. In addition, cooperative communications with relay stations (RSs) can be used to improve throughput performance of 4G downlink network. In this study a cognitive radio assisted cooperation (CRAC), which combines advantages of CR and cooperative communications with RSs, is considered for resources allocation in the downlink orthogonal frequency division multiple access (OFDMA) networks. Two adaptive resource allocation algorithms are presented and experimental results demonstrate that the proposed algorithms can not only enhance throughput, but also improve fairness and utility of users assuming available channels are known by base station.

This paper presents a compact self-decoupling dual band MIMO antenna that meets wireless local area network communication standards. The self-decoupling is taken place by purposely using a folded loop-shape element in the MIMO antenna. Furthermore, the feeding point of the element antenna is deliberately selected so as to produce both the two desired frequency bands. The whole loop structure is constructed to produce the lower frequency band while a portion of the loop is resonant to the higher frequency band. The MIMO antenna contains two folded loop-shape elements situated back-to-back with a closed inter-element distance of one-tenth wavelength of the lower band. The novelty of this MIMO antenna is attributed to the achievement of good isolation without using any other supplementary decoupling mechanisms. In addition, the properties of small in size, simple in structure, nearly omni-directional in radiation pattern with moderate radiation gain make the proposed MIMO antenna fully compatible and fairly attractive to the wireless and internet network applications.

We proposed an analysis-by-synthesis (AbS) frame dropping algorithm for the front end of a distributed speech recognition (DSR) system that preserves rapidly changing frames for being more related to speech perception but discards slowly changing frames for providing little information. When applying DSR over error prone packet-switched networks, speech data will inevitably suffer from frame loss since packets may be lost or delayed due to congestion at routers. We further employed a model adaptation error concealment decoder at the back-end for compensating the mismatch between the pre-trained models and the test data, which contain missing frames caused by frame dropping at the front end and packet loss over the transmitted channel. This approach, for convenience, is denoted as AbS-MA. In the decoding process of AbS-MA, the transition probabilities of the hidden Markov models are dynamically adapted according to the time difference between successive observations. Experiments on the recognition of Mandarin digits were conducted to investigate the effectiveness of the proposed AbSMA method for a wide range of combinations of frame rates and packet loss conditions. The performance of the proposed AbSMA approach was compared with a baseline approach, in which the error concealment was implemented by an interpolation as the estimate of the missing frame of the received observations at the back-end. The experimental results show that AbS-MA is not only superior to the baseline in word accuracy but also significantly reduces the computation time.

In this paper, both effective bandwidth extension and distributed issues are investigated to improve the energy efficiency for the orthogonal frequency division multiplexing (OFDM) wireless communication systems. A Fuzzy based energy efficient bandwidth expansion (FBEEBE) scheme is proposed to fast infer the bandwidth extension factors (BEFs) for users based on fuzzy inference systems. The proposed fuzzy inference system can infer the BEFs from the two inputs of SNR and bandwidth requirement of users. Simulation results show that the proposed FBEEBE can improve the energy efficiency comparing to that of same bandwidth extension (SBE) scheme.

In a client-server distributed speech recognition (DSR) application, speech features are extracted and quantized at the client-end, and are sent to a remote back-end server for recognition. Although the bandwidth constrains are mostly eliminated, data packets may be lost over error prone channels. In order to reduce the performance degradation because of frame missing, a frequently used error concealment approach is to restore a full frame rate (FFR) observation sequence for recognition at the back-end. In this paper, an alternative approach is proposed to deal with observations with lost frames. This approach at first extracts the most reliable reconstructed reduced-frame-rate (RFR) observation sequence from the received data at the back-end, and then decodes it with an adapted hidden Markov model (HMM) that compensates the mismatch between the FFR trained model and the RFR test data. Experimental results show that a DSR system using the proposed method can achieve the same level of accuracy as an FFR data reconstruction method and significantly lessens the computation time. From the viewpoint of user capacity of a DSR system, we find that the proposed method is capable of serving much more client users without any extra cost of installing new equipment.

A new approach named MPSO-TS, which combines the advantages of particle swarm optimization (PSO) in global search, Tabu search (TS) technique in local search, and the mutation operation in improving solution diversity, is presented in this study. Experimental result obtained with ten benchmark functions illustrates that the proposed MPSO-TS is superior to other optimization schemes. Further, MPSO-TS is applied to estimate the carrier frequency offsets (CFOs) of the uplink orthogonal frequency division multiple access (OFDMA) system. Experimental results indicate that MPSO-TS not only can achieve the best performance but also spends the minimum CPU time per generation as compared to the existing PSO-based optimization approaches.

In linear networks with physical layer network coding (PNC), the relay node performs the combination of the relaying data from both end users. Moreover, the adaptive power allocation (APA) scheme can optimize the transmission rate of PNC for relaying communication systems. However, when the relay nodes are deployed with different distance to both of end user nodes, it is called near-far effect (NFE). The NFE degrades the bit error rate (BER) performance with deteriorating the decoding performance of PNC. Therefore, in order to further optimize the transmission power allocation (PA) for both of end user nodes and relaying node we propose a total adaptive power allocation (TAPA) scheme in this paper. With the proposed TAPA scheme, the NFE of conventional PNC systems can be diminished and the BER performance is improved. Simulation results show that the proposed TAPA improves the BER performance especially for the severe NFE environments.

In orthogonal frequency division multiple access (OFDMA) system, the multiple access interference (MAI) is a critical factor that significantly degrades system performance. In this research, a genetic algorithm (GA) is employed for joint channel estimation and multi-user detection. For improving the weakness of GA in exploitation, an approach called simulated annealing mutated-GA (SAM-GA) is considered by embedding the simulated annealing (SA) to the mutation operation of GA. Experimental result demonstrates that the proposed SAM-GA scheme achieves the best performance in terms of mean squared error (MSE) and bit error rate (BER) for joint channel estimation and multi-user detection in the OFDMA system as compared to other existing schemes.

The number of mobile subscribers has increased tremendously in recent years. The orthogonal frequency- division multiplexing (OFDM) system carries the message data on orthogonal subcarriers for parallel transmission, combating the distortion caused by the frequency selective channel or equivalently, the inter-symbol-interference in the multi-path fading channel. Carrier frequency offset (CFO) in OFDM systems, which can induce the loss of orthogonality among subcarriers and result in significant performance degradation, is critical to be estimated and compensated for. In this paper, the pilot symbol is used to estimation CFO in OFDM systems. We use a Bisection method to obtain estimation performance of CFO well and fast.

In orthogonal frequency division multiple access (OFDMA) uplink systems, carrier frequency offsets (CFOs) caused by multiple users may significantly degrade system performance. Therefore, many approaches have recently been proposed to estimate the CFOs, such as Simplified Maximum Likelihood (SML) Scheme and Fast Algorithm. While those algorithms reveal acceptable performance, their computational burdens are still very high. For reducing the computational burden, a new particle swarm optimization (PSO)-based scheme is proposed in this study by introducing the mutation operation and Taguchi method. Experimental results indicate that the proposed approach can achieve performance close to SML and Fast Algorithm with much less computational burden.

Traditional restaurant service is typically passive: Waiters must interact with customers directly before processing their orders. However, a high-quality service system should be customer centered; it should immediately recognize customer identities, favorite menus, and expenditure records to provide customer-centric services. To achieve this goal, this study integrates radio frequency identification (RFID), wireless local area network, database technologies, and a menu recommender to develop an intelligent e-restaurant for customer-centric service. This system enables waiters to immediately identify customers via RFID-based membership cards and then actively recommend the most appropriate menus through menu recommender for customers. Experimental results that are obtained from a case study conducted in two Taipei restaurants indicate that the proposed system has practical potential in providing customer-centric service.

Carrier frequency offset (CFO) is a major factor that degrades performance of the orthogonal frequency division multiplexing (OFDM) system. For orthogonal frequency division multiple access (OFDMA) uplink systems, there are multiple CFOs need to be estimated simultaneously, which is a very challenging task. Particle swarm optimization (PSO) algorithm has been recognized a potential approach for CFOs estimation. However, its performance deteriorates as the number of users increases due to insufficient particles diversity. To overcome this difficulty, an enhanced PSO called MRPSO is proposed to increase particles diversity by employing mutation and repulsive operations. Furthermore, a least square (LS) scheme is adopted to improve the estimated CFOs. Simulation results indicate that the proposed MRPSO attains best BER and MSE performances in CFOs estimation as compared to other PSO-based schemes.

In multi-carrier code-division multiple-access (MC-CDMA) system the multiple access interference (MAI) is a critical factor that significantly degrades system performance. In this research the genetic algorithm (GA) is employed for joint channel estimation and multi-user detection. For improving exploration and exploitation strengths of GA, a GA-based approach called SAM-GA is developed by incorporating the simulated annealing (SA) to mutation operation. Furthermore, a threshold-based mutation is proposed to refine the SAM-GA scheme. The resulting scheme is called modified SAM-GA for simplicity. Experimental result demonstrates that the proposed modified SAM-GA scheme achieves the best performance in terms of mean squared error (MSE), bit error rate (BER), and convergence rate for joint CIR estimation and multi-user detection in the MC-CDMA system as compared to other schemes.

In this paper, we investigate the multiuser performance in a 2-D spreading orthogonal frequency coded division multiplexing (OFCDM) communication system over frequency and time selective fading channels. To compromise between the diversity gain and the multiple access interference (MAI), we propose an adaptive fuzzy logic to infer adequate 2-D spreading codes and to assign the orthogonal variable spreading factor (OVSF) codes to active users for OFCDM mobile communication systems. Simulation results show that the proposed fuzzy-based code allocation (FBCA) scheme can not only suppress the MAIs induced by the non-orthogonality but also exploit the diversity gain over time and frequency selective fading channels and then outperforms the orthogonal code allocation (OCA) scheme at all system load.

Partial transmit sequence (PTS) technique requires an exhaustive search over all combinations of allowed phase weighting factors, the search complexity increases exponentially with the number of sub-blocks. In this paper, a novel combing strategy that employs sub-block partition scheme and phase factors for PTS in orthogonal frequency division multiplexing (OFDM) system is proposed. We present an OFDM system, which through the use of sub-optimal PTS based on particle swarm optimization (PSO) algorithm, achieves low computation complexity to find optimum phase weighting factors and reduce the peak-to-average power ratio (PAPR). Furthermore, to work around potentially computational intractability, the proposed scheme exploits heuristics in consideration of both global and local exploration partial transmit sequence. Simulation results show that the proposed PSO-based PTS can efficiently find the optimum phase weighting factor, and can achieve the lower PAPR with moderate complexity.

Many issues have been identified in outdoor teaching, especially in places that lack the capacity to effectively present information. This study proposes an environment of ubiquitous learning with educational resources (EULER) based on radio frequency identification (RFID), the Internet and ubiquitous computing technologies to resolve such issues. A case study of natural science learning is conducted in classrooms and at the Guandu Nature Park in Taiwan. The results of evaluation show that the proposed EULER significantly improves student motivation and learning.

In this paper, we propose an adaptive multistage partial parallel interference cancellation (PPIC) multiuser detector for multi-carrier direct-sequence code-division multiple-access (MC-CDMA) communication systems over frequency selective fading channels. The PPIC, which tries to reduce the cancellation error in parallel interference cancellation (PIC) schemes due to the wrong interference estimations in the early stages, outperforms the conventional PIC under the heavy load for CDMA systems. Then, in this paper, a partial factor (PF) derived from the estimated multiple access interference (MAI) is proposed to adapt the channel variations and perform more effective PPIC for MC-CDMA systems. Simulation results show that the proposed five-stage adaptive PPIC scheme outperforms the conventional PIC (CPIC) and constant weight PPIC (CW-PPIC) with highly near-far resistant and robust to the channel variations, especially at heavy system load.

Many problems have been found in museum learning activities that apply tour guide systems. In this research, a ubiquitous museum learning environment is developed to overcome these problems by applying the radio frequency identification, wireless network, and embedded system technologies. A case study is conducted and experimental results verify the effectiveness of the proposed learning environment.

In this paper, we investigate the performance of multistage partial parallel interference cancellation (PPIC) multiuser detector for space-time block coded (STBC) multi-carrier direct-sequence code-division multiple-access (MC-CDMA) communication systems over frequency selective fading channels. The PPIC, which tries to reduce the cancellation error in parallel interference cancellation (PIC) schemes due to the wrong interference estimations in the early stages, performs the adequate interference cancellation for CDMA systems. Therefore, in this paper, the effectiveness of PPIC multiuser detection is further investigated for STBC MC-CDMA systems. Simulation results show that the PPIC scheme outperforms the conventional PIC (CPIC) especially at heavy system load.

The asynchronous impulsive noise is one of the most crucial factors that degrade the performance of power line communication (PLC) network. Several approaches have been presented to characterize the time domain behavior of the asynchronous impulsive noise over the PLC network. Among them a partitioned Markov-chains model has been validated with measured data. However, its model parameters estimation using the Simplex method can easily trap the final solution into the local optimum. To overcome this difficulty, a new estimation scheme based on the genetic algorithm (GA) is proposed in this paper. Experimental results show that the proposed scheme yields estimates which more closely match the experimental data statistics.

In this paper, we investigate the performance of multistage GA-based parallel interference cancellation (PIC) multiuser detectors for multi-carrier direct-sequence code-division multiple-access (MC-CDMA) communication systems over frequency selective fading channels. The genetic algorithm (GA), which tries to reduce the complexity in optimal detection due to the greedy exhausted searching, performs the adequate sub-optimal multiuser detection for CDMA systems. Therefore, in this paper, we further investigate the GA-based PIC multiuser detection for MC-CDMA systems. Simulation results show that with a lower generation of Gn=5, 10, the constant weight PIC scheme outperforms the conventional PIC (CPIC) especially at heavy system load. However, with higher generation Gn=20 in GA, the conventional PIC (CPIC) scheme contrarily outperforms the constant weight PIC for all system load.

In this paper a two-stage receiver called PRAKE-GA receiver which takes the advantages of the partial RAKE (PRAKE) receiver and genetic algorithm-based multiuser detector (GA-MUD) is proposed. In this new structure, the GA-MUD employs the output of the PRAKE receiver as its initial value to search for the best solution based on a formulated optimization mechanism. Simulation results indicate that the proposed receiver can effectively improve the performance of multiuser detection in the DS-CDMA UWB system over an extreme nonline-of-sight (NLOS) multipath channel.

This study designs and implements a learning management system - the WISH (Web-based instruction system for high school). With the friendly interface, complete functions, and plentiful materials, the WISH provides an Internet learning environment of good quality to high schools' teachers and students. A series of experiments is conducted in 5 high schools with 38 courses. The results of student questionnaire and teacher visit are presented to provide useful reference in using the WISH platform as instruction aiding system.

This research develops a mobile-based interactive learning environment (MOBILE) for aiding elementary school English learning. The MOBILE consists of a mobile learning server and mobile learning tools, which is able to support in- or outdoor learning activities. Several theme-based mobile learning activities including body parts learning and creation of species are conducted. Experimental results obtained from posttests and questionnaire indicate that the MOBILE can significantly increase students' interest and effect in learning English as compared to the traditional manner.

This study focuses on two issues: parametric modeling of the channel and index assignment of codevectors, to design a vector quantizer that achieves high robustness against channel errors. We first formulate the design of a robust zero-redundancy vector quantizer as a combinatorial optimization problem leading to a genetic search for a minimum-distortion index assignment. The performance is further enhanced by the use of the Fritchman (1967) channel model that more closely characterizes the statistical dependencies between error sequences. This study also presents an index assignment algorithm based on the Fritchman model with parameter values estimated using a real-coded genetic algorithm. Simulation results indicate that the global explorative properties of genetic algorithms make them very effective in estimating Fritchman model parameters, and use of this model can match index assignment to expected channel conditions.

Errors encountered in digital mobile radio channels are not independent but exhibit varying degrees of statistical dependencies. In many mobile applications, Gilbert's (1960) finite-state Markov chain model has been shown to adequately characterize the bursty-noise binary channels. An estimation method based on the simulated annealing algorithm is proposed for computing the model. Parameters from the sample error sequences. Experimental results indicate that the simulated annealing method yields more accurate identification at a faster convergence rate for comparison with the gradient iterative method.