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.

A novel adaptive sliding diagonal recurrent fuzzy cerebellar model articulation controller (ASDRC) is proposed by this study. ASDRC includes the inputs by a sliding surface into a diagonal recurrent fuzzy cerebellar model articulation controller (DRCMAC). The ASDRC enables cerebellar model articulation controller (CMAC) to exhibit both static and dynamic characteristics, indicating that the use of DRCMAC improves the disadvantage of conventional CMAC while exhibiting the advantages of fuzzy CMAC (FCMAC). Regarding the proposed ASDRC, the adaptive update law determining the memory weights, means of Gaussian functions, and standard deviations of Gaussian functions is yielded by the Lyapunov stability theory; moreover, the gradient descent method is applied to yield the recurrent weight update law. Using the adaptive update law and recurrent weight update law of the ASDRC to implement online adjustment ensures system stability. To demonstrate the performance of the proposed ASDRC, this study applies it to the direct torque control (DTC) systems of an induction motor to perform experiments. The root-mean-square error is used as an assessment indicator to compare the results of the proposed controller with those of the FCMAC. The experimental results prove that the ASDRC has more excellent response, and its performance is superior to that of the FCMAC.

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.

Localization is a very important issue to wireless sensor networks. The sensor node requires accurate location information in order to achieve the purpose of real-time monitoring and transmission of information. The more the sensor nodes and GPS modules are used in localization, the higher the localization accuracy can achieve, but employing more sensor nodes also leads to high costs. Thus, how to improve the localization accuracy while solving the problem of high costs has garnered attention. In this paper, an area-restriction based localization (ARBL) algorithm is proposed. The ARBL algorithm uses both range-based and range-free schemes, which combines area restriction setting and the sampling points from Monte Carlo localization (MCL) to improve the localization accuracy. The simulation results show that the ARBL algorithm proposed in this paper outperforms DuRT, MMRL and RL algorithms that employ a mobile anchor node, regardless of the number of the sensor nodes and the moving speed of the anchor node.

This paper investigates the type-2 T-S fuzzy multiple feedback-loop guaranteed cost controller design for uncertain singular systems with transmission and state delays. Based on the type-2 T-S fuzzy theory, this work constructs a mathematical model for a class of nonlinear uncertain singular time-delay systems. Using the parallel distributed compensation concept with Lyapunov theory and linear matrix inequality technique, a robust guaranteed cost fuzzy controller is designed with the multiple feedback-loop structure to stabilize the considered system with guaranteed cost performance. Finally, an optimization procedure is presented to reduce the upper bound of guaranteed cost. According to the simulation results, when parametric uncertainties and transmission and state delays exist simultaneously, it indicates that the proposed guaranteed cost controller demonstrates better design flexibility. Also, the closed-loop system is robustly stable and the upper bound of guaranteed cost is decreased.

The robust H-infinity controller design is investigated in this paper for a class of nonlinear singular systems with state and transmission delays. The proposed controller is configured in multiple feedback loops. Based on the interval type-2 T-S fuzzy modelling and parallel distributed compensation techniques, a fuzzy multiple feedback-loop controller with memoryless state and delayed states is proposed to stabilize the system with required H-infinity performance by using the Lyapunov stability theory. The controller gain matrices are able to be calculated by solving the linear matrix inequalities. To check the feasibility of the proposed method, a numerical example is given and the simulation results indicate that the proposed multiple-feedback-loop controller is effective.

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 distributed speech recognition applications, variable frame rate (VFR) analysis is a technique that can reduce the channel bandwidth and computation resources. In this method, slowly changing frames that provide little information are abandoned. Rapidly changing frames, on the other hand, that are more related to speech perception are preserved. In this paper, we proposed an analysis-by-synthesis (AbS) frame dropping algorithm together with a novel VFR decoding method for hidden Markov modeling of speech. A recursive formula for the calculation of forward probability function of the VFR observations was derived and was used to form a time-varying hidden Markov model (tvHMM) with transition probabilities that are depended on the time difference between successive observations. A generalized Viterbi decoding algorithm was developed to decode the VFR observations. We also use an example to explain the decoding process for a particular VFR observation sequence. Experiments were conducted to investigate the effectiveness of the proposed AbS-tvHMM method. The experimental results show that our method can achieve essentially the same accuracy as full frame rate observations at frame rate of only 40 % and significantly reduces the computation time.

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.

This paper proposes a generalized maximum a posteriori spectral amplitude (GMAPA) algorithm to spectral restoration for speech enhancement. The proposed GMAPA algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMAPA adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMAPA uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, Aurora-4 and Aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMAPA algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), maximum likelihood spectral amplitude estimator (MLSA), and maximum a posteriori spectral amplitude estimator (MAPA). The experimental results first confirm that GMAPA provides better objective evaluation scores than MMSE, MLSA, and MAPA in lower SNR conditions, with comparable scores to MLSA in higher SNR conditions. Moreover, our recognition results indicate that GMAPA outperforms the three conventional algorithms consistently over different testing conditions.

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 frame rate of the observation sequence in distributed speech recognition applications may be reduced to suit a resource-limited front-end device. In order to use models trained using full-frame-rate data in the recognition of reduced frame-rate (RFR) data, we propose a method for adapting the transition probabilities of hidden Markov models (HMMs) to match the frame rate of the observation. Experiments on the recognition of clean and noisy connected digits are conducted to evaluate the proposed method. Experimental results show that the proposed method can effectively compensate for the frame-rate mismatch between the training and the test data. Using our adapted model to recognize the RFR speech data, one can significantly reduce the computation time and achieve the same level of accuracy as that of a method, which restores the frame rate using data interpolation.

Most of low bit-rate speech coders based on the speech production model use line spectrum frequencies (LSFs) to represent short-term spectra of speech signals. A vector predictor for the LSFs which consists of a group of grey predictors is investigated in this paper for the purpose of estimating the current LSFs accurately by using previous LSFs. We impose a new parameter called fractional step (FS) on the grey predictor which is determined by the steepest descent method in achieving the optimal prediction performance. Furthermore, the vector predictor can easily be applied to a vector predictive coder for spectral quantization. The experimental results show that the direct scalar quantization and partitioned vector quantization for the LSFs need, in total, 34 bits/frame and 27 bits/frame, respectively to achieve the spectral distortion limen (DL) of 1 dB. The proposed vector predictor with scalar quantization scheme can maintain the same spectral distortion at only 24 bits/frame.

Abstruct- In this correspondence, a two-stage approach based on Karhunen-Loeve transform and 2-D prediction is proposed for efficient quantization of line spectrum pair (LSP) parameters of speech. Besides, a switched classifier is incorporated with this approach to reduce the outlier frames (spectral distortion greater than 2 dB) down to about 0.27% and to eliminate frames with spectral distortion greater than 4 dB at an average bit-rate below 19 b/frame.

This correspondence proposes a new CELP coding method which embeds speech classification in adaptive codebook search. This approach can retain the synthesized speech quality at bit-rates below 4 kb/s. A pitch analyzer is designed to classify each frame by its periodicity, and with a finite-state machine, one of four states is determined. Then the adaptive codebook search scheme is switched according to the state. Simulation results show that higher SEGSNR and lower computation complexity can be achieved, and the pitch contour of the synthesized speech is smoother than that produced by conventional CELP coders.>

A novel spectral coding method, two-dimensional differential line spectra pair coding (2DdLSP), is proposed. Taking advantage of the strong inter-frame, and intra-frame correlation of LSP parameters, a two-dimensional linear prediction technique is used to reduce the variance of the parameters to be quantized. One scalar quantization and two vector quantization schemes are designed to quantize the 2-D prediction residuals. Without further buffering delay, the spectral distortion of 1 dB/sup 2/ can be achieved at 19 b/frame when the frame period is 10 ms. Both within- and out-of-training tests show the robustness of the method to speech data variance.>