Lih-Jen Kau

A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System

We propose in this paper a novel algorithm as well as architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a fall accident event is detected, the users position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

Least-Squares-Based Switching Structure for Lossless Image Coding

Many coding methods are more efficient with some images than others. In particular, run-length coding is very useful for coding areas of little changes. Adaptive predictive coding achieves high coding efficiency for fast changing areas like edges. In this paper, we propose a switching coding scheme that will combine the advantages of both run-length and adaptive linear predictive coding. For pixels in slowly varying areas, run-length coding is used; otherwise least-squares (LS)-adaptive predictive coding is used. Instead of performing LS adaptation in a pixel-by-pixel manner, we adapt the predictor coefficients only when an edge is detected so that the computational complexity can be significantly reduced. For this, we use a simple yet effective edge detector using only causal pixels. This way, the proposed system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation in advance to prevent the occurrence of a large prediction error. With the proposed switching structure, very good prediction results can be obtained in both slowly varying areas and pixels around boundaries. Furthermore, only causal pixels are used for estimating the coding pixels in the proposed encoder; no additional side information needs to be transmitted. Extensive experiments as well as comparisons to existing state-of-the-art predictors and coders will be given to demonstrate its usefulness.

Adaptive lossless image coding using least squares optimization with edge-look-ahead

In predictive image coding, the least squares (LS)-based adaptive predictor is noted as an efficient method to improve prediction result around edges. However pixel-by-pixel optimization of the predictor coefficients leads to a high coding complexity. To reduce computational complexity, we activate the LS optimization process only when the coding pixel is around an edge or when the prediction error is large. We propose a simple yet effective edge detector using only causal pixels. The system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation to prevent the occurrence of a large prediction error. Our experiments show that the proposed approach can achieve a noticeable reduction in complexity with only a minor degradation in the prediction results.

Lossless image coding using a switching predictor with run-length encodings

In this paper, we propose a switching adaptive predictor (FSWAP) with run-length encoding for lossless image coding. The proposed FSWAP system has two operation modes; run mode and regular mode. If the members in the texture context of the coding pixel have identical grey values, the run mode is used, otherwise the regular mode is used. The run mode, using run-length coding, with an arithmetic coder, is very useful for images with flat regions. The regular mode borrows the switching predictor structure in SWAP (Lih-Jen Kau et al, IEEE Trans. Fuzzy Systems) with some modifications. Experiments show that simplified context clustering is very useful in error modeling for prediction refinement. Furthermore, the execution time of FSWAP can be accelerated with minor degradation in the bit rates associated with the modifications. Comparisons of the proposed system to existing state-of-the-art predictive coders are given to demonstrate its coding efficiency

A cloud network-based power management technology for smart home systems

With the fast development of network infrastructure, connecting to the Internet at any time and any place has been made easy and possible. On the other hand, as our world is suffering energy crisis on oil and natural resources shortages, how to make efficient use of limited power energy has remained a major problem to be conquered so far. Aimed to facilitate the life of human being as well as to use the limited power energy more efficiently, we propose in this paper a technology that can perform remote control and monitoring of electrical appliances on the Internet. To do this, an intelligent power socket (IPS) module that is able to control and monitoring the power of electricity is realized in this research. The IPS modules are placed in conjunction with the electrical appliances that are to be controlled from a far-end place. In addition, an embedded system-based home gateway that can be connected with the Internet is set up in which the electrical appliances are located. Moreover, the acquired power consumption information or the status of the appliances is stored in a database server in the Cloud. With the proposed structure, authorized users or system managers can log into the web server which is connected with the database, monitoring the power status and take actions on the appliances remotely. The control command from the far-end place, i.e., from the web server on the Internet, is first sent to the home gateway and then transmitted to the IPS modules through the Zigbee wireless communication protocol so that the remote control of appliances can be achieved. The proposed architecture can be easily applied to any kind of room space. Moreover, only a browser is needed for the client to communicate with the web server, no other application program is required. As the browser is now available almost on every information technology products, e.g., a notebook or a smart phone, the proposed architecture has been shown to be very convenient and useful for remote control and monitoring of electrical appliances, and hence can facilitate the life of human beings.

A smart phone-based pocket fall accident detection system

A smart phone-based pocket fall accident detection system is proposed in this paper. To realize the system, the angles acquired by the electronic compass and the waveform sequence of the triaxial accelerometer on the smart phone are used as the input signals of the proposed system. The acquired signals are then used to generate an ordered feature sequence and examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current stage, it can proceed to next stage; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. With the proposed cascade classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall detection accuracy up to 96% on the sensitivity and 99.71% on the specificity can be obtained when a set of 400 test actions in eight different kinds of activities are estimated by using the proposed approach, which justifies the superiority of the proposed algorithm.

A Gradient Intensity-Adapted Algorithm With Adaptive Selection Strategy for the Fast Decision of H.264/AVC Intra-Prediction Modes

H.264/Advanced Video Coding (AVC) is well known for its superiority of finding an optimal tradeoff between the visual quality and the bit-rate expense. Nevertheless, the highly complex procedures of finding an optimal intra- or inter-prediction mode can degrade the run-time performance of the coding process. To speed up the run-time performance during the encoding of H.264/AVC intra-prediction mode, we apply in this paper a simple yet effective gradient evaluation approach so that the texture orientation inside the coding block can be evaluated efficiently. Moreover, we propose an adaptive selection strategy in this paper so that only a subset with a variable number of the intra-prediction modes will be sent for the rate-distortion optimization process. With the proposed gradient evaluation and adaptive selection strategy, a noticeable speedup on the run-time performance can be achieved with only a minor degradation on the visual quality and the bit-rate expense. When compared with the existing state-of-the-art fast decision algorithms, a significant improvement over prior arts on the proposed cost performance metric can be obtained, which demonstrates the superiority of the proposed approach.

A real-time portable sign language translation system

In this paper, a wireless hand gesture recognition glove is proposed for real-time translation of Taiwanese sign language. To discriminate between different hand gestures, we have flex and inertial sensors embedded into the glove so that the three most important parameters, i.e., the posture of fingers, orientation of the palm, and motion of the hand, defined in Taiwanese Sign Language can be recognized without ambiguity. The finger flexion postures acquired by flex sensors, the palm orientation acquired by G-sensor, and the motion trajectory acquired by gyroscope are used as the input signals of the proposed system. The input signals will be acquired and examined periodically to see if it is a legal sign language gesture or not. Once the sampled signal can last longer than a predefined clock cycles, it is regarded as a valid gesture and will be sent to cell phone via Bluetooth for gesture discrimination and speech translation. With the proposed architecture and algorithm, the accuracy for gesture recognition is quite satisfactory. As we can see in experiments that an accuracy rate up to 94% on sensitivity for gesture recognition can be achieved which justifies the superiority of the proposed architecture.

Speeding up the runtime performance for lossless image coding on GPUs with CUDA

With the highly increased capability on parallel processing, computing on graphics processing units (GPUs) have been widely used in applications more than just graphics data processing. In this paper, we apply the compute unified device architecture (CUDA), a parallel computing architecture on GPUs proposed by NVIDIA, for the runtime performance enhancement in a predictively encoded lossless image compression system. For this, a least squares (LS)-adapted predictor, an effective approach for the removal of redundancy around boundaries, is applied. The adaptation process of an LS-based predictor requires multiplications of matrices for the construction of normal equations, which has been known to be the major complexity in LS adaptation process. Fortunately, matrices multiplication is most suitable to be parallel processed, which leads to the idea of speeding up the construction of normal equations with GPUs. With the proposed approach, a noticeable improvement on the runtime performance can be achieved as can be seen in the experiments.

Adaptive predictor with dynamic fuzzy K-means clustering for lossless image coding

This paper proposed a nonlinear predictor ADFK (Adaptive predictor with Dynamic Fuzzy K-means clustering error feedback) for lossless image coding based on multi-layered perceptrons. Since real images are usually nonstationary, a fixed predictor is not adequate to handle the varying statistics of input images. Using back propagation learning with causal neighbors of the coding pixel as training patterns to update network weights continuously, ADFK is made adaptive on the fly. Furthermore, prediction error is further refined in ADFK by applying error compensation different to compound context error modeling used in CALIC based on dynamic codebook design with adaptive fuzzy k-means clustering algorithm. Compensated errors are then entropy encoded using conditional arithmetic coding based on error strength estimation. The proposed compensation mechanism is proved to be very useful through experiments by further improving the bit rates in an average amount of about 0.2bpp in test images. Success in the use of proposed predictor is demonstrated through the reduction in the entropy and actual bit rate of the differential error signal as compared to that of existing linear and nonlinear predictors.