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At present, mature technology of images fusion is generally implemented by software. But it has been proved to have a bad real-time capability. This paper designs a new system to resolve the problems of bad real time performance on traditional method. Based upon FPGA, the system processes the infrared and low-level-light images then output images data to the display module in real time. In order to meet requirement of accuracy and optimization, this system takes a registration method based on parallel optical axis. Also, it adopts the bionic algorithm for color fusion of infrared and low Level light images. The images resultant increased by an average of 3.86% and 8.77% than Waxman algorithm in terms of mutual information and structural similarity respectively. The exceptional quality images we can obtain shows that the system is useful in images processing. The processing and transferring time of one frame image is 0.043434s in total shows that the system performs better in real-time. In addition, this system is valuable for defense and security.

Night vision technology is becoming ever-more widely used in military and civil fields, and it will be more accurate for target detection and recognition through color fusion of infrared and low light level images. Since the classic Waxman fusion model-only simulates the rattlesnake's IR-depressed Visual Cell and the target in fusion image is not obvious, a novel fusion model is proposed in this paper. We enhance the edge information through the ON neural network for the infrared and low light-level images and then establish a mathematical model to process the rattlesnake's “enhanced cells”and “depressed cells”. Next, we input the ON-central receptive field for fusion and RGB spatial mapping, which can fully realize the union function of the “enhanced cells”and “depressed cells”. Finally, we conduct comparative experiments and image quality evaluation with the classical Waxman fusion model. The results show that image targets are more obvious obtained by our algorithm and increased by an average of 51.97%, 4.07%, and 7.62% than Waxman algorithm in terms of color, mutual information, and structural similarity, respectively. It turned out that our fusion images are richer in color than the Waxman fusion images, which contain more source image information, and more similar to the source image structure.

In sparse fast Fourier transform algorithm, noise will increase the difficulty in frequency location. As to this problem, probability of detected frequency are analyzed with respect to noise level and bucket in this paper. Firstly different mean and variance of compressed vector in frequency domain are derived under the hypothesis of whether there is a signal, then these statistical characteristics are used to analyze the impact of signal-to-noise ratio and number of point per bucket on detection probability of frequency. Finally, simulation curves is given under the conditions of different noise and bucket. Simulation shows that frequency of signal with additive white Gaussian noise could be effectively detected when SNR is higher than 10dB and number of point per bucket smaller than 212. And in order to ensure effective detection of frequency, when SNR decrease, number of point per bucket should be reduced. Through the analysis, this paper provides a theoretical support to enhance the reliability of the algorithm.

Accidental fall can cause physical injury, fracture and other health complication, especially for elderly people living alone. Aimed to provide timely assistance after the occurrence of falling down, a pre-fall alarm system was proposed. In order to test the reliability of pre-fall alarm system, eighteen subjects who worn this device on the waist were required to participate in a series of experiments. The acceleration and angular velocity time series extracted from human motion processes were used to described human motion features. HMM-based SVM classifier was used to determine the maximum separation boundary between fall and Activities of Daily Living (ADLs). The fall detection results showed 94.91% accuracy, 97.22% Sensitivity and 93.75% Specificity. The proposed device can accurately recognize fall event, achieve additional functions, and have advantages of small size and low power consumption. Based on the findings, this pre-impact fall alarm system with detection algorithm could potentially be useful for monitoring the state of physical function in elderly population.

A new method of edge detection based on adaptive oriented double opponent neurons is presented in this paper, considering that the homocentric opponent receptive field is lack of directionality, and the anisotropic of receptive field in ODOG model will be badly restrained during weighting process. To get relatively complete image edges, the edge directional operators are introduced to choose Difference of Gaussians (DOG) model or the orientations of Oriented Difference of Gaussians (ODOG) model automatically. Compared with DOG and ODOG methods, the methods detect weak edges effectively with better edge connectivity and edge confidence.

This paper presents a smartphone application which has realized action recognition and fall detection. The application identifies the holding pattern of smartphone by the data of light sensor, distance sensor and accelerometer sensor, which reduce the impact of recognition resulting from the smartphone's different positions. And then the application uses data collected from the acceleration sensor, the direction angle sensor and the gyro sensor to distinguish fall from daily actions. The results of human motion recognition are uploaded to the server. For the purpose of real time, the network stability of the application is improved by the method of multi-layer detection based on heartbeat packet. Experiments prove that the way of improving network stability can reduce the rate of losing packet. The accuracy of action recognition achieves more than 90%.

The methods for parameter estimation of the frequency hopping signal(FHS) in compressed domain find the numeral chara-teristics of the compressed sampling data based on the measurement matrix, having a drawback of high computational complexity and a problem of base mismatch. We propose a method for parameter estimation of the FHS based on numeral charateristics in compressed domain and atomic norm. We Firstly establish a block diagonalization measurement matrix to piecewise compress signals, analyse the numeral charateristics of compressed sampling data to roughly estimate hop timing, and then separate signal segments without frequency hopping for estimating hop frequency precisely using atomic norm minimization. On the basis of the implementation of hop frequency estimation, design the dictionary of atoms and the hop timing precise estimation is realized in compressed domain. Simulation results show that the proposed methods frequency estimation performance is better than that based on grid CS, when the SNR is higher than −2dB and the compression ratio is higher than 0.5. The proposed method can also estimate precisely hop timing of FHSs.

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

Accidental falls are the main factors that endanger the health of elderly people in modern society. Timely and effective fall detection and alarm can reduce the risk of falls. With the increasing of the aging society, the design and development of the fall detection system which is portable, accurate and real-time, has gradually become one of the most urgent needs of the community. With the rapid development of mobile Internet, smart phones as another kind of wearable device have become essential products of people's life. Smart phone is portable and flexible, which can be built in various sensors. It can be used to monitor human motion data. In order to detect falls promptly and minimize the damage to the aged, a fall detection system based on Android phones was designed and implemented and a fall detection algorithm for phones was proposed in the paper. In this way, the elderly can detect falls without other equipment at any time. Meanwhile, the system integrated the phone system function greatly, provided a good platform for human-computer interaction in the detection, and gave remote alarm to help the elderly get timely rescue.

Motion sensing game is a novel game and operated by body segmental movements. It is respected by the majority of players of all ages because of its excellent interactivity. At present, many companies adopt the method of video capture to implement the collection of movement information, but the cost is much higher. And it also can achieve the accurate control of the game through the sensing technology of inertial sensor. This paper introduces a kind of wearable motion sensing game device presented in the form of glasses. Using a 9-axis sensor module determines the glasses posture, sequentially realizing real-time detection of the head movement. The inertial data is packaged and sent to the according base-station by the BLE4.0 wireless technology. The base-station contains an interface circuit of USART to USB and can transmit inertial data to the PC through this interface. Compared with using the RF module (e. g nrf24l01), the Bluetooth 4.0 technology has the advantage of low power consumption, low cost, small volume and easy to connect to phone. So this communication technology is suitable for the use in the motion sensing game controller.

Fall is one of great threats affecting people with old age. Aimed at the falling issue of aged, the paper explored a pre-impact fall early warning and protection system. This system consists of an early fall alarm, protection airbags, a remote monitoring platform and a guardian;s cellphone app. The early fall alarm and airbags are integrated in a belt, convenient for wearing and hip-protection. The inner of early fall alarm has a MEMS sensor, which collects 3-axis accelerated velocity and 3-axis angular velocity. A fall detection algorithm is applied to recognize falls from activities of daily living (ADL). When there is a dangerous movement approaching fall, the early fall alarm will warn the aged to stop the movement. When the fall happens, the early fall alarm will trigger the airbag system, then the airbags in the belt will inflate as soon as possible to reduce the damage to the aged. In addition, the early fall alarm will ring and send message to the guardian's cellphone for help. Meanwhile, the kinematics of the human body during falling time will be stored in TF card and sent to remote monitoring platform for storage. Then the monitoring platform can show the fall location where the fall incident happens in the electronic map. In order to test the reliability of this early fall alarm and protection system, a series of experiments have been designed. The results show that this system can be relatively accurate to detect falls, accomplishing functions including early fall warning and alarming, airbag inflation, statics transferring and storage, realtime location, which has significant benefit for reducing the direct damage and shortening the aiding time.

Falls have caused extensive interest of the researchers for it becomes the second largest accidental injury to death in the world. And there are lots of approaches to fall detection at present. However, on account for the complexity of this problem, a preferable effective method for fall detection hasn’t been present so far. This paper adopts a relatively high-predicted and stable SVM classifier to predict falls. 10 healthy young subjects participated in this study based on the Xsens MVN Biomech system. With the extraction of feature vectors, as well as the exploration of the best position, it found that the waist would be the best to measure body’s motion, and the simple accelerometer can offer the preferable features for the classifier to determinate the falls well. Meanwhile it can get a high accuracy up to 96% by setting an optimal C and g with five-fold cross-validation testing.

This paper is concerned with the design of type systems for logic programming so as to satisfy the requirements of modern logic programming. The design of type systems is based on the language Godel which has a strongly type system based on many-sorted logic with parametric polymorphism. The definitions of the basic logic programming concepts of terms, atoms, programs are given in the setting of polymorphic many-sorted logic. In particular, an unification algorithm for typed predicates is proposed for the compiler construction of Godel.