Two-Step k-means Clustering Based Information Entropy for Detecting Environmental Barriers Using Wearable Sensor

Walking is the most basic means of transportation. Therefore, continuous management of the walking environment is very important. In particular, the identification of environmental barriers that can impede walkability is the first step in improving the pedestrian experience. Current practices for identifying environmental barriers (e.g., expert investigation and survey) are time-consuming and require additional human resources. Hence, we have developed a method to identify environmental barriers based on information entropy considering that every individual behaves differently in the presence of external stimuli. The behavioral data of the gait process were recorded for 64 participants using a wearable sensor. Additionally, the data were classified into seven gait types using two-step k-means clustering. It was observed that the classified gaits create a probability distribution for each location to calculate information entropy. The values of calculated information entropy showed a high correlation in the presence or absence of environmental barriers. The results obtained facilitated the continuous monitoring of environmental barriers generated in a walking environment.

A Quantitative Index Representing Crack Shapes and Corresponding Techniques

In order to quantificationally describe the soil cracks due to dry-wet cycles, the concept of gray level entropy is applied according to the physical significance of the information entropy to represent various shapes of cracks. Then a piece of simple and easy-to-use equipment for taking photos is used to monitor and record the crack propagation. A grayscale image and the corresponding gray level entropy are obtained automatically by a program. Test results showed that gray level entropy can quantificationally describe the shape of cracks reasonably well and evaluate the degree of crack development effectively.

Sharpness evaluation of microscopic detection image for micro parts

According to the characteristics of micro parts microscopic detection image, including the image texture is similar, the edge information is too little and the gray distribution Range is limited, based on the basic principles of algorithm, analyzes the traditional sharpness evaluation function. Aiming at the defect that the traditional sharpness evaluation function cannot have both high sensitivity and noise immunity, an algorithm based on local variance information entropy is proposed. The method uses the local variance to weight the self-information of each gray level, on the one hand, it makes up for the lack of spatial information of information entropy and avoids misjudgement of sharpness; on the other hand, it can increase the weights of clear region pixels when they participate in the calculation of information, while reducing the weights of background and noise region pixels, thereby improve the function sensitivity. The experimental results show that compared with the traditional sharpness evaluation function, the local variance information entropy function not only has high sensitivity, but also has better noise immunity and is suitable for actual auto-focusing systems.

IoT-IE: An Information-Entropy-Based Approach to Traffic Anomaly Detection in Internet of Things

Security issues related to the Internet of Things (IoTs) have attracted much attention in many fields in recent years. One important problem in IoT security is to recognize the type of IoT devices, according to which different strategies can be designed to enhance the security of IoT applications. However, existing IoT device recognition approaches rarely consider traffic attacks, which might change the pattern of traffic and consequently decrease the recognition accuracy of different IoT devices. In this work, we first validate by experiments that traffic attacks indeed decrease the recognition accuracy of existing IoT device recognition approaches; then, we propose an approach called IoT-IE that combines information entropy of different traffic features to detect traffic anomaly. We then enhance the robustness of IoT device recognition by detecting and ignoring the abnormal traffic detected by our approach. Experimental evaluations show that IoT-IE can effectively detect abnormal behaviors of IoT devices in the traffic under eight different types of attacks, achieving a high accuracy value of 0.977 and a low false positive rate of 0.011. It also achieves an accuracy of 0.969 in a multiclassification experiment with 7 different types of attacks.

A Vehicle Can Bus Anomaly Detection Method for Periodic Attacks Based on the Entropy Model

Because of the rapid development of automobile intelligence and networking, cyber attackers can invade the vehicle network via wired and wireless interfaces, such as physical interfaces, short-range wireless interfaces, and long-range wireless interfaces. Thus, interfering with regular driving will immediately jeopardises the drivers’ and passengers’ personal and property safety. To accomplish security protection for the vehicle CAN (Controller Area Network) bus, we propose an anomaly detection method by calculating the information entropy based on the number of interval messages during the sliding window. It detects periodic attacks on the vehicle CAN bus, such as replay attacks and flooding attacks. First, we calculate the number of interval messages according to the CAN bus baud rate, the number of bits of a single frame message, and the time required to calculate information entropy within the window. Second, we compute the window information entropy of regular packet interval packets and determine the normal threshold range by setting a threshold coefficient. Finally, we calculate the information entropy of the data to be measured, determine whether it is greater than or less than the threshold, and detect the anomaly. The experiment uses CANoe software to simulate the vehicle network. It uses the body frame CAN bus network of a brand automobile body bench as the regular network, simulates attack nodes to attack the regular network periodically, collects message data, and verifies the proposed detection method. The results show that the proposed detection method has lower false-negative and false-positive rates for attack scenarios such as replay attacks and flood attacks across different attack cycles.

Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm

In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management.

A Personalized Recommendation Algorithm Based on Weighted Information Entropy and Particle Swarm Optimization

Similarity calculation is the most important basic algorithm in collaborative filtering recommendation. It plays an important role in calculating the similarity between users (items), finding nearest neighbors, and predicting scores. However, the existing similarity calculation is affected by over reliance on item scores and data sparsity, resulting in low accuracy of recommendation results. This paper proposes a personalized recommendation algorithm based on information entropy and particle swarm optimization, which takes into account the similarity of users’ score and preference characteristics. It uses random particle swarm optimization to optimize their weights to obtain the comprehensive similarity value. Experimental results on public data sets show that the proposed method can effectively improve the accuracy of recommendation results on the premise of ensuring recommendation coverage.

Information Entropy, Fractional Revivals and Schrödinger Equation with Position-dependent Mass

Information entropy has played a key role in a wide range of disciplines, for instance, classical and quantum information processing, quantum computing, quantum dynamics and quantum metrology. Here, we develop an information theoretic formalism using Shannon entropy, to investigate the quantum dynamics of Hamiltonian systems with position-dependent mass. Such systems are of fundamental interest in many areas, for instance, condensed matter, mathematical physics and foundations of quantum mechanics. We explore the phenomenon of fractional revivals for the temporal evolution of wave-packet solutions of Schrödinger equation with position-dependent mass by studying, analytically and numerically, the time-development of Shannon information entropy in position and momentum spaces. It is shown by our numerical results that the effect of spatially varying mass on the fractional revivals can not be fully harnessed using conventional measures, for instance, autocorrelation function. However, based on our numerical analysis it is concluded that information entropy is not only more sensitive to identify the fractional revivals but it also better elucidates the effect of position-dependent mass on the structure of fractional revivals in the form of symmetry breaking.