The effects of directive self‐explanation prompts to support active processing of multiple representations in a simulation‐based learning environment

Processing of multiple representations in multimedia learning environments is considered to help learners obtain a more complete overview of the domain and gain deeper knowledge. This is based on the idea that relating and translating different representations leads to reflection beyond the boundaries and details of the separate representations. To achieve this, the design of a learning environment should support learners in adequately processing multiple representations. In this study, we compared a scientific inquiry learning environment providing instructional support with directive self‐explanation prompts to relate and translate between representations with a scientific inquiry learning environment providing instructional support with general self‐explanation prompts. Learners who received the directive prompts outperformed the learners who received general prompts on test items assessing domain knowledge. These positive results did not stretch to transfer items and items measuring learners' capabilities to relate and translate representations in general. The results suggest that learner support should promote the active relation of representations and translation between them to foster domain knowledge, and that other forms of support (e.g. extended training) might be necessary to make learners more expert processors of multiple representations.     

Application of the computer‐aided analysis for modeling the behavior of a TCXO

Quartz crystal oscillators for many radio frequency (RF) applications must provide a high‐frequency stability in a wide temperature range. To do this, a temperature compensation network is commonly included. This study analyzes and determines the frequency behavior of an oscillator circuit with variations in temperature and shows how simulation may be employed for the design of high‐stability quartz crystal oscillators. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 22–32, 2001.     

Neuro‐filters used to enhance the image quality of video displays

Abstract— Digital images can be affected by external factors. There are many types of noise which affect digital images. Image filtration is a basic method used to suppress such hindrances. The disadvantage of most filtration methods and hardware filters created on their behalf is their inability to react to changes in the input signal. The structure of the filters used for image processing is similar to the structure of a bi‐dimensional neural‐network matrix. Investigations have shown that a system with serial‐parallel filters of any degree of complexity can be created on the basis of the neural‐network matrices. Each neural‐network matrix layer acts as a separate neuro‐filter which can be trained and adapted to changes in the characteristics of the images. The neural‐network matrices allow for the creation of various types of linear and nonlinear filters, as well as combinations on the basis of a uniform structure. It allows for the design of a universal hardware neuro‐filter structure that can perform as different types of filters by means of loading the connectors weight. In our paper, we consider the realization of neuro‐filters based on a neural‐network matrix, which allows the processing of both static and moving images and increases the image sharpness, suppresses the noise, and detects movable objects in the processed image.     

Application of Fuzzy Logic to Vehicle Classification Algorithm in Loop/Piezo‐Sensor Fusion Systems

Individual vehicle information, especially, vehicle classification data play a key role in Advanced Traffic Management and Information Systems (ATMIS). In inductive loop and piezo‐sensor fusion systems, traffic data such as the vehicle length and the distance between axles are used for vehicle classification. However, classification errors often occur in distinguishing passenger cars from small trucks and in distinguishing medium‐sized trucks from small trucks. It is mainly attributed to the fact that they are similar in lengths and have similar inter‐axle distances. To improve the performance in vehicle classification, we develop a new algorithm using a fuzzy logic. Vehicle weight and speed are used as the inputs to the fuzzy logic block. The output of the fuzzy logic block is a weighting factor to modify the calculated vehicle length. Experimental results show that the developed algorithm significantly improves the classification performance.     

The role of self‐explanation in learning to use a spreadsheet through examples

Abstract This papers describes an exploratory study into the early phase of getting to know end‐user software during which users make use of a variety of information resources, including the user interface/program itself, manuals, on‐line help, examples provided in the manuals and other sources. In particular, how do novices make use of the worked‐out examples often provided in manuals and during training? Building on earlier research on the self‐explanation effect, thinking aloud data from 10 participants were analysed to see how examples were studied and how they were used during problem solving. Important effects of self‐explaining comparable to findings in other domains were found in this study. For instance, those participants who self‐explain with the goal to discover meaning prove to be better problem solvers than those who do not self‐explain or who focus more on syntactical aspects of examples.