Spatial reasoning is defined as the ability to generate, retain, and manipulate abstract visual images. In chemistry, spatial reasoning skills are typically taught using 2-D paper-based models, 3-D handheld models, and computerized models. These models are designed to aid student learning by integrating information from the macroscopic, microscopic, and symbolic domains of chemistry. Research has shown that increased spatial reasoning abilities translate directly to improved content knowledge. The recent explosion in the popularity of smartphones and the development of augmented reality apps for them provide, a yet to be explored, way of teaching spatial reasoning skills to chemistry students. Augmented reality apps can use the camera on a smartphone to turn 2-D paper-based molecular models into 3-D models the user can manipulate. This paper will discuss the development, implementation, and assessment of an augmented reality app that transforms 2-D molecular representations into interactive 3-D structures. AbstractSpatial reasoning is defined as the ability to generate, retain, and manipulate abstract visual images. In chemistry, spatial reasoning skills are typically taught using 2-D paper-based models, 3-D handheld models, and computerized models. These models are designed to aid student learning by integrating information from the macroscopic, microscopic, and symbolic domains of chemistry. Research has shown that increased spatial reasoning abilities translate directly to improved content knowledge. The recent explosion in the popularity of smartphones and the development of augmented reality apps for them provide, a yet to be explored, way of teaching spatial reasoning skills to chemistry students. Augmented reality apps can use the camera on a smartphone to turn 2-D paper-based molecular models into 3-D models the user can manipulate. This paper will discuss the development, implementation, and assessment of an augmented reality app that transforms 2-D molecular representations into interactive 3-D structures.

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Abstract-With the advance of technologies such as the Internet, Wi-Fi Internet availability and mobile access, it is becoming harder than ever to safeguard intellectual property in a digital form. Digital watermarking is a steganographic technique that is used to protect creative content. Copyrighted work can be accessed from many different computing platforms; the same image can exist on a handheld personal digital assistant, as well as a laptop and desktop server computer. For those who want to pirate, it is simple to copy, modify and redistribute digital media. Because this impacts business profits adversely, this is a highly researched field in recent years. This paper examines a technique for digital watermarking which utilizes properties of the Discrete Wavelet Transform (DWT).The digital watermarking algorithm is explained. This algorithm uses a database of 40 images that are of different types. These images, including greyscale, black and white, and color, were chosen for their diverse characteristics. Eight families of wavelets, both orthogonal and biorthogonal, are compared for their effectiveness. Three distinct watermarks are tested. Since compressing an image is a common occurrence, the images are compacted to determine the significance of such an action. Different types of noise are also added. The PSNR for each image and each wavelet family is used to measure the efficacy of the algorithm. This objective measure is also used to determine the influence of the mother wavelet. The paper asks the question:"Is the wavelet family chosen to implement the algorithm of consequence?" In summary, the results support the concept that the simpler wavelet transforms, e.g. the Haar wavelet, consistently outperform the more complex ones when using a non-colored watermark.

This paper studies the edge-detecting characteristics of the 2-D discrete wavelet transform. Our problem is to automatically detect edges. Since a common claim about the wavelet transform is that it splits images into an approximation and details, which contain edges, we use it in our experiments. First, to determine its efficacy, the 2-D discrete wavelet transform is compared to other common edgedetection methods. Also, a number of combinatorial methods for the octaves are examined in the comparison. Due to this work, a novel boundary-tracing algorithm is developed, to follow edges around an object of interest.