This paper attempts to uncover one possible method for the IMR (indoor mobile robot) to perform indoor exploration associated with SLAM (simultaneous localization and mapping) using LiDAR. Specifically, the IMR is required to construct a map when it has landed on an unexplored floor of a building. We had implemented the e-SLAM (exploration-based SLAM) using the coordinate transformation and the navigation prediction techniques to achieve that purpose in the engineering school building which consists of many 100-m2 labs, corridors, elevator waiting space and the lobby. We first derive the LiDAR mesh for the orthogonal walls and filter out the static furniture and dynamic humans in the same space as the IMR. Then, we define the LiDAR pose frame including the translation and rotation from the orthogonal walls. According to the MSC (most significant corner) obtained from the intersection of the orthogonal walls, we calculate the displacement of the IMR. The orientation of the IMR is calculated from the alignment of orthogonal walls in the consecutive LiDAR pose frames, which is also assisted by the LQE (linear quadratic estimation) method. All the computation can be done in a single processor machine in real-time. The e-SLAM technique leads to a potential for the in-house service robot to start operation without having pre-scan LiDAR maps, which can save the installation time of the service robot. In this study, we use only the LiDAR and compared our result with the IMU to verify the consistency between the two navigation sensors in the experiments. The scenario of the experiment consists of rooms, corridors, elevators, and the lobby, which is common to most office buildings.

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This paper attempts to develop a diagnosis method for classifying the machining error sources on the external/internal contour surfaces which are fabricated using a two-axis cylindrical grinding machine. A derivation of the contour profile to grinding wheel motion trajectory and also the grinding wheel motion trajectory to the contour profile equations has been performed. Due to the contours having a sinusoidal polynomial curve instead of a rose curve, the grinding wheel motion trajectory equation is then used to generate the NC code for the machining. The grinding wheel motion trajectory equation is needed for the synthesis of the profile error caused by the motion error as well as the variation of the grinding wheel diameter. The two-axis motion equation is obtained by transforming the non-circular contour equation through the coordinates of the grinding wheel and workpiece. The influence of different machining parameters such as grinding wheel outer diameter error, grinding wheel feed rate, workpiece rotation speed on the external/internal contour profile is being studied and analyzed. The diagnosis method is based on the least square method to assemble the components of the profile error due to each individual error sources. The actual machining with probe system is used to verify the diagnosis result.

Pancreatic cancer is one of the deadliest cancers, with less than 9% survival rates. Pancreatic Ductal Adeno Carcinoma (PDAC) is common with the general public affecting most people older than 45. Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages (advanced stage). One of the earlier symptoms of PDAC is Jaundice. Patients with diabetes, obesity, and alcohol consumption are also at higher risk of having pancreatic cancer. A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge. Features such as Mean Hue, Mean Saturation, Mean Value, and Mean Standard Deviation are computed after color space conversion from RGB to HSV. Fuzzy k-Nearest Neighbor (F-kNN) is designed for classification. The system proposed is trained and tested using features extracted from jaundiced eye images. The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible, helping clinicians make better decisions for surgical planning.