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.

In this paper we present a working model of an automatic pill reminder and dispenser setup that can alleviate irregularities in taking prescribed dosage of medicines at the right time dictated by the medical practitioner and switch from approaches predominantly dependent on human memory to automation with negligible supervision, thus relieving persons from error-prone tasks of giving wrong medicine at the wrong time in the wrong amount.

Indoor mobile robot (IMR) motion control for e-SLAM techniques with limited sensors, i.e., only LiDAR, is proposed in this research. The path was initially generated from simple floor plans constructed by the IMR exploration. The path planning starts from the vertices which can be traveled through, proceeds to the velocity planning on both cornering and linear motion, and reaches the interpolated discrete points joining the vertices. The IMR recognizes its location and environment gradually from the LiDAR data. The study imposes the upper rings of the LiDAR image to perform localization while the lower rings are for obstacle detection. The IMR must travel through a series of featured vertices and perform the path planning further generating an integrated LiDAR image. A considerable challenge is that the LiDAR data are the only source to be compared with the path planned according to the floor map. Certain changes still need to be adapted into, for example, the distance precision with relevance to the floor map and the IMR deviation in order to avoid obstacles on the path. The LiDAR setting and IMR speed regulation account for a critical issue. The study contributed to integrating a step-by-step procedure of implementing path planning and motion control using solely the LiDAR data along with the integration of various pieces of software. The control strategy is thus improved while experimenting with various proportional control gains for position, orientation, and velocity of the LiDAR in the IMR.

Mason’s gain formula can grow factorially because of growth in the enumeration of paths in a directed graph. Each of the (n − 2)! permutation of the intermediate vertices includes a path between input and output nodes. This paper presents a novel method for analyzing the loop gain of a signal flow graph based on the transform matrix approach. This approach only requires matrix determinant operations to determine the transfer function with complexity O(n³) in the worst case, therefore rendering it more efficient than Mason’s gain formula. We derive the transfer function of the signal flow graph to the ratio of different cofactor matrices of the augmented matrix. By using the cofactor expansion, we then obtain a correspondence between the topological operation of deleting a vertex from a signal flow graph and the algebraic operation of eliminating a variable from the set of equations. A set of loops sharing the same backward edges, referred to as a loop group, is used to simplify the loop enumeration. Two examples of feedback networks demonstrate the intuitive approach to obtain the transfer function for both numerical and computer-aided symbolic analysis, which yields the same results as Mason’s gain formula. The transfer matrix offers an excellent physical insight, because it enables visualization of the signal flow.

Sampling moiré effect is well-known in signal processing. When a continuous periodic signal $x(t)$ is sampled using a sampling rate $f_{s}$ that does not respect the Nyquist condition, and the signal-frequency $f_{x}$ folds over and gives a special sequence in the sampled signal. The coprime reconstruction is made possible when the periodic signal frequency $f_{x}$ and the sampling frequency $f_{s}$ satisfy $f_{s}/f_{x}=N/M$ with integer $M$, $N$, where $N/M$ is a reduced fraction. This article shows how to rearrange the N sample data into one entire period of the signal using the M periods of the signal. The reconstruction is equivalent to the decryption of the sample data using the method referred to the mathematic operator “multiplicative inverse modulo.” In the implementation to verify the coprime reconstruction, we used a commodity DSP board with 4.2 MHz sampling rate to reconstruct the 13.56 MHz signal acquired from the ADC circuit. The signal reconstruction result is comparable to the sampling result for the same signal sampled at 475 MHz sampling rate. The other simulation on the 234 kHz signal under 1 MHz sampling rate yields a very detail information on the signal with a finding that the multiplicative inverse modulo may also be used as the encryption of signal for network security use.

This paper attempts to introduce a design architecture for the contour mill-tool grinding. Due to the unexpected three-dimensional interference known as the undercut/overcut between the grind wheel and the contour mill-tool, the corresponding design needs to go through a complicated iterative process in order to obtain the path generation parameters to compromise the flute constraints at different cross-sections. Neural networks (NNK) in recent years have proven to be of great significance in decrypting nonlinear relationships, use of which greatly reduces the computation time from hours to minutes for the tool designer. The key to success is either using a database or the NNK result to reduce the computational burden in the conventional numerical iterations. The aim of this work is to develop an automated NNK search using reinforced adaptation to learn the domain where the undercut/overcut problem is needed to be compromised. Furthermore, a new neural architecture algorithm guarantees the model convergence and result precision. The grind wheel position and orientation are then converted into NC codes for a specific five-axis grinding machine to perform either the cutting simulation or manufacturing. The experiment on machining and design comparisons to the state-of-art software have been included in this paper.