This paper presents a new algorithm for detecting and characterizing potholes and bumps directly from noisy signals acquired using an Accelerometer. A wavelet transformation based filter was used to decompose the signals into multiple scales. These coefficients were correlated across adjacent scales and filtered using a spatial filter. Road anomalies were then detected based on a fixed threshold system, while characterization was achieved using unique features extracted from the filtered wavelet coefficients. Our analyses show that the proposed algorithm detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates.

Modern shopping centres are undoubtedly a beehive of intense shopping activities. However, customers are often plagued by salient challenges, which may include fatigue derived from pushing trolleys around the mall and prolonged sorting of bills by the cashier. These shopping challenges could be daunting for the elderly, disabled, pregnant and nursing mothers. In this paper, we addressed these shopping challenges by developing an autonomous shopping cart with the following characteristics; (1) it follows the customer’s movement relieving the need to push a cart, (2) it bills automatically all stock placed in the cart, (3) it prompts the customer to make payment and updates each stock via a local database. Our design adopts a Raspberry Pi, a camera and a few direct current motors programmed to achieve autonomy. We used an open-source cross platform software called XAMPP to create the database and used RFID tags to bill the items placed in the cart automatically. The system updates payments and communicates these transactions to a local database via nRF24 wireless transceivers. The experimental tests conducted demonstrate that our system successfully followed customers accurately within the mall. We consider our design a major contribution to the vision of automated shopping systems for the near future.

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods.

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