Abstract Real estate is potentially contributing to the economic growth. It has a strong correlation between property owners and beneficiaries. The accurate forecast of future property prices is particularly important. Therefore, real estate prices are reflecting the economic level of counties, and their price ranges are of great interest to both buyers and sellers. Developing a land‐price forecast model could significantly assist in predicting future land prices and setting real estate regulations. In contrast, machine learning (ML) algorithms have demonstrated a great potential to perform predictions. Motivated by these assumptions, we develop in this article a set of ML algorithms to build models capable to increase the effectiveness of land price estimation. The ML algorithms adopted in this work include the decision tree, random forest (RF), and linear regression. We collected data from 5946 lands localized in the northern area of Riyadh, KSA. This data has been collected using GeoTech's DAAL website. The performance of the developed models has been assessed based on state‐of‐art performance metrics including mean absolute error, mean squared error, and median squared error. The experiments show that the RF based model outperforms the remaining models.

There is an increasing popularity in exploiting modern vehicles as mobile sensors to obtain important road information such as potholes, black ice and road profile. Availability of such information has been identified as a key enabler for next-generation vehicles with enhanced safety, efficiency, and comfort. However, existing road information discovery approaches have been predominately performed in a single-vehicle setting, which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations, this paper presents a novel cloud-assisted collaborative estimation framework that can utilize multiple heterogeneous vehicles to iteratively enhance estimation performance. Specifically, each vehicle combines its onboard measurements with a cloud-based Gaussian process (GP), crowdsourced from prior participating vehicles as “pseudo-measurements”, into a local estimator to refine the estimation. The resultant local onboard estimation is then sent back to the cloud to update the GP, where we utilize a noisy input GP (NIGP) method to explicitly handle uncertain GPS measurements. We employ the proposed framework to the application of collaborative road profile estimation. Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.

This paper presents a new scan-based at-speed test signal scheme called One Clock Alternated Shift (OCAS) for minimizing the potential impact of the power distribution network PDN impedance variation. The strategy behind this new scheme is to mimic the clock signal of the functional mode as closely as possible. As a case study, we consider the PDN impedance variation that can occur with 3-D ICs, more specifically when a top die under test is bounded over a stack of different sizes. HSpice simulation results show that OCAS is less sensitive to such impedance variation when compared to existing scan-based at-speed testing techniques, mainly, SeBoS, and BurstMode.