Sensing Systems in Construction and the Built Environment: Review, Prospective, and Challenges.

This article is a comprehensive review of state-of-the-art sensors of the built environment, applicable in construction, structural engineering, management, and planning industries. This review is framed within the technical definition of sensing systems and their components. Existing sensors are listed and described in two broad categories of structural health monitoring (SHM) and building environment monitoring (BEM). The SHM systems are used for monitoring the long-term performance of structures, such as bridges and buildings. BEM systems are employed to ensure the safety and comfort of the built environment's occupants, as well as the general monitoring of the environment for any required maintenance. The applications and implementation challenges of both systems are discussed, with emphasis on common sensing system limitations such as energy supply, packaging, network layout, and performance validation. Finally, the prospect of sensing systems as part of a digital twin that incorporates multifunctional sophisticated monitoring systems and intelligent analysis methods is discussed.

Mass detection in automated 3-D breast ultrasound using a patch Bi-ConvLSTM network.

Breast cancer mortality can be significantly reduced by early detection of its symptoms. The 3-D Automated Breast Ultrasound (ABUS) has been widely used for breast screening due to its high sensitivity and reproducibility. The large number of ABUS slices, and high variation in size and shape of the masses, make the manual evaluation a challenging and time-consuming process. To assist the radiologists, we propose a convolutional BiLSTM network to classify the slices based on the presence of a mass. Because of its patch-based architecture, this model produces the approximate location of masses as a heat map. The prepared dataset consists of 60 volumes belonging to 43 patients. The precision, recall, accuracy, F1-score, and AUC of the proposed model for slice classification were 84%, 84%, 93%, 84%, and 97%, respectively. Based on the FROC analysis, the proposed detector obtained a sensitivity of 82% with two false positives per volume.

A Multitasking Electrical Impedance Tomography System Using Titanium Alloy Electrode.

This paper presents a multitasking electrical impedance tomography (EIT) system designed to improve the flexibility and durability of an existing EIT system. The ability of the present EIT system to detect, locate, and reshape objects was evaluated by four different experiments. The results of the study show that the system can detect and locate an object with a diameter as small as 1.5 mm in a testing tank with a diameter of 134 mm. Moreover, the results demonstrate the ability of the current system to reconstruct an image of several dielectric object shapes. Based on the results of the experiments, the programmable EIT system can adapt the EIT system for different applications without the need to implement a new EIT system, which may help to save time and cost. The setup for all the experiments consisted of a testing tank with an attached 16-electrode array made of titanium alloy grade 2. The titanium alloy electrode was used to enhance EIT system's durability and lifespan.