# Recent Trends in AI-Based Intelligent Sensing
## 4 Practical Applications of Intelligent Sensing
### Applications of Intelligent Sensing
In this project, an AI-powered application uses a smartphone to capture the image of a cash note. After recognizing the value of that particular note, an audio sequence signifies the value of the note. In order to work on realistic images taken from a smartphone, a transfer learning-based pre-trained model on ImageNet of VGG-16 is used for training deep neural networks and for verifying the approach. Recently, ML algorithms have improved the intelligibility of speech in both hearing-impaired and normal-hearing listeners. In [161], speech separation is considered as a binary classification problem in which each true or false unit needs to classify noise dominant as 0 and speech dominant as 1. In speech recognition for normal-hearing persons, Gaussian mixture models have been used. Figure 9 shows how AI, ML, and DL techniques are used for the visually impaired by taking gestures as input and converting text to speech using algorithms. These technologies improve the way of communication between ordinary people and visually impaired people. 6. Smart Communication Networks--As a recent trend in communication technologies, it is observed through 4G, 5G, and ongoing research in 6G networks, that ubiquitous sensor network is going to be a feature of intelligence sensing, which means that information of sensor nodes could be easily retrieved remotely and processed. This also requires adopting new techniques related to nondestructive data transfer mechanism, fast and lightweight computational nodes for signal and communication requirements, multichannel modulation schemes, and opportunistic channel sensing schemes. To control, transfer, and supervision of sensor information, Intelligent sensing can be observed in Internet of Things, Industry 4.0, etc. [162] presents the behavior analysis using the Latent Dirichlet Allocation (LDA), the Non-negative Matrix Factorization (NMF) and the Probabilistic Latent Semantic Analysis (PLSA) for a comparative study using three different datasets from ubiquitous sensors. LDA, NMF, and PLSA have all been successfully used in text analysis tasks such as document clustering and are closely related to each other. In particular, [163] has formally shown the equivalence between NLM and PLSA. The PLSA, also known as the probabilistic latent semantic indexing (PLSI), is a statistical approach used to analyze two-mode and co-occurrence data. Further, PLSA can be treated as LDA with a uniform Dirichlet prior distribution. The semi-supervised learning approach was implemented in [164] for gait recognition for person identification using ubiquitous sensor data. Sparse labels and low modality factors were analyzed in [165]. For intelligent sensing in communication, the steps illustrated in Figure 8 can be used. The initial steps include basic signal processing with sensing, filtering, amplification, sampling, quantization, data acquisition, and conversion. After that, information processing and digital communication procedures need to be adopted, where edge computing plays an important role. The edge computing system includes a low-power compute unit specifically tailored to the requirements. In such scenarios, it is important to notice that hardware restrictions exist. Because of this, the algorithm development should take all such limitations into account. The gateway is an important medium to transfer data from local nodes to the main computational platform, i.e., the server node. Communication protocols such as 4G, 5G, UWB, WiMax, etc. can be implemented as per the design requirements. Figure 4: Intelligent Sensing in Intruder Detection and Surveillance. Figure 3: Intelligent Sensing in Agriculture. Figure 5: Intelligent Sensing in Traffic Management. Figure 6: Intelligent Sensing in Healthcare Management. Figure 8: Intelligent Sensing in Communication Networks. Figure 7: Intelligent Sensing in Pandemic Monitoring.