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Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning is to create models that can process and link information using various modalities. Despite the extensive development made for unimodal learning, it still cannot cover all the aspects of human learning. Multimodal learning helps to understand and analyze better when various senses are engaged in the processing of information. This paper focuses on multiple types of modalities, i.e., image, video, text, audio, body gestures, facial expressions, and physiological signals. Detailed analysis of past and current baseline approaches and an in-depth study of recent advancements in multimodal deep learning applications has been provided. A fine-grained taxonomy of various multimodal deep learning applications is proposed, elaborating on different applications in more depth. Architectures and datasets used in these applications are also discussed, along with their evaluation metrics. Last, main issues are highlighted separately for each domain along with their possible future research directions.CCS Concepts: • Computing methodologies → Machine learning; • Information systems → Multimedia and multimodal retrieval.

Industry 4.0 is a new paradigm of digitalization and automation that demands high data rates and real-time ultra-reliable agile communication. Industrial communication at sub-6 GHz industrial, scientific, and medical (ISM) bands has some serious impediments, such as interference, spectral congestion, and limited bandwidth. These limitations hinder the high throughput and reliability requirements of modern industrial applications and mission-critical scenarios. In this paper, we critically assess the potential of the 60 GHz millimeter-wave (mmWave) ISM band as an enabler for ultra-reliable low-latency communication (URLLC) in smart manufacturing, smart factories, and mission-critical operations in Industry 4.0 and beyond. A holistic overview of 60 GHz wireless standards and key performance indicators are discussed. Then the review of 60 GHz smart antenna systems facilitating agile communication for Industry 4.0 and beyond is presented. We envisage that the use of 60 GHz communication and smart antenna systems are crucial for modern industrial communication so that URLLC in Industry 4.0 and beyond could soar to its full potential.

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This study presents a low-cost, compact, flexible, and wideband wearable antenna for different wireless body area network (WBAN) band applications, covering medical body area network band of 2.4 GHz, industrial, scientific, and medical band of 2.45 GHz, WiMAX band of 3.5 GHz, and wireless local area network band of 5.2 GHz. The final antenna topology is obtained by hexagonal microstrip radiator patch fabricated on commercially available low-cost photo paper substrate and defected ground plane below the feed line acting as a partial ground to achieve a conformable structure wideband operation. The overall size of the fabricated antenna is 30 � 40 mm 2 and yields a widebandwidth of 3 GHz (2.30-5.30 GHz), radiation efficiency of 84.35%, and the highest gain of 3.48 dBi at the centre frequency of 5.2 GHz, and minimum gain of 1.91 dBi at 2.45 GHz. Furthermore, our detailed numerical and experimental investigations involving specific absorption rate performance assessment and bending analysis revealed the proposed design's excellent robustness to both human body loading and structural deformation scenarios. Therefore, simulated and measured results strongly advocate that the proposed design has profound implications for flexible and body-worn devices in WBAN applications.