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The 57–71-GHz millimeter-wave (mmWave) industrial, scientific, and medical (ISM) band holds significant potential for enhancing the performance of next-generation industrial wireless applications. This article first presents the design and analysis of a compact and high-performance eight-element series-fed frequency beam-scanning array designed to cover the entire 21.87% fractional bandwidth (FBW) of the 57–71-GHz ISM band. Using this as a subarray, a hybrid parallel–series feed topology is designed to construct a 64-element ( $8\times8$ ) planar array with high-gain directional beams. The planar array provides a peak measured gain of 20.12 dBi at 64 GHz and maintains a flat gain of over 19.23 dBi throughout the band, with a 1-dB gain bandwidth of 13 GHz. Its narrow directional beams provide an average half-power beamwidth (HPBW) of 9.7° and 11.78° in the elevation and azimuth planes, respectively, facilitating point-to-point mmWave connectivity and high-resolution beam scanning. The inherent phase variation of the series-fed topology is employed to produce a beam-scanning range of 40° within the 57–71-GHz band, with a scan loss of less than 1 dB. The proposed array is a low-cost and reproducible solution for seamless integration with V-band mmWave equipment, as elucidated through practical demonstration frameworks using the mmWave power sensor and EK1HMC6350 evaluation board. The proposed array is well-suited for emerging industrial wireless sensing and imaging applications, and mmWave frequency scanning radars. Its versatility extends to various 60-GHz protocols, such as IEEE 802.11ay, IEEE 802.11ad, IEEE 802.15.3c/d, and WirelessHD, and other customized industrial protocols such as WirelessHP.

The detection of head movement plays a crucial role in human–computer interaction systems. These systems depend on control signals to operate a range of assistive and augmented technologies, including wheelchairs for Quadriplegics, as well as virtual/augmented reality and assistive driving. Driver drowsiness detection and alert systems aided by head movement detection can prevent major accidents and save lives. Wearable devices, such as MagTrack consist of magnetic tags and magnetic eyeglasses clips and are intrusive. Vision-based systems suffer from ambient lighting, line of sight, and privacy issues. Contactless sensing has become an essential part of next-generation sensing and detection technologies. Wi-Fi and radar provide contactless sensing, however, in assistive driving they need to be inside enclosures or dashboards, which for all practical purposes in this article have been considered as through walls. In this study, we propose a contactless system to detect human head movement with and without walls. We used ultra-wideband (UWB) radar and Wi-Fi signals, leveraging machine and deep learning (DL) techniques. Our study analyzes the six common head gestures: right, left, up, and down movements. Time-frequency multiresolution analysis based on wavelet scalograms is used to obtain features from channel state information values, along with spectrograms from radar signals for head movement detection. Feature fusion of both radar and Wi-Fi signals is performed with state-of-the-art DL models. A high classification accuracy of 83.33% and 91.8% is achieved overall with the fusion of VGG16 and InceptionV3 model features trained on radar and Wi-Fi time–frequency maps with and without the walls, respectively.

The distributed consensus intends to improve the reliability of critical decision making in wireless connected autonomous systems. The performance of distributed consensus heavily depends on the reliability of wireless links, which should be stochastic with limited communication resources. Therefore, advanced communication resource allocation schemes are needed to achieve high reliability and low latency for the distributed consensus. This article first derives optimized resource allocation schemes for the distributed consensus. The optimal number of nodes for the best reliability performance of the distributed consensus is also investigated to solve the inadequate overall communication resources issue. The revealed derivation and simulation results can provide guidelines to deploy the appropriate paradigm of communication resource allocation in autonomous wireless systems.

This article presents a bio-inspired circularly polarized ultrahigh-frequency (UHF) radio frequency identification (RFID) tag antenna for metallic and low-permittivity substances. This tag design is based on a leaf-shaped radiator, two shorting stubs, and slots etched on F4B substrate. Initially, the tag antenna is designed using a characteristics mode analysis (CMA) by analyzing the first six characteristic modes (CM) and characteristic angles. The width of orthogonal slots is varied to get the resonance of CM modes in the required US RFID band. Moreover, the edges are blended to get orthogonal current distribution, which is necessary for circular polarization. In addition, the proposed tag design is optimized further using CST Microwave studio and an RFID chip is exploited as a capacitive coupling element (CCE) to run CM modes with the orthogonal current pattern. This tag can also be tunable to European RFID (EU) band (866–868 MHz) by changing the length of shorter diagonal slot. The tag design offers a read range of 7–4.5 m on $100\times100$ mm2 metals plate and low-permittivity substrates (for the 902–928-MHz band). In the EU band, the corresponding read ranges are 5.7 and 3.5 m above metal and low-permittivity objects, respectively. This circularly polarized tag antenna is advantageous in terms of cost, circular polarization feature, and ease of fabrication due to the absence of vias, shorting pins, and matching circuits. Therefore, this tag design is suitable for labeling various low-permittivity objects, industrial conveyer belt applications, baggage handling systems, and Internet of Things (IoT) applications.