Space modulation techniques (SMTs) have emerged as promising candidates for spectral-and energy-efficient wireless communication systems since they strike a good balance among error performance, power efficiency, spectrum efficiency, and receiver complexity. In SMTs, the information is not only conveyed by the habitual M-ary signal constellations; rather, it is also conveyed by the indices of the transmit antennas. As such, the indices of the transmit antennas are harnessed in such a manner that they enhance the transmission efficiency compared with the other multiple-input multiple-output opponents. Despite their exceptional advantages, SMTs suffer from a major drawback, which lies in the logarithmic proportion between their achievable data rates and the number of transmit antennas. In this regard, the fully generalized spatial modulation (F-GSM) and the fully quadrature spatial modulation (F-QSM) are proposed in this paper in order to vanquish this controversial drawback. In F-GSM and F-QSM, the transmit antennas used for data transmission are varied from the state in which only one transmit antenna is activated to the state in which multiple/all transmit antennas are activated. Therefore, a linear relationship between the achievable data rates and the number of transmit antennas is acquired. Moreover, a novel mathematical framework for assessing the average bit error rate performance of different SMTs is delineated. The driven mathematical framework is considered as the first major attempt to generalize the analytical analysis of different SMTs. In addition, the receiver's computational complexity of the proposed schemes is obtained and analyzed in terms of the computational complexity of different SMTs. The simulation results substantiate the validity of the analytical analysis conducted throughout the paper, as they are very akin to the obtained analytical formulas.

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A novel transmission scheme called fully generalised spatial modulation (FGSM) is proposed for underwater communication, where any subset of available transmitting antennas (N t) is activated at a time instant to transmit the data constellation symbol and the index of the active antenna is also harnessed to carry information. The FGSM offers better energy efficiency (EE) than previous spatial modulation (SM) and generalised SM systems. The proposed FGSM system is tested in an acoustic underwater multipath channel. Simulation results show that it can significantly improve the average bit error rate (ABER) as well as EE.

Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of specialists in such areas. Automating the initial screening process using artificial intelligence (AI) to detect pre-cancerous lesions can prove to be an effective and inexpensive technique that would allow patients to be triaged accordingly to receive appropriate clinical management. In this study, we have applied and evaluated the efficacy of six deep convolutional neural network (DCNN) models using transfer learning, for identifying pre-cancerous tongue lesions directly using a small dataset of clinically annotated photographic images to diagnose early signs of OCC. DCNN models were able to differentiate between benign and pre-cancerous tongue lesions and were also able to distinguish between five types of tongue lesions, i.e. hairy tongue, fissured tongue, geographic tongue, strawberry tongue and oral hairy leukoplakia with high classification performances. Preliminary results using an (AI + Physician) ensemble model demonstrate that an automated pre-screening process of oral tongue lesions using DCNNs can achieve ‘near-human’ level classification performance for diagnosing early signs of OCC in patients.