Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

In this paper, a novel interference free dual-hop device-to-device (D2D) aided cooperative relaying strategy (CRS) based on spatial modulation (SM) (termed D2D-CRS-SM) is proposed. In D2D-CRS-SM, two cellular users (e.g., a near user (NU) and a relay-aided far user (FU)) and a pair of D2D transmitter (D${}_1$)-receivers (D${}_2$) are served in two time-slots. Two different scenarios are investigated considering information reception criteria at the NU. Irrespective of the scenarios, the base station (BS) exploits SM to map information bits into two sets: modulation bits and antenna index, in phase-1. In the first scenario, the BS maps FU information as the modulation bits and NU information as antenna index, whereas modulation bits correspond to NU information and the antenna index carries FU’s information in scenario-2. The iterative-maximum ratio combining (i-MRC) technique is then used by NU and D${}_1$ to de-map their desired information bits. During phase-2, D${}_1$ also exploits SM to forward FU’s information received from BS and its own information bits to the D2D receiver D${}_2$. Then, FU and D${}_2$ retrieve their desired information by using i-MRC. Due to exploiting SM in both phases, interference free information reception is possible at each receiving node without allocating any fixed transmit power. The performance of D2D-CRS-SM is studied in terms of bit-error rate and spectral efficiency considering M-ary phase shift keying and quadrature amplitude modulation. Finally, the efficiency of D2D-CRS-SM is demonstrated via the Monte Carlo simulation.

Bridges are expensive and important constituents in the transportation network and any failure associated with it results in a huge loss of money, time and sometimes, lives. Bridges are important structures considering its use and purpose-serving. It may fail due to aging, loading conditions and natural disasters. Every bridge construction specifies a limit of weight of the vehicles that can ride it. However, still there is a very large record of bridge failures due to overloading. The proposed system is a wireless sensor network which monitors the weight of the vehicles on the bridge and provides immediate protection when the failure of the structure is nearly estimated due to excessive load. This design uses embedded sensors (piezoelectric sensors and accelerometers) over the bridge deck and its girder for the continuous governance of the bridge conditions. Data collected from the sensors are sent to a database server connected to the internet (forming an advanced wireless technology called Internet of things (IoT)) to activate traffic control when the limit crosses in order to eliminate high losses which would’ve occurred if the bridge have collapsed.

We propose an interference free half-duplex cooperative relaying scheme with the aid of device-to-device (D2D) communication by employing spatial modulation (SM), where a D2D user helps the base station (BS) by forwarding the signal of the far user (FU). In phase-I, BS exploits SM to map information bits into two sets: modulation bits (near user's (NU) signal) and antenna index (FU's signal). The iterative-maximum ratio combining (i-MRC) technique is used by NU and D2D user D ₁ (relay) to de-map their desired information bits. During phase-2, the D2D enabled relay D ₁ also exploits SM to forward FU's signal received from BS and its own data bits for another D2D user (D2). Then FU and D2 retrieve their desired data by utilizing i-MRC. There will be no interference in and between cellular and D2D communications as SM is invoked both at BS and D ₁ . So, interference cancellation techniques are not needed at the receivers. Finally, through the Monte Carlo simulation, the efficiency of the proposed protocol is demonstrated.