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With yearly output exceeding 70 million units, the automotive industry is one of the world's largest manufacturing industries. According to worldwide estimates, the car industry's global revenue was an astounding 3 trillion dollars equating to a combined global GDP of 3.65 percent. The emergence of IoT in the automobile sector has created new opportunities for automakers and purchasers worldwide. With industrial and commercial applications, IoT in the automobile industry has developed into a significant hotspot for a variety of multipurpose applications. From linked automobiles to automated transportation systems, Internet of Things applications have had a significant impact on the worldwide automotive business. The Internet of Things, along with other disruptive technologies, is reshaping the automobile sector as a whole. The evolution of this sector has resulted in the birth of ground-breaking advancements in automobiles, namely linked and autonomous vehicles. Different types of internet of things technology have significant qualities that make them viable candidates as a technology for use in automotive industry. This paper focuses on internet of things latest findings done by previous researcher and describes the operation of the technology. Moreover, this paper also provides insights into some countermeasures against internet of things.

The use of water in diesel is an alternative to counteracting the increase in nitrogen oxide (NOx) emissions from diesel engines. However, some other criteria such as combustion performance and thermal efficiency were slightly reduced due to the impact of water content in diesel fuel. In other words, optimizing the balance between engine performance and emissions has always been a major problem in automotive industries. The purpose of this article is to demonstrate the use of RSM (response surface methodology) to improve the performance and emissions of a compression ignition (CI) engine which works on 5%, and 10% water mixes dubbed WD5 and WD10. This mixture of water emulsion to diesel is produced by using emulsifier ultrasonic. Tests were then performed on the Isuzu 4JJ1 4-cylinder 3000cc test bed engine with water cooled. Changes to engine load 20% to 50% and speeds of 1000rpm to 3000rpm. Experimental results data is then included in the Design Expert software to analyze the performance, emissions and optimization values using CCD Techniques in RSM. In performance analysis, pure diesel and WD5 are comparable with respect to power, heat efficiency of brakes and brakes of certain fuel consumption (BSFC). At higher loads, increased BTE levels and a neat BSFC drop were found. However, the combustion period is shorter. NOx and CO data slightly decreased with the same increase in water percentage at the same engine speed and load. However, the CO2 data showed a sharp increase with the increase in water content in fuel. Optimum engine operating parameters found as 5% water ratio, 50% of load and 2446 rpm while the best output parameters found as 103.7 Nm. of torque, 26.3 kW power, 43.8% BTE, 172 g/kWh BSFC, 521.8ppm NOx and 3.1% of CO.

Cardiovascular auscultation is a process of listening to the sound of a heartbeat to pick up on any abnormalities. One of these abnormalities is heart murmurs, which are the result of blood turbulence, in or near the heart. Heart murmurs can be innocent, or they can indicate the existence of very serious diseases. Normally the process is performed with a stethoscope, by a medical professional, where murmurs are identified by the subtle difference in timing and pitch from a normal heartbeat. These professionals, however, are not always available; hence, the need for the automation of this process rises. This paper aims at testing the performance of pre-trained CNN models at the classification of heartbeats. A database of phonocardiogram (PCG) heartbeat recordings, under the name of the PASCAL CHSC database was used to train four pre-trained models: VGG16, VGG19, MobileNet, and inceptionV3. The data was processed, and the features were extracted using Spectrogram signal representation. They were then split into training and testing data, and the results were compared using the metrics of accuracy and loss. The classification accuracies of the VGG16, VGG19, MobileNet, and inceptionV3 models are 80.25%, 85.19%, 72.84% and 54.32%, respectively. The findings of the paper indicate that the use of different transfer learning models can, to a certain extent, enhance the overall accuracy at detecting the murmurs of the heart.

Continuous searching in new energy sources has been a crucial issue for sustaining the increasing energy demand. Due to the present economic and social modernization as well as petroleum oil depletion crisis, makespromising alternatives such as renewable energy sources an important choice for the next power generation. Petroleum fuel includesdiesel currently used in power generation, transportation, and industrial sectors. The introduction of biodiesel as a secondary fuel for diesel engines has revolutionized the use of different fuels with fuel blending in current diesel. Though biodiesel-diesel fuel can substitute diesel fuel at an acceptable blending ratio rate up to 20%, fuel properties could be affected with beyond the limit from the engine manufacturer's standard when blending at high volume ratio. Thus, in the present study, the use of the diesel-biodiesel fuel (B20) was investigated corresponding to the fuel properties and engine cyclic variations. Also, the tested fuels include mineral diesel were tested experimentally in a diesel engine with the in-cylinder pressure data measurement for 1000 cycles. These data were analyzed using the coefficient of variation (COV) and wavelet power spectrum (WPS). Fuel properties test results showed significant differences in density and acid value with a significant reduction in viscosity when diesel is blended with biodiesel at 20%. Despite that, the low heating value was significantly affected for B20 compared to pure biodiesel. While as for the wavelet analysis results, the short period oscillations appear periodically in pure biodiesel and mineral diesel, but in contrast, the long and intermediate-term periodicities has are found in B20. Moreover, the spectral power has increased with B20, which attributed significantly to the engine cyclic variations. This characteristic validated the coefficient of variation (COV) for the indicated mean effective pressure (IMEP) time series that B20 produces the lowest fluctuation in cyclic variations compared to other fuels.

A five-Port Reflectometer (FPR) with the integration of ultra-wideband (UWB) Epsilon Negative (ENG) Double Split Ring Resonator (DSRR) metamaterial array is introduced in this paper for microwave imaging (MWI) application. The designed DSRR consists of two concentric rings with a split in each which are spatially rotated by 180°, formed an inverted structure to exhibit a wide negative epsilon bandwidth of 187 % (from 0.5 GHz to 15 GHz). The FPR is designed using a ring junction topology and semi-circularly curved inter-port transmission lines (TLs) which are placed between five equally spaced ports. Localizing the DSRR metamaterial in a periodic array of 5 × 4 at the ground plane of FPR lead to 79.79 % fractional bandwidth and reflection coefficient within the operating frequencies of 0.991 GHz–2.2576 GHz. Equivalent circuit model has been alluded with an intricate description of different array configurations of the metamaterial unit cell. Comparison of EM simulation and circuit simulation has been performed to validate the equivalent circuit model. It is found that the existence of stray capacitance, C s t r a y which is represented by the DSRR configurations, significantly influenced the resonant frequency and bandwidth of FPR. Measured results of the proposed design suits well with the simulations and prove higher efficacious applicability of the proposed design for microwave imaging application. A comparison of the reconstructed image also proves its suitability for the microwave imaging application.

The rise of crude oil price and the implications of exhaust emissions to the environment from combustion application call for a new reliable alternative fuel. A potential alternative fuel for compression ignition (C.I.) engine is the compressed natural gas (CNG). For C.I. engines to operate using CNG, or to be converted as a retrofitted CNG engine, further modifications are required. Previous works reported loss in brake power (BP) and increase in hydrocarbon (HC) emission for C.I. engine retrofitted with CNG fuelling. Verification of performance characteristics for CNG retrofitted engine through experimental analysis requires high cost and is very time consuming. Thus, a 1-Dimensional simulation software, GT-Power, was introduced in this study to reduce the experimental process and setup. A 4-cylinder medium duty C.I. engine (DE) and CNG retrofitted engine (RE) GT-Power models were used in this simulation work over various operational conditions: low, medium and high load conditions. As compared with DE model, results from RE model showed that RE model achieved an average 4.9% improvement for brake specific fuel consumption (BSFC) and loss in BP by 37.3%. For nitrogen oxides (NOX) and carbon dioxides (CO2) RE model predicted reduction of 48.1% (engine mode 1-9) and 33.4% (all engine modes), respectively. Moreover, RE produced 72.4% more carbon monoxide (CO) and 90.3% more HC emission.

The scarcity of oil resources and the rise of crude oil price had driven the whole world to seek for an alternative fuel for automotive industry. One of the prospective alternative fuels for compression ignition (C.I.) engine is compressed natural gas (CNG). In order to operate CNG in a C.I. engine as mono-gas engine (RE), several modifications are required. The modifications that involves are compression ratio, fuel injection type, addition of spark plug and fuel itself. So as to reduce the time in preparing the experimental test bed and high cost analytical study a 1-dimensional simulation software GT-Power was introduce. The GT-Power simulation model for a 4 cylinder medium duty C.I. engine (DE) and RE has been built to study the effects of conversion process to the performance and emissions of the engine at various operational conditions: low, medium and high load conditions. As compared with DE model, results from RE model showed loss in brake power (BP) and brake thermal efficiency (BTE) by 37.3% and 19% respectively. Meanwhile, for brake specific air consumption (BSAC) RE predicted to undergo an average of 19412.6 g/kW-h and increment in volumetric efficiency by percentage of difference 22%. In other side, oxides of nitrogen (NOx) RE engine model predicted reduction of 48.1% (engine mode 1-9) and increased in hydrocarbons (HC) by 90.3.