Low insertion loss of surface mount device low pass filter at 700 MHz

The paper involved with the design, simulation and fabrication of 6th order elliptical-based Surface Mount Device (SMD) LPF with cutoff frequency at 700 MHz. Fabricated LPF is consisted of four PCB layers which components of SMD are soldered on the top layer. Another three layers is for grounding and shielding, power supply and grounding void. The four layers is crucial to avoid interference between components. The research has find out that the momentum simulation is definitely required to improve the signals response compared to a normal simulation by ADS software. The comparison between momentum simulated versus measured and normal simulated versus measured is 0.2 dB and 29 dB correspondingly. Such huge difference leads to conclusion that momentum simulation is saving time without having much struggles and efforts to get optimum readings. The Proposed SMD LPF has a very low insertion loss of 0.965dB with a transition region of 195 MHz which is good steepness to avoid any image frequency

A triangular MIMO array antenna with a double negative metamaterial superstrate to enhance bandwidth and gain

Multiple-input-multiple-output (MIMO) array antenna integrated with the double negative metamaterial superstrate is presented. The triangular metamaterial unit cell is designed by combining two triangular elements positioned in complementary on the same plane at different sizes. Such design with more gaps is used to excite rooms for more capacitance effects to shift the resonance frequency thus enlarging the bandwidth of the MIMO antenna. The unit cell is arranged in 7 × 7 periodic array created a superstrate metamaterial plane where the Cstray exists in parallel between the two consecutive cells. It is found that the existence of Cstray and gaps for each unit cells significantly influenced the bandwidth of the MIMO antenna. The higher value of the capacitance will lead to the negativity of permittivity. The superstrate plane is then located on top of the 4 × 2 MIMO with a gap of 5 mm. The integration resulted in improving the bandwidth to 12.45% (5.65-6.4GHz) compared to only 3.49% bandwidth (5.91-6.12GHz) of the MIMO antenna itself. Moreover, the negative permeability characteristic is created by a strong magnetic field between the complementary unit cells to have 14.05-dBi peak gain. Besides that, the proposed antenna managed to minimize the mutual coupling and improve the mean effective gain, envelope correlation coefficient, and multiplexing efficiency

Dynamic Spectrum Algorithm Based on D2D Communication

Device-to-device (D2D) communication is a concept that promises the overall performance enhancement by allowing direct communication between the devices which are in proximity. The idea of implementing in-band and out-band spectrums together in a D2D assisted mobile users will be relevant to the landscape of the 5G networks. Nevertheless, limited research works are available on efficient transmission of the data when both spectrums are used simultaneously. In this paper, we propose an efficient dynamic spectrum that utilize the licensed and unlicensed bands, based on the distance between the D2D link, in such a way that it selects the best band for establishing the D2D links in the network. The proposed algorithm is based on the distance between the D2D link, where it selects the most efficient band that reduces the interference of the D2D connection and maximizes the network throughput. The simulation results show that the proposed algorithm, using dynamic spectrum, achieves a higher network performance compared with other static spectrums

Real-time and predictive analytics of air quality with IoT system: A review

Environmental pollution particularly due to the emission of combus-tible gas from industry, haze, and vehicles, that has always been a major concern. Continuous monitoring of the air quality is hence essential to ensure early pre-caution or preventive measure can be taken in eliminating potential health risk which may be done via Smart Environmental Monitoring system with the Internet of Things (IoT), which is cost-effective and efficient way to control air pollution and curb climate change, IoT applications along with Machine Learning(ML) can make the data prediction in real-time. ML can be used to predict the previous and current data obtained by sensors. This review describes the existence of an inte-grated research field in the development of the environmental monitoring system and ML method. The findings of this review interestingly show that (i) various communication module is used for environmental monitoring system. (ii) Very less integration of IoT together with predictive analytics, it is separately to study for air pollution monitoring system. (iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can as-sist in the development of real-time, and continuous high precision environmen-tal monitoring systems. v) Machine Learning (ML) Regression algorithm is suit-able for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting

Real-time and predictive analytics of air quality with IoT system: a review

Environmental pollution particularly due to the emission of combustible gas from industry, haze, and vehicles, that has always been a major concern. Continuous monitoring of the air quality is hence essential to ensure early precaution or preventive measure can be taken in eliminating potential health risk which may be done via Smart Environmental Monitoring system with the Internet of Things (IoT), which is cost-effective and efficient way to control air pollution and curb climate change, IoT applications along with Machine Learning(ML) can make the data prediction in real-time. ML can be used to predict the previous and current data obtained by sensors. This review describes the existence of an integrated research field in the development of the environmental monitoring system and ML method. The findings of this review interestingly show that (i) various communication module is used for environmental monitoring system. (ii) Very less integration of IoT together with predictive analytics, it is separately to study for air pollution monitoring system. (iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can assist in the development of real-time, and continuous high precision environmental monitoring systems. (v) Machine Learning (ML) Regression algorithm is suitable for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting

DSRC technology in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) IoT system for Intelligent Transportation System (ITS): a review

Intelligent Transportation System (ITS) consisting of Vehicle Ad-hoc Networks (VANET) offers a major role in ensuring a safer environment in cities for drivers and pedestrians. VANET has been classified into two main parts which are Vehicle to Infrastructure (V2I) along with Vehicle to Vehicle (V2V) Communication System. This technology is still in development and has not been fully implemented worldwide. Currently, Dedicated Short Range Communication (DSRC) is a commonly used module for this system. This paper focuses on both V2V and V2I latest findings done by previous researcher and describes the operation of DSRC along with its architecture including SAE J2735, Basic Safety Message (BSM) and different type ofWireless Access in Vehicular Environment (WAVE) which is being labeled as IEEE 802.11p. Interestingly, (i) DSRC technology has been significantly evolved from electronic toll collector application to other V2V and V2I applications such as Emergency Electronics Brake Lights (EEBL), Forward Collision Warning (FCW), Intersection Moving Assist (IMA), Left Turn Assist (LTA) and Do Not Pass Warning (DNPW) (ii) DSRC operates at different standards and frequencies subject to the country regulations (e.g. ITS-G5A for Europe (5.875–5.905 GHz), US (5.850– 5.925 GHz), Japan (755.5–764.5MHz) and most other countries (5.855–5.925 GHz)) where the frequencies affected most on the radius of coverage

DSRC Technology in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) IoT System for Intelligent Transportation System (ITS): A Review

Intelligent Transportation System (ITS) consisting of Vehicle Ad-hoc Networks (VANET) offers a major role in ensuring a safer environment in cities. For drivers and pedestrians. VANET has been classified into two main parts which are Vehicle to Infrastructure (V2I) along with Vehicle to Vehicle (V2V) Communication System. This technology is still in development and has not been fully implemented worldwide. Currently, Dedicated Short Range Communication (DSRC) is a commonly used module for this system. This paper focuses on both V2V and V2I latest findings done by previous researcher and describes the operation of DSRC along with its architecture including SAE J2735, Basic Safety Message (BSM) and different type of Wireless Access in Vehicular Environment (WAVE) which is being labeled as IEEE 802.11p. Interestingly, (i) DSRC technology has been significantly evolved from electronic toll collector application to other V2V and V2I applications such as Emergency Electronics Brake Lights (EEBL), Forward Collision Warning (FCW), Intersection Moving Assist (IMA), Left Turn Assist (LTA) and Do Not Pass Warning (DNPW) (ii) DSRC operates at different standards and frequencies subject to the country regulations (e.g. ITS-G5A for Europe (5.875–5.905 GHz), US (5.850–5.925 GHz), Japan (755.5–764.5 MHz) and most other countries (5.855–5.925 GHz)) where the frequencies affected most on the radius of coverage

A triangular MIMO array antenna with a double negative metamaterial superstrate to enhance bandwidth and gain

Multiple-input-multiple-output (MIMO) array antenna integrated with thedouble negative metamaterial superstrate is presented. The triangularmetamaterial unit cell is designed by combining two triangular elements posi-tioned in complementary on the same plane at different sizes. Such designwith more gaps is used to excite rooms for more capacitance effects to shift theresonance frequency thus enlarging the bandwidth of the MIMO antenna. Theunit cell is arranged in 7×7 periodic array created a superstrate metamaterialplane where theCstrayexists in parallel between the two consecutive cells. It isfound that the existence ofCstrayand gaps for each unit cells significantlyinfluenced the bandwidth of the MIMO antenna. The higher value of thecapacitance will lead to the negativity of permittivity. The superstrate plane isthen located on top of the 4×2 MIMO with a gap of 5 mm. The integrationresulted in improving the bandwidth to 12.45% (5.65-6.4GHz) compared toonly 3.49% bandwidth (5.91-6.12GHz) of the MIMO antenna itself. Moreover,the negative permeability characteristic is created by a strong magnetic fieldbetween the complementary unit cells to have 14.05-dBi peak gain. Besidesthat, the proposed antenna managed to minimize the mutual coupling andimprove the mean effective gain, envelope correlation coefficient, and mul-tiplexing efficiency

Near-infrared spectroscopy for ganoderma boninense detection in oil palm: An outlook

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causing a serious threat to the palm oil industry. This catastrophic disease ultimately destructs the basal tissues of oil palm that causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This mini-review describes past and future prospects of integrated research of near infrared spectroscopy (NIRS) towards early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and hyperspectral in reacting with organic tissues, ii) NIR spectrum is more precise and sensitive to particular diseases include G. boninense compared to visible light iii) hand-held NIRS for in-situ measurement is to explore the efficacy for early detection system in real-time using machine learning (ML) classifier algorithms and predictive analytics model. This non-destructive, environmentally friendly (no chemical involved), mobile and sensitive leads the integrated hand-held NIRS with ML, and predictive analytics has significant potential as a platform towards early detection of G. boninense in the future