Microstrip diplexer for recent wireless communities

The diplexer is a dual-filter circuit with three ports that may share an antenna across two different frequency channels. As long as each band can be employed for sending and receiving signals, this technology can be used for multiple transmitters running on various frequencies. This paper will first present an overview of the diplexer concept, its importance in wireless networks, and its difference with duplexers. Then, the microstrip transmission line and its variation are discussed, as the filters and diplexers are designed using transmission lines. Typical electrical specifications are presented with measurement methods as well

Developing capacity sharing strategy for vehicular networks with integrated use of licensed and unlicensed spectrum

A widely deployed cellular network, supported by direct connections, can offer a promising solution that supports new services with strict requirements on access availability, reliability, and end-to-end (E2E) latency. The communications between vehicles can be made using different radio interfaces: One for cellular communication (i.e., cellular communication over the cellular network based on uplink (UL)/downlink (DL) connections) and the other for direct communication (i.e., D2D-based direct communications between vehicles which allows vehicular users (V-UEs) to communicate directly with others). Common cellular systems with licensed spectrum backed by direct communication using unlicensed spectrum can ensure high quality of service requirements for new intelligent transportation systems (ITS) services, increase network capacity and reduce overall delays. However, selecting a convenient radio interface and allocating radio resources to users according to the quality of service (QoS) requirements becomes a challenge. In this regard, let’s introduce a new radio resource allocation strategy to determine when it’s appropriate to establish the communication between the vehicles over a cellular network using licensed spectrum resources or D2D-based direct connections over unlicensed spectrum sharing with Wi-Fi. The proposed strategy aims at meeting the quality of service requirements of users, including reducing the possibility of exceeding the maximum delay restrictions and enhancing network capacity utilization in order to avoid service interruption. The proposed solution is evaluated by highlighting different conditions for the considered scenario, and it is demonstrated that the proposed strategy improves network performance in terms of transmitted data rate, packet success rate, latency, and resource usag

Design of n-Bit Adder without Applying Binary to Quaternary Conversion

Microprocessor has been considered as most important part in ICs manufacturing and making progress since more than 50 years, so increasing microprocessor speed is paid attention in all technologies. ALU is known as the slowest part in microprocessor because of the ripple carry, nowadays microprocessor uses 8-uints as pipeline, each one has 8-bits for implementing 64-bit, working in this form has been captured the microprocessor development and limited its speed for all its computations. Parallel processing and high speed ICs always trying to increase this speed but unfortunately it remains limited. The contemporary solution for increasing microprocessors speed is the Multiple Valued Logic (MVL) technology that will reduce the 8-bits to 4-qbits, this paper proposes a new design of a 2-qbit full adder (FA) as a basic unit to implement MVL ALU (AMLU) that has 8-units as pipeline, each one consists of 4-qbits to implement 32-qbit which is equivalent to 64-bit, without applying binary to quaternary conversion and vice versa. The proposed design increases microprocessors speed up to 1.65 times, but also a little increase of implementation

Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers

We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let’s performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environment

Real-Time classification of various types of falls and activities of daily livings based on CNN LSTM network

In this research, two multiclass models have been developed and implemented, namely, a standard long-short-term memory (LSTM) model and a Convolutional neural network (CNN) combined with LSTM (CNN-LSTM) model. Both models operate on raw acceleration data stored in the Sisfall public dataset. These models have been trained using the TensorFlow framework to classify and recognize among ten different events: five separate falls and five activities of daily livings (ADLs). An accuracy of more than 96% has been reached in the first 200 epochs of the training process. Furthermore, a real-time prototype for recognizing falls and ADLs has been implemented and developed using the TensorFlow lite framework and Raspberry PI, which resulted in an acceptable performance

Emerging wireless communication technologies in Iraqi government: Exploring cloud, edge, and fog computing

This study aims to structure the implementation of a governmental cloud of things (CoT), edge computing (EC), and fog computing in Iraq in the context of sustainable wireless communication. A base of literature was built that included any challenges, opportunities, and best practices relevant to these innovative technologies to set up the background for this paper. A concept model was created that included core components (cognitive technologies and fog computing), key processes (resource analysis, infrastructure design), and stakeholders (governments, industry, community). A strategic methodology made up of stakeholder involvement, capacity building, and pilot projects was used in the project. Concerning IoT planned deployment and services provision, network infrastructure was put in place to support the devices and a higher level of security measures were recommended. Using scenario hypothesis, MATLAB simulator was employed to simulate data value distribution as well as received power distribution based on different institutions for 12 months. Monitoring and evaluation should be followed to measure performance indicators and effects on this process. Continuously improvement strategies were the highlight of the session which further stimulated innovations. Acquainted projects will be put in the function to extend the range of activities by including additional government agencies, regions, or sectors. Reporting of the collected data and funding will be done with stakeholders to share and pool knowledge

Cloud of Things and fog computing in Iraq: Potential applications and sustainability

This paper depicts the principles of Cloud of Things and fog computing and discusses its possible uses in Iraq with sustainability measures. The capacity of cloud computing to supply elastic, as-needed computer resources has garnered widespread interest worldwide. However, fog computing and a Cloud of Things enhance the Internet of Things by relocating computation to devices on the network's periphery. This study looks at how the Cloud of Things and fog computing are used now in Iraq, the obstacles, and the future uses of these technologies in various fields. To fully reap the benefits of the Cloud of Things and fog computing in Iraq, the study also emphasizes the significance of infrastructure development, policy design, cybersecurity, and other measures. This study will discuss the use of questionnaires in research. There are two distinct components to this. The first section includes questions regarding the respondents' affiliations, including their roles, departments, organization sizes, and ministries. The rest of the study's factors are discussed with inquiries in line with issues of cyber security, privacy, sustainability, cost of implementation, feasibility, trust, IT infrastructure, and government support. The survey's final open-ended inquiry will help us to compile a wide range of perspectives on what kinds of Cloud of Things and fog computing services based on the Iraqi government's needs

Development of A Convolutional Neural Network Joint Detector for Non-orthogonal Multiple Access Uplink Receivers

We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot Fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let's performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environment

Process simulation of methanol production via carbon dioxide hydrogenation

The performance of a packed bed reactor for CO2 conversion to methanol was investigated. Because of the exothermic reaction, temperature and concentration gradients happen along the reactor. A 2D packed-bed reactor model was applied for numerical investigations, coupled with detailed reaction kinetics and transport phenomena. The effect of feed inlet pressure and temperature on CO2 conversion and temperature profiles along the reactor was evaluated. The results showed that firstly increase in the temperature in the reactor because of the presence of exothermic reactions and then slight decrease in the temperature at top section of the reactor. Pressure was decrease from 55 bar to around 54 bar along the reactor. It was found that the CO2 conversion at outlet of reactor was 18.18 % at T = 475 K and it was increased to 23 % at temperature of 498 K and 555 K. The CO2 conversion was reached its maximum at short distance from the reactor entrance at T = 555 K. Also, the outlet temperature of gas mixture was increased from 522.53 K to 534.56 K with increasing feed inlet pressure from 35 bar to 55 bar. The CO2 conversion was 18.12 %, 20.77 %, and 22.96 % at pressures equal to 35, 45, and 55 bar respectively

Investigation of the potential of Apigenin and Kaempferol for toxic gas sensing: A theoretical perspective

Here, we investigate the chemical as well as the structural attributes of Apigenin and Kaempferol as viable gas sensors with high sensitiveness via density functional theory (DFT). The adhesion behavior of typical gas molecules was examined on Apigenin and Kaempferol by performing DFT calculations. These compounds can sense the physisorption of SO2, NO, and NO2 because of the considerable charge transport. The adhesion of NO2 in particular led to a considerable increase in the transport performance because of the considerable charge transport, which makes Apigenin and Kaempferol) promising candidates to selectively detect NO2 with high sensitiveness. The present study can provide insights into the potential use of natural products in order to sense gasses with high toxicity