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Design Of Analog Cmos Integrated Circuits

This paper presents a low power boost converter for thermoelectric energy harvesting that demonstrates an efficiency that is 15% higher than the state-of-the-art for voltage conversion ratios above 20. This is achieved by utilizing a technique allowing synchronous rectification in the discontinuous conduction mode. A low-power method for input voltage monitoring is presented. The low input voltage requirements allow operation from a thermoelectric generator powered by body heat. The converter, fabricated in a 0.13 μm CMOS process, operates from input voltages ranging from 20 mV to 250 mV while supplying a regulated 1 V output. The converter consumes 1.6 (1.1) μW of quiescent power, delivers up to 25 (175) μW of output power, and is 46 (75)% efficient for a 20 mV and 100 mV input, respectively.

A 20 mV Input Boost Converter With Efficient Digital Control for Thermoelectric Energy Harvesting

In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

/pdf/a-survey-on-resource-allocation-for-5g-heterogeneous-4gmdtz8zbn.pdf

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

https://hal.archives-ouvertes.fr/hal-02395411/document

Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

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Design Of Integrated Circuits For Optical Communications

Abstract Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling. 

/pdf/an-optimized-collaborative-scheduling-algorithm-for-2yitsant.pdf

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

In this paper, We implement the Discrete Cosine Transform (DCT) coding (lossy compression method) followed by the proposed coding technique which called “Quantized Huffman Coding” in order to minimize the EEG data size. Therefore, adding a lossless compression algorithm after the lossy compression is a good idea to get a high compression ratio with acceptable distortion in the original signal. Here, we use DCT encoder followed by either quantized Huffman Coding or Run Length Encoding (RLE) then compare between them. Our work shows that, at the same Root Mean Square Error (RMSE), the quantized Huffman coding outperforms the RLE in some aspects as Compression Ratio (CR) and time consumed in compression and decompression, but Structural Similarity Index (SSIM) is the same for the two techniques.

Hybrid DCT/Quantized Huffman compression for electroencephalography data

With the development of the 5G and Internet of things (IoT) applications, which lead to an enormous amount of data, the need for efficient data-driven algorithms has become crucial. Security concerns are therefore expected to be raised using state-of-the-art information technology (IT) as data may be vulnerable to remote attacks. As a result, this paper provides a high-level overview of machine-learning use-cases for data-driven, maintaining security, or easing telecommunications operating processes. It emphasizes the importance of analyzing the role of machine learning in the telecommunications sector in terms of network operation.

A Review of Machine learning Use-Cases in Telecommunication Industry in the 5G Era

In this paper, we investigate rate adaptive radio resource allocation in visible light communication systems (VLC), to maximize the total output data rate. The proposed allocation algorithm assigns power and subcarriers to each user based on its downlink channel gain and rate constraint, while satisfying the total power constraint. The performance of the proposed algorithm on multi cell VLC systems is evaluated over unity and partial frequency reuse schemes. Furthermore, the system throughput and the blocking probability are presented. It is shown that the proposed algorithm improved the system capacity and the blocking probability as well as it enhanced the computational complexity .

Optimized radio resource allocation scheme for indoor optical wireless communication

In this paper, we present an indoor visible light communication (VLC) system to estimate the position of a moving user. This system uses two approaches based on received signal strength, trilateration estimation, and neural network estimation. In the VLC system, each transmitter sends its position information via light. A photo-detector receiver supported with the moving user is used to receive the transmitted power from each transmitter. The receiver position is calculated using the estimation of trilateration and the prediction of the neural network. We consider the sight line (LOS) and non-line of sight (NLOS) cases in our simulation. The results showed that the neural network estimation approach offers more accurate positioning than the trilateration estimation in case of the normal of the receiver has a tilted angle with the normal of the transmitter with 94% accuracy.

Tawfik Ismail

Papers

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

Hybrid DCT/Quantized Huffman compression for electroencephalography data

A Review of Machine learning Use-Cases in Telecommunication Industry in the 5G Era

Optimized radio resource allocation scheme for indoor optical wireless communication

A Neural Network-Based VLC Indoor Positioning System for Moving Users