Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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How PV systems can be cost-competitive for mobile BTS in remote areas of Algeria
Get PDFSolar electricity for telecom infrastructure has became one of the main contributors of electrical energy to the energy system in the future, but solar technology needs support to find its way to the market. Since photovoltaic (PV) electricity always involves very high costs compared to electricity from other sources, a striking question is: how can PVs achieve competitiveness? There are different strategies for promoting applied PV electricity generation around the world. Here, we simulated and designed a 40 KWp injected PV power, to evaluate the energy of the incident photon flux, that produced by PV panels and the energy injected into the electricity grid, and judge whether the installation is profitable, and if the technology is economical. The results reveal that the site of Tamanrasset (in the extreme south of Algeria) could produce an annual energy production 73879 kWh, which justify the reason the German Academy has chosen such site for the famous desert project, lastly launched between the two countries
A Viability Approach For Management Of IEEE 802.15.4 Wireless Sensor Node Performance
Get PDFThe long-term use of wireless sensors node while guaranteeing a good Quality of Services (QoS) is a major challenge in wireless sensor networks. Most of the relevant solutions which exist are proposed under Mac layer level but they use an optimization technique which requires a regular update of parameters and leads to unnecessary energy consumptiom which reduces the expected liftime and QoS. So in order to adress this issue, we propose in this paper, an adaptive management of wireless sensor node resources to meet application requirements in terms of energy consumption, reliability and delay. To do this, we have used the theory of viability, which is an approach that allows controling the evolution of a system in a set of desirable states. Here we have proposed an enhanced analytical model of sensor node’s energy dynamic, and we control it based on both Mac layer parameters of the IEEE 802.15.4 standard and the packet sampling frequency. The simulation results have shown that the proposed model is more accurate and efficient as a node can send more information without violating energy, reliability and delay constraints
Internet of Things Based Smart Vending Machine using Digital Payment System
Get PDFThe advent of the Internet envisions a cashless society by enabling financial transactions through digital payments. Significantly, the emergence of coronavirus (COVID-19) disrupted our traditional cash handling means and triggered an inflection point for switching towards contactless digital payments from physical cash payments. Furthermore, Internet of Things (IoT) technology escalates digital payments to the next level by enabling devices to render goods and services without requiring any human interaction. This research proposed an IoT-enabled cashless vending machine that incorporates both cloud computing and payment gateway for ordering and purchasing items through digital payment systems by using a mobile application. The system enables a pre-installed mobile application to scan the Quick Response (QR) code attached to the body of a vending machine, opens the portal of a web-based virtual machine through the code, allows user to choose and order items from the virtual vending, initiates and authorizes a digital payment through an IoT gateway installed inside the physical vending machine by establishing a connection between user's and vendor's financial entities, and finally, dispenses the ordered items by unlocking the shelves of the vending machine after the successful payment transaction. It operates in the Arduino platform with an ATmega 2560 Microcontroller and Esp8266 Wi-fi module as hardware components, mobile application software, and payment gateway API. The system performed an average response time of 14500 milliseconds to pick a product after running 150 consecutive API test calls. This result shows a satisfying time for enhancing customers' buying experiences with digital payment systems and a customizable and cost-effective IoT-based intelligent vending machine to introduce for mass production
The review of heterogeneous design frameworks/Platforms for digital systems embedded in FPGAs and SoCs
Get PDFSystems-on-a-chip integrate specialized modules to provide well-defined functionality. In order to guarantee its efficiency, designersare careful to choose high-level electronic components. In particular,FPGAs (field-programmable gate array) have demonstrated theirability to meet the requirements of emerging technology. However,traditional design methods cannot keep up with the speed andefficiency imposed by the embedded systems industry, so severalframeworks have been developed to simplify the design process of anelectronic system, from its modeling to its physical implementation.This paper illustrates some of them and presents a comparative studybetween them. Indeed, we have selected design methods of SoC(ESP4ML and HLS4ML, OpenESP, LiteX, RubyRTL, PyMTL,SysPy, PyRTL, DSSoC) and NoC networks on OCN chip (PyOCN)and in general on FPGA (PRGA, OpenFPGA, AnyHLS, PYNQ, andPyLog).The objective of this article is to analyze each tool at several levelsand to discuss the benefit of each in the scientific community. Wewill analyze several aspects constituting the architecture and thestructure of the platforms to make a comparative study of thehardware and software design flows of digital systems.
Approach to Object Hardness Prediction by Rubber Ball Hardness Prediction Using Capsule Network
Get PDFA hardness is often used as an index to compare similar objects such as fruits or wood. To measure an object’s hardness, a hardness meter is required, and certain conditions must be met. The conditions are that the hardness meter is compatible with the object and must be close at hand. This research shows the possibility of measuring hardness without a hardness meter using a neural network. The method employs machine learning using a capsule network (CapsNet) of a neural network model. This research experimented using CapsNet with routing-by-agreement, CapsNet with expectation-maximization routing (EM routing) and the EM routing method with the addition of Tasks-Constrained Deep Convolutional Network (TCDCN). The four-layer CapsNet with EM routing implemented has achieved the state-of-the-art. Multi-layered CapsNet with EM routing was a very effective method for regression analysis as well. And, CapsNet has higher discriminative power using EM-routing than routing-by-agreement
A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments
Get PDFThe environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.
LPV Control Synthesis for Sensor Senescence in Feedback Control System
Get PDFThe senescence of system sensor(s) probably causing degenerate system behavior or systembreakdown. Motivated by that, this work considers the synthesis of a gain scheduling controlfor linear parameter varying (LPV) system via linear matrix inequalities (LMIs) techniquessuch that sensors senescence information incorporated in the design of the controller. That is,the degradation of sensor effectiveness due to senescence is modeled by the variation of sensormeasurements’ noise co-variance. The sensors’ senescence information is incorporated as apart of the scheduling parameters for the LPV controller. The synthesis control matrices via linearmatrix inequalities have been re-structured and re-formalized in a way that they incorporatethe sensors senescence information to synthesize a gain scheduling dynamic output feedback(GS - DOF) control. That is, the existing GS - DOF control design LMIs have been modifiedto include the noise co-variance matrix. The significant achievement is the control design conditionsdescription of GS - DOF control in a way that ensures the desired integrated H2&H1 performances in the presence of sensors senescence, where sensors’ senescence information isincorporated as a part of the scheduling parameters for the LPV controller. In addition, two setsof controllers have been synthesized and studied. The simulation expresses the benefits of theproposed controllers, and closed loop system H1 and H2 performances are also studied. Thesynthesized controller ensures the performance associate with a closed loop, the closed loopsystem stability, and the scheme of control is simple enough for real time implementations
Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks
Get PDFThe human brain is considered as one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: a headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. This chip offers an advanced filtering technology that has a high noise immunity along with an embedded Bluetooth module using which the acquired signal is transferred wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in real-time and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The work presented here gives a basic understanding of the functionality of a BCI system, and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis
Dynamic Spectrum Allocation Access Using Cognitive Radio Networks in a Maritime
Get PDFThe maritime environment is unique due to radio wave propagation over water, surface reflection and wave obstruction. In dealing with the challenging maritime environment, a dynamic spectrum allocation access using cognitive radio network through optimization is proposed. Existing works in this area are limited in performance due to the long duration in achieving the probability of false alarm. Matched filtering technique which is known as the optimum method for detection of primary users (PUs) faces the challenge of large power consumption as various receiver’s algorithm are needed to be executed for detection. This work provides a platform that enables minimum energy utilization by secondary users (SUs) thereby, enhancing throughput. An algorithm for throughput maximum in spectrum allocation was developed and used based on demand based model. The implementation of the developed model was carried out using Java program and the spectrum analysis using long distance path loss model and adaptive modulation code to estimate the minimum bandwidth of the secondary users. A simulation of cognitive radio mesh network for the testing and validation of the demand based algorithm preference, and also the cognitive radio network traffic was carried out using Cisco packet tracer and results shown on MATLAB. Simulation results indicate that using the demand based algorithm, the throughput rose with time and almost stabilized. This increase and steady throughput indicates effectiveness in the algorithm which shows that the PUs and SUs activities increase as holes’ detection effort varies, unlike that of genetic algorithm where the throughput rose gradually, got to a peak value at certain time and then fell which indicates instability in the variation of the throughput. Also, the average throughput of the demand based algorithm is far greater than that of genetic algorithm which shows that demand based algorithm outperforms the genetic by a far greater percentage. The percentage of optimization is approximately 26%
Autocorrelation Based White Space Detection in Energy Harvesting Cognitive Radio Network
Get PDFAccurate detection of White Space (WS) is of paramount importance in a Cognitive Radio Network (CRN) to prevent authorized users from harmful interference. However, channel impairment such as multipath fading and shadowing affects accurate detection of WS resulting in interference. The Existing Feature Detection (EFD) technique used to address the problem is faced with computational complexity and synchronization resulting in long sensing time, bandwidth inefficiency, energy constrain and poor detection rate. Hence, this paper proposes autocorrelation based multiple antenna with energy harvesting for WS detection in a CRN using Radio Frequency (RF) energy harvesting and autocorrelation of the received signal with a modified Equal Gain Combiner (mEGC). Antenna Switching (AS) RF energy harvesting with mEGC are used to harvest energy and information from the received PU signal in a multiple antenna configuration. Autocorrelation is then obtained and compared with the set threshold of zero to determine the presence or absence of WS. The proposed technique is evaluated using Spectral Efficiency (SE), Probability of Detection (PD) and Sensing Time (ST) by comparing with EFD technique. The results obtained revealed that the proposed technique shows better performance than EFD