Mohammad Shakeri

In the past few years, the internet of things (IoT) has garnered a lot of attention owing to its significant deployment for fulfilling the global demand. It has been seen that power-efficient devices such as sensors and IoT play a significant role in our regular lives. However, the popularity of IoT sensors and low-power electronic devices is limited due to the lower lifetime of various energy resources which are needed for powering the sensors over time. For overcoming this issue, it is important to design and develop better, high-performing, and effective energy harvesting systems. In this article, different types of ambient energy harvesting systems which can power IoT-enabled sensors, as well as wireless sensor networks (WSNs), are reviewed. Various energy harvesting models which can increase the sustainability of the energy supply required for IoT devices are also discussed. Furthermore, the challenges which need to be overcome to make IoT-enabled sensors more durable, reliable, energy-efficient, and economical are identified.

Amid growing demand for solar photovoltaic (PV) energy, the output from PV panels/cells fails to deliver maximum power to the load, due to the intermittency of ambient conditions. Therefore, utilizing maximum power point tracking (MPPT) becomes essential for PV systems. In this paper, a novel internet of things (IoT)-equipped MPPT solar charge controller (SCC) is designed and implemented. The proposed circuit system utilizes IoT-based sensors to send vital data to the cloud for remote monitoring and controlling purposes. The IoT platform helps the system to be monitored remotely. The PIC16F877A is used as a main controller of the proposed MPPT-SCC besides implementing the perturb and observe (P&O) technique and a customized buck-boost converter. To validate the proposed system, both simulation and hardware implementation are carried out by the MATLAB/SIMULINK environment and laboratory set up, respectively. The proposed MPPT-SCC can handle the maximum current of 10 A at 12 V voltage. Results show that the efficiency of the proposed system reaches up to 99.74% during a month of performance testing duration.

Research interests on various scientific aspects of photovoltaic (PV) systems has increased over the past decade. However, these systems are still undergoing further developments, and new designs are being demonstrated every year. To minimize cost, reduce size, and increase the efficiency of PV systems, the use of transformerless PV grid-connected inverters has gained the interest of the residential market. This study describes the main challenges in transformerless topologies as well as provides a review on new single-phase grid-connected PV systems, which are categorized into six groups based on the number of switches required in the system. The basic operational principles of various schemes under the six categories of inverters are presented and compared in terms of leakage current, efficiency, strengths and weaknesses. Furthermore, the proposed inverter structure and a compensation strategy for eliminating leakage current have been discussed followed by a comparative study with the available ones. Consequently, our proposed system has found noteworthy results in PSIM environment with a reduction in leakage current as compared with those in three other different topologies.

This study investigates a novel control algorithm for the home energy management system (HEMS) to monitor and schedule the electrical appliances to apply in any conventional houses. This is to reduce the electricity consumption and consequently electricity cost in places with time-of-use (TOU) pricing model. A hardware implementation is executed in a testbed house premise using in-house built control system, smart plugs as well as PV system as the supplementary source. In this system architecture, the overall consumption of the electrical appliances that run on the grid is limited by the algorithm and kept under the certain value that is fixed depending on the total consumption of the building and the capacity of the battery. If the predefined limit is crossed, the new appliances are shifted to run on battery instead of shifting to other time, and thereby human comfort is less violated. In the worst case scenario, where the electrical appliances are assumed to consume the maximum amount of electricity, a prototype implementation of the proposed algorithm achieves up to 15% of electricity cost reduction and ensures the minimum sacrifice in dweller's comfort.

The Home Energy Management System (HEMS) is an important part of the smart grid that enables the residential customers to execute demand response programs autonomously. This study presents theoutcome of a new system architecture and control algorithm that can use both battery storage andmanage the temperature of thermal appliances. The proposed algorithm receives the price information from the utility company in advance and purchases the electricity at off-peak hours and utilizes thebattery as well as manages the temperature of the thermal appliances during peak hours. The proposed algorithm assures that the power consumption of the electrical appliances is always less than certainlevel. The proposed house is supported by the battery system and Photovoltaic system as to increase thegreen index by utilizing alternative energy resource. The amount of the power that can be drained fromthe battery is limited by the algorithm to remain more during a day. The simulation results indicate thatthe proposed system is able to reduce the electricity price up to 20% a day without sacrificing the user’s comfort.

Designing the low power devices are becoming very important field of research due to the increment of the number of portable devices. In this research, 16-bits shift register circuit design method is proposed using Single Edge Triggered (SET) D-Flip-flop. Moreover, a comparison study between the conventional circuit design and modified design is presented. The proposed circuit is designed using CEDEC 0.18 μm CMOS process. The simulated results show that SET D-FF circuit required lower power than the conventional shift register circuit. However, the conventional circuit required 16-transistors and the proposed design required 10-transistors. Therefore, 10-transistors SET D-flip-flop is the better option for 16-bits shift register.

Complementary Metal Oxide Semiconductor (CMOS) Image Sensors technology since their innovation has been changed a lot. Previously, CMOS technology was not so powerful to be considered as the competitor with couple charged devices. However, current CMOS Image Sensors (CIS) gain a huge market share by offering many advantages such as lower voltage operation, on-chip functionality and low cost. Nevertheless, they have the noise and low sensitivity problems. There are variousCIS to satisfy the huge demand in different areas, such as digital photography, industrial vision, medical and space applications, electrostatic sensing, automotive, instrumentation and 3D vision systems. Recently, a lot of research is done to solve the problems of sensitivity, noise, power consumption, voltage operation, speed imaging and dynamic range. In this paper, advanced CIS are reviewed, gathering data on the newest advances, their applications and the recent challenges.

In an ever-growing state of electricity demand due to population growth as well as
modernization of societies, it has compelled us to look for many options to cope with the
situations. However, for a balanced electrical power demand and supply, it is necessary to
respond requirement at any time without any interruption with the strategy of demand
response programs (DRP) to the users. In order to promote smart usage of electrical
power, smart grid networks are gradually transforming conventional grids in many
places. As a part of smart grid, conventional houses may be transformed to smart house
by simply implementing some intelligent controller with interfaces like smart plugs to the
conventional electrical appliances. This chapter elaborates a new strategy of home energy
management system (HEMS) in a smart grid environment to transform any ordinary
premises to smart house to be energy efficient by simply rescheduling operation time.

With the growth in smart technology, customers have a chance to contribute to demand response programs and reduce their bills of electricity actively. This paper presents an intelligent wireless smart plug demonstration, which is designed to control the electrical appliances in the home energy management system (HEMS) application with a response to the utility company’s signal. Besides, a linear model of an energy management system utilizing a dynamic priority for electrical appliances is used as an energy management strategy. This can be useful for decreasing energy consumption in peak hours. Proposed hardware is tested with two different price strategies such as real-time pricing and a combination of this and incremental block rate (IBR) pricing. A small one-story house with ordinary electrical appliances is used as a test-bed for the proposed hardware and strategy. Initial results show that intelligent plugs can decrease the energy cost by 9% per day with an effective peak-to-ave...

Conventional wireless sensor networks (WSNs) in smart home-building (SHB) are typically driven by batteries, limiting their lifespan and the maximum number of deployable units. To satisfy the energy demand for the next generation of SHB which can interconnect WSNs to make the internet of smart home-building (IoSHB), this study introduces the design and implementation of a 250 mW to 2.3 W energy harvesting device. The proposed device is dynamically autonomous owing to the integration of embedded solar photovoltaic (PV) modules and power storage through a supercapacitor (SC; 5 V, 0.47 F) capable of powering WSNs for 95 s (up to 4.11 V). The deployed device can harvest indoor and outdoor ambient light at a minimum illumination of 50 lux and a maximum illumination of 200 lux. Moreover, the proposed system supports wireless fidelity (Wi-Fi) and Bluetooth Low Energy (BLE) to do data transfer to a webserver as a complete internet of things (IoT) device. A customized android dashboard is fu...

Nowadays, chemotherapy is one of the crucial and common therapies in the world. So far, it has been revealed to be highly promising, yet patients suffer from the consequences of severe negative medical dosages. In order to overcome these issues, the enhancement of photothermal chemotherapy with reduced graphene oxide (rGO) as a photothermal agent (PTA) is widely utilised in current medical technologies. This is due to its high near-infrared region (NIR) response, in vitro or in vivo organism biocompatibility, low risk of side effects, and effective positive results. Moreover, rGO not only has the ability to ensure that selective cancer cells have a higher mortality rate but can also improve the growth rate of recovering tissues that are untouched by necrosis and apoptosis. These two pathways are specific diverse modalities of cell death that are distinguished by cell membrane disruption and deoxyribonucleic acid (DNA) disintegration of the membrane via phosphatidylserine exposure in...

This paper elucidated the properties of cellulose nanoparticles (CNPs) extracted from microcrystalline cellulose by hydrolysis reaction by using 1-butyl-3-methylimidazolium acetate (BmimOAc) as a catalyst and solvent at various temperatures (i.e. 70, 80, 90, 100 and 110°C). X-ray diffraction (XRD), Fourier-transform infrared spec-troscopy (FTIR), field emission scanning electron microscopy (FESEM) and thermogravimetric analysis (TGA) were used to characterise the samples and the relevant analysis was presented in detail. It was found that the initial peak from microcrystalline cellulose at the preferential orientation of (200) split into two broad peaks, with the preferential orientations found to be (110) and (020) as per XRD analysis. This showed that native cellulose experienced a structural transformation from its initial cellulose type I to the terminated phase of cellulose type II in CNPs, with a remarkable reduction in crystallinity after the hydrolysis reaction in BmimOAc. The reaction temperature was found to refine the individual cellulosic fibres with a smooth, homogenous, and defined width, which was obtained at an optimum temperature of 80°C. The application of BmimOAc as both catalyst and solvent thus introduces a green chemistry approach as it does not produce any hazardous waste products. Additionally , it is an economical process as the recovery of the ionic liquid is high, reaching up to 86%.

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aim...