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Murizah Kassim ◽  
Fadila Lazim

<span>This paper presents an intelligent of single axis automatic adaptive photovoltaic solar module. A static solar panel has an issue of efficiency on shading effects, irradiance of sunlight absorbed, and less power generates. This aims to design an effective algorithm tracking system and a prototype automatic adaptive solar photovoltaic (PV) module connected through </span><span>internet of things (IoT). The system has successfully designated on solving efficiency optimization. A tracking system by using active method orientation and allows more power and energy are captured. The solar rotation angle facing aligned to the light-dependent resistor (LDR) voltage captured and high solar panel voltage measured by using Arduino microcontroller. Real-time data is collected from the dynamic solar panel, published on Node-Red webpage, and running interactive via android device. The system has significantly reduced time. Data captured by the solar panel then analyzed based on irradiance, voltage, current, power generated and efficiency. Successful results present a live data analytic platform with active tracking system that achieved larger power generated and efficiency of solar panel compared to a fixed mounted array. This research is significant that can help the user to monitor parameters collected by the solar panel thus able to increase 51.82% efficiency of the PV module.</span>

2022 ◽  
Vol 464 ◽  
pp. 109836
Shangge Li ◽  
Jinfeng Jian ◽  
Rama Krishnan Poopal ◽  
Xinyu Chen ◽  
Yaqi He ◽  

2022 ◽  
Vol 21 (1) ◽  
pp. 1-18
Fei Wen ◽  
Mian Qin ◽  
Paul Gratz ◽  
Narasimha Reddy

Hybrid memory systems, comprised of emerging non-volatile memory (NVM) and DRAM, have been proposed to address the growing memory demand of current mobile applications. Recently emerging NVM technologies, such as phase-change memories (PCM), memristor, and 3D XPoint, have higher capacity density, minimal static power consumption and lower cost per GB. However, NVM has longer access latency and limited write endurance as opposed to DRAM. The different characteristics of distinct memory classes render a new challenge for memory system design. Ideally, pages should be placed or migrated between the two types of memories according to the data objects’ access properties. Prior system software approaches exploit the program information from OS but at the cost of high software latency incurred by related kernel processes. Hardware approaches can avoid these latencies, however, hardware’s vision is constrained to a short time window of recent memory requests, due to the limited on-chip resources. In this work, we propose OpenMem: a hardware-software cooperative approach that combines the execution time advantages of pure hardware approaches with the data object properties in a global scope. First, we built a hardware-based memory manager unit (HMMU) that can learn the short-term access patterns by online profiling, and execute data migration efficiently. Then, we built a heap memory manager for the heterogeneous memory systems that allows the programmer to directly customize each data object’s allocation to a favorable memory device within the presumed object life cycle. With the programmer’s hints guiding the data placement at allocation time, data objects with similar properties will be congregated to reduce unnecessary page migrations. We implemented the whole system on the FPGA board with embedded ARM processors. In testing under a set of benchmark applications from SPEC 2017 and PARSEC, experimental results show that OpenMem reduces 44.6% energy consumption with only a 16% performance degradation compared to the all-DRAM memory system. The amount of writes to the NVM is reduced by 14% versus the HMMU-only, extending the NVM device lifetime.

Akey Sungheetha

Recently, various indoor based sensors that were formerly separated from the digital world, are now intertwined with it. The data visualization may aid in the comprehension of large amounts of information. Building on current server-based models, this study intends to display real environmental data acquired by IoT agents in the interior environment. Sensors attached to Arduino microcontrollers are used to collect environmental data for the smart campus environment, including air temperature, light intensity, and humidity. This proposed framework uses the system's server and stores sensor readings, which are subsequently shown in real time on the server platform and in the environment application. However, most current IoT installations do not make use of the enhanced digital representations of the server and its graphical display capabilities in order to improve interior safety and comfort conditions. The storage of such real-time data in a standard and organized way is still being examined even though sensor data integration with storing capacity server-based models has been studied in academics.

10.29007/b1th ◽  
2022 ◽  
Cong Hoa Vu ◽  
Ngoc Thien Ban Dang

Today, freight is an extremely important industry for the world we are living. Fast transportation, large volume...will optimize the cost, time and effort. Besides, ensuring the products safety is a matter of concern. During transporting, it is inevitable that the vibration caused by the engine, rough road surface...the cargo inside can be damaged. Automobile industries have prime importance to vibration testing. Sine vibration testing is performed when we have been given with only one frequency at given time instant. Trend to perform random vibration testing has been increased in recent times. As random vibration considers all excited frequencies in defined spectrum at known interval of time, it gives real-time data of vibration severities. The vibration severity is expressed in terms of Power Spectral Density (PSD). KLT box is an industrial stacking container conforming to the VDA 4500 standard that was defined by German Association of the Automotive Industry (VDA) for the automotive industry. The aim of this paper is study about random vibration and power spectral density analysis, how it can be used to predict the impact of hash road to the KLT box on container / truck during transportation. Finite element model is developed in ANSYS, modal analysis and random vibration analysis were done.

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Muhammad Asghar Khan ◽  
Insaf Ullah ◽  
Mohammed H. Alsharif ◽  
Abdulaziz H. Alghtani ◽  
Ayman A. Aly ◽  

Internet of drones (IoD) is a network of small drones that leverages IoT infrastructure to deliver real-time data communication services to users. On the one hand, IoD is an excellent choice for a number of military and civilian applications owing to key characteristics like agility, low cost, and ease of deployment; on the other hand, small drones are rarely designed with security and privacy concerns in mind. Intruders can exploit this vulnerability to compromise the security and privacy of IoD networks and harm the information exchange operation. An aggregate signature scheme is the best solution for resolving security and privacy concerns since multiple drones are connected in IoD networks to gather data from a certain zone. However, most aggregate signature schemes proposed in the past for this purpose are either identity-based or relied on certificateless cryptographic methods. Using these methods, a central authority known as a trusted authority (TA) is responsible for generating and distributing secret keys of every user. However, the key escrow problem is formulated as knowing the secret key generated by the TA. These methods are hampered by key distribution issues, which restrict their applicability in a variety of situations. To address these concerns, this paper presents a certificate-based aggregate signature (CBS-AS) scheme based on hyperelliptic curve cryptography (HECC). The proposed scheme has been shown to be both efficient in terms of computation cost and unforgeable while testing its toughness through formal security analysis.

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 132
Jeffrey Chi Wai Lee ◽  
Christy Yan Yu Leung ◽  
Mang Hin Kok ◽  
Pak Wai Chan

A comparison was made of two eddy dissipation rate (EDR) estimates based on flight data recorded by commercial flights. The EDR estimates from real-time data using the National Center for Atmospheric Research (NCAR) Algorithm were compared with the EDR estimates derived using the Netherlands Aerospace Centre (NLR) Algorithm using quick assess recorder (QAR) data. The estimates were found to be in good agreement in general, although subtle differences were found. The agreement between the two algorithms was better when the flight was above 10,000 ft. The EDR estimates from the two algorithms were also compared with the vertical acceleration experienced by the aircraft. Both EDR estimates showed good correlation with the vertical acceleration and would effectively capture the turbulence subjectively experienced by pilots.

Erin Feser ◽  
Kyle Lindley ◽  
Kenneth Clark ◽  
Neil Bezodis ◽  
Christian Korfist ◽  

This study established the magnitude of systematic bias and random error of horizontal force-velocity (F-v) profile variables obtained from a 1080 Sprint compared to that obtained from a Stalker ATS II radar device. Twenty high-school athletes from an American football training group completed a 30 m sprint while the two devices simultaneously measured velocity-time data. The velocity-time data were modelled by an exponential equation fitting process and then used to calculate individual F-v profiles and related variables (theoretical maximum velocity, theoretical maximum horizontal force, slope of the linear F-v profile, peak power, time constant tau, and horizontal maximal velocity). The devices were compared by determining the systematic bias and the 95% limits of agreement (random error) for all variables, both of which were expressed as percentages of the mean radar value. All bias values were within 6.32%, with the 1080 Sprint reporting higher values for tau, horizontal maximal velocity, and theoretical maximum velocity. Random error was lowest for velocity-based variables but exceeded 7% for all others, with slope of the F-v profile being greatest at ±12.3%. These results provide practitioners with the information necessary to determine if the agreement between the devices and the magnitude of random error is acceptable within the context of their specific application.

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