Fossil fuels bring about concerns like air pollution, rising greenhouse gas emissions due to its combustion and depletion issues. The need to more to biofuels as an alternative and renewable energy source is for a sus...
Fossil fuels bring about concerns like air pollution, rising greenhouse gas emissions due to its combustion and depletion issues. The need to more to biofuels as an alternative and renewable energy source is for a sustainable and environment for the future. This research project studies the biofuels in Malaysian perspective, the debates concerning the issues as well as the advantages and disadvantages of biofuels. The production of biofuels and comparison with fossil fuels has been done based on the social, economic and environmental context. Research is also conducted on the organizations producing biofuels in Malaysia, volume of fossil fuels are being currently replaced by biofuels, and where the projection in the next 20 years will be. After all the studies and observations are carried out, personal evaluation of further requirements or developments is made. It can be concluded that Malaysia will not be able to replace its fossil fuels with biofuels in the near future.
Electric Vehicles (EV s) such as e-bikes, e-scooters, and e-skateboards become the most popular Eco-friendly personal modes of transportation in the United States. These EVs are mostly recharged via the power grid'...
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Electric Vehicles (EV s) such as e-bikes, e-scooters, and e-skateboards become the most popular Eco-friendly personal modes of transportation in the United States. These EVs are mostly recharged via the power grid's stations. Grid power is generated through multiple means including hydroelectric, thermal, solar, and wind. Even with using EVs to reduce the dangerous effects of fuel burning, there is a real need to take major steps towards green charging approaches. Charging EVs through renewable energy resources is maximizing the ecologically friendly potentials. The aim of this work is to provide a green and sustainable charging system for personal electric vehicles. Hence, a solar-powered charging dock has been built and controlled by an Arduino Mega 2560 adding to a Raspberry Pi 4. Subsequently, solar panel tracker linear actuator has been applied to be integrated with the installed dock solar panel tracking system for delivering maximum precision while operating. To get economic and reliable dock, it has been constructed from wood.
This study presents preliminary results of a randomized controlled trial comparing a novel passive arm orthosis training system, the Therapy Wilmington Robotic Exoskeleton (T-WREX), with conventional self-directed upp...
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This study presents preliminary results of a randomized controlled trial comparing a novel passive arm orthosis training system, the Therapy Wilmington Robotic Exoskeleton (T-WREX), with conventional self-directed upper extremity exercises. Chronic stroke survivors (n = 23) with moderate to severe upper limb hemiparesis trained three times per week for eight weeks with minimal supervision from an occupational therapist. Both groups demonstrated significant improvements in arm movement ability according to the Fugl-Meyer (3.7 point mean improvement in T-WREX group, p = 0.001, and 2.7 point improvement in control group, p = 0.003). Individuals who completed T-WREX training also demonstrated significant gains in self-rated quality of arm movement on the Motor Activity Log (p=0.05), and showed a trend towards greater gains on all clinical measures, although this trend was not significant at the current study size. Post-treatment surveys revealed a subjective preference for T-WREX training over conventional gravity-supported exercises. These preliminary results suggest that the T-WREX is a safe device feasible for clinical use, and effective in enhancing upper extremity motor recovery and patient motivation. Next steps are discussed.
In this paper we propose a technique to incorporate contextual information into object classification. In the real world there are cases where the identity of an object is ambiguous due to the noise in the measurement...
In this paper we propose a technique to incorporate contextual information into object classification. In the real world there are cases where the identity of an object is ambiguous due to the noise in the measurements based on which the classification should be made. It is helpful to reduce the ambiguity by utilizing extra information referred to as context, which in our case is the identities of the accompanying objects. This technique is applied to white blood cell classification. Comparisons are made against "no context" approach, which demonstrates the superior classification performance achieved by using context. In our particular application, it significantly reduces false alarm rate and thus greatly reduces the cost due to expensive clinical tests.
Reuse has been proposed as a microarchitecture-level mechanism to reduce the amount of executed instructions, collapsing dependencies and freeing resources for other instructions. Previous works have used reuse domain...
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Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires ...
Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV’s decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments.
In multi-converter power electronic systems, different converters such as DC/DC choppers, DC/AC inverters, and AC/DC rectifiers are used in source, load, and distribution subsystems to provide power at different volta...
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In multi-converter power electronic systems, different converters such as DC/DC choppers, DC/AC inverters, and AC/DC rectifiers are used in source, load, and distribution subsystems to provide power at different voltage levels and forms. Most of the loads are also in the form of power electronic converters and motor drives. The most popular examples of these systems are automotive systems and more electric/hybrid electric vehicles. These systems have unique characteristics, dynamics, and stability problems that are just beginning to be appreciated. In this paper, we take a closer look at multiconverter power electronic systems and address the fundamental problems faced in these systems.
Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicitly for human motion synthesis. Howeve...
Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicitly for human motion synthesis. However, these compact text representations may overemphasize the action names at the expense of other important properties and lack fine-grained details to guide the synthesis of subtly distinct motion. In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into hierarchical semantic graphs including three levels of motions, actions, and specifics. Such global-to-local structures facilitate a comprehensive understanding of motion description and fine-grained control of motion generation. Correspondingly, to leverage the coarse-to-fine topology of hierarchical semantic graphs, we decompose the text-to-motion diffusion process into three semantic levels, which correspond to capturing the overall motion, local actions, and action specifics. Extensive experiments on two benchmark human motion datasets, including HumanML3D and KIT, with superior performances, justify the efficacy of our method. More encouragingly, by modifying the edge weights of hierarchical semantic graphs, our method can continuously refine the generated motion, which may have a far-reaching impact on the community. Code and pre-trained weights are available at https://***/jpthu17/GraphMotion.
Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT...
Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).
The initial years of an infant’s life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plasticity. In recent studies, an AI agent,...
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