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61.
Aishani Mazumder Chung Kim Nguyen Thiha Aung Mei Xian Low Md. Ataur Rahman Salvy P. Russo Sherif Abdulkader Tawfik Shifan Wang James Bullock Vaishnavi Krishnamurthi Nitu Syed Abhishek Ranjan Ali Zavabeti Irfan H. Abidi Xiangyang Guo Yongxiang Li Taimur Ahmed Torben Daeneke Akram Al-Hourani Sivacarendran Balendhran Sumeet Walia 《Advanced functional materials》2023,33(36):2303641
Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In2O3) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand. 相似文献
62.
Md. Moklesur Rahman Heung-Gyoon Ryu 《International Journal of Communication Systems》2023,36(8):e5475
In this paper, in order to improve the received signal strength (RSS) and signal quality, three arrays of electronically steerable parasitic array radiator (ESPAR) antennas are suggested for the ultra-high frequency (UHF) radio frequency identification (RFID) communication and sensing system applications. Instead of the single antenna, the array antennas have recently been widely used in many communication systems because of their peak gains, better radiation patterns, and higher radiation efficiency. Also, there are some important issues to use the antenna array like high data rates in wireless communication systems and to better understand the many targets or sensors. In this article, a wireless sensor network (WSN) is being investigated to overcome multipath fading and interference by antenna nulling technology that can be achieved through beam control ESPAR array antennas. The proposed ESPAR array antennas exhibit higher gains like 9.63, 10.2, and 12 dBi and proper radiation patterns from one array to another. Moreover, we investigate the mutual coupling effect on the performance of array antennas with different spacing (0.5λ, 0.75λ, λ) and configurations. It is found that the worst mutual coupling reduced by −28 to −34 dB for 2 × 2 array, −3 to −43 dB for 2 × 3 array, and finally −42 dB to −51 dB due to the antenna spacing from 0.5λ to λ. Thus, these suggested antennas could effectively be applied in the WSN communication systems, internet of things (IoT) networks, and massive wireless and backscatter communication systems. 相似文献
63.
Mostafizur Rahman Rana Erik Upol Biswas Sultan Mahmud Salman Meem Sahel Syeda Sarita Hassan Mahdy Rahman Chowdhury Mahdy 《Advanced Engineering Materials》2023,25(20):2300438
Electromagnetic wideband absorption is still perceived as a critical and formidable challenge to address with an unambiguous photonic absorber. Subwavelength metamaterial (MM) unit cells with unique and controlled features have recently gained considerable interest. However, meta-atoms, generated using a quantum-inspired pattern distribution, are underwhelming in existing literature to design photonic absorbers and their potential application to manufacture solar sails is still quite uncommon. In this article, to create a flexible, polarization-insensitive, ultrathin, and broadband MM absorber, quantum interference pattern-inspired design is utilized. Herein, a novel approach to fabricating solar sails for the space exploration incorporates the proposed broadband photonic absorber rather than conventional reflectors. The quantum-inspired meta-absorber (QIMA) exhibits an absorption of over 91% for the visible domain, i.e., 380–800 nm under a conventional plane-polarized source. It is shown in the study that broadband absorbers are almost equivalent to excellent reflectors to design the solar sails in terms of the time-averaged force calculated by utilizing the Maxwell stress tensor method. Thus, the QIMA has the potential to be a viable alternative to reflectors in the design of futuristic solar sails for space exploration. The interference theory model is also utilized to assure the dependability of calculated data, and additionally, the standard AM1.5 solar spectrum is utilized to demonstrate the QIMA's solar-harvesting potentiality. 相似文献
64.
Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results. 相似文献
65.
Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M. Alqhtani Omar AlShorman 《计算机、材料和连续体(英文)》2023,76(1):711-729
A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region. 相似文献
66.
S Brady CM Hiam R Saemann L Humbert MZ Fleming K Dawkins-Brickhouse 《Canadian Metallurgical Quarterly》1996,32(6):573-578
The treatment of "dual diagnosis", co-occurring substance abuse and mental illness, calls for addressing two serious and often confounding problems. The authors introduce an expanded version of the transtheoretical model of change as formulated by J.O. Prochaska and C.C. DiClemente, and suggest that this new version offers a pragmatic approach to the conceptualization and treatment of dual diagnosis. The potential utility of the treatment model is presented through the authors' experiences in working with inner-city, chronic mentally ill individuals with substance abuse problems. Practical guidelines for dual diagnosis group therapy are discussed. 相似文献
67.
This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th... 相似文献
68.
The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in speaker’s utterance. In this paper, a new cue-based model for dialogue act recognition is presented. The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network’s random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. To evaluate the proposed approaches of design, three stages of experiments have been conducted. In the initial stage, the dynamic Bayesian network model is constructed using sets of lexical cues selected manually from the dialogue corpus. The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks’ random variables. The developed approach is evaluated against some of the previously used ranking approaches and the results provide experimental evidences on its ability to avoid the drawbacks of the ranking approaches. In the third stage, the dynamic Bayesian networks model is constructed using random variables constituted from the sets of lexical cues generated in the second stage and the results confirm the effectiveness of the proposed approaches for designing dialogue act recognition model. 相似文献
69.
Haijun Zhang Author Vitae Author Vitae M.K.M. Rahman Author Vitae 《Pattern recognition》2009,42(11):2950-2719
A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval. 相似文献
70.
Tommy W.S. Chow Haijun Zhang M.K.M. Rahman 《Expert systems with applications》2009,36(10):12023-12035
This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval. 相似文献