This paper presents a deep reinforcement learning-based beam-user selection and hybrid beamforming design for the multiuser massive multiple-input multiple-output (MIMO) non-orthogonal multiple ...access (NOMA) downlink systems. The conventional hybrid beamforming in massive MIMO provides multiple directional beams, but each beam serves only one user. The integration of NOMA with the massive MIMO enables power domain multiplexing within a beam, hence increasing the system capacity. In this paper, we first design a channel gain and correlation-based users grouping algorithm per beam, and then using the deep reinforcement learning-based beam selection, a beamspace orthogonal analog precoder is obtained. The deep Q-network consists of a main network and target network with Adam optimizer. Finally, optimal power is allocated to the users in each beam. Simulation results show that at transmit SNR of 10 dB, the proposed scheme provides a 42% increase in sum-rate and energy efficiency performance as compared to the state-of-the-art <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means users' grouping and Stable Matching-based beam selection NOMA scheme.
This paper reviews a number of biomedical engineering approaches to help aid in the detection and treatment of tropical diseases such as dengue, malaria, cholera, schistosomiasis, lymphatic ...filariasis, ebola, leprosy, leishmaniasis, and American trypanosomiasis (Chagas). Many different forms of non-invasive approaches such as ultrasound, echocardiography and electrocardiography, bioelectrical impedance, optical detection, simplified and rapid serological tests such as lab-on-chip and micro-/nano-fluidic platforms and medical support systems such as artificial intelligence clinical support systems are discussed. The paper also reviewed the novel clinical diagnosis and management systems using artificial intelligence and bioelectrical impedance techniques for dengue clinical applications.
In this paper, we present an energy-efficient Self Organizing Network (SON) architecture based on a tunable eNodeB (eNB) antenna tilt design for macrocells in a mobile network environment. This is an ...imperative element of mobility management in high speed and low latency wireless networks. The SON architecture follows a fully distributed approach with optional network information exchange with neighboring cells and core network. Antenna tilt directly affects its radiation pattern thus changes in eNB antenna tilt can be used to optimize cell coverage and reduce interference in mobile networks. We apply and compare two reinforcement machine learning techniques for optimizing the eNB antenna tilts, i.e., Deep Q-learning using Artificial Neural Network (ANN) and a simple Stochastic Cellular Learning Automata (SCLA). ANN is well known for its ability to learn from a vast number of inputs, while the stochastic learning technique relies on a simple action based probability vector updated based on system feedback. Neighboring cells for any one cell in the network environment are selected based on their separation distance and antenna orientation. We validate the data call performance of the network for edge users as they directly impact the Quality of Service (QoS) in the mobile environment. Our simulated results show that ANN performs better for edge users as compared to SCLA. The model also satisfies the SON requirement of scalability and agility. This work is a follow-up to our earlier work, where we showed that SCLA performs better than Q-learning in a similar network environment and optimizing strategy due to its low complexity, but within the same Q-learning algorithm more input learning parameters gave better performance.
Many applications which require accurate location point positioning systems utilize global position system for pseudosciences. One of the main challenges faced by the system occurs due to the ...inherent errors that are a resultant of outliers. This considerably reduces the accuracy of the observations of global position system device. In this paper, we briefly introduce the problem of position estimation when the pseudo range measurements have an outlier. Moving horizon estimation algorithm has been adapted for the simulation result compared with the extended Kalman filter model, which is still imperfect for the case of outlier. The point at which a pseudo range becomes an outlier is considered at a fixed time instance. A simulation example is presented using an existing model with a moving horizon estimator and an extended Kalman filter. The moving horizon filter turns to be more robust than Kalman filtering with presence of outlier under certain choice of tunning parameter.
Dengue disease is considered as one of the life threatening disease that has no vaccine to reduce its case fatality. In clinical practice the case fatality of dengue disease can be reduced to 1% if ...the dengue patients are hospitalized and prompt intravenous fluid therapy is administrated. Yet, it has been a great challenge to the physicians to decide whether to hospitalize the dengue patients or not due to the overlapping of the medical diagnosis criteria of the disease. Beside that physicians cannot decide to admit all patients because this will have major impact on health care cost saving due to the huge incident of dengue disease in the country. Even if the physicians managed to identify the critical cases to be hospitalized, most of the tools that have been used for monitoring those patients are invasive. Therefore, this study was conducted to develop a non-invasive accurate diagnostic system that can assist the physicians to diagnose the risk in dengue patients and therefore attain the correct decision. Bioelectrical Impedance Analysis measurements, Symptoms and Signs presented with dengue patients were incorporated with Adaptive Neuro-Fuzzy Inference System (ANFIS) to construct two diagnostic models. The first model was developed by systematically optimizing the initial ANFIS model parameters while the second model was developed by employing the subtractive clustering algorithm to optimize the initial ANFIS model parameters. The results showed that the ANFIS model based on subtractive clustering technique has superior performance compared with the other model. Overall diagnostic accuracy of the proposed system is 86.13% with 87.5% sensitivity and 86.7% specificity.
The scope of the difficulties that has been addressed in dengue disease includes the definition of the risk criteria in dengue disease and the prediction of the risk in dengue patients. It is ...critical to precisely and efficiently predict the level of risk in dengue disease for clinical care, surveillance and lifesaving. Even though some studies showed significant results in this area, a complete, systematic approach for predicting the risk in dengue disease has never been attained yet.
Therefore, this study was carried out to develop a noninvasive intelligent technique for predicting the risk in dengue patients. A combination of the self-organizing map (SOM) and multilayer feed-forward neural networks (MFNN) was employed for this task. Clinical manifestations and bioelectrical impedance analysis (BIA) parameters belonging to the dengue patients were considered for this aim. The SOM was used to define the significant risk predictors, whereas the MFNN was employed for constructing the prediction model.
Seven significant risk predictors as defined by SOM were employed for the dengue patient risk classification. The MFNN prediction model defined by 10 hidden neurons, momentum of 0.99, learning rate of 0.1 and iteration rate of 20,000 achieved a 70% predicative accuracy with 0.121 sum squared error.
Advancements in globalization have significantly seen a rise in road travel. This has also led to increased car accidents and fatalities, which become a global cause of concern. Driver's behavior, ...including drowsiness, contributes to many of the road deaths. The main objective of this study is to develop a system to diminish mishaps caused by the driver's drowsiness. Recently deep convolutional neural networks have been used in multiple applications, including identifying and anticipate driver drowsiness. However, limited studies investigated the systematic optimization of convolutional neural networks (CNNs) hyperparameters, which could lead to better anticipation of driver drowsiness. To bridge this gap, a holistic approach based on the deep learning method is proposed in this paper to anticipate the drivers' drowsiness and provide an alerting mechanism to prevent drowsiness related accidents. To ensure optimal performance achievement by the system, a database of real-time images preprocessed via Haar cascade's classifiers is used to systematically optimize the CNN model's hyperparameters. Different metrics, including accuracy, precision, recall, F1-score, and confusion matrix, are used to evaluate the performance of the model. The training evaluation results of the optimal model achieved an accuracy of 99.87%, while the testing results accurately classify the drowsy driver with 97.98%.
An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for ...linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation.
Smart Cart With Multi-shopping Solutions Faisal, Tarig; Awawdeh, Moath; Habte, Daniel ...
International journal of interactive mobile technologies,
12/2021, Letnik:
15, Številka:
24
Journal Article
Recenzirano
Consumerism has constantly been growing, and visiting marketplaces and purchasing is a key development factor for the economic development of a city. The spending habits of people are a reflector of ...many development indices. As people go out for purchasing in malls for shopping essential items, they often tend to find long queues at the billing counter, and the queues are more longer during weekends and during sale seasons. This has been deterring people from going out shopping and instead resort to online shopping. This has affected the sales and revenue of departmental chains. Also, people spend quite a considerable amount of time searching through rows in order to find the items of their choice. Proposing a solution to these above-mentioned problems using technology is a real boon. Installing a payment option on the cart itself would result in cutting down the waiting times in queues. Moreover, the customers can also benefit from navigation through the virtual maps that would be displayed on the screens of the carts. This reduces the time wasted by customers in searching for products. Hence the customers are self-dependent during their shopping. For the retail group, their transactions and management are truly simplified with these developments. Additionally, the counter labor will decrease if not cease to zero, which in turn reduces the labor cost drastically. In times of pandemic like now, people have been sticking with online shopping due to various reasons, but they long to go out and spend time in the malls as they would in normal times. The proposed solution would definitely assist in the normalization of lives and bring back on-ground retail back to life.
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