The design and optimization of the high-speed digital subscriber line (HDSL) need powerful computational strategies. Traditional techniques of distributing poles and zeros on Smith charts generally do not work. In the past, such approaches have lead to suboptimal designs for applications where the data capacity sought is considerably less than the Shannon capacity of the lines. Typical subscriber loops are less than perfect and for the current demands on the HDSL at T1/T2 and El rates every possible venue for the HDSL design needs to be investigated, if not exploited. In this Chapter, flexible computational techniques are presented that explore and optimize system components in view of the operating environment of the HDSL and/or the asymmetric digital subscriber line (ADSL) and inherent limitations of system components. The optimization occurs automatically by forcing the computer to track the effects of incremental changes of the subsystem performance (e.g., echo cancelers or equalizers), or the component values (Rs and Cs in the matching circuits) in context to the functional constraints of the (HDSL, ADSL, duplex, dual-duplex, triplex, etc.) line in conjunction with various subscriber loop environments (CSA loops, loops
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In regional hyperthermia, optimization techniques are valuable in order to obtain amplitude/phase settings for the applicators to achieve maximal tumour heating without toxicity to normal tissue. We implemented a temperature-based optimization technique and maximized tumour temperature with constraints on normal tissue temperature to prevent hot spots. E-field distributions are the primary input for the optimization method. Due to computer limitations we are restricted to a resolution of 1 x 1 x 1 cm3 for E-field calculations, too low for reliable treatment planning. A major problem is the fact that hot spots at low-resolution (LR) do not always correspond to hot spots at high-resolution (HR), and vice versa. Thus, HR temperature-based optimization is necessary for adequate treatment planning and satisfactory results cannot be obtained with LR strategies. To obtain HR power density (PD) distributions from LR E-field calculations, a quasi-static zooming technique has been developed earlier at the UMC Utrecht. However, quasi-static zooming does not preserve phase information and therefore it does not provide the HR E-field information required for direct HR optimization. We combined quasi-static zooming with the optimization method to obtain a millimetre resolution temperature-based optimization strategy. First we performed a LR (1 cm) optimization and used the obtained settings to calculate the HR (2 mm) PD and corresponding HR temperature distribution. Next, we performed a HR optimization using an estimation of the new HR temperature distribution based on previous calculations. This estimation is based on the assumption that the HR and LR temperature distributions, though strongly different, respond in a similar way to amplitude/phase steering. To verify the newly obtained settings, we calculate the corresponding HR temperature distribution. This method was applied to several clinical situations and found to work very well. Deviations of this estimation method for the AMC-4 system were typically smaller than 0.2 degrees C in the volume of interest, which is accurate enough for treatment planning purposes.
In this work, it is shown that DoA estimation capabilities of a lens-loaded cavity can be systematically enhanced by converting it into a lens-loaded dynamic aperture optimized efficiently. This is implemented by introducing dynamic reconfigurability into the lens-loaded cavity by adding a mechanically controlled mode-mixing mechanism; thus, adding another dimension to physically control the aperture performance. The benefit of using a lens structure placed in front of the cavity is that it enhances the quasi-random variations in the radiation modes previously shown in19,20; hence, impacting positively to the spatio-temporal bases, which in turn improves the DoA estimation accuracy. This is then followed by dynamically reconfiguring the lens-loaded aperture optimized by a machine learning (ML)-assisted evolutionary algorithm for a given wireless channel, which enhances the DoA estimator performance further, shown in this paper. To circumvent the need for a reasonably good initial design, ad-hoc process, and a large number of full-wave electromagnetic (EM) simulations which are often needed by popular global optimization techniques (e.g., evolutionary algorithms), an ML-assisted antenna design optimization algorithm from the surrogate model-assisted differential evolution for antenna synthesis (SADEA) series21,22,23,24,25 is employed for the targeted aperture optimization. In comparison to standard global optimization methods (e.g., particle swarm optimization), the selected algorithm (i.e., SADEA-I21) provides up to 20 times speed improvement, while obtaining design solutions of comparable or better quality for many antenna cases26, making it a good choice for the targeted problem. SADEA-I employs the surrogate model-aware evolutionary search (SMAS) framework for surrogate model management27, which shows a harmonious balance between evolutionary algorithm-based global search and surrogate modeling.
Considering the targeted simulation-driven aperture design optimization problem, there are several local and global optimization methods in the literature, such as26,31,32,33. Local optimization techniques rely on good initial designs that the designer needs to specify as starting points31. However, in our case, it is difficult to find a good initial design. Global optimization-based EM device design techniques (e.g.33,34,35) do not require initial designs, but they often require a large (sometimes prohibitive) number of EM simulations to obtain optimal results26,33,34,35. For our targeted aperture, each EM simulation costs more than one hour. Hence, both kinds of methods are not suitable.
In recent years, the incorporation of ML techniques into the optimization kernel of standard EAs has been demonstrated to lower the computational cost of the optimization process, which is applied to EM device design36,37,38. This is mainly achieved through surrogate model-based optimization in which many computationally expensive EM simulations in the optimization process are replaced with surrogate model-based predictions. These surrogate models, also called metamodels, are computationally cheap approximation models of expensive full-wave EM simulations. They are often constructed using ML techniques and are used to emulate the characterization or behavior of the EM simulation model, as closely as possible. Even though many paradigms and methods are currently available for the ML-assisted optimization of EM designs as reported in36,37,38,39, some of these approaches still have the drawbacks of standard optimization methods and are not general due to the ad-hoc processes required to ensure their efficiencies.
Now let us examine when the SADEA-I-based optimization process is initialized and the lens-loaded cavity static state is updated via the rotation of the stepper motor shaft for the first design from the initial database of SADEA-I. The set of modes generated by the previous state of the lens-loaded cavity are no longer valid, and a new set of modes are generated, given the frequency-diverse functionality of high-Q chaotic cavity. Hence, the previous \(E(r,\omega ,1)\) and estimated \(P_est,M,1\) are also no longer valid; however, they are buffered to be used by the surrogate model to evaluated \(E(r,\omega ,2)\) and \(P_est,M,2\) for the updated state of the lens-loaded cavity (i.e., the subsequent designs generated by SADEA and each new design is numbered in 3rd subscript), here, represented as \(k\in [0^\circ , \, 360^\circ ]\) i.e., updated cavity state number. Note that only one (the best) state of the cavity is used for DoA estimation. Also, consider the number of resonances over a specific bandwidth, \(N_R\), from the full-wave EM simulation of each design (i.e., each static state of the lens-loaded cavity for a single E). These designs (i.e., k) and the associated \(N_R\) are used for a SADEA-I-based optimization of the lens-loaded cavity aperture for the first time in this paper, as described in the next section.
(a) Prominence and width of a resonance for a given frequency response. (b) Comparison between the frequency responses of selected designs generated during the SADEA-I-based optimization.
In this paper, it has been shown that a mechanically controlled mode-mixing scatterer can dynamically update the state of the lens-loaded aperture, optimized by the SADEA-I method, to provide a state best suited for improved DoA estimation accuracy. To quantitatively analyze the achievable improvement, we first optimized the aperture to maximize the number of radiation modes, and afterward optimized it to simultaneously have a large number of radiation modes as well as a reduced amount of correlation between the radiation modes at adjacent frequency points. The optimization process shown in this work is purely simulation-driven, while it verifies the functionality of our unique enabling technology of real-time lens-loaded cavity optimization in practical channels. It is shown that a mechanical rotation of the mode-mixing scatterer inside the lens-loaded cavity can produce a unique set of frequency-diverse modes and radiation masks. If this rotation is optimized using SADEA-I based on a given criterion, it can improve the dynamic aperture conditioning to enable accurate DoA estimation verified in this paper by full-wave EM simulations campaign. To quantify the benefits of the proposed technique, we show the singular value decomposition spectrum against the initial, intermediate, and final state of the lens-loaded cavity, revealing a 25\(\%\) reduction in the CN when comparing the initial with final state. Finally, DoA estimation patterns using initial and final cavity modes are compared with the ground truth to verify the validity of the dynamic aperture optimisation method. Future works include investigation of practical mode-mixing mechanism in a lens-loaded cavity hardware and practical verification of dynamic aperture optimization using the SADEA-I method. 2ff7e9595c
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