This paper introduces an improved miniaturization method for 5G hexagonal patch antennas using the corrugation technique. The antenna is initially optimized to operate at 28 GHz, achieving a return loss (RL) of 17dB. By adding corrugated slots to the radiator element, the antenna size is reduced by $75 \%$ while enhancing its performance. The tiny design achieves an RL of 40dB, a bandwidth of 3.3 GHz, a gain of $\mathbf{5 ~ d B i}$, and compact dimensions of $\mathbf{5. 4 4} \times \mathbf{5. 4 3} \times \mathbf{0. 8 2 1} \mathrm{mm}^{3}$. These results show the effectiveness of the proposed design in providing high performance within a much smaller footprint, making it a promising option for 5G millimeter-wave applications.

Mobile edge computing (MEC) has recently become a key technology that appeared in response to the increasing focus on the emergence of new heterogeneous service chain requests, the resource-constrained mobile devices, and the long delay provided by the conventional Cloud Data Centers. Although many researchers have investigated how the heterogeneous service function chain (SFC) requests can interact with the MEC system, very few have tackled how to deploy a flexible MEC infrastructure at the mobile operator for the expected mobile traffic. This paper addresses the design and dimensioning of an agile MEC infrastructure problem with aid of Network Function Virtualization (NFV) technology. To improve physical resource utilization, we consider virtual instances resources overheads and virtual instances shareability in the deployment process. A mixed-Integer Linear Program (MILP) is used to construct a physical MEC system design by mapping the received SFCs' virtual networks to the dimensioned MEC nodes while minimizing the deployment cost. Numerical results highlight the value gained in reducing the deployment cost while guaranteeing the delay requirements for different SFCs with high acceptance rates.

Epilepsy is a brain disorder that can significantly affect a patient's health. Therefore, seizure prediction techniques have gained a lot of attention to minimize the potential damages caused by epilepsy and improve the quality-of-life of epileptic patients. In this paper, an algorithm based on the linear Support Vector Machine (SVM) tool was proposed to classify intracranial electroencephalography (iEEG) signals as ictal or interical, in order to efficiently perform human seizure prediction. One of the most important parameters in predicting seizure is the size of the sliding window, whose optimization may significantly affect performance, as well as overlapping between windows. In this study, an optimum sliding window and overlapping rate are proposed for efficient seizure prediction. They allow accurate prediction of seizure events from a large set of EEG data. Applied to iEEG recordings of eight patients in the Freiburg EEG database, the proposed approach exhibits a sensitivity of 68% and specificity of 100% using 2-second-long window and 50% overlapping via 10 fold-cross validation.

This paper presents a compact high performance clock multiplier based on an injection locked ring oscillator (ILRO) with replica oscillator tuning. All cells were synthesized utilizing vendor supplied digital cells and automatically placed and routed to improve scalability and speed up the design process. The ILRO, fabricated in TSMC 65 nm CMOS process technology, occupies 102 μm × 170 μm chip area. At 1.1 V and 1.7 GHz, the measured phase noise for third harmonic injection locking was found to be -128 dBc/Hz at 1 MHz offset. Furthermore, it consumes 11.77 mW and achieves 200 fs RMS jitter (1 kHz to 40 MHz) and 86 MHz locking bandwidth.

Because devices in wireless transceivers are small and packed closely together, the effects of anisotropic printed-circuit-board materials can significantly affect the whole system performance. This paper deals with the hybrid-mode analysis of multilayer anisotropic circuits. Different orientations of the optical axis were investigated and the propagation characteristics studied. Based on the spectral domain approach, the proposed technique takes into account the multilayer aspect of these circuits. Computed results showed good agreement with data available in the literature as well as with simulations performed in electromagnetic commercial tools.

Clustering is an efficient topology control approach for maximizing the lifetime and scalability of Wireless Sensor Networks (WSNs). Many cluster-based routing techniques have been proposed in the literature. However, in most of the proposed protocols, the communication between a sensor node and

In this study, different pulse modulation parameters, such as symbol error rate (SER), power loss and bandwidth were investigated to highlight the most suitable parameter for narrowband interference cancellation in ultra‐wide‐band (UWB) Rake receivers. Thus, the performance of transmitter pulse was evaluated at the UWB Rake receiver. Finally, comparison of various filter bank receivers in terms of number of filters and SER was achieved by using a new accurate indoor UWB IEEE model channel with different propagation scenarios.

For Radio Frequency Identification (RFID) system, this paper presents an anti collision circuit design for fast of multi tags identification system. Principally, the authors use the Blind Signal Separation (BSS) method by the Independent Component Analysis (ICA) algorithm based computation of anti-collision circuit for speedily extracting the hidden factor of signal independence in neural network learning. Then, to reach the separation performance of BSS method, the design of the anti-collision circuit in the reader is based on the processing for more inputs of signal samples by one neuron hardware model of neural network through one multiplexer, which by means of a control circuit the original signal are estimated through one decoder. Thus, the performance results of the proposed approach is estimated since the speed rate of the anti-collision circuit provides numerical results, which evaluates the performance of an anti-collision circuit through the number of identified tags of RFID system.

Computation of passive and compact macromodels of distributed interconnects has gained considerable importance during the recent years. Method of characteristics (MoC) is widely used for macromodeling of transmission lines, however, it may not be guaranteed passive. This paper presents a new algorithm for passivity enforcement of MoC-based macromodels of multiconductor transmission lines. The algorithm is based on the first-order perturbation of the related delay differential equations and can handle single as well as coupled interconnects. Necessary theoretical foundations and validating numerical results are presented.

This paper presents a robust method for guaranteed passive macromodeling of high-speed interconnects characterized by multiconductor transmission lines with frequency-dependent per-unit-length (FD-PUL) parameters, using integrated-congruence-transformation-based model-order-reduction techniques. For this purpose, a new formulation is developed to directly incorporate state-space equations corresponding to the positive-real (PR) rational functions representing the frequency dependence of PUL parameters, in Telegrapher's equations. The proposed formulation overcomes the need for explicit lumped-circuit realization with all positive elements for PR rational functions of FD-PUL parameters. Proof of passivity and numerical examples are presented to validate the theory.

In this paper, we propose a VLSI (very large scale integrated) implementation of a hybrid neural fuzzy control scheme on a Xilinx Virtex2 Pro 2VP30 field programmable gate array (FPGA) for a flexible-joint robot manipulators with uncertain dynamics. The control strategy is based on a feedforward artificial neural network that approximates the manipulator's inverse dynamics. An adaptive feedback fuzzy sliding mode controller is used to compensate for residual errors. A systolic top-down hardware design methodology takes full advantage of the neural networks' inherent parallelism that allows the controller to operate at high frequencies. Furthermore, a pipeline strategy was used to speed-up the feedback fuzzy controller's inference process. Numerical simulations and the synthesis of the results highlight the effectiveness of the proposed controller in compensating for the nonlinear unknown manipulator's dynamics.

A new technique based on neural networks is presented for dynamic modeling of nonlinear telecommunication circuits in continuous time domain. The proposed feedforward dynamic neural network (FDNN) model can be developed directly from input-output large-signal measurements or simulations, without having to rely on internal details of the circuit. New formulations are derived in order to handle the important circuit-load effects in system level simulation. The resulting model is fast and can be used with connections to other circuit models, allowing us to perform efficient high-level system simulation and design. It is observed that the proposed FDNN approach provides the best overall performance of being much faster than original detailed system simulation and much more accurate than the conventional behavioral modeling approach. Examples of feedforward dynamic modeling of amplifiers, mixer and their use in telecommunication system simulation are presented, demonstrating the increased efficiency in designing telecommunications systems using the proposed technique.