The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm behavior as a function of these two parameters. This paper studies the steady-state behavior and the transient behavior of the Gaussian KLMS algorithm for Gaussian inputs and a finite order nonlinearity model. In particular, we derive recursive expressions for the mean-weight-error vector and the mean-square-error. The model predictions show excellent agreement with Monte Carlo simulations in transient and steady state. This allows the explicit analytical determination of stability limits, and gives opportunity to choose the algorithm parameters a priori in order to achieve prescribed convergence speed and quality of the estimate. Design examples are presented which validate the theoretical analysis and illustrates its application.

Cryptography is considered indispensable among security measures applied to data concerning insecure means of transmission. Among various existent algorithms on asymmetric cryptography, we may cite Elliptic Curve Cryptography (ECC), which has been widely used due to its security level and reduced key sizes. When compared to Rivest, Shamir and Adleman (RSA), for example, ECC can maintain security levels with a shorter key. Elliptic Curve Point Multiplication (ECPM) is the main function in ECC, and is the component with the highest hardware cost. Lots of ECPM implementations have been applied on hardware targeting the acceleration of its calculus. This article presents a systematic review of literature on ECPM implementations on both Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC). The obtained results show which methods and technologies have been used to implement ECPM on hardware and present some findings of the choices available to the hardware designers.

The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient’s no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law.

No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling.

Since the early 2000s, life in cities has changed significantly due to the Internet of Things (IoT). This concept enables developers to integrate different devices collecting, storing, and processing a large amount of data, enabling new services to improve various professional and personal activities. However, privacy issues arise with a large amount of data generated, and solutions based on blockchain technology and smart contract have been developed to address these issues. Nevertheless, several issues must still be taken into account when developing blockchain architectures aimed at the IoT scenario because security flaws still exist in smart contracts, mainly due to the lack of ease when building the code. This article presents a blockchain storage architecture focused on license plate recognition (LPR) systems for smart cities focusing on privacy, performance, and security. The proposed architecture relies on the Ethereum platform. Each smart contract matches the privacy preferences of a license plate to be anonymized through public encryption. The storage of data captured by the LPR system can only be done if the smart contract enables it. However, in the case of motivation foreseen by the legislation, a competent user can change the smart contract and enable the storage of the data captured by the LPR system. Experimental results show that the performance of the proposed architecture is satisfactory, regarding the scalability of the built private network. Furthermore, tests on our smart contract using security and structure analysis tools on the developed script demonstrate that our solution is fraud-proof. The results obtained in all experiments bring evidence that our architecture is feasible to be used in real scenarios.

The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.