RF Energy Harvesting Techniques for Battery-less Wireless Sensing, Industry 4.0 and Internet of Things: A Review

As the Internet of Things (IoT) continues to expand, the demand for the use of energy-efficient circuits and battery-less devices has grown rapidly. Battery-less operation, zero maintenance and sustainability are the desired features of IoT devices in fifth generation (5G) networks and green Industry 4.0 wireless systems. The integration of energy harvesting systems, IoT devices and 5G networks has the potential impact to digitalize and revolutionize various industries such as Industry 4.0, agriculture, food, and healthcare, by enabling real-time data collection and analysis, mitigating maintenance costs, and improving efficiency. Energy harvesting plays a crucial role in envisioning a low-carbon Net Zero future and holds significant political importance. This survey aims at providing a comprehensive review on various energy harvesting techniques including radio frequency (RF), multi-source hybrid and energy harvesting using additive manufacturing technologies. However, special emphasis is given to RF-based energy harvesting methodologies tailored for battery-free wireless sensing, and powering autonomous low-power electronic circuits and IoT devices. The key design challenges and applications of energy harvesting techniques, as well as the future perspective of System on Chip (SoC) implementation, data digitization in Industry 4.0, next-generation IoT devices, and 5G communications are discussed

kNN and SVM classification for EEG: a review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances

Achievable tolerances in robotic feature machining operations using a low-cost hexapod

Portable robotic machine tools potentially allow feature machining processes to be brought to large parts in various industries, creating an opportunity for capital expenditure and operating cost reduction. However, robots lack the machining capability of conventional equipment, which ultimately results in dimensional errors in parts. This work showcases a low-cost hexapod-based robotic machine tool and presents experimental research conducted to investigate how the widely researched robotic machining challenges, e.g. structural dynamics and kinematics, translate to achievable tolerance ranges in real-world production to highlight currently feasible applications and provide a context for considering technology improvements. Machining trials assess the total dimensional errors in the final part over multiple geometries. A key finding is error variation which is in the sub-millimetre range, although, in some cases, upper tolerance limits < 100 μm are achieved. Practical challenges are also noted. Most significantly, it is demonstrated that dimensional machining error is mainly systematic in nature and therefore that the total error can be dramatically reduced with in situ measurement and compensation. Potential is therefore found to achieve a flexible, high-performance robotic machining capability despite complex and diverse underlying scientific challenges. Overall, the work presented highlights achievable tolerances in low-cost robotic machining and opportunities for improvement, also providing a practical benchmark useful for process selection

Process capability analysis in non normal linear regression profiles

When the distribution of a process characterized by a profile is non normal, process capability analysis using normal assumption often leads to erroneous interpretations of the process performance. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is represented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that the quality characteristics follow a normal distribution; however, in certain applications this assumption may fail to hold and may yield misleading results. In this article, we consider process capability analysis of non normal linear profiles. We investigate and compare five methods to estimate non normal process capability index (PCI) in profiles. In three of the methods, an estimation of the cumulative distribution function (cdf) of the process is required to analyze process capability in profiles. In order to estimate cdf of the process, we use a Burr XII distribution as well as empirical distributions. However, the resulted PCI with estimating cdf of the process is sometimes far from its true value. So, here we apply artificial neural network with supervised learning which allows the estimation of PCIs in profiles without the need to estimate cdf of the process. Box-Cox transformation technique is also developed to deal with non normal situations. Finally, a comparison study is performed through the simulation of Gamma, Weibull, Lognormal, Beta and student-t data

A note on multivariate non-normal process capability analysis using "mvtnorm" library in R

In this note we examine using Genzbretz and Miwa algorithms to improve estimation of proposition of non-conformance in multivariate normal distributions. This estimation is required in the procedure outlined in Abbasi and Niaki (Int J Adv Manuf Technol 50(5-8):823-830, 2010) to determine process capability index of multivariate non-normal processes

On the issuing policies for perishable items such as red blood cells and platelets in blood service

Red blood cells (RBCs) and platelets are examples of perishable items with a fixed shelf life. Recent studies show that transfusing fresh RBCs may lead to an improvement of patient outcomes. In addition, to better manage their inventory, hospitals prefer to receive fresh RBCs and platelets. Therefore, as well as minimizing outdates and shortages, reducing the average age of issue is a key performance criterion for blood banks. The issuing policy in a perishable inventory system has a substantial impact on the age of issue and outdate and shortage rates. Although several studies have compared the last in first out (LIFO) and the first in first out (FIFO) policies for perishable products, only a few studies have considered the situation of blood banks where replenishment is not controllable. In this study, we examine various issuing policies for a perishable inventory system with uncontrollable replenishment, and outline a modified FIFO policy. Our proposed modified FIFO policy partitions the inventory into two parts such that the first part holds the items with age less than a threshold. It then applies the FIFO policy in each part and the LIFO policy between the parts. We present two approximation techniques to estimate the average age of issue, the average time between successive outdates and the average time between successive shortages of the modified FIFO policy. Our analysis shows in several cases that where the objective function is a single economic function, or it is formulated as a multiobjective model, the modified FIFO policy outperforms the FIFO and LIFO policies

Evaluation of process capability indices of linear profiles

In profile monitoring, which is a growing research area in the field of statistical process control, the relationship between response and explanatory variables is monitored over time. The purpose of this paper is to focus on the process capability analysis of linear profiles. Process capability indices give a quick indication of the capability of a manufacturing process

Tail conditional expectation for multivariate distributions: a game theory approach

This paper proposes using the Shapley values in allocating the total tail conditional expectation (TCE) to each business line (Xj , j = 1, . . . ,n) when there are n correlated business lines. The joint distributions of Xj and S (S = X1 + X2 + · · · + Xn) are needed in the existing methods, but they are not required in the proposed method

Intensive care unit discharge policies prior to treatment completion

In this study we explore a model to optimize the Intensive Care Unit (ICU) discharging decisions prior to service completion as a result of capacity-constrained situation under uncertainty. Discharging prior to service completion, which is called demand-driven discharge or premature discharging, increases the chance that a patient to be readmitted to the ICU in the near future. Since readmission imposes an additional load on ICUs, the cost of demand-driven discharge is pertained to the surge of readmission chance and the length of stay (LOS) in the ICU after readmission. Hence, the problem is how to select a current patient in the ICU for demand-driven discharge to accommodate a new critically ill patient. In essence, the problem is formulated as a stochastic dynamic programming model. However, even in the deterministic form i.e. knowing the arrival and treatment times in advance, solving the dynamic programming model is almost unaffordable for a sizable problem. This is illustrated by formulating the problem by an integer programming model. The uncertainties and difficulties in the problem are convincing reasons to use the optimization-simulation approach. Thus, using simulations, we evaluate various scenarios by considering Weibull distribution for the LOS. While it is known that selecting a patient with the lowest readmission risk is optimum under certain conditions and supposing a memory-less distribution for LOS; we remark that when LOS is non-memory-less, considering readmission risk and remaining LOS rather than just readmission risk leads to better results