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The speed difference between high-performance CPUs and energy-efficient CPUs, which are found in asymmetric performance multicore processors, affects the current form of Amdahl’s law equation. This paper proposes two updates to that equation based on the performance evaluation results of a simple parallel pi program written with OpenCilk. Performance evaluation was done by measuring execution time and instructions per cycle (IPC). The performance evaluation of the parallel program executed on the Intel Core i5 1240P processor did not indicate decreased performance due to asymmetric performance. Instead, the program with efficient work-stealing advantages from OpenCilk performed well. In the case of using the execution time of the P-CPU as a reference to obtain speedup, the evaluation results in a sublinear speedup. Conversely, in the case of using the execution time of the E-CPU as a reference, the evaluation results in a superlinear speedup. This paper proposes two updates to Amdahl’s law equation based on these two evaluation results.

This research work presents a depleted demand inventory model with constant deterioration. The rate of change of demand is assumed to be a time-dependent function. Initial non-zero demand occurs due to advertisements. Advertisement cost is assumed to be constant. Two types of models are considered for two replenishment strategies viz., without shortages and with shortages. This study aims to obtain a suitable policy for replenishment for minimizing the total inventory cost. Four examples about the alterations made in the optimal solutions due to different values of independent parameters used in the models are considered and discussed. Sensitivity analysis is done and numerical illustrations are provided for validating the approach presented.

Background and objective

Electronic fetal heart monitoring is currently used during pregnancy throughout most of the developed world to detect risk conditions for both the mother and the fetus. Non-invasive fetal electrocardiogram (NI-fECG), recorded in the maternal abdomen, represents an alternative to cardiotocography, which could provide a more accurate estimate of fetal heart rate. Different methodologies, with varying advantages and disadvantages, have been developed for NI-fECG signal detection and processing.

Methods

In this context, we propose a hybrid methodology, combining independent component analysis, signal quality indices, empirical mode decomposition, wavelet thresholding and correlation analysis for NI-fECG optimized signal extraction, denoising, enhancement and addressing the intrinsic mode function selection problem.

Results

The methodology has been applied in four different datasets, and the obtained results indicate that our method can produce accurate fetal heart rate (FHR) estimations when tested against different datasets of variable quality and acquisition protocols, on the FECGDARHA dataset our method achieved average values of Sensitivity = 98.55%, Positive Predictive Value = 91.73%, F1 = 94.92%, Accuracy = 90.91%, while on the ARDNIFECG dataset it achieved average values of Sensitivity = 92.96%, Positive Predictive Value = 91.66%, F1 = 93.60%, Accuracy = 90.45%.

Conclusions

The proposed methodology is completely unsupervised, has been proven robust in different signal-to-noise ratio scenarios and abdominal signals, and could potentially be applied to the development of real-time fetal monitoring systems.

In this article a real-time method is proposed that reduces the pose estimate error for robots capable of motion on the 2D plane. The solution that the method provides addresses the recent introduction of low-cost panoramic range scanners (2D LIDAR range sensors whose field of view is 360${}^{\circ }$), whose use in robot localisation induces elevated pose uncertainty due to their significantly increased measurement noise compared to prior, costlier sensors. The solution employs scan-to-map-scan matching and, in contrast to prior art, its novelty lies in that matching is performed without establishing correspondences between the two input scans; rather, the matching problem is solved in closed form by virtue of exploiting the periodicity of the input signals. The correspondence-free nature of the solution allows for dispensing with the calculation of correspondences between the input range scans, which (a) becomes non-trivial and more error-prone with increasing input noise, and (b) involves the setting of parameters whose output effects are sensitive to the parameters’ correct configuration, and which does not hold universal or predictive validity. The efficacy of the proposed method is illustrated through extensive experiments on public domain data and over various measurement noise levels exhibited by the aforementioned class of sensors. Through these experiments we show that the proposed method exhibits (a) lower pose errors compared to state of the art methods, and (b) more robust pose error reduction rates compared to those which are capable of real-time execution. The source code of its implementation is available for download.

Healthcare is experiencing a fast change from a hospital-centric and specialist-focused model to one that is dispersed and patient-centric. Numerous technological advancements are driving this fast evolution of the healthcare sector. Communication technologies, among others, have permitted the delivery of customized and distant healthcare services. The present 4G networks and other wireless communication technologies are being utilized by the healthcare industry to create smart healthcare applications. These technologies are continuously evolving to meet the expectations and requirements of future smart healthcare applications. At the moment, current communication technologies are incapable of meeting the dynamic and complex demands of smart healthcare applications. Thus, the future 5G and beyond 5G networks are expected to support smart healthcare applications such as remote surgery, tactile internet and Brain-computer Interfaces. Future smart healthcare networks will combine IoT and advanced wireless communication technologies that will address current limitations related to coverage, network performance and security issues. This paper presents 5G-based smart healthcare architecture, key enabling technologies and a deep examination of the threats and solutions for maintaining the security and privacy of 5G-based smart healthcare networks.

There are various types of obstacles in an emergency, and the traffic environment is complicated. It is critical to detect obstacles accurately and quickly in order to improve traffic safety. The obstacle detection algorithm based on deep learning cannot detect all types of obstacles because it requires pre-training. The VIDAR (Vision-IMU-based Detection and Range method) can detect any three-dimensional obstacles, but at a slow rate. In this paper, an improved VIDAR and machine learning-based obstacle detection method (hereinafter referred to as the IVM) is proposed. In the proposed method, morphological closing operation and normalized cross-correlation are used to improve VIDAR. Then, the improved VIDAR is used to quickly match and remove the detected unknown types of obstacles in the image, and the machine learning algorithm is used to detect specific types of obstacles to increase the speed of detection with the average detection time of 0.316s. Finally, the VIDAR is used to detect regions belonging to unknown types of obstacles in the remaining regions, improving detection performance with the accuracy of 92.7%. The flow of the proposed method is illustrated by the indoor simulation test. Moreover, the results of outdoor real-world vehicle tests demonstrate that the method proposed in this paper can quickly detect obstacles in real-world environments and improve detection accuracy.

Depression is a common psychiatric disorder that is becoming more prevalent in developing countries like Bangladesh. Depression has been found to be prevalent among youths and influences a person’s lifestyle and thought process. Unfortunately, due to the public and social stigma attached to this disease, the mental health issue of individuals are often overlooked. Early diagnosis of patients who may have depression often helps to provide effective treatment. This research aims to develop mechanisms to detect and predict depression levels and was applied to university students in Bangladesh. In this work, a questionnaire containing 106 questions has been constructed. The questions in the questionnaire are primarily of two kinds – (i) personal, and (ii) clinical. The questionnaire was distributed amongst Bangladeshi students and a total of 684 responses (aged between 19 and 35) were obtained. After appropriate consents from the participants, they were allowed to take the survey. After carefully scrutinizing the responses, 520 samples were taken into final consideration. A hybrid depression assessment scale was developed using a voting algorithm that employs eight well-known existing scales to assess the depression level of an individual. This hybrid scale was then applied to the collected samples that comprise personal information and questions from various familiar depression measuring scales. In addition, ten machine learning and two deep learning models were applied to predict the three classes of depression (normal, moderate and extreme). Five hyperparameter optimizers and nine feature selection methods were employed to improve the predictability. Accuracies of 98.08%, 94.23%, and 92.31% were obtained using Random Forest, Gradient Boosting, and CNN models, respectively. Random Forest accomplished the lowest false negatives and highest F Measure with its optimized hyperparameters. Finally, LIME, an explainable AI framework, was applied to interpret and retrace the prediction output of the machine learning models.

Breast cancer, lung cancer, skin cancer, and blood malignancies such as leukemia and lymphoma are just a few instances of cancer, which is a collection of cells that proliferate uncontrollably within the body. Acute lymphoblastic leukemia is of one the significant form of malignancy. The hematologists frequently makes an oversight while determining a blood cancer diagnosis, which requires an excessive amount of time. Thus, this research reflects on a novel method for the grouping of the leukemia with the aid of the modern technologies like Machine Learning and Deep Learning. The proposed research pipeline is occupied into some interconnected parts like dataset building, feature extraction with pre-trained Convolutional Neural Network (CNN) architectures from each individual images of blood cells, and classification with the conventional classifiers. The dataset for this study is divided into two identical categories, Benign and Malignant, and then reshaped into four significant classes, each with three subtypes of malignant, namely, Benign, Early Pre-B, Pre-B, and Pro-B. The research first extracts the features from the individual images with CNN models and then transfers the extracted features to the features selections such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and SVC Feature Selectors along with two nature inspired algorithms like Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). After that, research has applied the seven Machine Learning classifiers to accomplish the multi-class malignant classification. To assess the efficacy of the proposed architecture a set of experimental data have been enumerated and interpreted accordingly. The study discovered a maximum accuracy of 98.43% when solely using pre-trained CNN and classifiers. Nevertheless, after incorporating PSO and CSO, the proposed model achieved the highest accuracy of 99.84% by integrating the ResNet50 CNN architecture, SVC feature selector, and LR classifiers. Although the model has a higher accuracy rate, it does have some drawbacks. However, the proposed model may also be helpful for real-world blood cancer classification.

With the proliferation of smartphones, digital data collection has become trivial. The ability to analyze images has increased, but source authentication has stagnated. Editing and tampering of images has become more common with advancements in signal processing technology. Recent developments have introduced the use of seam carving (insertion and deletion) techniques to disguise the identity of the camera, specifically in the child pornography market. In this article, we focus on the available features in the image based on PRNU (photo response nonuniformity). The forced-seam sculpting technique is a well-known method to create occlusion for camera attribution by injecting seams into each 50 × 50 pixel block. To counter this, we perform camera identification using a 1D CNN integrated with feature extractions on 20 × 20 pixel blocks. We achieve state-of-the-art performance for our proposed IMG_CAT (image categorization) in three-class classification over the baselines (original, seam removed, seam inserted). Based on our experimental findings, our model is robust when dealing with blind facts related to the questionable camera.

A large number of sensor and actuator devices are being deployed for sensing and automation in a smart environment. While enabling communication for a large number of stations with RAW in IEEE 802.11ah, the state-of-the-art solutions for channel access are deficient in dealing with both periodic uplink and event-driven downlink actuation at the same time, as per the application’s criteria. In this paper, we propose IoT-MAC, a downlink traffic-aware Medium Access Control (MAC) protocol for automation in smart spaces. The proposed scheme uses new RAW frames to schedule downlink actuation traffic, considering the periodicity and freshness of uplink traffic. IoT-MAC identifies the periodicity of uplink traffic and schedules a frame without further contention. It then prioritizes critical downlink traffic without losing fresh uplink data. The performance analysis of the proposed scheme shows significant improvement in terms of throughput, delay, power consumption and packet loss for running different IoT applications.