The unexpectedly high number of deaths caused by inadequate medical care is, to date, considered as a serious problem. Besides, the ratio of elderly people who require continuous care is rising. Therefore, a patient monitoring system (PMS) also known as remote patient monitoring (RPM) using the latest Internet of Things (IoT) technology becomes a viable solution that can provide efficient healthcare from a remote distance. PMS monitors timely physiological signals of a patient’s health and can reduce the healthcare costs of treatment significantly. In PMS, different health and vital signs issues such as body temperature, heart rate, sleep monitoring, fall detection, and blood pressure can be checked effectively in real-time. To this end, this paper provides a clear vision of electronic healthcare assistance based on PMS and explores the applications of IoT that allow efficient medical services in healthcare systems. In particular, the objective of this paper is to provide a review of PMS, current research, and the challenges associated with this area. Besides, the essential services that can be offered by PMS for monitoring human activities are also discussed. Furthermore, the communication networks and protocols that are required to endure efficient healthcare systems are explained. Finally, this paper discusses several research challenges and open issues that can be investigated for further work. Overall, this paper offers valuable insights for both industry professionals and academic researchers, exploring potential avenues for new research directions.

The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes, process data, and communicate seamlessly within interconnected networks. Typically, such devices integrate low-power radio transmitters and multiple smart sensors, hence enabling efficient functionality across wide ranges of applications. Alongside these technological developments, the concept of the IoT has emerged as a transformative paradigm, facilitating the interconnection of uniquely identifiable devices through internet-based networks. This paper aims to provide a comprehensive exploration of sensor technologies, detailing their integral role within IoT frameworks and examining their impact on optimizing efficiency and service delivery in modern wireless communications systems. Also, it presents a thorough review of sensor technologies, current research trends, and the associated challenges in this evolving field, providing a detailed explanation of recent advancements and IoT-integrated sensor systems, with a particular emphasis on the fundamental architecture of sensors and their pivotal role in modern technological applications. It explores the core benefits of sensor technologies and delivers an in-depth classification of their fundamental types. Beyond reviewing existing developments, this study identifies key open research challenges and outlines prospective directions for future exploration, offering valuable insights for both academic researchers and industry professionals. Ultimately, this paper serves as an essential reference for understanding sensor technologies and their potential contributions to IoT-driven solutions. This study offers meaningful contributions to academic and industrial sectors, facilitating advancements in sensor innovation.

Deep submicron technologies continue to develop according to Moore’s law allowing hundreds of processing elements and memory modules to be integrated on a single chip forming multi/many-processor systems-on-chip (MPSoCs). Network on chip (NoC) arose as an interconnection for this large number of processing modules. However, the aggressive scaling of transistors makes NoC more vulnerable to both permanent and transient faults. Permanent faults persistently affect the circuit functionality from the time of their occurrence. The router represents the heart of the NoC. Thus, this research focuses on tolerating permanent faults in the router’s input buffer component, particularly the virtual channel state fields. These fields track packets from the moment they enter the input component until they leave to the next router. The hardware redundancy approach is used to tolerate the faults in these fields due to their crucial role in managing the router operation. A built-in self-test logic is integrated into the input port to periodically detect permanent faults without interrupting router operation. These approaches make the NoC router more reliable than the unprotected NoC router with a maximum of 17% and 16% area and power overheads, respectively. In addition, the hardware redundancy approach preserves the network performance in the presence of a single fault by avoiding the virtual channel closure.

Machine learning, a part of artificial intelligence, is recently used in speech enhancement algorithms (SE). The primary focus of SE is finding the original speech signal from the distorted one. Specifically, deep learning is used in SE because it handles nonlinear mapping problems for complicated features. In this paper, Charlier polynomials-based discrete transform, simply discrete Charlier transform (DCHT), has been used to get the spectra of the noisy signal using a fully connected neural network. Deep learning effectively acquires the context information of speech signal and gets enhanced speech with good quality and intelligibility properties. The proposed algorithm is tested experimentally through self-comparison to obtain the best speech enhancement models corresponding to the DCHT parameter. The experiment is performed with different values of the DCHT parameter. In addition, the well-known TIMIT database is used for evaluation purposes. Different speech measures are used in the experiment. The realized results show the ability of the trained model based on DCHT to enhance the speech signal and provide good results on specific conditions.

في السنوات الأخيرة، أدى التقدم التكنولوجي في إنترنت الأشياء (IoT) وأجهزة الاستشعار الذكية إلى فتح اتجاهات جديدة وإعطاء حلول عملية في مختلف قطاعات الحياة. يتم التعرف على إنترنت الأشياء كتنولوجيا حديثة تربط بين مختلف انواع الشبكات. تم تحسين أنواع مختلفة من قطاعات الرعاية الصحية في المجال الطبي بناءً على هذه التكنولوجيا. أحد هذه القطاعات الهامة هو نظام مراقبة الصحة (HMS). تعتبر مراقبة المريض عن بعد لاسلكيًا وبتكلفة منخفضة أمرًا بالغ الأهمية في هذا المجال. في هذا العمل، تم اقتراح نظام مراقبة صحة المريض في الوقت الحقيقي وبتكلفة منخفضة. يتم دمج أنواع مختلفة من أجهزة الاستشعار في شبكة اتصالات لاسلكية لجمع الإشارات الفسيولوجية للمريض عن بعد وإرسالها إلى المختص في أسرع وقت ممكن. يتكون االنظام المقترح من عدد من أجهزة الاستشعار الذكية التي تقيس معلمات مختلفة وهي: عدد نبضات القلب بالدقيقة، درجة حرارة الجسم، وSPO2 (تشبع الأكسجين). تُستخدم هذه الحساسات لحساب معدل ضربات قلب المريض ودرجة حرارة الجسم ونسبة تشبع الأكسجين للمريض على التوالي. يتم عرض حالة المريض اما على شاشة OLED اوباستخدام تطبيق Blynk. في هذا النظام نستخدم لوحة Raspberry Pi Pico W...