Epilepsy is a serious neurological disorder which affects every aspect of patients’ life, including added socio-economic burden. Unfortunately, only a few suppressive medicines are available, and a complete cure for the disease has not been found yet. Excluding the effectiveness of available therapies, the timely detection and monitoring of epilepsy are of utmost priority for early remediation and prevention. Inability to detect underlying epileptic signatures at early stage causes serious damage to the central nervous system (CNS) and irreversible detrimental variations in the organ system. Therefore, development of a multi-task solving novel smart biosensing systems is urgently required. The present review highlights advancements in state-of-art biosensing technology investigated for epilepsy diseases diagnostics and progression monitoring or both together. State of art epilepsy biosensors are composed of nano-enabled smart sensing platform integrated with micro/electronics and display. These diagnostics systems provide bio-information needed to understand disease progression and therapy optimization timely. The associated challenges related to the development of an efficient epilepsy biosensor and vision considering future prospects are also discussed in this report. This review will serve as a guide platform to scholars for understanding and planning of future research aiming to develop a smart bio-sensing system to detect and monitor epilepsy for point-of-care (PoC) applications.

Biosensors for Epilepsy Management: State-of-Art and Future Aspects

The blind and visually impaired people are facing a number of challenges in their daily life. One of the major challenges is finding their way both indoor and outdoor. For this reason, routing and navigation independently, especially in urban areas are important for the blind. Most of the blind undertake route finding and navigation with the help of a guide. In addition, other tools such as a cane, guide dog or electronic aids are used by the blind. However, in some cases these aids are not efficient enough in a wayfinding around obstacles and dangerous areas for the blind. As a result, the need to develop effective methods as decision support using a non-visual media is leading to improve quality of life for the blind through their increased mobility and independence. In this study, we designed and implemented an outdoor mobile sensor-based wayfinding system for the blind. The objectives of this study are to guide the blind for the obstacle recognition and the design and implementation of a wayfinding and navigation mobile sensor system for them. In this study an ultrasonic sensor is used to detect obstacles and GPS is employed for positioning and navigation in the wayfinding. This type of ultrasonic sensor measures the interval between sending waves and receiving the echo signals with respect to the speed of sound in the environment to estimate the distance to the obstacles. In this study the coordinates and characteristics of all the obstacles in the study area are already stored in a GIS database. All of these obstacles were labeled on the map. The ultrasonic sensor designed and constructed in this study has the ability to detect the obstacles in a distance of 2cm to 400cm. The implementation and the results obtained from the interview of a number of blind persons who employed the sensor verified that the designed mobile sensor system for wayfinding was very satisfactory.

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Design and Development of a Mobile Sensor Based the Blind Assistance Wayfinding System

—In recent years, the existence of a signiﬁcant cross-impact between Cloud computing and Internet of Things (IoT) has lead to a dichotomy that gives raise to Cloud-Assisted IoT (CAIoT) and IoT-Based Cloud (IoTBC) . Although it is pertinent to study both technologies, this paper focuses on CAIoT, and especially its security issues, which are inherited from both Cloud computing and IoT. This study starts with reviewing existing relevant surveys, noting their shortcomings, which motivate a comprehensive survey in this area. We proceed to highlight existing approaches towards the design of Secure CAIoT (SCAIoT) along with related security challenges and controls. We develop a layered architecture for SCAIoT. Furthermore, we take a look at what the future may hold for SCAIoT with a focus on the role of Artiﬁcial Intelligence(AI). data and assure reliable trafﬁc ﬂow prediction. This work proposes a novel

The Dichotomy of Cloud and IoT: Cloud-Assisted IoT From a Security Perspective

Intelligent computing is an environment-adaptive computing method developed by using various mechanisms of physical or natural phenomena. The university stage is a critical period for a person’s physical and mental development. Healthy psychology and physical quality are the basic characteristics of students’ talents and a reliable guarantee for students’ normal study, life, and work. The purpose of this article is to study how to analyze and study the impression of jogging on the physical health of college students based on intelligent computing and describe the intelligent computing algorithm. This article puts forward the problem of the impact of jogging on the body, which is based on intelligent computing, elaborates on the concept of intelligent computing and related algorithms, and designs and analyzes the impact of jogging. The experimental results showed that after 5 months of jogging, there was no significant difference in the height and weight of the subjects. The body fat percentage of boys decreased significantly before and after exercise, but there was no significant difference after the statistical test (P > 0.05), from 20.42 ± 2.53 to 20.21 ± 3.28. The body fat percentage of girls decreased significantly, with a significant difference (P < 0.05), from 29.43 ± 4.27 to 25.72 ± 2.94.

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The Effect of Jogging on the Physical Health of College Students Based on Intelligent Computing

ABSTRACT In this study, the effects of sustainable development of agricultural IoT are being investigated in 13 cities of Anhui province in China by Principal Component Analysis (PCA) and drivers-pressures-state-impact-response (DPSIR) models. The relevant theories are combined with agricultural IoT research, and an effective evaluation system is proposed. The main goals of this study are to recognize the essential parameters in the study of agricultural IoT; investigate the effects of the technological and economic environment of agricultural IoT. The sustainable development model and evaluation index system are combined with the driving force-state-response framework. The PCA algorithm is used for index value assignment. The scores of sustainable developments of agricultural IoT in each city of province A are calculated. In the balance ranking of sustainable development of agricultural IoT, city A ranks first with the driving force score of 0.233, the state score of 0.2, and the response score of 0.281. The results provided the data support for the investigation of supportable expansion of emerging industries in the economic globalization field.

Effects of Internet of things (IoT) on performance of agricultural in China: A case study

Multi-core heterogeneous architectures are playing the important role in server, mobile and all commercial devices. With the advent of internet of things in today's applications and increase in the workloads inputs, predictive simulating and computing tools are mandatory for the effective implementation of the multi-core heterogeneous architectures for the different applications. Many tools such as MacPACT, ESEC has been into existence but an intelligent computing framework tool for the predictive selection of the cores depending on the workloads remains in the darker side of the research. Hence the new computing framework called VEERBENCH has been proposed which works on the learning and training mechanisms for the usage of the cores in the heterogeneous architectures depending on the workloads. The framework uses the fuzzy clustering with the extreme learning machines and formulation of adaptive and cognitive energy (FACE) rule sets which are used for the energy and performance-based allocation of the cores. The proposed knowledge-based test bench has been compared with the other tools such as MACPACT, ESEC and with the other energy-based scheduler benchmarks and the obtained results are shown.

VEERBENCH - an intelligent computing framework for workload characterisation in multi-core heterogeneous architectures

Epilepsy is a serious, potentially fatal neurological disorder which often requires the immediate attention of a caregiver. Common symptoms of epileptic seizures include sudden fluctuations in heart rate and involuntary muscular contractions (seizures). They can also be accompanied by nausea, dizziness, etc. The sudden onset of a seizure during driving may lead to accidents and its occurrence during sleeping hours could prove fatal if no immediate, proper attention is provided by a caregiver or a doctor. Since the onset of seizures is sudden and its consequences can be severe, it is risky to leave the patient alone. We propose a wireless electronic monitoring system that can accurately predict the onset of seizures a few minutes in advance. We will measure two critical health indicators—brain and cardiac bio-potential signal in the form of EEG and ECG using body sensor networks. The system will utilize a wearable device that detects the symptoms and transmits a coded signal to produce control signals for switching on an alarm device, alerting a doctor or caregiver’s mobile phone using wireless communication with help of GSM modem. A GPS module is used to trace out the exact location of the patient. We will use machine learning to classify whether the sensors indicate an onset of a seizure or not. With this system, we hope to improve the patient’s quality of life and give them dignity.

Continuous Monitoring and Detection of Epileptic Seizures Using Wearable Device

Security is an important case study of iot which gives a specific output for highly secure physical devices. The usage of physical and cloud-based security measures allows the elevation of various security parameters required for successfully securing physical everyday objects after collaborating them with IOT. The security devices which are available cannot detect variables, so there is a requirement of developing a novel system. The model proposed provides highly secure device for lower cost and survival of the new device will be measured to pick up various signs such as forced entry via IR, software unlocking before physical unlocking system through a smart phone app that communicates to a cloud based back end over a secure line by sending encrypted key data. A cloud-based system has been designed to implement via Email, SMS via GSM module and improved security in the IOT

Cloud Integrated Smart Security Implementation in IoT

The development of blockchain has led to the emergence and widespread use of decentralized cryptocurrencies around the globe. As of 2021, the global market capitalization of cryptocurrencies has crossed two trillion dollars. With increasing popularity and adoption, investors have begun to see cryptocurrencies as an alternative to conventional financial assets. However, the volatility associated with cryptocurrencies makes them a highly risky investment. This gives rise to the need for accurate and efficient price prediction models which can help reduce risks associated with cryptocurrency investments. The model aims at predicting the price of two popular cryptocurrencies: Bitcoin and Ethereum. Tensor processing unit (TPU) is used for providing a distributed environment for the proposed model. The results show that the distributed TPU-trained model performed significantly better than the conventional CPU-trained model in terms of training time while maintaining a high degree of accuracy.

Cryptocurrency Price Prediction Using TPU-Based Distributed Machine Learning

Visually impaired individuals have relied on a variety of techniques to actively participate in society. There are many available technologies both wearable and non-wearable to assist them. These either provide them assistance in walking, or some help them to recognize things by audio or touch feedback. Our approach is to provide a low-cost wearable assistive system which can not only help blinds to navigate freely, but also tells them the time. The system will also enable a distant mobile user to monitor body vitals of the blind person and in case of any emergency the blind can call him with just one press of a button. So overall, the system provides not only aids to the blind, but also to his/her family members.

Yokesh Babu Sundaresan

Papers

VEERBENCH - an intelligent computing framework for workload characterisation in multi-core heterogeneous architectures

Continuous Monitoring and Detection of Epileptic Seizures Using Wearable Device

Cloud Integrated Smart Security Implementation in IoT

Cryptocurrency Price Prediction Using TPU-Based Distributed Machine Learning

Smart wearable prototype for visually impaired