Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

In this paper, we present DoppleSleep -- a contactless sleep sensing system that continuously and unobtrusively tracks sleep quality using commercial off-the-shelf radar modules. DoppleSleep provides a single sensor solution to track sleep-related physical and physiological variables including coarse body movements and subtle and fine-grained chest, heart movements due to breathing and heartbeat. By integrating vital signals and body movement sensing, DoppleSleep achieves 89.6% recall with Sleep vs. Wake classification and 80.2% recall with REM vs. Non-REM classification compared to EEG-based sleep sensing. Lastly, it provides several objective sleep quality measurements including sleep onset latency, number of awakenings, and sleep efficiency. The contactless nature of DoppleSleep obviates the need to instrument the user's body with sensors. Lastly, DoppleSleep is implemented on an ARM microcontroller and a smartphone application that are benchmarked in terms of power and resource usage.

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DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar

Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.

Applications of artificial intelligence for disaster management

The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.

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iPathology: Robotic Applications and Management of Plants and Plant Diseases

Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for automatic sleep stage classification is presented. Compared with existing sleep stage methods, our method can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract features from raw data. To translate open sleep stage standards into machine rules recognized by computers, a new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages. The new model combines complex-valued backpropagation and the Fisher criterion. It can learn discriminative features and overcome the negative effect of imbalance dataset. More importantly, the orthogonal decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron are proven. A speed-up algorithm is proposed to reduce computational workload and yield improvements of over an order of magnitude compared to the normal convolution algorithm. The classification performances of handcrafted features and different convolutional neural networks are compared with that of the FDCCNN. The total accuracy and kappa coefficient of the proposed method are 92% and 0.84, respectively. Experiment results demonstrated that the performance of our system is comparable to those of human experts.

A New Method for Automatic Sleep Stage Classification

In recent years, robotics in agriculture sector with its implementation based on precision agriculture concept is the newly emerging technology. The main reason behind automation of farming processes are saving the time and energy required for performing repetitive farming tasks and increasing the productivity of yield by treating every crop individually using precision farming concept. Designing of such robots is modeled based on particular approach and certain considerations of agriculture environment in which it is going to work. These considerations and different approaches are discussed in this paper. Also, prototype of an autonomous Agriculture Robot is presented which is specifically designed for seed sowing task only. It is a four wheeled vehicle which is controlled by LPC2148 microcontroller. Its working is based on the precision agriculture which enables efficient seed sowing at optimal depth and at optimal distances between crops and their rows, specific for each crop type.

Precision agriculture robot for seeding function

Floods are the natural disasters that cause catastrophic destruction and devastation of natural life, agriculture, property and infrastructure every year. Flooding is influenced by various hydrological & meteorological factors. A number of researches have been done in flood disaster management and food prediction systems. However, it has now become significant to shift from individual monitoring and prediction frameworks to smart flood prediction systems which include stakeholders and the flood affecting people equally with help of recent technological advancements. Internet of Things (IoT) is a technology that is a combination of embedded system hardware and wireless communication network which further transfers sensed data to computing device for analysis in real-time. Researches in direction of flood prediction have shifted from mathematical models or hydrological models to algorithmic based approaches. Flood data is dynamic data and non-linear in nature. To predict floods, techniques such as artificial neural networks are used to devise prediction algorithms. Here an IoT based flood monitoring and artificial neural network (ANN) based flood prediction is designed with the aim of enhancing the scalability and reliability of flood management system. The main aim of this system is to monitor humidity, temperature, pressure, rainfall, river water level and to find their temporal correlative information for flood prediction analysis. The IoT approach is deployed for data collection from the sensors and communication over Wi-Fi and an ANN approach is used for analysis of data in flood prediction.

Smart flood disaster prediction system using IoT & neural networks

In this paper the feature extraction of the EEG Signal is done by computing the Discrete Wavelet Transform. The wavelet transform coefficients compress the number of data points into few features. Various statistics were used to further reduce the dimensionality. The Classification of the EEG sleep stages is done by using neural network which provides more accurate sleep stage classification compared to other techniques.

Sleep stages classification using wavelettransform & neural network

Authentication has become an essential part of our everyday lives which is used at almost every place from banks to experimental labs, from car automation to home automation. This authentication is generally provided through systems like passwords, PIN codes, card readers. At some places biometrics like fingerprint and retina scans are used. All designed with one purpose; to confirm a person's identity. Brain wave based authentication is another addition to the wide range of authentication systems, which has many advantages over other authentication systems. With a standard password someone can watch or ‘shoulder-surf’ what others type, but no one ca n watch thoughts. Cards and keys can be lost, but the brain wave is always present. Differently abled persons can't use systems which uses fingerprints or retina scans but they can use system using brain-waves. This clears that using brain waves as biometric to provide authentication is very beneficial. A system is designed and implemented which allows user to set a pattern of brain waves which must be provided as an unlock pattern to get the access. This pattern can be any combination of eye blink, attention and various brain rhythms like Alpha, Beta, Theta and Delta. The system described in this paper provides two-level authentication. First level of which is brain waves. Once the correct pattern of brain signal is provide the system will ask for a pass key as a second level of authentication. This paper describes the design and implementation of the system.

Biometric user authentication using brain waves

Digital signal processors (DSPS) fundamentally contains multipliers as its core element. The speed of the multipliers affects the speed of the DSPs. The execution of most DSPs is dependent on its multipliers, and hence need for high-speed multipliers arises. In this digitalization era, it becomes necessary to increase the speed of the digital circuits while reducing on-chip area and memory consumption. Latency and throughput are the basic parameters associated with multiplication algorithms where latency is a total delay in computing a function while throughput is the measure of computations performed in a given stipulated time. For increasing multiplication speed and reducing delay there is more and more emphasis on designing faster multipliers. There are many algorithms like standard modified booth algorithm, Wallace tree methods and several new techniques are worked on to enhance the speed of the multiplier. Among this, algorithms based on Vedic mathematics are under focused as they can be used to design faster and low power multipliers. Vedic mathematics is based on sixteen sutras, out of them “Urdhva Tiryakbhyam” and “Nikhilam Navatashcaramam Dashatah” are noticed most. In this paper 32 bit implementation of “Urdhva Tiryakbhyam” and “Nikhilam Navatashcaramam Dashatah”. Multipliers in this paper are coded using Verilog language, it is synthesised and simulated using Xilinx ISE 14.5. In this paper, Urdhva Tiryakbhyam and Nikhilam sutra both algorithms are compared in terms of propagation delay and found that Urdhva Tiryakbhyam sutra performs faster for less bit input while Nikhilam sutra is faster for larger inputs.

V. V. Shete

Papers

Precision agriculture robot for seeding function

Smart flood disaster prediction system using IoT & neural networks

Sleep stages classification using wavelettransform & neural network

Biometric user authentication using brain waves

Design and comparison of multiplier using vedic mathematics