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Chlorophyll-A concentration is one of the most commonly measured water quality parameters. It is an indicator of algal biomass and provides insight into stressors such as eutrophication and bloom risk. It is also a widely used metric in terrestrial ecosystems as an indicator of photosynthetic activity and nutrient limitation. Laboratory-based methods for measuring chlorophyll-A require expensive instrumentation. In this paper, we proposed a smart, low-cost, and portable smart sensor system to measure the concentration of chlorophyll-A in an extracted solution using two consumer-grade spectral sensors that read the reflectance at 12 discrete wavelengths in visible and near-infrared spectra. The system was tuned for an optimal distance from the sensors to the solution and an enclosure was printed to maintain the distance, as well as to avoid natural light interference. Extracted chlorophyll solutions of 51 different concentrations were prepared, and at least 100 readings per sample were taken using our smart sensor system. The ground truth values of the samples were measured in the laboratory using Thermo Nano 2000C. After cleaning the anomalous data, different machine learning models were trained to determine the significant wavelengths that contribute most towards chlorophyll-A measurement. Finally, a decision tree model with 5 important features was chosen based on the lowest Root Mean Square and Mean Absolute Error when it was tested on the validation set. Our final model resulted in a mean error of $\pm 0.9\mu \text{g}$ /L when applied on our test set. The total cost was around $150.

Chlorophyll-a is a highly measured metric in water quality and plant health monitoring. Current laboratory-based methods exploit spectroscopic techniques and require expensive instrumentation like spectrophotometer or fluorometer. In this work, a rapid approach has been proposed using electrical impedance spectroscopy (EIS) to measure chlorophyll-a that is extracted from lettuce leaves into 95% (v/v) ethanol. Multiple readings of 70 differently concentrated samples were taken, and the corresponding impedances were measured using the proposed EIS system by varying frequency from 1.5 to 7.5 kHz. Pearson coefficient, variance inflation factor (VIF), and backward elimination were used to identify the significant component of the frequency. Finally, a multiple linear regression model with 11 features in the range of 2.3 kHz to 4.7 kHz was chosen based on the lowest root mean square error (RMSE) and mean absolute error (MAE). The results show high index of positive determination of chlorophyll-a with the coefficient of determination being 0.932, RMSE of 1.05 μgL-1, and MAE of 0.904 μgL-1.

Long-term and continuous monitoring of physiological parameters like Blood Pressure (BP), Heart-Beat Rate (HBR), and Electro-cardiogram (ECG) are of very important in ensuring proper medical care. Presently, all these measurements involve separate medical equipment. Most of the portable or wearable solutions require high-power, and are often too heavy to wear. In this paper, the design of a wearable wireless sensor node has been proposed that can collect all the required data and send them to the smartphone. The smartphone is used to perform the desired processing tasks like digital filtering, normalization, feature extraction, etc. In the proposed design, the smartphone further acts as an IoMT (Internet of Medical Things) gateway to send all processed data to the IoT cloud for medical use. This will eventually reduce the amount of power consumption at the sensor-node.

A minirhizotron is an in situ root imaging system that captures components of root system architecture dynamics over time. Commercial minirhizotrons are expensive, limited to white-light imaging, and often need human intervention. The implementation of a minirhizotron needs to be low cost, automated, and customizable to be effective and widely adopted. We present a newly designed root imaging system called SoilCam that addresses the above mentioned limitations. The imaging system is multi-modal, i.e., it supports both conventional white-light and multispectral imaging, with fully automated operations for long-term in-situ monitoring using wireless control and access. The system is capable of taking 360&deg, images covering the entire area surrounding the tube. The image sensor can be customized depending on the spectral imaging requirements. The maximum achievable image quality of the system is 8 MP (Mega Pixel)/picture, which is equivalent to a 2500 DPI (dots per inch) image resolution. The length of time in the field can be extended with a rechargeable battery and solar panel connectivity. Offline image-processing software, with several image enhancement algorithms to eliminate motion blur and geometric distortion and to reconstruct the 360&deg, panoramic view, is also presented. The system is tested in the field by imaging canola roots to show the performance advantages over commercial systems.

Beat tracking from music signals has significant importance in multimedia information retrieval systems, especially in cover song detection. A predictive real-time beat tracking system can also be used to assist musicians performing live. In this paper we present a real-time beat tracking algorithm, fast enough to be implemented on an embedded system. The onset of a note is detected using a maximum filter approach that suppresses the effect of vibrato. Beats are predicted a second in advance using a causal variant of Dynamic Programming. We have employed an onset memoization algorithm, to reduce the computational resources required. Raspberry Pi was chosen as our preferred development board. We have demonstrated through experimental results that the proposed approach can satisfactorily estimate beat positions from a music signal in real-time with an average continuity score (AMLt) of 0.67.

For the disabled and handicapped, the world of brain-computer interface has opened a new horizon to lead an unassisted and least supervised life. But the use of prosthetic limbs and artificial organs are still not commercially available in most of the countries. Recently developed some other brainwave based systems are mostly expensive, immobile or task specific. In order to overcome these problems, analogous to the pen-drive, we have focused on developing a low cost control unit namely “Brain-drive” which transforms ones thoughts into output digital electric signal. Certain thoughts (mental tasks) with hard eye blinking (neural driven physical outcome) develops certain action potential, no matter the eye blinking is visible or not, it creates a significant change in electroencephalogram (EEG) or brain signal. Single channel EEG signal is collected from frontal lobe as it plays a vital role in voluntary movement using a wearable Mindwave Mobile from Neurosky. The EEG data thus recorded is preprocessed to reduce the effect of noise and artifacts and then analyzed in time domain. A grid of cells are displayed on a monitor in front of the subject, which is connected to individual digital output pin onboard an embedded system. To select a cell, a user just needs to look and generate a sustainable eye blink which can easily be identified from the raw value of brainwave. Since eye blinking can be both conscious and involuntary, automatic periodic eye blink of very low frequency oscillation is removed to avoid unwanted selection of command. Unlike webcam based eye blink detector, Brain-Drive captures the signal for blink rather than the visual output of blinking. The system can be trained for person specific customized performance, considering special needs of a disabled or handicapped people who cannot communicate with the outside world using voice, motion or other methods. With the help of cognitive command, the ‘Brain-Drive’ can be used to control wheelchair, home appliances and even industrial machines.

Brain-computer interface can have a profound impact on the life of paralyzed or elderly citizens as they offer control over various devices without any necessity of movement of the body parts. This technology has come a long way and opened new dimensions in improving our life. Use of electroencephalogram (EEG wave) based control schemes can change the shape of the lives of the disabled citizens if incorporated with an electric wheelchair through a wearable device. Electric wheelchairs are nowadays commercially available which provides mobility to the disabled persons with relative ease. But most of the commercially available products are much expensive and controlled through the joystick, hand gesture, voice command, etc. which may not be viable control scheme for severely disabled or paralyzed persons. In our research work, we have developed a low-cost electric wheelchair using locally available cheap parts and incorporated brain-computer interface considering the affordability of people from developing countries. So, people who have lost their control over their limbs or have the inability to drive a wheelchair by any means can control the proposed wheelchair only by their attention and willingness to blink. To acquire the signal of attention and blink, single channel electroencephalogram (EEG wave) was captured by a wearable Neurosky MindWave Mobile. One of the salient features of the proposed scheme is ‘Destination Mapping’ by which the wheelchair develops a virtual map as the user moves around and autonomously reaches desired positions afterward by taking command from a smart interface based on EEG signal. From the experiments that were carried out at different stages of the development, it was exposed that, such a wheelchair is easy to train and calibrate for different users and offers a low cost and smart alternative especially for the elderly people in developing countries.

Proper healthcare is one of the fundamental needs of human beings. However, the doctor-to-patient ratio in developing and underdeveloped countries, especially in remote rural areas is quite low. A smart digital stethoscope, which can make preliminary health monitoring by itself and communicate with a doctor from remote areas can minimize such problem. Though there are several such devices from various medical equipment vendors, most of them are costly for our target community and lack wireless communication facilities with the doctors. Our research work aims at developing a smartphone-based personalized healthcare scheme at low cost by providing primary health monitoring focusing on cardiac activity and regularly tracking the condition and reporting to a doctor because almost one-third of all global deaths are attributed to cardiovascular diseases. A low-cost digital stethoscope chest piece was therefore developed from a conventional one using piezoelectric element which picks up body sounds and provides a corresponding electrical signal. The signal is then fed to a smartphone which evaluates the health condition based on the analysis of heart sounds using the acquired Phonocardiogram (PCG) signals. In order to improve the computational efficiency and accuracy, heart sound segmentation, probabilistic models such as hidden Markov model, application of the k-nearest neighbor algorithm to train the network for binary classification have been implemented. Based on the results analyzed, the proposed low-cost scheme shows great promise for preliminary screening of healthcare issues using heart sounds, especially in remote areas with scarce medical assistance available.

the efficient and effective utilization of limited fossil fuel with a potential leverage from renewable energy will certainly reduce the footprint and will improve the power generation process at the same time. Offshore covering two third of the world also a vast source of renewable energy is always a better location to place such hybrid plants especially for those well already bored at offshore. Such kind of plants will also reduce the risk of human loss in case of a disaster. Our work therefore, focuses on producing electricity in a floating power plant based on a recent discovery of gas in the Bay of Bengal. The objective is to design a hybrid floating gas turbine power plant, which will harness tidal and solar energy as well. Produced electricity can be added to the grid using submarine cable. A statistical analysis, coupled with simulated data will be presented to investigate the feasibility of such power plant. As Bangladesh, India and Myanmar surround the Bay of Bengal, surpassed electricity can be exported to neighboring countries. For countries like Bangladesh with high population density, this type of plants will eliminate the risk of pollution and the subsequent adverse effects on locality and surrounding wildlife.

In recent times, landmines have created unaccounted fatalities across the world. The detection of mines buried beneath the surface is paramount to avert inhuman risks associated with mine deployment and explosion. In this paper, a substantive design of an intelligible landmine detective system is proposed based on precise control over aerial motion planning and metal detection. The proposed mine detector is capable of providing virtual mapping of the traversed area and a concatenated map of the points of metal signatures, no matter whether it is a rough terrain or inclined surface. Since the aerial route is considered for surface exploration, the avoidance of assimilating the mine paths is reliably possible. The dynamism of path generation correlates to GPS coordinates of detected mines. A modified metal detection technique is developed based on electromagnetic radiation estimates to discriminate metals, mines and bare explosives like RDX. The proposed system can identify the explosion type of the detonator and recognize electrically controlled explosives. This paper appends several test studies of the system under different consequences, such as different location of metals, variation of metals in size and type, varied experimental arena and the evaluated results affirm the reliability and cost-effectiveness of the reported framework.