Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data

Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN

An Energy-Efficient, Dynamic Voltage Scaling Neural Stimulator for a Proprioceptive Prosthesis

Accepted versio

An Implantable Stimulator with Safety Sensors in Standard CMOS Process for Active Books

This paper presents a second-generation integrated circuit for the Active Books neural stimulation microsystem. It provides multi-channel stimulation with versatile control of stimulation profiles and reduced crosstalk from other stimulation channels. The new design features enhanced safety by monitoring the temperature and humidity inside the micropackage, and the peak electrode voltage at any stimulating electrode. The humidity sensor uses an interdigitated capacitor covered by a passivation layer and a polyimide covering. To boost sensitivity in the operating range of interest, the temperature sensor uses a temperature-insensitive current that is subtracted from a proportional-to-absolute-temperature current. A 3-b analog-todigital converter is used to record the peak electrode voltage. All sensor data is sent to an implanted central hub using bidirectional connection with error checking. Both the stimulation electronics and sensors are integrated on a 6.2 mm × 4 mm silicon die using XFAB's 0.6-μm CMOS high-voltage process. No post-processing steps are involved. The stimulator uses a fivewire cable to provide the power supply and bidirectional data signals. The chip operates from a 7.5-18 V power supply and can generate stimulation currents of 1 mA, 4 mA or 8 mA with a pulse duration of 2 μs-1.07 ms. The humidity sensor output varies linearly with relative humidity (RH) with a normalized sensitivity of 0.04%/%RH over the range of 20-90%RH. The temperature sensor has a nonlinearity of 0.4% over the range of 20-90 °C and a resolution of 0.12 °C. The stimulator is the first of its kind to include integrated temperature and humidity sensors. Index Terms-Active Books, humidity sensor, implant safety, integrated stimulator, temperature sensor, voltage sensor

Development of Low-Cost Current Controlled Stimulator for Paraplegics

A spinal cord injury (SCI) has a severe impact on human life in general as well as on the physical status and condition. The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising way to restore mobility to SCI by applying low-level electrical current to the paralyzed muscles so as to enhance that person’s ability to function and live independently. However, due to the limited number of commercially available FES assisted exerciser systems and their rather high cost, the conventional devices are unaffordable for most peoples. It is also inconvenient because of wired based system that creates a limitation in performing exercise. Thus, this project is concerned with the development of low-cost current controlled stimulator mainly for the paraplegic subjects. The developed device is based on a microcontroller, wireless based system using Zigbee module, voltage-to-current converter circuit and should produce proper monopolar and bipolar current pulses, pulse trains, arbitrary current waveforms, and a trigger output for FES applications. This device has been developed as in the new technique of the stimulator development with low cost and one of the contributing factors in Rehabilitation Engineering for patients with SCI

Wearable High Voltage Compliant Current Stimulator for Restoring Sensory Feedback

Transcutaneous Electrical Nerve Stimulation (TENS) is a promising technique for eliciting referred tactile sensations in patients with limb amputation. Although several studies show the validity of this technique, its application in daily life and away from laboratories is limited by the need for more portable instrumentation that guarantees the necessary voltage and current requirements for proper sensory stimulation. This study proposes a low-cost, wearable high-voltage compliant current stimulator with four independent channels based on Components-Off-The-Shelf (COTS). This microcontroller-based system implements a voltage-current converter controllable through a digital-to-analog converter that delivers up to 25 mA to load up to 3.6 kΩ. The high-voltage compliance enables the system to adapt to variations in electrode-skin impedance, allowing it to stimulate loads over 10 kΩ with currents of 5 mA. The system was realized on a four-layer PCB (115.9 mm × 61 mm, 52 g). The functionality of the device was tested on resistive loads and on an equivalent skin-like RC circuit. Moreover, the possibility of implementing an amplitude modulation was demonstrated

A Multi-Channel Stimulator With High-Resolution Time-to-Current Conversion for Vagal-Cardiac Neuromodulation

This paper presents an integrated stimulator for a cardiac neuroprosthesis aiming to restore the parasympathetic control after heart transplantation. The stimulator is based on time-to-current conversion. Instead of the conventional current mode digital-to-analog converter (DAC) that uses ten of microamp for biasing, the proposed design uses a novel capacitor time-based DAC offering close to 10 bit of current amplitude resolution while using only a bias current 250 nA. The stimulator chip was design in a 0.18 m CMOS high-voltage (HV) technology. It consists of 16 independent channels, each capable of delivering 550 A stimulus current under a HV output stage that can be operated up to 30 V. Featuring both power efficiency and high-resolution current amplitude stimulation, the design is suitable for multi-channel neural simulation applications

A power efficient time-to-current stimulator for vagal-cardiac connection after heart transplantation

This paper presents a stimulator for a cardiac neuroprosthesis aiming to restore the parasympathetic control after heart transplantation. The stimulator is based on time-to-current conversion, instead of the conventional current mode digital-to-analog converter (DAC) that drives the output current mirrors. It uses a DAC based on capacitor charging to drive a power efficient voltage-to-current converter for output. The stimulator uses 1.8 V for system operation and 10 V for stimulation. The total power consumption is Istim × 10 V +18. u μW during the biphasic current output, with a maximum Istim of 512 μA. The stimulator was designed in CMOS 0.18 μm technology and post-layout simulations are presented

Embedded Electronic Systems for Electronic Skin Applications

The advances in sensor devices are potentially providing new solutions to many applications including prosthetics and robotics. Endowing upper limb prosthesis with tactile sensors (electronic/sensitive skin) can be used to provide tactile sensory feedback to the amputees. In this regard, the prosthetic device is meant to be equipped with tactile sensing system allowing the user limb to receive tactile feedback about objects and contact surfaces. Thus, embedding tactile sensing system is required for wearable sensors that should cover wide areas of the prosthetics. However, embedding sensing system involves set of challenges in terms of power consumption, data processing, real-time response and design scalability (e-skin may include large number of tactile sensors). The tactile sensing system is constituted of: (i) a tactile sensor array, (ii) an interface electronic circuit, (iii) an embedded processing unit, and (iv) a communication interface to transmit tactile data. The objective of the thesis is to develop an efficient embedded tactile sensing system targeting e-skin application (e.g. prosthetic) by: 1) developing a low power and miniaturized interface electronics circuit, operating in real-time; 2) proposing an efficient algorithm for embedded tactile data processing, affecting the system time latency and power consumption; 3) implementing an efficient communication channel/interface, suitable for large amount of data generated from large number of sensors. Most of the interface electronics for tactile sensing system proposed in the literature are composed of signal conditioning and commercial data acquisition devices (i.e. DAQ). However, these devices are bulky (PC-based) and thus not suitable for portable prosthetics from the size, power consumption and scalability point of view. Regarding the tactile data processing, some works have exploited machine learning methods for extracting meaningful information from tactile data. However, embedding these algorithms poses some challenges because of 1) the high amount of data to be processed significantly affecting the real time functionality, and 2) the complex processing tasks imposing burden in terms of power consumption. On the other hand, the literature shows lack in studies addressing data transfer in tactile sensing system. Thus, dealing with large number of sensors will pose challenges on the communication bandwidth and reliability. Therefore, this thesis exploits three approaches: 1) Developing a low power and miniaturized Interface Electronics (IE), capable of interfacing and acquiring signals from large number of tactile sensors in real-time. We developed a portable IE system based on a low power arm microcontroller and a DDC232 A/D converter, that handles an array of 32 tactile sensors. Upon touch applied to the sensors, the IE acquires and pre-process the sensor signals at low power consumption achieving a battery lifetime of about 22 hours. Then we assessed the functionality of the IE by carrying out Electrical and electromechanical characterization experiments to monitor the response of the interface electronics with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system. In addition, we implemented filtering methods on the IE that reduced the effect of noise in the system. Furthermore, we evaluated our proposed IE by integrating it in tactile sensory feedback system, showing effective deliver of tactile data to the user. The proposed system overcomes similar state of art solutions dealing with higher number of input channels and maintaining real time functionality. 2) Optimizing and implementing a tensorial-based machine learning algorithm for touch modality classification on embedded Zynq System-on-chip (SoC). The algorithm is based on Support Vector Machine classifier to discriminate between three input touch modality classes \u201cbrushing\u201d, \u201crolling\u201d and \u201csliding\u201d. We introduced an efficient algorithm minimizing the hardware implementation complexity in terms of number of operations and memory storage which directly affect time latency and power consumption. With respect to the original algorithm, the proposed approach \u2013 implemented on Zynq SoC \u2013 achieved reduction in the number of operations per inference from 545 M-ops to 18 M-ops and the memory storage from 52.2 KB to 1.7 KB. Moreover, the proposed method speeds up the inference time by a factor of 43 7 at a cost of only 2% loss in accuracy, enabling the algorithm to run on embedded processing unit and to extract tactile information in real-time. 3) Implementing a robust and efficient data transfer channel to transfer aggregated data at high transmission data rate and low power consumption. In this approach, we proposed and demonstrated a tactile sensory feedback system based on an optical communication link for prosthetic applications. The optical link features a low power and wide transmission bandwidth, which makes the feedback system suitable for large number of tactile sensors. The low power transmission is due to the employed UWB-based optical modulation. We implemented a system prototype, consisting of digital transmitter and receiver boards and acquisition circuits to interface 32 piezoelectric sensors. Then we evaluated the system performance by measuring, processing and transmitting data of the 32 piezoelectric sensors at 100 Mbps data rate through the optical link, at 50 pJ/bit communication energy consumption. Experimental results have validated the functionality and demonstrated the real time operation of the proposed sensory feedback system