Spintronic Nanodevices for Neuromorphic Sensing Chips

Recent developments in spintronics materials and physics are promising to develop a new type of magnetic sensors which can be embedded into the silicon chips. These neuromorphic sensing chips will be designed to capture the biomagnetic signals from active biological tissue exploited as brain-machine interface. They lead to machines that are able to sense and interact with the world in humanlike ways and able to accelerate years of fitful advance in artificial intelligence. To detect the weak biomagnetic signals, this work aims to develop a CMOS-compatible spintronic sensor based on the magnetoresistive (MR) effect. As an alternative to bulky superconducting quantum interference device (SQUID) systems, the miniaturised spintronic devices can be integrated with standard CMOS technologies makes it possible to detect weak biomagnetic signals with micron-sized, non-cooled and low-cost. Fig. 1 shows the finite element method (FEM)-based simulation results of a Tunnelling-Magnetoresistive (TMR) sensor with an optimal structure in COMSOL Multiphysics. The finest geometry and material are demonstrated and compared with the state-of-the-art. The proposed TMR sensor achieves a linear response with a high TMR ratio of 172% and sensitivity of 223 μV/Oe. The results are promising for utilizing the TMR sensors in future miniaturized brain-machine interface, such as Magnetoencephalography (MEG) systems for neuromorphic sensing

Perovskite Photodiode for Wearable Electronics

Photodetectors are sensing devices that have been used for a broad range electromagnetic wave sensing applications. We are currently investigating the use of photovoltaic cells for implantable and wearable applications [1] [2]. In this work, we have demonstrated the use of CH3NH3PbI3-xClx perovskite materials for photo sensing applications in wearable electronic devices. Our photodetectors were fabricated from two different structures. The first involves the formation of a thin film perovskite material that is sandwiched between bottom and top contact electrodes, while the second involves using hole and electron transport layers between the bottom and top electrodes. Despite a poorer device stability, our experimental results confirmed that devices without an interlayer yield superior performance. Furthermore, AFM results show that the perovskite film formed on top of the PEDOT: PSS layer is non-uniform with more crystalline domains, while it has better surface coverage on top of bare ITO substrates [3] [4]

Artificial Intelligence for Solar Energy Harvesting in Wireless Sensor Networks

Solar cells have been extensively investigated for wireless sensor networks (WSN). In comparison to other energy harvesting techniques, solar cells are capable of harnessing the highest amount of power density. Furthermore, the energy conversion process does not involve any moving parts and does not require any intermediate energy conversion steps. Their main drawback is the inconsistent amount of energy harvested due to the intermittency and variability of the incoming solar radiation [1]. Consequently, being able to predict the amount of solar radiation is important for making necessary decisions regarding the amount of energy that can be utilized at the sensor node. We demonstrate that artificial intelligence (AI) can be used as an effective technique for predicting the amount of incoming solar radiation at these sensor nodes. We show that a Support Vector Machine (SVM) regression technique can effectively predict the amount of solar radiation for the next 24 hours based on weather data from previous days. We reveal that this technique outperforms other state of the art prediction methods for WSNs. To assess the performance of our proposed solution, we use experimental measurements that were collected for a period of two years from a weather station installed by Beijing Sunda Solar Energy Technology Company [2]. We also demonstrate how the harvested energy can be regulated using an innovative Power Management Unit [3]

Teaching teamwork to transnational students in engineering and technology

China is producing over 4 million STEM graduates each year. According to the literature, many of these graduates lack the professional skills that are required by the global job market. Consequently, a new course was designed to help Chinese students cultivate the necessary professional knowledge and practical skills in the field of electronic engineering. The aim of this innovative course was to build experience of working in a team to design and develop a rover that can perform specific tasks. The course’s team project covers areas such as electronic design, sensing, instrumentation, measurement, computing, communications, as well as project management, report writing and technical presentation. The learning outcomes and course details are described herein. Furthermore, feedback from 152 Chinese students y is discussed and. Statistical analysis from surveys completed by 152 students are discussed and compared with a similar learning activity that was implemented in the UK. Contrary to popular misconceptions, the survey’s results clearly show that this team based active learning activity was ideally suited to the culture and background of Chinese students.

Do you call that a lab notebook?

"Electronic laboratory notebooks (ELNs)? What are those?" At the start of every academic year, I prepare myself for a battle with my third-year engineering students about laboratory notebooks. More often than not, they either hand me a neatly written diary or loose bits of paper that are half folded and randomly glued to the back cover of a jotter. At first, my students dismiss the idea of keeping a properly documented lab notebook, but I notice a change in attitude when they are introduced to electronic notebooks

Wrist-worn gesture sensing with wearable intelligence

This paper presents an innovative wrist-worn device with machine learning capabilities and a wearable pressure sensor array. The device is used for monitoring different hand gestures by tracking tendon movements around the wrist. Thus, an array of PDMS-encapsulated capacitive pressure sensors is attached to the user to capture wrist movement. The sensors are embedded on a flexible substrate and their readout requires a reliable approach for measuring small changes in capacitance. This challenge was addressed by measuring the capacitance via the switched capacitor method. The values were processed using a programme on LabVIEW to visually reconstruct the gestures on a computer. Additionally, to overcome limitations of tendon’s uncertainty when the wristband is re-worn, or the user is changed, a calibration step based on the Support Vector Machine (SVM) learning technique is implemented. Sequential Minimal Optimization (SMO) algorithm is also applied in the system to generate SVM classifiers efficiently in real-time. The working principle and the performance of the SVM algorithms demonstrate through experiments. Three discriminated gestures have been clearly separated by SVM hyperplane and correctly classified with high accuracy (>90%) during real-time gesture recognition

Spintronic Nanodevices for Neuromorphic Sensing Chips

Recent developments in spintronics materials and physics are promising to develop a new type of magnetic sensors which can be embedded into the silicon chips. These neuromorphic sensing chips will be designed to capture the biomagnetic signals from active biological tissue exploited as brain-machine interface. They lead to machines that are able to sense and interact with the world in humanlike ways and able to accelerate years of fitful advance in artificial intelligence. To detect the weak biomagnetic signals, this work aims to develop a CMOS-compatible spintronic sensor based on the magnetoresistive (MR) effect. As an alternative to bulky superconducting quantum interference device (SQUID) systems, the miniaturised spintronic devices can be integrated with standard CMOS technologies makes it possible to detect weak biomagnetic signals with micron-sized, non-cooled and low-cost. Fig. 1 shows the finite element method (FEM)-based simulation results of a Tunnelling-Magnetoresistive (TMR) sensor with an optimal structure in COMSOL Multiphysics. The finest geometry and material are demonstrated and compared with the state-of-the-art. The proposed TMR sensor achieves a linear response with a high TMR ratio of 172% and sensitivity of 223 μV/Oe. The results are promising for utilizing the TMR sensors in future miniaturized brain-machine interface, such as Magnetoencephalography (MEG) systems for neuromorphic sensing

Smart Multi-Sensor Wristband for Gesture Classification

No abstract available

Perovskite Photodiode for Wearable Electronics

Photodetectors are sensing devices that have been used for a broad range electromagnetic wave sensing applications. We are currently investigating the use of photovoltaic cells for implantable and wearable applications [1] [2]. In this work, we have demonstrated the use of CH3NH3PbI3-xClx perovskite materials for photo sensing applications in wearable electronic devices. Our photodetectors were fabricated from two different structures. The first involves the formation of a thin film perovskite material that is sandwiched between bottom and top contact electrodes, while the second involves using hole and electron transport layers between the bottom and top electrodes. Despite a poorer device stability, our experimental results confirmed that devices without an interlayer yield superior performance. Furthermore, AFM results show that the perovskite film formed on top of the PEDOT: PSS layer is non-uniform with more crystalline domains, while it has better surface coverage on top of bare ITO substrates [3] [4]

Artificial Intelligence for Solar Energy Harvesting in Wireless Sensor Networks

Solar cells have been extensively investigated for wireless sensor networks (WSN). In comparison to other energy harvesting techniques, solar cells are capable of harnessing the highest amount of power density. Furthermore, the energy conversion process does not involve any moving parts and does not require any intermediate energy conversion steps. Their main drawback is the inconsistent amount of energy harvested due to the intermittency and variability of the incoming solar radiation [1]. Consequently, being able to predict the amount of solar radiation is important for making necessary decisions regarding the amount of energy that can be utilized at the sensor node. We demonstrate that artificial intelligence (AI) can be used as an effective technique for predicting the amount of incoming solar radiation at these sensor nodes. We show that a Support Vector Machine (SVM) regression technique can effectively predict the amount of solar radiation for the next 24 hours based on weather data from previous days. We reveal that this technique outperforms other state of the art prediction methods for WSNs. To assess the performance of our proposed solution, we use experimental measurements that were collected for a period of two years from a weather station installed by Beijing Sunda Solar Energy Technology Company [2]. We also demonstrate how the harvested energy can be regulated using an innovative Power Management Unit [3]