Hybrid Stochastic Synapses Enabled by Scaled Ferroelectric Field-effect Transistors

Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution and emergence of stochastic non-idealities. While prior work has mainly focused on binary transitions, in this work we leverage the stochastic switching of a three-state ferroelectric field effect transistor (FeFET) to implement a long-term and short-term 2-tier stochastic synaptic memory with a single device. Experimental measurements are performed on a scaled 28nm high-$k$ metal gate technology-based device to develop a probabilistic model of the hybrid stochastic synapse. In addition to the advantage of ultra-low programming energies afforded by scaling, our hardware-algorithm co-design analysis reveals the efficacy of the 2-tier memory in comparison to binary stochastic synapses in on-chip learning tasks -- paving the way for algorithms exploiting multi-state devices with probabilistic transitions beyond deterministic ones

Augmented reality marker-based technology for augmenting newspaper advertisement

In this research, we describe an augmented reality android-based idea in which we utilize augmented reality marker-based technology for augmenting newspaper advertisement with electronic information that does not modify the format of the newspaper document and remains exactly the same, substantially improves the utility of paper by reducing the portion of the printed Ad on the newspaper. An implementation on a camera phone is discussed that lets users retrieve data and access links from newspaper advertisements to electronic data. We carefully examined over twenty people of different ages and occupations who participated in the newspaper-based AR and we got a significant overall response. Further analysis implies that this may assist students in understanding the complex 3D objects, which they can manipulate, learn tasks and improve skills

Kinematic Analysis of a Clamp-Type Picking Device for an Automatic Pepper Transplanter

Pepper is one of the most vital agricultural products with high economic value, and pepper production needs to satisfy the growing worldwide population by introducing automatic seedling transplantation techniques. Optimal design and dimensioning of picking device components for an automatic pepper transplanter are crucial for efficient and effective seedling transplantation. Therefore, kinematic analysis, virtual model simulation, and validation testing of a prototype were conducted to propose a best-suited dimension for a clamp-type picking device. The proposed picking device mainly consisted of a manipulator with five grippers and a picking stand. To analyze the influence of design variables through kinematic analysis, 250- to 500-mm length combinations were considered to meet the trajectory requirements and suit the picking workspace. Virtual model simulation and high-speed photography tests were conducted to obtain the kinematic characteristics of the picking device. According to the kinematic analysis, a 350-mm picking stand and a 380-mm manipulator were selected within the range of the considered combinations. The maximum velocity and acceleration of the grippers were recorded as 1.1, 2.2 m/s and 1.3, 23.7 m/s2, along the x- and y-axes, respectively, for 30 to 90 rpm operating conditions. A suitable picking device dimension was identified and validated based on the suitability of the picking device working trajectory, velocity, and acceleration of the grippers, and no significant difference (p ≤ 0.05) occurred between the simulation and validation tests. This study indicated that the picking device under development would increase the pepper seedling picking accuracy and motion safety by reducing the operational time, gripper velocity, acceleration, and mechanical damage

Astromorphic Self-Repair of Neuromorphic Hardware Systems

While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse. Shifting from this neuro-synaptic perspective, this paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. The work investigates stronger correlations with astrocyte computational neuroscience models to develop macro-models with a higher degree of bio-fidelity that accurately captures the dynamic behavior of the self-repair process. Hardware-software co-design analysis reveals that bio-morphic astrocytic regulation has the potential to self-repair hardware realistic faults in neuromorphic hardware systems with significantly better accuracy and repair convergence for unsupervised learning tasks on the MNIST and F-MNIST datasets. Our implementation source code and trained models are available at https://github.com/NeuroCompLab-psu/Astromorphic_Self_Repair

Human identification based on color stimuli

Human identification by using Electroencephalogram is becoming promising field and reliable to improve security systems. It is difficult to acquire EEG at a certain mental condition always such as concentration or relaxation. This paper represents a simple model to identify individuals and finding most effective primary color by using features of EEG by means of color stimuli. A comparison between primary and secondary colors for identification has also been made. Standard additive primary colors blue, green, red and one secondary color yellow were selected for experiment. Four neural networks were built by extracting various features of EEG in the domain of time and frequency. All artificial neural networks showed satisfactory performance with minimum mean square error for identification. Among the four selected colors blue color based ANN showed minimum mean square error of 6.238×10-08.</p

Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision

Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. The aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB camera and an ultrasonic sensor, was developed to record beef cattle drinking actions. We developed an algorithm for tracking the beef cattle’s key body parts, such as head–ear–neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a long short-term memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validation purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet immediate needs and expand farmers’ capability in monitoring animal health and well-being by identifying drinking behavior

Corrigendum to “Augmented reality marker-based technology for augmenting newspaper advertisement"

Recent Research in Science and Technology, 2021, 13, 13–18. https://doi.org/10.25081/rrst.2021.13.7005One of the authors’ (Md. Imdadul Hoque3) affiliations was mentioned incorrectly. It should be read as follows: 3Department of Computer Science and Telecommunication Engineering, Noakhali Science andTechnology University, Noakhali – 3814, Banglades