Memristor Based Multi-State Shift Register Architecture

Bio-inspiring circuit design attracts a great deal of attention among researchers in the field of electronics. Memristor has emerged not only because of their potential use in neuromorphic circuits but also because of their feasible fabrication using low-cost techniques. This research presents the use of memristors to build multi-state shift registers. Memristors are capable of storing and processing multi-state logic and design of an architecture for their use in shift register have potential applications in bio-inspired integrated circuits, telecommunication systems, cryptology, display technologies, data storage, chaotic circuits, etc. The designed shift register consists of stages with capability to store and transfer multiple bits. The number of stages can be adjusted depending on the requirements of the specific applications. Each stage of the shift register consists of two memristors for a continuous signal generation at the output of each stage. Reading and writing are executed in sequential order so that when reading operation is performed by a memristor, new data is transferred to another for writing. The amplitude of the voltage corresponds to the logic state and voltage levels are classified into logic states using comparators. For n-state logic, 2n-1 comparators are required at each stage. Yakopcic’s memristor model is used in the simulations conducted in LTSPICE. The multi-state shift register architecture provided in this research successfully stores and shifts the data in the desired logic state

Fine-grained Classification of Solder Joints with {\alpha}-skew Jensen-Shannon Divergence

Solder joint inspection (SJI) is a critical process in the production of printed circuit boards (PCB). Detection of solder errors during SJI is quite challenging as the solder joints have very small sizes and can take various shapes. In this study, we first show that solders have low feature diversity, and that the SJI can be carried out as a fine-grained image classification task which focuses on hard-to-distinguish object classes. To improve the fine-grained classification accuracy, penalizing confident model predictions by maximizing entropy was found useful in the literature. Inline with this information, we propose using the {\alpha}-skew Jensen-Shannon divergence ({\alpha}-JS) for penalizing the confidence in model predictions. We compare the {\alpha}-JS regularization with both existing entropyregularization based methods and the methods based on attention mechanism, segmentation techniques, transformer models, and specific loss functions for fine-grained image classification tasks. We show that the proposed approach achieves the highest F1-score and competitive accuracy for different models in the finegrained solder joint classification task. Finally, we visualize the activation maps and show that with entropy-regularization, more precise class-discriminative regions are localized, which are also more resilient to noise. Code will be made available here upon acceptance.Comment: Submitted to IEEE Transactions on Components, Packaging and Manufacturing Technolog

Synthesis of conductive carbon aerogels decorated with β-tricalcium phosphate nanocrystallites

There has been substantial interest in research aimed at conductive carbon-based supports since the discovery that the electrical stimulus can have dramatic effect on cell behavior. Among these carbon-aerogels decorated with biocompatible polymers were suggested as future materials for tissue engineering. However, high reaction temperatures required for the synthesis of the aerogels tend to impair the stability of the polymeric networks. Herein, we report a synthetic route towards carbon-aerogel scaffolds decorated with biocompatible ceramic nanoparticles of tricalcium phosphate. The composites can be prepared at temperature as high as 1100 °C without significant effect on the morphology of the composite which is comparable with the original aerogel framework. Although the conductivity of the composites tends to decrease with the increasing ceramic content the measured conductivity values are similar to those previously reported on polymer-functionalized carbon-aerogels. The cell culture study revealed that the developed constructs support cell proliferation and provide good cell attachment suggesting them as potentially good candidates for tissue-engineering applications

Composites of Functionalized Multi-Walled Carbon Nanotube and Sodium Alginate for Tactile Sensing Applications

Flexible–tactile sensors are predicted to soon be extensively used in wearable devices. Various materials in flexible-sensor fabrication offer sensing properties with multiple capabilities. There is a crucial research opportunity in the field of flexible–tactile sensors for these materials, including nanocomposites. While the nanocomposites’ electrical properties mainly depend on nanofillers, the mechanical properties are determined by their polymer components. Carbon nanotubes (CNTs) are one of the most promising materials among nanofillers due to their high electrical conductivity, thermal stability, and durability. However, CNTs should be processed to increase the binding capacity of the polymer structure. In this study, the nanocomposite used for sensor manufacturing consisted of acid-functionalized CNTs and sodium alginate as the nanofiller and the polymer material, respectively. The sensor material was cross-linked using calcium chloride and glycerin was involved in the sensor fabrication to test its effect on sensing and flexibility. It is critical to note that sodium alginate and glycerin are biocompatible and biodegradable substances. In the scope of this study, the impedance changes of the fabricated tactile sensors were examined in the 100 Hz–10 MHz frequency range and equivalent circuits of the sensors were created. Additionally, impedance changes were obtained when alternating forces were applied to the sensors. The results showed that the frequency responses of the sensors differed from each other in different frequency ranges. In addition, each sensor had different sensing mechanisms in specific frequency ranges and the sensor made with glycerin had higher flexibility but less sensitivity

Microwave-Assisted Synthesis of Stretchable and Transparent Poly(Ethyleneglycol-Sebacate) Elastomers with Autonomous Self-Healing and Capacitive Properties

Introducing functional synthetic biomaterials to the literature became quite essential in biomedical technologies. For the growth of novel biomedical engineering approaches, progressive functional properties as well as the robustness of the manufacturing processes are essential. By using acid-induced epoxide ring-opening polymerizations through catalysts, a wide variety of biodegradable and functionalized biomaterials can be synthesized. Sebacic acid (SA) and poly(ethylene glycol) diglycidyl ether (PEGDGE) are amongst the FDA-approved biocompatible materials. In this study, we focused on the rapid synthesis of caffeine-catalyzed self-healable elastomer via a facile microwave-assisted synthesis route. The elastomer prepared can be used in various applications, including tactile sensors, wearable electronics, and soft robotics. SA and PEGDGE were catalyzed in the presence of caffeine under microwave irradiation followed by crosslinkingin vacuo, yielding an elastomeric material. The chemical characterizations of the obtained elastomer were carried out. The resulting material is transparent, highly stretchable, and has capacitive and self-healing properties even at room temperature. The material developed can be easily applied for the aforementioned applications

Gestational Outcomes Of Pregnant Women Who Have Had Invasive Prenatal Testing For The Prenatal Diagnosis Of Duchenne Muscular Dystrophy

Aim . To show the importance of prenatal diagnosis of Duchenne Muscular Dystrophy (DMD) and to demonstrate the effect of DMD gene mutations on gestational outcomes. Materials and Methods . We retrospectively evaluated 89 pregnancies in 81 individuals who were referred to Hacettepe University for prenatal diagnosis of DMD between January 2000 and December 2015. Prenatal diagnostic methods (chorionic villus sampling (CVS): 66, amniocentesis (AC): 23) were compared for test results, demographic features, and obstetric outcomes of pregnancies. The female fetuses were divided into two groups according to the DMD status (healthy or carrier) to understand the effect of DMD gene mutations on obstetric outcomes. Results . Eight prenatally diagnosed disease-positive fetuses were terminated. There was no statistically significant difference between the CVS and AC groups in terms of study variables. There were 46 male fetuses (51.6%) and 43 female fetuses (48.4%). Fifteen of the female fetuses were carriers (34.8%). Median birthweight values were statistically insignificantly lower in the carrier group. Conclusion . Pregnancies at risk for DMD should be prenatally tested to prevent the effect of disease on families and DMD carrier fetuses had obstetric outcomes similar to DMD negative female fetuses.PubMedScopu