Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

An approach to merge superconducting Josephson Junction (JJ) devices with nanoscale Graphene devices in order to achieve high gain-bandwidth-product on the order of THz is proposed. While JJ-based devices have existed since nearly 1962, interfacing or interacting with them with standard electrical systems has been a challenge for several reasons, including operating temperature, switching speed, and low-signal sensitivity. In this study, we investigated the possibility of utilizing JJ's so-called AC-effect in order to generate high frequency signals that can be accommodated by high speed nanoscale devices. Graphene Nano Ribbon Field Effect Transistors (GNRFET) have been emphasized for their very high frequency operation that is appropriate to accommodate the very high switching speed of the JJ devices. The study is based on the AC JJ effect with generating a sinusoidal current whose frequency directly scales with voltage. For a single-stage GNRFET CS amplifier, a gain of 35.7dB with a bandwidth of 217GHz was achieved, while 10.8dB with a unitary gain of 2.5THz was also accomplished. The paper details the design and interface of the JJ with GNRFET devices, targeting high frequency signal generation on the order of hundreds of GHz to THz, with the purpose of providing a foundation on top of which further developments can be made, especially in the areas of communications, medical devices, instrumentation and measurement devices, high-speed imaging, sub-infrared antenna arrays, and computer architecture and hardware, by providing a system which can respond to speeds on the order of fifty to one hundred times faster than current commercial computer equipment or signal generation/processing. The paper details the design and interface of the JJ with GNRFET devices, targeting high frequency signal generation on the order of hundreds of GHz to THz, with the purpose of providing a foundation on top of which further developments can be made, especially in the areas of communications, medical devices, instrumentation and measurement devices, high-speed imaging, sub-infrared antenna arrays, and computer architecture and hardware, by providing a system which can respond to speeds on the order of fifty to one hundred times faster than current commercial computer equipment or signal generation/processing.

Acoustic detection of gunshots has many applications in the field of security and military. This paper deals with the detection of gunshots, using microphone sensor arrays placed in different locations which are processed using MATLAB. The time difference of arrivals of acoustic signals at different sensors is used to determine the direction of arrival, elevation and the location of the shooter. The aim of the project is to develop a high speed, low power sensor array which can be used for both military and civilian safety along with efficient network security. A mathematical model will be developed and five microphone system will be used in localizing the gunshot.

Generally falls may occur from moving or resting postures. This may include slipping from bed and fall from a sitting, or from running or walking. The pre-fall is a non-equilibrium state of human position that may lead to serious injuries, and may negatively impact the quality life condition, particularly for elders. Physical disabilities resulting from the fall incidences may lead to high costs involved with the healing process. In this work, an embedded sensor system using Arduino micro-controller was utilized to coordinate the data received from accelerometer and gyroscope. For a given threshold voltage and fall pattern, the fall decision is made by the microcontroller, citing an incoming fall. The study addresses the number of sensors to be coordinated for enhancing probability of receiving a real fall. Sensors are suggested to be placed on the human body within a belt, and safety devices at human body as well as incorporated in a smart room will be coordinated with the processor commands. Near 150 ms time frame was detected from the simulation results, suggesting a safety device to be triggered and activated for protection within this time frame. This paper discusses the research parameters such as response time for the device activation and interfacing the microcontroller to airbag switch, and means of activating the safety devices within the sharp edges in the smart unit care to minimize the impact of the fall injuries.