Every communication system has a receiver and a transmitter. Irrespective if it is wired or wireless.The future of wireless communication consists of a massive number of transmitters and receivers. The question arises, can we use computer vision to help wireless communication? To satisfy the high data requirement, a large number of antennas are required. The devices that employ large-antenna arrays have other sensors such as RGB camera, depth camera, or LiDAR sensors.These vision sensors help us overcome the non-trivial wireless communication challenges, such as beam blockage prediction and hand-over prediction.This is further motivated by the recent advances in deep learning and computer vision that can extract high-level semantics from complex visual scenes, and the increasing interest of leveraging machine/deep learning tools in wireless communication problems.[1] <br/><br/>The research was focused solely based on technology like 3D cameras,object detection and object tracking using Computer vision and compression techniques. The main objective of using computer vision was to make Milli-meter Wave communication more robust, and to collect more data for the machine learning algorithms. Pre-build lossless and lossy compression algorithms, such as FFMPEG, were used in the research. An algorithm was developed that could use 3D cameras and machine learning models such as YOLOV3, to track moving objects using servo motors and low powered computers like the raspberry pi or the Jetson Nano. In other words, the receiver could track the highly mobile transmitter in 1 dimension using a 3D camera. Not only that, during the research, the transmitter was loaded on a DJI M600 pro drone, and then machine learning and object tracking was used to track the highly mobile drone. In order to build this machine learning model and object tracker, collecting data like depth, RGB images and position coordinates were the first yet the most important step. GPS coordinates from the DJI M600 were also pulled and were successfully plotted on google earth. This proved to be very useful during data collection using a drone and for the future applications of position estimation for a drone using machine learning. <br/><br/>Initially, images were taken from transmitter camera every second,and those frames were then converted to a text file containing hex-decimal values. Each text file was then transmitted from the transmitter to receiver, and on the receiver side, a python code converted the hex-decimal to JPG. This would give an efect of real time video transmission. However, towards the end of the research, an industry standard, real time video was streamed using pre-built FFMPEG modules, GNU radio and Universal Software Radio Peripheral (USRP). The transmitter camera was a PI-camera. More details will be discussed as we further dive deep into this research report.

Graduating from college is an important time of life transitions and career development for undergraduates and their future. Future self-identification, the connection between an individual’s current and future self, can negatively predict depression and utilize self-control as a mechanism to achieve later academic goals. Investigating an individual’s future self- identification, depression scores, and behavioral outcomes in the face of the COVID-19 pandemic can help optimize college graduate success in an uncertain world. The present study aimed to (1) determine if earlier future self-identification moderated the changes between later outcomes (e.g., depression, perceived alcohol consumption, and academic and career goals) from pre-COVID-19 to during COVID-19, (2) investigate if psychological resources (e.g., self-control and emotion regulation) had any intermediary effects between earlier future self-identification and later depression and behavioral outcomes during the pandemic, and (3) test for any moderation effects of future self-identification on the relationship between available psychological resources before COVID-19 and during COVID-19. The present research demonstrated that students with greater earlier future self-identification were less likely to change their academic and career goals and were less likely to experience symptoms of depression during the pandemic. Additionally, self-control was demonstrated as an intermediary factor between earlier future self-identification and later academic and career goal changes. These findings may help college graduates develop resilience in other stressful situations.

The Beck Depression Inventory II (BDI-II) and the Patient Health Questionnaire 9 (PHQ-9) are highly valid depressive testing tools used to measure the symptom profile of depression globally and in South Asia, respectively (Steer et al., 1998; Kroenke et al, 2001). Even though the South Asian population comprises only 23% of the world’s population, it represents one-fifth of the world’s mental health disorders (Ogbo et al., 2018). Although this population is highly affected by mental disorders, there is a lack of culturally relevant research on specific subsections of the South Asian population.<br/><br/>As such, the goal of this study is to investigate the differences in the symptom profile of depression in native and immigrant South Asian populations. We investigated the role of collective self-esteem and perceived discrimination on mental health. <br/><br/>For the purpose of this study, participants were asked a series of questions about their depressive symptoms, self-esteem and perceived discrimination using various depressive screening measures, a self-esteem scale, and a perceived discrimination scale.<br/><br/>We found that immigrants demonstrated higher depressive symptoms than Native South Asians as immigration was viewed as a stressor. First-generation and second-generation South Asian immigrants identified equally with somatic and psychological symptoms. These symptoms were positively correlated with perceived discrimination, and collective self-esteem was shown to increase the likelihood of these symptoms.<br/><br/>This being said, the results from this study may be generalized only to South Asian immigrants who come from highly educated and high-income households. Since seeking professional help and being aware of one’s mental health is vital for wellbeing, the results from this study may spark the interest in an open communication about mental health within the South Asian immigrant community as well as aid in the restructuring of a highly reliable and valid measurement to be specific to a culture.

Communication skills are vital for the world we inhabit. Both oral and written communication are some of the most sought-after skills in the job market today; this holds true in science, technology, engineering and mathematics (STEM) fields. Despite the high demand for communication skills, communication classes are not required for some STEM majors (Missingham, 2006). STEM major maps are often so packed with core classes that they nearly exclude the possibility of taking communication courses. Students and job seekers are told they need to be able to communicate to succeed but are not given any information or support in developing their skills. Scientific inquiry and discovery cannot be limited to only those that understand high-level jargon and have a Ph.D. in a subject. STEM majors and graduates must be able to translate information to communities beyond other experts. If they cannot communicate the impact of their research and discoveries, who is going to listen to them?<br/>Overall, the literature around communication in STEM fields demonstrate the need for and value of specific, teachable communication skills. This paper will examine the impact of a communication training module that teaches specific communication skills to BIO 182: General Biology II students. The communication training module is an online module that teaches students the basics of oral communication. The impact of the module will be examined through the observation of students’ presentations.