Cameras that take lens flare-free photos at night, sunlight-readable displays and enhanced durability with tougher and lighter frames: all are …

This week, Verizon has announced 5G Ultra Wideband partnerships with a pair of US universities, aiming to use the network to help explore drone flight alongside the California Institute of Technology (Caltech) and Industry 4.0 advancements with Pennsylvania State University. At CAST, the operator said it would use the 5G deployment to explore how the low latency, high speeds, and massive capacity of 5G can be used to help reduce drones' need for in-built heavy computing hardware. Making use of edge computing, the AI systems the drone makes use of can function more efficiently, allowing for better real-time interpretation of data and near instantaneous in-flight adjustments. More specifically, the technology will be explored in the context of difficult weather conditions, with researchers hoping the new capabilities will allow drones to detect, interpret, and adjust to weather conditions in real-time. The CAST lab includes a three-story-tall aerodrome filled with adjustable fans, allowing the researchers to mimic weather conditions from a gentle breeze to gale-force winds; it can even be tilted 90 degrees to simulate vertical take-off under challenging conditions.

There are three leading trends currently shaping the future of technology-based industries. At the nexus of these emergent areas of IoT, Cloud, and artificial intelligence is a new player on the Vancouver tech scene. Motorola Solutions, a leader in advanced security technology and a new addition to the realm, is looking for skilled tech talent to join its local team. "Motorola is a well-known name, but people often associate it with the consumer mobile phone company. That's not Motorola Solutions today, though," Hamish Dobson, vice president of product management at Motorola Solutions, tells Daily Hive.

How does language differ across one's Facebook status updates vs. one's text messages (SMS)? In this study, we show how Facebook and SMS use differs in psycho-linguistic characteristics and how these differences drive downstream analyses with an illustration of depression diagnosis. We use a sample of consenting participants who shared Facebook status updates, SMS data, and answered a standard psychological depression screener. We quantify domain differences using psychologically driven lexical methods and find that language on Facebook involves more personal concerns, experiences, and content features while the language in SMS contains more informal and style features. Next, we estimate depression from both text domains, using a depression model trained on Facebook data, and find a drop in accuracy when predicting self-reported depression assessments from the SMS-based depression estimates. Finally, we evaluate a simple domain adaption correction based on words driving the cross-platform differences and applied it to the SMS-derived depression estimates, resulting in significant improvement in prediction. Our work shows the Facebook vs. SMS difference in language use and suggests the necessity of cross-domain adaption for text-based predictions.

Mobile user verification is to authenticate whether a given user is the legitimate user of a smartphone device. Unlike the current methods that commonly require users active cooperation, such as entering a short pin or a one-stroke draw pattern, we propose a new passive verification method that requires minimal imposition of users through modelling users subtle mobility patterns. Specifically, our method computes the statistical ambience features on WiFi and cell tower data from location anonymized data sets and then we customize Hidden Markov Model (HMM) to capture the spatial-temporal patterns of each user's mobility behaviors. Our learned model is subsequently validated and applied to verify a test user in a time-evolving manner through sequential likelihood test. Experimentally, our method achieves 72% verification accuracy with less than a day's data and a detection rate of 94% of illegitimate users with only 2 hours of selected data. As the first verification method that models users' mobility pattern on location-anonymized smartphone data, our achieved result is significant showing the good possibility of leveraging such information for live user authentication.

Recently, an increasing number of mobile users are eagerly using the cellular network in data applications. In particular, multimedia downloads generated by Internet-capable smart phones and other portable devices has been widely recognized as the major source for strains in cellular networks, to a degree where service quality for all users is significantly impacted. In this paper we explore the novel concept of proactive content caching using evolutionary algorithms inspired by the inherent predictability of the mobile user behavior. Users can then use the cached version of the content in order to achieve a better user experience and reduce the peak-to-average ratio in mobile networks, especially during peak hours of the day. Finally, we confirm the merits of the proposed scheduler using real data traces of different user's requests and Wi-Fi availability. The results after applying the proposed scheduling algorithm show that up to 70% of the user content requests can be fulfilled i.e. the content were successfully cached before request. We also observe that proposed scheduler outperforms a baseline scheduler based on simulated annealing.

Fast and reliable wireless communication has become a critical demand in human life. When natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications in mission-critical (MC) scenarios. Due to the unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered as an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control a UAV-BS in both static and dynamic environments. We investigate a situation in which a macro BS is destroyed as a result of a natural disaster and a UAV-BS is deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. We present a data collection system, signaling procedures and machine learning applications for this use case. A deep reinforcement learning algorithm is developed to jointly optimize the tilt of the access and backhaul antennas of the UAV-BS as well as its three-dimensional placement. Evaluation results show that the proposed algorithm can autonomously navigate and configure the UAV-BS to satisfactorily serve the MC users on the ground.

In the wake of network densification and multi-band operation in emerging cellular networks, mobility and handover management is becoming a major bottleneck. The problem is further aggravated by the fact that holistic mobility management solutions for different types of handovers, namely inter-frequency and intra-frequency handovers, remain scarce. This paper presents a first mobility management solution that concurrently optimizes inter-frequency related A5 parameters and intra-frequency related A3 parameters. We analyze and optimize five parameters namely A5-time to trigger (TTT), A5-threshold1, A5-threshold2, A3-TTT, and A3-offset to jointly maximize three critical key performance indicators (KPIs): edge user reference signal received power (RSRP), handover success rate (HOSR) and load between frequency bands. In the absence of tractable analytical models due to system level complexity, we leverage machine learning to quantify the KPIs as a function of the mobility parameters. An XGBoost based model has the best performance for edge RSRP and HOSR while random forest outperforms others for load prediction. An analysis of the mobility parameters provides several insights: 1) there exists a strong coupling between A3 and A5 parameters; 2) an optimal set of parameters exists for each KPI; and 3) the optimal parameters vary for different KPIs. We also perform a SHAP based sensitivity to help resolve the parametric conflict between the KPIs. Finally, we formulate a maximization problem, show it is non-convex, and solve it utilizing simulated annealing (SA). Results indicate that ML-based SA-aided solution is more than 14x faster than the brute force approach with a slight loss in optimality.

Federated Learning (FL) over wireless network enables data-conscious services by leveraging the ubiquitous intelligence at network edge for privacy-preserving model training. As the proliferation of context-aware services, the diversified personal preferences causes disagreeing conditional distributions among user data, which leads to poor inference performance. In this sense, clustered federated learning is proposed to group user devices with similar preference and provide each cluster with a personalized model. This calls for innovative design in edge association that involves user clustering and also resource management optimization. We formulate an accuracy-cost trade-off optimization problem by jointly considering model accuracy, communication resource allocation and energy consumption. To comply with parameter encryption techniques in FL, we propose an iterative solution procedure which employs deep reinforcement learning based approach at cloud server for edge association. The reward function consists of minimized energy consumption at each base station and the averaged model accuracy of all users. Under our proposed solution, multiple edge base station are fully exploited to realize cost efficient personalized federated learning without any prior knowledge on model parameters. Simulation results show that our proposed strategy outperforms existing strategies in achieving accurate learning at low energy cost.

The United States government has begun to take significant steps to eliminate robocalls. The FCC requires that phone companies use a cryptography-based technology called STIR/SHAKEN to authenticate all callers' IDs beginning June 30, 2021. Anyone hoping for robocalls to evaporate in a puff of regulation will be sorely disappointed. However, respite may be on the way, albeit slowly. The technology to block robocalls is developing, and STIR/SHAKEN is part of a trend in which phone consumers in the United States are no longer solely responsible for deciding whether or not to accept robocalls.