Systems and Methods for Diagnosis and Treatment of Psychiatric Disorders

A device for diagnosing and treating psychiatric disorders is described. The device may be configured to provide a graphical user interface that enables a user to select at least one of: entering information related to a diagnosis of the psychiatric disorder and alleviating symptoms caused by the psychiatric disorder. Upon a user selecting entering information related to the diagnosis of a psychiatric disorder, the device may receive information related to the diagnosis of the psychiatric disorder. The device may determine the severity of a user\u27s condition based at least in part on the received information. The device may provide a treatment based on the determined severity of the user\u27s condition. A treatment may include providing feedback to a user

Secure Communications Over Hybrid Military Networks

Stealthiness can be described as a disposition to be sly and to do things surreptitiously. This paper presents a new architecture for flexible and secure networking in battlefields that enables stealthy and covert communication in the presence of node mobility. Our architecture is based on the combination of optical (fiber) and wireless links. Our objective is to be able to carry on undeterred communication without the attack/eavesdropping nodes being able to detect the presence of any communication. This objective is not only crucial for successful completion of the operation, but also for the safety of our mobile nodes, by not giving out their locations. We combine the advantages of optical links, such as high bandwidth, low delays, low error rates, good security, with the advantages of wireless links, such as mobility and flexibility, along with directional antennas for communication. From security point of view, we also assume presence of red zones, which are the ones controlled by the adversary or where the adversary can trace wireless activities

TTL based Packet Marking for IP Traceback

Distributed Denial of Service Attacks continue to pose major threats to the Internet. in order to traceback attack sources (i.e., IP addresses), a well-studied approach is Probabilistic Packet Marking (PPM), where each intermediate router of a packet marks it with a certain probability, enabling a victim host to traceback the attack source. in a recent study, we showed how attackers can take advantage of probabilistic nature of packet markings in existing PPM schemes to create spoofed marks, hence compromising traceback. in this paper, we propose a new PPM scheme called TTL-Based PPM (TPM) scheme, where each packet is marked with a probability inversely proportional to the distance traversed by the packet so far. Thus, packets that have to traverse longer distances are marked with higher probability, compared to those that have to traverse shorter distances. This ensures that a packet is marked with much higher probability by intermediate routers than by traditional mechanisms, hence reducing the effectiveness of spoofed packets reaching victims. using formal analysis and simulations using real Internet topology maps, we show how our TPM scheme can effectively trace DDoS attackers even in presence of spoofing when compared to existing schemes. © 2008 IEEE

On Optimizing Traffic Signal Phase Ordering in Road Networks

Traffic signals are an elementary component of all urban road networks and play a critical role in controlling the flow of vehicles. However, current road transportation systems and traffic signal implementations are very inefficient. the objective of this research is to evaluate optimal phase ordering within a signal cycle to minimize the average waiting delay and thus in turn minimizing fuel consumption and greenhouse gas (GHG) emissions. through extensive simulation analysis, we show that by choosing optimal phase ordering, the stopped delay can be reduced by 40% per car at each signal resulting in a saving of up to 100 gallons of fuel per traffic signal each day. © 2010 IEEE

Defending Wireless Sensor Networks Against Adversarial Localization

In this paper, we study the issue of defending against adversarial localization in wireless sensor networks. Adversarial localization refers to attacks where an adversary attempts to disclose physical locations of sensors in the network. the adversary accomplishes this by physically moving in the network while eavesdropping on communication messages exchanged by sensors and measuring raw physical properties of messages like Angle of Arrival, Signal Strength of the detected signal. in this paper, we aim to defend sensor networks against such kinds of adversarial localization. the core challenge comes from the sensors performing two conflicting objectives simultaneously: localize the adversary and hide from the adversary. the principle of our approach and the subsequent defense protocol is to allow sensors intelligently predict their own importance as a measure of these two conflicting requirements. Only a few important sensors will participate in any message exchanges. This ensures high degree of adversary localization, while also protecting location privacy of many sensors. Extensive simulations are conducted to demonstrate the performance of our protocol. © 2010 IEEE

Systems and Methods for Emergency Situation Communications

A system for enabling communications during an emergency situation is described. A system may be configured to generate graphical user interfaces including a map displaying a location and a status of the one or more users located at the scene of an emergency situation. The graphical user interfaces may be displayed on a user\u27s portable computing device. The graphical user interfaces may be displayed at a computing device located at a dispatcher site

A Multi-tiered Architecture for Content Retrieval in Mobile Peer-to-peer Networks

In this paper, we address content retrieval in Mobile Peer-to-Peer (P2P) Networks. We design a multi-tiered architecture for content retrieval, where at Tier 1, we design a protocol for content similarity governed by a parametera that trades accuracy with search overhead. at Tier 2, we introduce a novel concept called Chained Bloom Filters and design a protocol where popular search items are linked with popular content at each node in an efficient manner for subsequent retrieval. Extensive analysis and numerical simulations demonstrate the effectiveness of our techniques. © 2011 IEEE

Scaling Laws of Key Predistribution Protocols in Wireless Sensor Networks

Many key predistribution (KP) protocols have been proposed and are well accepted in randomly deployed wireless sensor networks (WSNs). Being distributed and localized, they are perceived to be scalable as node density and network dimension increase. While it is true in terms of communication/computation overhead, their scalability in terms of security performance is unclear. in this paper, we conduct a detailed study on this issue. in particular, we define a new metric called Resilient Connectivity (RC) to quantify security performance in WSNs. We then conduct a detailed analytical investigation on how KP protocols scale with respect to node density and network dimension in terms of RC in randomly deployed WSNs. based on our theoretical analysis, we state two scaling laws of KP protocols. Our first scaling law states that KP protocols are not scalable in terms of RC with respect to node density. Our second scaling law states that KP protocols are not scalable in terms of RC with respect to network dimension. in order to deal with the Un scalability of the above two scaling laws, we further propose logical and physical group deployment, respectively. We validate our findings further using extensive theoretical analysis and simulations. © 2006 IEEE

Providing End-to-end Secure Communications in Wireless Sensor Networks

In many Wireless Sensor Networks (WSNs), providing end to end secure communications between sensors and the sink is important for secure network management. While there have been many works devoted to hop by hop secure communications, the issue of end-to-end secure communications is largely ignored. in this paper, we design an end-to-end secure communication protocol in randomly deployed WSNs. Specifically, our protocol is based on a methodology called differentiated key pre-distribution. the core idea is to distribute different number of keys to different sensors to enhance the resilience of certain links. This feature is leveraged during routing, where nodes route through those links with higher resilience. using rigorous theoretical analysis, we derive an expression for the quality of end-to-end secure communications and use it to determine optimum protocol parameters. Extensive performance evaluation illustrates that our solutions can provide highly secure communications between sensor nodes and the sink in randomly deployed WSNs. We also provide detailed discussion on a potential attack (i.e. biased node capturing attack) to our solutions and propose several countermeasures to this attack. © 2011 IEEE

Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25,867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are deadly vectors) is amongst the highest. We present important lessons learned and practical impact of our techniques towards the end of the paper